1
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Muller L, Churchland PS, Sejnowski TJ. Transformers and cortical waves: encoders for pulling in context across time. Trends Neurosci 2024:S0166-2236(24)00149-8. [PMID: 39341729 DOI: 10.1016/j.tins.2024.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 06/07/2024] [Accepted: 08/09/2024] [Indexed: 10/01/2024]
Abstract
The capabilities of transformer networks such as ChatGPT and other large language models (LLMs) have captured the world's attention. The crucial computational mechanism underlying their performance relies on transforming a complete input sequence - for example, all the words in a sentence - into a long 'encoding vector' that allows transformers to learn long-range temporal dependencies in naturalistic sequences. Specifically, 'self-attention' applied to this encoding vector enhances temporal context in transformers by computing associations between pairs of words in the input sequence. We suggest that waves of neural activity traveling across single cortical areas, or multiple regions on the whole-brain scale, could implement a similar encoding principle. By encapsulating recent input history into a single spatial pattern at each moment in time, cortical waves may enable a temporal context to be extracted from sequences of sensory inputs, the same computational principle as that used in transformers.
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Affiliation(s)
- Lyle Muller
- Department of Mathematics, Western University, London, Ontario, Canada; Fields Laboratory for Network Science, Fields Institute, Toronto, Ontario, Canada.
| | - Patricia S Churchland
- Department of Philosophy, University of California at San Diego, San Diego, CA, USA.
| | - Terrence J Sejnowski
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, San Diego, CA, USA; Department of Neurobiology, University of California at San Diego, San Diego, CA, USA.
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2
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Polat L, Harpaz T, Zaidel A. Rats rely on airflow cues for self-motion perception. Curr Biol 2024; 34:4248-4260.e5. [PMID: 39214088 DOI: 10.1016/j.cub.2024.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 07/12/2024] [Accepted: 08/01/2024] [Indexed: 09/04/2024]
Abstract
Self-motion perception is a vital skill for all species. It is an inherently multisensory process that combines inertial (body-based) and relative (with respect to the environment) motion cues. Although extensively studied in human and non-human primates, there is currently no paradigm to test self-motion perception in rodents using both inertial and relative self-motion cues. We developed a novel rodent motion simulator using two synchronized robotic arms to generate inertial, relative, or combined (inertial and relative) cues of self-motion. Eight rats were trained to perform a task of heading discrimination, similar to the popular primate paradigm. Strikingly, the rats relied heavily on airflow for relative self-motion perception, with little contribution from the (limited) optic flow cues provided-performance in the dark was almost as good. Relative self-motion (airflow) was perceived with greater reliability vs. inertial. Disrupting airflow, using a fan or windshield, damaged relative, but not inertial, self-motion perception. However, whiskers were not needed for this function. Lastly, the rats integrated relative and inertial self-motion cues in a reliability-based (Bayesian-like) manner. These results implicate airflow as an important cue for self-motion perception in rats and provide a new domain to investigate the neural bases of self-motion perception and multisensory processing in awake behaving rodents.
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Affiliation(s)
- Lior Polat
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Tamar Harpaz
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Adam Zaidel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel.
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3
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Pujar AA, Barua A, Dey PS, Singh D, Roy U, Jolly MK, Hatzikirou H. Microenvironmental entropy dynamics analysis reveals novel insights into Notch-Delta-Jagged decision-making mechanism. iScience 2024; 27:110569. [PMID: 39318535 PMCID: PMC11420447 DOI: 10.1016/j.isci.2024.110569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/31/2024] [Accepted: 07/19/2024] [Indexed: 09/26/2024] Open
Abstract
Notch-Delta-Jagged (NDJ) signaling among neighboring cells contributes crucially to spatiotemporal pattern formation and developmental decision-making. Despite numerous detailed mathematical models, their high-dimensionality parametric space limits analytical treatment, especially regarding local microenvironmental fluctuations. Using the low-dimensional dynamics of the recently postulated least microenvironmental uncertainty principle (LEUP) framework, we showcase how the LEUP formalism recapitulates a noisy NDJ spatial patterning. Our LEUP simulations show that local phenotypic entropy increases for lateral inhibition but decreases for lateral induction. This distinction allows us to identify a critical parameter that captures the transition from a Notch-Delta-driven lateral inhibition to a Notch-Jagged-driven lateral induction phenomenon and suggests random phenotypic patterning in the case of lack of dominance of either Notch-Delta or Notch-Jagged signaling. Our results enable an analytical treatment to map the high-dimensional dynamics of NDJ signaling on tissue-level patterning and can possibly be generalized to decode operating principles of collective cellular decision-making.
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Affiliation(s)
- Aditi Ajith Pujar
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India
- Undergraduate Program, Indian Institute of Science, Bangalore 560012, India
| | - Arnab Barua
- Tata Institute of Fundamental Research, Hyderabad 500046, India
| | - Partha Sarathi Dey
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India
| | - Divyoj Singh
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India
- Undergraduate Program, Indian Institute of Science, Bangalore 560012, India
| | - Ushasi Roy
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India
| | - Mohit Kumar Jolly
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India
| | - Haralampos Hatzikirou
- Mathematics Department, Khalifa University, P.O. Box: 127788, Abu Dhabi, UAE
- Technische Univesität Dresden, Center for Information Services and High Performance Computing, Nöthnitzer Straße 46, P.O. Box: 01062, Dresden, Germany
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4
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Wang R, Gong J, Zhao C, Xu Y, Hong B. Distinct neural pathway and its information flow for blind individual's Braille reading. Neuroimage 2024; 300:120852. [PMID: 39265958 DOI: 10.1016/j.neuroimage.2024.120852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/22/2024] [Accepted: 09/10/2024] [Indexed: 09/14/2024] Open
Abstract
Natural Braille reading presents significant challenges to the brain networks of late blind individuals, yet its underlying neural mechanisms remain largely unexplored. Using natural Braille texts in behavioral assessments and functional MRI, we sought to pinpoint the neural pathway and information flow crucial for Braille reading performance in late blind individuals. In the resting state, we discovered a unique neural connection between the higher-order 'visual' cortex, the lateral occipital cortex (LOC), and the inferior frontal cortex (IFC) in late blind individuals, but not in sighted controls. The left-lateralized LOC-IFC connectivity was correlated with individual Braille reading proficiency. Prolonged Braille reading practice led to increased strength of this connectivity. During a natural Braille reading task, bidirectional information flow between the LOC and the IFC was positively modulated, with a predominantly stronger top-down modulation from the IFC to the LOC. This stronger top-down modulation contributed to higher Braille reading proficiency. We thus proposed a two-predictor multiple regression model to predict individual Braille reading proficiency, incorporating both static connectivity and dynamic top-down communication between the LOC-IFC link. This work highlights the dual contributions of the occipito-frontal neural pathway and top-down cognitive strategy to superior natural Braille reading performance, offering guidance for training late blind individuals.
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Affiliation(s)
- Ruxue Wang
- School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing 100084, PR China
| | - Jiangtao Gong
- The Future Laboratory, Tsinghua University, Beijing 100084, PR China; Academy of Arts & Design, Tsinghua University, Beijing 100084, PR China
| | - Chenying Zhao
- School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing 100084, PR China
| | - Yingqing Xu
- The Future Laboratory, Tsinghua University, Beijing 100084, PR China; Academy of Arts & Design, Tsinghua University, Beijing 100084, PR China.
| | - Bo Hong
- School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing 100084, PR China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, PR China.
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5
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Hoven M, Mulder T, Denys D, van Holst RJ, Luigjes J. Lower confidence and increased error sensitivity in OCD patients while learning under volatility. Transl Psychiatry 2024; 14:370. [PMID: 39266521 PMCID: PMC11393329 DOI: 10.1038/s41398-024-03042-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 07/16/2024] [Accepted: 07/26/2024] [Indexed: 09/14/2024] Open
Abstract
A decoupling between confidence and action could relate to compulsive behaviour as seen in obsessive-compulsive disorder (OCD). The link between confidence and action in OCD has been investigated in clinical case-control studies and in the general population with discrepant findings. The generalizability of findings from highly-compulsive general population samples to clinical OCD samples has been questioned. Here, we investigate action-confidence coupling for 38 OCD patients compared to 37 healthy controls (HC), using a predictive inference task. We compared those results to a comparison between matched high and low compulsive individuals from the general population. Action-updating, confidence and their coupling were compared between the groups. Moreover, computational modeling was performed to compare groups on error sensitivity and environmental parameters. OCD patients showed lower confidence and higher learning rates in reaction to (small) prediction errors than HC, signaling hyperactive error signaling and lower confidence estimation. No evidence was found for differences in action-confidence coupling between groups. In contrast high the compulsive group showed higher confidence and stronger decoupling than the low compulsive group, both of which were related to symptoms. The underlying mechanisms of obsessive-compulsive behaviour might differ between clinical and highly-compulsive general population samples, resulting in different (meta)cognitive profiles.
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Affiliation(s)
- Monja Hoven
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands
| | - Tosca Mulder
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands
| | - Damiaan Denys
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands
| | - Ruth J van Holst
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands
| | - Judy Luigjes
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands.
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6
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Rohe T, Hesse K, Ehlis AC, Noppeney U. Multisensory perceptual and causal inference is largely preserved in medicated post-acute individuals with schizophrenia. PLoS Biol 2024; 22:e3002790. [PMID: 39255328 DOI: 10.1371/journal.pbio.3002790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 08/06/2024] [Indexed: 09/12/2024] Open
Abstract
Hallucinations and perceptual abnormalities in psychosis are thought to arise from imbalanced integration of prior information and sensory inputs. We combined psychophysics, Bayesian modeling, and electroencephalography (EEG) to investigate potential changes in perceptual and causal inference in response to audiovisual flash-beep sequences in medicated individuals with schizophrenia who exhibited limited psychotic symptoms. Seventeen participants with schizophrenia and 23 healthy controls reported either the number of flashes or the number of beeps of audiovisual sequences that varied in their audiovisual numeric disparity across trials. Both groups balanced sensory integration and segregation in line with Bayesian causal inference rather than resorting to simpler heuristics. Both also showed comparable weighting of prior information regarding the signals' causal structure, although the schizophrenia group slightly overweighted prior information about the number of flashes or beeps. At the neural level, both groups computed Bayesian causal inference through dynamic encoding of independent estimates of the flash and beep counts, followed by estimates that flexibly combine audiovisual inputs. Our results demonstrate that the core neurocomputational mechanisms for audiovisual perceptual and causal inference in number estimation tasks are largely preserved in our limited sample of medicated post-acute individuals with schizophrenia. Future research should explore whether these findings generalize to unmedicated patients with acute psychotic symptoms.
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Affiliation(s)
- Tim Rohe
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
- Institute of Psychology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Klaus Hesse
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Ann-Christine Ehlis
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
- Tübingen Center for Mental Health (TüCMH), Tübingen, Germany
| | - Uta Noppeney
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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7
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Powers A, Angelos PA, Bond A, Farina E, Fredericks C, Gandhi J, Greenwald M, Hernandez-Busot G, Hosein G, Kelley M, Mourgues C, Palmer W, Rodriguez-Sanchez J, Seabury R, Toribio S, Vin R, Weleff J, Woods S, Benrimoh D. A computational account of the development and evolution of psychotic symptoms. Biol Psychiatry 2024:S0006-3223(24)01584-1. [PMID: 39260466 DOI: 10.1016/j.biopsych.2024.08.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 08/08/2024] [Accepted: 08/13/2024] [Indexed: 09/13/2024]
Abstract
The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing a compensatory relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.
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Affiliation(s)
- Albert Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA.
| | - P A Angelos
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Alexandria Bond
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Emily Farina
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Carolyn Fredericks
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jay Gandhi
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Maximillian Greenwald
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | | | - Gabriel Hosein
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Megan Kelley
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Catalina Mourgues
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - William Palmer
- Yale University Department of Psychology, New Haven, CT, USA
| | | | - Rashina Seabury
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Silmilly Toribio
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Raina Vin
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jeremy Weleff
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Scott Woods
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, Canada
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8
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Chen F, Fahimi Hnazaee M, Vanneste S, Yasoda-Mohan A. Effective Connectivity Network of Aberrant Prediction Error Processing in Auditory Phantom Perception. Brain Connect 2024. [PMID: 39135479 DOI: 10.1089/brain.2024.0013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2024] Open
Abstract
Introduction: Prediction error (PE) is key to perception in the predictive coding framework. However, previous studies indicated the varied neural activities evoked by PE in tinnitus patients. Here, we aimed to reconcile the conflict by (1) a more nuanced view of PE, which could be driven by changing stimulus (stimulus-driven PE [sPE]) and violation of current context (context-driven PE [cPE]) and (2) investigating the aberrant connectivity networks that are engaged in the processing of the two types of PEs in tinnitus patients. Methods: Ten tinnitus patients with normal hearing and healthy controls were recruited, and a local-global auditory oddball paradigm was applied to measure the electroencephalographic difference between the two groups during sPE and cPE conditions. Results: Overall, the sPE condition engaged bottom-up and top-down connections, whereas the cPE condition engaged mostly top-down connections. The tinnitus group showed decreased sensitivity to the sPE and increased sensitivity to the cPE condition. Particularly, the auditory cortex and posterior cingulate cortex were the hubs for processing cPE in the control and tinnitus groups, respectively, showing the orientation to an internal state in tinnitus. Furthermore, tinnitus patients showed stronger connectivity to the parahippocampus and pregenual anterior cingulate cortex for the establishment of the prediction during the cPE condition. Conclusion: These results begin to dissect the role of changes in stimulus characteristics versus changes in the context of processing the same stimulus in mechanisms of tinnitus generation.
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Affiliation(s)
- Feifan Chen
- Lab for Clinical and Integrative Neuroscience, Trinity College Institute for Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Mansoureh Fahimi Hnazaee
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Sven Vanneste
- Lab for Clinical and Integrative Neuroscience, Trinity College Institute for Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
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Sun Q, Wang SY, Zhan LZ, You FH, Sun Q. A Bayesian inference model can predict the effects of attention on the serial dependence in heading estimation from optic flow. J Vis 2024; 24:11. [PMID: 39269364 PMCID: PMC11407482 DOI: 10.1167/jov.24.9.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024] Open
Abstract
It has been demonstrated that observers can accurately estimate their self-motion direction (i.e., heading) from optic flow, which can be affected by attention. However, it remains unclear how attention affects the serial dependence in the estimation. In the current study, participants conducted two experiments. The results showed that the estimation accuracy decreased when attentional resources allocated to the heading estimation task were reduced. Additionally, the estimates of currently presented headings were biased toward the headings of previously seen headings, showing serial dependence. Especially, this effect decreased (increased) when the attentional resources allocated to the previously (currently) seen headings were reduced. Furthermore, importantly, we developed a Bayesian inference model, which incorporated attention-modulated likelihoods and qualitatively predicted changes in the estimation accuracy and serial dependence. In summary, the current study shows that attention affects the serial dependence in heading estimation from optic flow and reveals the Bayesian computational mechanism behind the heading estimation.
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Affiliation(s)
- Qi Sun
- Department of Psychology, Zhejiang Normal University, Jinhua, P. R. China
- Intelligent Laboratory of Zhejiang Province in Mental Health and Crisis Intervention for Children and Adolescents, Jinhua, P. R. China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, P. R. China
| | - Si-Yu Wang
- Department of Psychology, Zhejiang Normal University, Jinhua, P. R. China
| | - Lin-Zhe Zhan
- Department of Psychology, Zhejiang Normal University, Jinhua, P. R. China
| | - Fan-Huan You
- Department of Psychology, Zhejiang Normal University, Jinhua, P. R. China
| | - Qian Sun
- Department of Psychology, Zhejiang Normal University, Jinhua, P. R. China
- Intelligent Laboratory of Zhejiang Province in Mental Health and Crisis Intervention for Children and Adolescents, Jinhua, P. R. China
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Goodwin I, Hester R, Garrido MI. Temporal stability of Bayesian belief updating in perceptual decision-making. Behav Res Methods 2024; 56:6349-6362. [PMID: 38129733 PMCID: PMC11335944 DOI: 10.3758/s13428-023-02306-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2023] [Indexed: 12/23/2023]
Abstract
Bayesian inference suggests that perception is inferred from a weighted integration of prior contextual beliefs with current sensory evidence (likelihood) about the world around us. The perceived precision or uncertainty associated with prior and likelihood information is used to guide perceptual decision-making, such that more weight is placed on the source of information with greater precision. This provides a framework for understanding a spectrum of clinical transdiagnostic symptoms associated with aberrant perception, as well as individual differences in the general population. While behavioral paradigms are commonly used to characterize individual differences in perception as a stable characteristic, measurement reliability in these behavioral tasks is rarely assessed. To remedy this gap, we empirically evaluate the reliability of a perceptual decision-making task that quantifies individual differences in Bayesian belief updating in terms of the relative precision weighting afforded to prior and likelihood information (i.e., sensory weight). We analyzed data from participants (n = 37) who performed this task twice. We found that the precision afforded to prior and likelihood information showed high internal consistency and good test-retest reliability (ICC = 0.73, 95% CI [0.53, 0.85]) when averaged across participants, as well as at the individual level using hierarchical modeling. Our results provide support for the assumption that Bayesian belief updating operates as a stable characteristic in perceptual decision-making. We discuss the utility and applicability of reliable perceptual decision-making paradigms as a measure of individual differences in the general population, as well as a diagnostic tool in psychiatric research.
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Affiliation(s)
- Isabella Goodwin
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville Campus, Melbourne, Victoria, 3010, Australia.
| | - Robert Hester
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville Campus, Melbourne, Victoria, 3010, Australia
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville Campus, Melbourne, Victoria, 3010, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
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11
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Smeeton NJ, Meyer J, Varga M, Klatt S. Is Anticipation Skill Learning Bayesian? RESEARCH QUARTERLY FOR EXERCISE AND SPORT 2024; 95:664-679. [PMID: 38324767 DOI: 10.1080/02701367.2023.2294100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 11/21/2023] [Indexed: 02/09/2024]
Abstract
Purpose: The aims of this study were to examine the learning of anticipation skill in the presence of kinematic and outcome probabilities information, and to see if this learning exhibited characteristics of Bayesian integration. Method: Participants with no competitive tennis playing experience watched tennis player stimuli playing forehand tennis shots and were tasked with predicted shot outcomes. Accuracy, response times and perceived task effort were recorded, pre, post and during four acquisition blocks where outcome feedback was provided. In both Experiment 1 and 2, kinematic information about shot direction was either present in the training group stimuli or absent. In Experiment 1, left/right shot probability information remained equi-probable for both groups. In Experiment 2, both groups also trained with a bias in the shot outcome probability toward one shot direction on 80% of the trials across acquisition blocks (and were not told about this manipulation). Results: Pre-to-post anticipation performance improved in the presence of kinematic (EXP 1) or both information sources (EXP 2). Pre-to-post improvements in the presence of shot outcome probability information were congruent with the bias in the shot direction trained (EXP 2). Superior anticipation performance was found when both information sources were present. The presence of kinematic information resulted in increased perceived effort during early training (EXP 1 & 2). Bayesian odds ratios indicated that shot direction probabilities and kinematic information were integrated during anticipation skill learning. Conclusion: Learning with shot direction probabilities and kinematic information shows characteristics of Bayesian integration.
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12
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Medrano J, Sajid N. A Broken Duet: Multistable Dynamics in Dyadic Interactions. ENTROPY (BASEL, SWITZERLAND) 2024; 26:731. [PMID: 39330066 PMCID: PMC11431444 DOI: 10.3390/e26090731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 08/07/2024] [Accepted: 08/20/2024] [Indexed: 09/28/2024]
Abstract
Misunderstandings in dyadic interactions often persist despite our best efforts, particularly between native and non-native speakers, resembling a broken duet that refuses to harmonise. This paper delves into the computational mechanisms underpinning these misunderstandings through the lens of the broken Lorenz system-a continuous dynamical model. By manipulating a specific parameter regime, we induce bistability within the Lorenz equations, thereby confining trajectories to distinct attractors based on initial conditions. This mirrors the persistence of divergent interpretations that often result in misunderstandings. Our simulations reveal that differing prior beliefs between interlocutors result in misaligned generative models, leading to stable yet divergent states of understanding when exposed to the same percept. Specifically, native speakers equipped with precise (i.e., overconfident) priors expect inputs to align closely with their internal models, thus struggling with unexpected variations. Conversely, non-native speakers with imprecise (i.e., less confident) priors exhibit a greater capacity to adjust and accommodate unforeseen inputs. Our results underscore the important role of generative models in facilitating mutual understanding (i.e., establishing a shared narrative) and highlight the necessity of accounting for multistable dynamics in dyadic interactions.
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Affiliation(s)
- Johan Medrano
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Noor Sajid
- Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany;
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13
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Castejón J, Chen F, Yasoda-Mohan A, Ó Sé C, Vanneste S. Chronic pain - A maladaptive compensation to unbalanced hierarchical predictive processing. Neuroimage 2024; 297:120711. [PMID: 38942099 DOI: 10.1016/j.neuroimage.2024.120711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 06/30/2024] Open
Abstract
The ability to perceive pain presents an interesting evolutionary advantage to adapt to an ever-changing environment. However, in the case of chronic pain (CP), pain perception hinders the capacity of the system to adapt to changing sensory environments. Similar to other chronic perceptual disorders, CP is also proposed to be a maladaptive compensation to aberrant sensory predictive processing. The local-global oddball paradigm relies on learning hierarchical rules and processing environmental irregularities at a local and global level. Prediction errors (PE) between actual and predicted input typically trigger an update of the forward model to limit the probability of encountering future PEs. It has been hypothesised that CP hinders forward model updating, reflected in increased local deviance and decreased global deviance. In the present study, we used the local-global paradigm to examine how CP influences hierarchical learning relative to healthy controls. As hypothesised, we observed that deviance in the stimulus characteristics evoked heightened local deviance and decreased global deviance of the stimulus-driven PE. This is also accompanied by respective changes in theta phase locking that is correlated with the subjective pain perception. Changes in the global deviant in the stimulus-driven-PE could also be explained by dampened attention-related responses. Changing the context of the auditory stimulus did not however show a difference in the context-driven PE. These findings suggest that CP is accompanied by maladaptive forward model updating where the constant presence of pain perception disrupts local deviance in non-nociceptive domains. Furthermore, we hypothesise that the auditory-processing based biomarker identified here could be a marker of domain-general dysfunction that could be confirmed by future research.
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Affiliation(s)
- Jorge Castejón
- Lab for Clinical and Integrative Neuroscience, Trinity College Institute for Neuroscience, School of Psychology, Trinity College Dublin, Ireland; Senior MSK Physiotherapist CompassPhysio LTD, Ireland
| | - Feifan Chen
- Lab for Clinical and Integrative Neuroscience, Trinity College Institute for Neuroscience, School of Psychology, Trinity College Dublin, Ireland
| | - Anusha Yasoda-Mohan
- Lab for Clinical and Integrative Neuroscience, Trinity College Institute for Neuroscience, School of Psychology, Trinity College Dublin, Ireland; Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Colum Ó Sé
- Lab for Clinical and Integrative Neuroscience, Trinity College Institute for Neuroscience, School of Psychology, Trinity College Dublin, Ireland
| | - Sven Vanneste
- Lab for Clinical and Integrative Neuroscience, Trinity College Institute for Neuroscience, School of Psychology, Trinity College Dublin, Ireland; Global Brain Health Institute, Trinity College Dublin, Ireland.
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14
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Costa C, Pezzetta R, Masina F, Lago S, Gastaldon S, Frangi C, Genon S, Arcara G, Scarpazza C. Comprehensive investigation of predictive processing: A cross- and within-cognitive domains fMRI meta-analytic approach. Hum Brain Mapp 2024; 45:e26817. [PMID: 39169641 PMCID: PMC11339134 DOI: 10.1002/hbm.26817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 07/15/2024] [Accepted: 07/30/2024] [Indexed: 08/23/2024] Open
Abstract
Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain-general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain-general predictive network that encompasses all well-established cognitive domains exists remains unanswered. The present meta-analysis aims to address this gap by testing the hypothesis that PP relies on a large-scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta-analytic approach was employed, along with Meta-Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena-independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross-domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large-scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross-domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta-Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain-general, large-scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context- and stimulus-dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world.
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Affiliation(s)
| | | | | | - Sara Lago
- Padova Neuroscience CenterPaduaItaly
- IRCCS Ospedale San CamilloVeniceItaly
| | - Simone Gastaldon
- Padova Neuroscience CenterPaduaItaly
- Dipartimento di Psicologia dello Sviluppo e della SocializzazioneUniversità degli Studi di PadovaPaduaItaly
| | - Camilla Frangi
- Dipartimento di Psicologia GeneraleUniversità degli Studi di PadovaPaduaItaly
| | - Sarah Genon
- Institute for Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Centre JülichJülichGermany
| | | | - Cristina Scarpazza
- IRCCS Ospedale San CamilloVeniceItaly
- Dipartimento di Psicologia GeneraleUniversità degli Studi di PadovaPaduaItaly
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15
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Huang YT, Wu CT, Fang YXM, Fu CK, Koike S, Chao ZC. Crossmodal hierarchical predictive coding for audiovisual sequences in the human brain. Commun Biol 2024; 7:965. [PMID: 39122960 PMCID: PMC11316022 DOI: 10.1038/s42003-024-06677-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 08/02/2024] [Indexed: 08/12/2024] Open
Abstract
Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge.
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Affiliation(s)
- Yiyuan Teresa Huang
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan
- Department of Multidisciplinary Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Chien-Te Wu
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan
- School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Yi-Xin Miranda Fang
- School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chin-Kun Fu
- School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Shinsuke Koike
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan
- Department of Multidisciplinary Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan
| | - Zenas C Chao
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan.
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16
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Malkin J, O'Donnell C, Houghton CJ, Aitchison L. Signatures of Bayesian inference emerge from energy-efficient synapses. eLife 2024; 12:RP92595. [PMID: 39106188 DOI: 10.7554/elife.92595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2024] Open
Abstract
Biological synaptic transmission is unreliable, and this unreliability likely degrades neural circuit performance. While there are biophysical mechanisms that can increase reliability, for instance by increasing vesicle release probability, these mechanisms cost energy. We examined four such mechanisms along with the associated scaling of the energetic costs. We then embedded these energetic costs for reliability in artificial neural networks (ANNs) with trainable stochastic synapses, and trained these networks on standard image classification tasks. The resulting networks revealed a tradeoff between circuit performance and the energetic cost of synaptic reliability. Additionally, the optimised networks exhibited two testable predictions consistent with pre-existing experimental data. Specifically, synapses with lower variability tended to have (1) higher input firing rates and (2) lower learning rates. Surprisingly, these predictions also arise when synapse statistics are inferred through Bayesian inference. Indeed, we were able to find a formal, theoretical link between the performance-reliability cost tradeoff and Bayesian inference. This connection suggests two incompatible possibilities: evolution may have chanced upon a scheme for implementing Bayesian inference by optimising energy efficiency, or alternatively, energy-efficient synapses may display signatures of Bayesian inference without actually using Bayes to reason about uncertainty.
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Affiliation(s)
- James Malkin
- Faculty of Engineering, University of Bristol, Bristol, United Kingdom
| | - Cian O'Donnell
- Faculty of Engineering, University of Bristol, Bristol, United Kingdom
- Intelligent Systems Research Centre, School of Computing, Engineering, and Intelligent Systems, Ulster University, Derry/Londonderry, United Kingdom
| | - Conor J Houghton
- Faculty of Engineering, University of Bristol, Bristol, United Kingdom
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17
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Hirao Y, Amemiya T, Narumi T, Argelaguet F, Lecuyer A. Leveraging Tendon Vibration to Enhance Pseudo-Haptic Perceptions in VR. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:5861-5874. [PMID: 37647196 DOI: 10.1109/tvcg.2023.3310001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Pseudo-haptic techniques are used to modify haptic perception by appropriately changing visual feedback to body movements. Based on the knowledge that tendon vibration can affect our somatosensory perception, this article proposes a method for leveraging tendon vibration to enhance pseudo-haptics during free arm motion. Three experiments were performed to examine the impact of tendon vibration on the range and resolution of pseudo-haptics. The first experiment investigated the effect of tendon vibration on the detection threshold of the discrepancy between visual and physical motion. The results indicated that vibrations applied to the inner tendons of the wrist and elbow increased the threshold, suggesting that tendon vibration can augment the applicable visual motion gain by approximately 13% without users detecting the visual/physical discrepancy. Furthermore, the results demonstrate that tendon vibration acts as noise on haptic motion cues. The second experiment assessed the impact of tendon vibration on the resolution of pseudo-haptics by determining the just noticeable difference in pseudo-weight perception. The results suggested that the tendon vibration does not largely compromise the resolution of pseudo-haptics. The third experiment evaluated the equivalence between the weight perception triggered by tendon vibration and that by visual motion gain, that is, the point of subjective equality. The results revealed that vibration amplifies the weight perception and its effect was equivalent to that obtained using a gain of 0.64 without vibration, implying that the tendon vibration also functions as an additional haptic cue. Our results provide design guidelines and future work for enhancing pseudo-haptics with tendon vibration.
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18
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Heng JG, Zhang J, Bonetti L, Lim WPH, Vuust P, Agres K, Chen SHA. Understanding music and aging through the lens of Bayesian inference. Neurosci Biobehav Rev 2024; 163:105768. [PMID: 38908730 DOI: 10.1016/j.neubiorev.2024.105768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 06/05/2024] [Accepted: 06/10/2024] [Indexed: 06/24/2024]
Abstract
Bayesian inference has recently gained momentum in explaining music perception and aging. A fundamental mechanism underlying Bayesian inference is the notion of prediction. This framework could explain how predictions pertaining to musical (melodic, rhythmic, harmonic) structures engender action, emotion, and learning, expanding related concepts of music research, such as musical expectancies, groove, pleasure, and tension. Moreover, a Bayesian perspective of music perception may shed new insights on the beneficial effects of music in aging. Aging could be framed as an optimization process of Bayesian inference. As predictive inferences refine over time, the reliance on consolidated priors increases, while the updating of prior models through Bayesian inference attenuates. This may affect the ability of older adults to estimate uncertainties in their environment, limiting their cognitive and behavioral repertoire. With Bayesian inference as an overarching framework, this review synthesizes the literature on predictive inferences in music and aging, and details how music could be a promising tool in preventive and rehabilitative interventions for older adults through the lens of Bayesian inference.
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Affiliation(s)
- Jiamin Gladys Heng
- School of Computer Science and Engineering, Nanyang Technological University, Singapore.
| | - Jiayi Zhang
- Interdisciplinary Graduate Program, Nanyang Technological University, Singapore; School of Social Sciences, Nanyang Technological University, Singapore; Centre for Research and Development in Learning, Nanyang Technological University, Singapore
| | - Leonardo Bonetti
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus, Aalborg, Denmark; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United Kingdom; Department of Psychiatry, University of Oxford, United Kingdom; Department of Psychology, University of Bologna, Italy
| | | | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus, Aalborg, Denmark
| | - Kat Agres
- Centre for Music and Health, National University of Singapore, Singapore; Yong Siew Toh Conservatory of Music, National University of Singapore, Singapore
| | - Shen-Hsing Annabel Chen
- School of Social Sciences, Nanyang Technological University, Singapore; Centre for Research and Development in Learning, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; National Institute of Education, Nanyang Technological University, Singapore.
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19
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Lakshminarayanan K, Ramu V, Shah R, Haque Sunny MS, Madathil D, Brahmi B, Wang I, Fareh R, Rahman MH. Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation. PeerJ Comput Sci 2024; 10:e2174. [PMID: 39145233 PMCID: PMC11323104 DOI: 10.7717/peerj-cs.2174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 06/12/2024] [Indexed: 08/16/2024]
Abstract
Background The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients. Methods We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks. The experiment utilized EEG signals captured via a gel-free cap, processed through various stages including signal verification, training, and testing. The training involved MI tasks with concurrent vibrotactile stimulation, utilizing common spatial pattern (CSP) training and linear discriminant analysis (LDA) for signal classification. The testing stage introduced a real-time feedback system and a virtual game environment where participants controlled a virtual iTbot robot. Results Results showed varying accuracies in motor intention detection across participants, with an average true positive rate of 63.33% in classifying MI signals. Discussion The study highlights the potential of MI-based BCI in robotic rehabilitation, particularly in terms of engagement and personalization. The findings underscore the feasibility of BCI technology in rehabilitation and its potential use for stroke survivors with upper limb dysfunctions.
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Affiliation(s)
- Kishor Lakshminarayanan
- Department of Sensors and Biomedical Tech, School of Electronics Engineering, Vellore Institute of Technology University, Vellore, Tamil Nadu, India
| | - Vadivelan Ramu
- Department of Sensors and Biomedical Tech, School of Electronics Engineering, Vellore Institute of Technology University, Vellore, Tamil Nadu, India
| | - Rakshit Shah
- Department of Orthopaedic Surgery, University of Arizona, Tucson, AZ, United States of America
| | - Md Samiul Haque Sunny
- Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, United States of America
| | - Deepa Madathil
- Jindal Institute of Behavioural Sciences, O.P. Jindal Global University, Haryana, India
| | - Brahim Brahmi
- Electrical Engineering, Collège Ahuntsic, Montreal, QC, Canada
| | - Inga Wang
- Department of Occupational Science & Technology, University of Wisconsin-Milwaukee, Milwaukee, WI, United States of America
| | - Raouf Fareh
- Department of Electrical and Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates
| | - Mohammad Habibur Rahman
- Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, United States of America
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20
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Chao ZC, Komatsu M, Matsumoto M, Iijima K, Nakagaki K, Ichinohe N. Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder. Commun Biol 2024; 7:851. [PMID: 38992101 PMCID: PMC11239931 DOI: 10.1038/s42003-024-06545-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 07/03/2024] [Indexed: 07/13/2024] Open
Abstract
In autism spectrum disorder (ASD), atypical sensory experiences are often associated with irregularities in predictive coding, which proposes that the brain creates hierarchical sensory models via a bidirectional process of predictions and prediction errors. However, it remains unclear how these irregularities manifest across different functional hierarchies in the brain. To address this, we study a marmoset model of ASD induced by valproic acid (VPA) treatment. We record high-density electrocorticography (ECoG) during an auditory task with two layers of temporal control, and applied a quantitative model to quantify the integrity of predictive coding across two distinct hierarchies. Our results demonstrate a persistent pattern of sensory hypersensitivity and unstable predictions across two brain hierarchies in VPA-treated animals, and reveal the associated spatio-spectro-temporal neural signatures. Despite the regular occurrence of imprecise predictions in VPA-treated animals, we observe diverse configurations of underestimation or overestimation of sensory regularities within the hierarchies. Our results demonstrate the coexistence of the two primary Bayesian accounts of ASD: overly-precise sensory observations and weak prior beliefs, and offer a potential multi-layered biomarker for ASD, which could enhance our understanding of its diverse symptoms.
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Affiliation(s)
- Zenas C Chao
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, 113-0033, Tokyo, Japan.
| | - Misako Komatsu
- Institute of Innovative Research, Tokyo Institute of Technology, 226-8503, Tokyo, Japan.
- RIKEN Center for Brain Science, 351-0198, Wako, Japan.
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry (NCNP), 187-8502, Tokyo, Japan.
| | - Madoka Matsumoto
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry (NCNP), 187-8553, Tokyo, Japan
| | - Kazuki Iijima
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry (NCNP), 187-8553, Tokyo, Japan
| | - Keiko Nakagaki
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry (NCNP), 187-8502, Tokyo, Japan
| | - Noritaka Ichinohe
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry (NCNP), 187-8502, Tokyo, Japan.
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21
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Sarodo A, Yamamoto K, Watanabe K. The role of perceptual processing in the oddball effect revealed by the Thatcher illusion. Vision Res 2024; 220:108399. [PMID: 38603924 DOI: 10.1016/j.visres.2024.108399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 03/23/2024] [Accepted: 03/24/2024] [Indexed: 04/13/2024]
Abstract
When a novel stimulus (oddball) appears after repeated presentation of an identical stimulus, the oddball is perceived to last longer than the repeated stimuli, a phenomenon known as the oddball effect. We investigated whether the perceptual or physical differences between the repeated and oddball stimuli are more important for the oddball effect. To manipulate the perceptual difference while keeping their physical visual features constant, we used the Thatcher illusion, in which an inversion of a face hinders recognition of distortion in its facial features. We found that the Thatcherized face presented after repeated presentation of an intact face induced a stronger oddball effect when the faces were upright than when they were inverted (Experiment 1). However, the difference in the oddball effect between face orientations was not observed when the intact face was presented as the oddball after repeated presentation of a Thatcherized face (Experiment 2). These results were replicated when participants performed both the intact-repeated and Thatcherized-repeated conditions in a single experiment (Experiment 3). Two control experiments confirmed that the repeated presentation of the preceding stimuli is necessary for the difference in duration distortion to occur (Experiments 4 and 5). The results suggest the considerable role of perceptual processing in the oddball effect. We discuss the discrepancy in the results between the intact-repeated and Thatcherized-repeated conditions in terms of predictive coding.
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Affiliation(s)
- Akira Sarodo
- Faculty of Science and Engineering, Waseda University, 3-4-1 Ohkubo, Shinjuku, Tokyo 169-8555, Japan.
| | - Kentaro Yamamoto
- Faculty of Human-Environment Studies, Kyushu University, Fukuoka, Japan
| | - Katsumi Watanabe
- Faculty of Science and Engineering, Waseda University, 3-4-1 Ohkubo, Shinjuku, Tokyo 169-8555, Japan
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22
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Zhu H, Beierholm U, Shams L. BCI Toolbox: An open-source python package for the Bayesian causal inference model. PLoS Comput Biol 2024; 20:e1011791. [PMID: 38976678 PMCID: PMC11257388 DOI: 10.1371/journal.pcbi.1011791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 07/18/2024] [Accepted: 06/05/2024] [Indexed: 07/10/2024] Open
Abstract
Psychological and neuroscientific research over the past two decades has shown that the Bayesian causal inference (BCI) is a potential unifying theory that can account for a wide range of perceptual and sensorimotor processes in humans. Therefore, we introduce the BCI Toolbox, a statistical and analytical tool in Python, enabling researchers to conveniently perform quantitative modeling and analysis of behavioral data. Additionally, we describe the algorithm of the BCI model and test its stability and reliability via parameter recovery. The present BCI toolbox offers a robust platform for BCI model implementation as well as a hands-on tool for learning and understanding the model, facilitating its widespread use and enabling researchers to delve into the data to uncover underlying cognitive mechanisms.
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Affiliation(s)
- Haocheng Zhu
- Department of Psychology, University of California, Los Angeles, California, United States of America
- Department of Psychology, Research Center for Psychology and Behavioral Sciences, Soochow University, Suzhou, China
| | - Ulrik Beierholm
- Department of Psychology, University of Durham, Durham, United Kingdom
| | - Ladan Shams
- Department of Psychology, University of California, Los Angeles, California, United States of America
- Department of Bioengineering, and Neuroscience Interdepartmental Program, University of California, Los Angeles, California, United States of America
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23
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Ampollini S, Ardizzi M, Ferroni F, Cigala A. Synchrony perception across senses: A systematic review of temporal binding window changes from infancy to adolescence in typical and atypical development. Neurosci Biobehav Rev 2024; 162:105711. [PMID: 38729280 DOI: 10.1016/j.neubiorev.2024.105711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/14/2024] [Accepted: 05/03/2024] [Indexed: 05/12/2024]
Abstract
Sensory integration is increasingly acknowledged as being crucial for the development of cognitive and social abilities. However, its developmental trajectory is still little understood. This systematic review delves into the topic by investigating the literature about the developmental changes from infancy through adolescence of the Temporal Binding Window (TBW) - the epoch of time within which sensory inputs are perceived as simultaneous and therefore integrated. Following comprehensive searches across PubMed, Elsevier, and PsycInfo databases, only experimental, behavioral, English-language, peer-reviewed studies on multisensory temporal processing in 0-17-year-olds have been included. Non-behavioral, non-multisensory, and non-human studies have been excluded as those that did not directly focus on the TBW. The selection process was independently performed by two Authors. The 39 selected studies involved 2859 participants in total. Findings indicate a predisposition towards cross-modal asynchrony sensitivity and a composite, still unclear, developmental trajectory, with atypical development associated to increased asynchrony tolerance. These results highlight the need for consistent and thorough research into TBW development to inform potential interventions.
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Affiliation(s)
- Silvia Ampollini
- Department of Humanities, Social Sciences and Cultural Industries, University of Parma, Borgo Carissimi, 10, Parma 43121, Italy.
| | - Martina Ardizzi
- Department of Medicine and Surgery, Unit of Neuroscience, University of Parma, Via Volturno 39E, Parma 43121, Italy
| | - Francesca Ferroni
- Department of Medicine and Surgery, Unit of Neuroscience, University of Parma, Via Volturno 39E, Parma 43121, Italy
| | - Ada Cigala
- Department of Humanities, Social Sciences and Cultural Industries, University of Parma, Borgo Carissimi, 10, Parma 43121, Italy
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24
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Su Y, Shi Z, Wachtler T. A Bayesian observer model reveals a prior for natural daylights in hue perception. Vision Res 2024; 220:108406. [PMID: 38626536 DOI: 10.1016/j.visres.2024.108406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/20/2024] [Accepted: 03/25/2024] [Indexed: 04/18/2024]
Abstract
Incorporating statistical characteristics of stimuli in perceptual processing can be highly beneficial for reliable estimation from noisy sensory measurements but may generate perceptual bias. According to Bayesian inference, perceptual biases arise from the integration of internal priors with noisy sensory inputs. In this study, we used a Bayesian observer model to derive biases and priors in hue perception based on discrimination data for hue ensembles with varying levels of chromatic noise. Our results showed that discrimination thresholds for isoluminant stimuli with hue defined by azimuth angle in cone-opponent color space exhibited a bimodal pattern, with lowest thresholds near a non-cardinal blue-yellow axis that aligns closely with the variation of natural daylights. Perceptual biases showed zero crossings around this axis, indicating repulsion away from yellow and attraction towards blue. These biases could be explained by the Bayesian observer model through a non-uniform prior with a preference for blue. Our findings suggest that visual processing takes advantage of knowledge of the distribution of colors in natural environments for hue perception.
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Affiliation(s)
- Yannan Su
- Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany; Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
| | - Zhuanghua Shi
- General and Experimental Psychology, Ludwig-Maximilians-Universität München, Munich, Germany.
| | - Thomas Wachtler
- Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany; Bernstein Center for Computational Neuroscience Munich, Planegg-Martinsried, Germany.
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Brocard S, Wilson VAD, Berton C, Zuberbühler K, Bickel B. A universal preference for animate agents in hominids. iScience 2024; 27:109996. [PMID: 38883826 PMCID: PMC11177197 DOI: 10.1016/j.isci.2024.109996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/15/2024] [Accepted: 05/14/2024] [Indexed: 06/18/2024] Open
Abstract
When conversing, humans instantaneously predict meaning from fragmentary and ambiguous mspeech, long before utterance completion. They do this by integrating priors (initial assumptions about the world) with contextual evidence to rapidly decide on the most likely meaning. One powerful prior is attentional preference for agents, which biases sentence processing but universally so only if agents are animate. Here, we investigate the evolutionary origins of this preference, by allowing chimpanzees, gorillas, orangutans, human children, and adults to freely choose between agents and patients in still images, following video clips depicting their dyadic interaction. All participants preferred animate (and occasionally inanimate) agents, although the effect was attenuated if patients were also animate. The findings suggest that a preference for animate agents evolved before language and is not reducible to simple perceptual biases. To conclude, both humans and great apes prefer animate agents in decision tasks, echoing a universal prior in human language processing.
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Affiliation(s)
- Sarah Brocard
- Department of Comparative Cognition, Institute of Biology, University of Neuchatel, Neuchatel, Switzerland
| | - Vanessa A D Wilson
- Department of Comparative Cognition, Institute of Biology, University of Neuchatel, Neuchatel, Switzerland
- Department of Comparative Language Science, University of Zurich, Zurich, Switzerland
- Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Zurich, Switzerland
| | - Chloé Berton
- Department of Comparative Cognition, Institute of Biology, University of Neuchatel, Neuchatel, Switzerland
| | - Klaus Zuberbühler
- Department of Comparative Cognition, Institute of Biology, University of Neuchatel, Neuchatel, Switzerland
- Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Zurich, Switzerland
- School of Psychology & Neuroscience, University of St Andrews, St Andrews, Scotland (UK)
| | - Balthasar Bickel
- Department of Comparative Language Science, University of Zurich, Zurich, Switzerland
- Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Zurich, Switzerland
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Nair SS, Chakravarthy S. A Computational Model of Deep Brain Stimulation for Parkinson's Disease Tremor and Bradykinesia. Brain Sci 2024; 14:620. [PMID: 38928620 PMCID: PMC11201485 DOI: 10.3390/brainsci14060620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 06/05/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
Parkinson's disease (PD) is a progressive neurological disorder that is typically characterized by a range of motor dysfunctions, and its impact extends beyond physical abnormalities into emotional well-being and cognitive symptoms. The loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) leads to an array of dysfunctions in the functioning of the basal ganglia (BG) circuitry that manifests into PD. While active research is being carried out to find the root cause of SNc cell death, various therapeutic techniques are used to manage the symptoms of PD. The most common approach in managing the symptoms is replenishing the lost dopamine in the form of taking dopaminergic medications such as levodopa, despite its long-term complications. Another commonly used intervention for PD is deep brain stimulation (DBS). DBS is most commonly used when levodopa medication efficacy is reduced, and, in combination with levodopa medication, it helps reduce the required dosage of medication, prolonging the therapeutic effect. DBS is also a first choice option when motor complications such as dyskinesia emerge as a side effect of medication. Several studies have also reported that though DBS is found to be effective in suppressing severe motor symptoms such as tremors and rigidity, it has an adverse effect on cognitive capabilities. Henceforth, it is important to understand the exact mechanism of DBS in alleviating motor symptoms. A computational model of DBS stimulation for motor symptoms will offer great insights into understanding the mechanisms underlying DBS, and, along this line, in our current study, we modeled a cortico-basal ganglia circuitry of arm reaching, where we simulated healthy control (HC) and PD symptoms as well as the DBS effect on PD tremor and bradykinesia. Our modeling results reveal that PD tremors are more correlated with the theta band, while bradykinesia is more correlated with the beta band of the frequency spectrum of the local field potential (LFP) of the subthalamic nucleus (STN) neurons. With a DBS current of 220 pA, 130 Hz, and a 100 microsecond pulse-width, we could found the maximum therapeutic effect for the pathological dynamics simulated using our model using a set of parameter values. However, the exact DBS characteristics vary from patient to patient, and this can be further studied by exploring the model parameter space. This model can be extended to study different DBS targets and accommodate cognitive dynamics in the future to study the impact of DBS on cognitive symptoms and thereby optimize the parameters to produce optimal performance effects across modalities. Combining DBS with rehabilitation is another frontier where DBS can reduce symptoms such as tremors and rigidity, enabling patients to participate in their therapy. With DBS providing instant relief to patients, a combination of DBS and rehabilitation can enhance neural plasticity. One of the key motivations behind combining DBS with rehabilitation is to expect comparable results in motor performance even with milder DBS currents.
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Affiliation(s)
| | - Srinivasa Chakravarthy
- Department of Biotechnology, Bhupat and Mehta Jyoti School of Biosciences, Chennai 600036, India;
- Department of Medical Science and Technology, Indian Institute of Technology Madras, Sardar Patel Road, Adyar, Chennai 600036, India
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27
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Gehrke L, Terfurth L, Akman S, Gramann K. Visuo-haptic prediction errors: a multimodal dataset (EEG, motion) in BIDS format indexing mismatches in haptic interaction. FRONTIERS IN NEUROERGONOMICS 2024; 5:1411305. [PMID: 38903905 PMCID: PMC11188399 DOI: 10.3389/fnrgo.2024.1411305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 05/20/2024] [Indexed: 06/22/2024]
Affiliation(s)
- Lukas Gehrke
- Biological Psychology and Neuroergonomics, Department of Psychology and Ergonomics, Technological University Berlin, Berlin, Germany
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Bays PM, Schneegans S, Ma WJ, Brady TF. Representation and computation in visual working memory. Nat Hum Behav 2024; 8:1016-1034. [PMID: 38849647 DOI: 10.1038/s41562-024-01871-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 03/22/2024] [Indexed: 06/09/2024]
Abstract
The ability to sustain internal representations of the sensory environment beyond immediate perception is a fundamental requirement of cognitive processing. In recent years, debates regarding the capacity and fidelity of the working memory (WM) system have advanced our understanding of the nature of these representations. In particular, there is growing recognition that WM representations are not merely imperfect copies of a perceived object or event. New experimental tools have revealed that observers possess richer information about the uncertainty in their memories and take advantage of environmental regularities to use limited memory resources optimally. Meanwhile, computational models of visuospatial WM formulated at different levels of implementation have converged on common principles relating capacity to variability and uncertainty. Here we review recent research on human WM from a computational perspective, including the neural mechanisms that support it.
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Affiliation(s)
- Paul M Bays
- Department of Psychology, University of Cambridge, Cambridge, UK
| | | | - Wei Ji Ma
- Center for Neural Science and Department of Psychology, New York University, New York, NY, USA
| | - Timothy F Brady
- Department of Psychology, University of California, San Diego, La Jolla, CA, USA.
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29
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Yasoda-Mohan A, Chen F, Ó Sé C, Allard R, Ost J, Vanneste S. Phantom perception as a Bayesian inference problem: a pilot study. J Neurophysiol 2024; 131:1311-1327. [PMID: 38718414 DOI: 10.1152/jn.00349.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 04/18/2024] [Accepted: 05/01/2024] [Indexed: 06/19/2024] Open
Abstract
Tinnitus is the perception of a continuous sound in the absence of an external source. Although the role of the auditory system is well investigated, there is a gap in how multisensory signals are integrated to produce a single percept in tinnitus. Here, we train participants to learn a new sensory environment by associating a cue with a target signal that varies in perceptual threshold. In the test phase, we present only the cue to see whether the person perceives an illusion of the target signal. We perform two separate experiments to observe the behavioral and electrophysiological responses to the learning and test phases in 1) healthy young adults and 2) people with continuous subjective tinnitus and matched control subjects. We observed that in both parts of the study the percentage of false alarms was negatively correlated with the 75% detection threshold. Additionally, the perception of an illusion goes together with increased evoked response potential in frontal regions of the brain. Furthermore, in patients with tinnitus, we observe no significant difference in behavioral or evoked response in the auditory paradigm, whereas patients with tinnitus were more likely to report false alarms along with increased evoked activity during the learning and test phases in the visual paradigm. This emphasizes the importance of integrity of sensory pathways in multisensory integration and how this process may be disrupted in people with tinnitus. Furthermore, the present study also presents preliminary data supporting evidence that tinnitus patients may be building stronger perceptual models, which needs future studies with a larger population to provide concrete evidence on.NEW & NOTEWORTHY Tinnitus is the continuous phantom perception of a ringing in the ears. Recently, it has been suggested that tinnitus may be a maladaptive inference of the brain to auditory anomalies, whether they are detected or undetected by an audiogram. The present study presents empirical evidence for this hypothesis by inducing an illusion in a sensory domain that is damaged (auditory) and one that is intact (visual). It also presents novel information about how people with tinnitus process multisensory stimuli in the audio-visual domain.
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Affiliation(s)
- Anusha Yasoda-Mohan
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
- Lab for Clinical and Integrative Neuroscience, Trinity College Institute for Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Feifan Chen
- Lab for Clinical and Integrative Neuroscience, Trinity College Institute for Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Colum Ó Sé
- Lab for Clinical and Integrative Neuroscience, Trinity College Institute for Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Remy Allard
- School of Optometry, University of Montreal, Montreal, Quebec, Canada
| | - Jan Ost
- Brain Research Center for Advanced, International, Innovative and Interdisciplinary Neuromodulation, Ghent, Belgium
| | - Sven Vanneste
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
- Lab for Clinical and Integrative Neuroscience, Trinity College Institute for Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
- Brain Research Center for Advanced, International, Innovative and Interdisciplinary Neuromodulation, Ghent, Belgium
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Jordan J, Sacramento J, Wybo WAM, Petrovici MA, Senn W. Conductance-based dendrites perform Bayes-optimal cue integration. PLoS Comput Biol 2024; 20:e1012047. [PMID: 38865345 PMCID: PMC11168673 DOI: 10.1371/journal.pcbi.1012047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 03/31/2024] [Indexed: 06/14/2024] Open
Abstract
A fundamental function of cortical circuits is the integration of information from different sources to form a reliable basis for behavior. While animals behave as if they optimally integrate information according to Bayesian probability theory, the implementation of the required computations in the biological substrate remains unclear. We propose a novel, Bayesian view on the dynamics of conductance-based neurons and synapses which suggests that they are naturally equipped to optimally perform information integration. In our approach apical dendrites represent prior expectations over somatic potentials, while basal dendrites represent likelihoods of somatic potentials. These are parametrized by local quantities, the effective reversal potentials and membrane conductances. We formally demonstrate that under these assumptions the somatic compartment naturally computes the corresponding posterior. We derive a gradient-based plasticity rule, allowing neurons to learn desired target distributions and weight synaptic inputs by their relative reliabilities. Our theory explains various experimental findings on the system and single-cell level related to multi-sensory integration, which we illustrate with simulations. Furthermore, we make experimentally testable predictions on Bayesian dendritic integration and synaptic plasticity.
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Affiliation(s)
- Jakob Jordan
- Department of Physiology, University of Bern, Bern, Switzerland
- Electrical Engineering, Yale University, New Haven, Connecticut, United States of America
| | - João Sacramento
- Department of Physiology, University of Bern, Bern, Switzerland
- Institute of Neuroinformatics, UZH / ETH Zurich, Zurich, Switzerland
| | - Willem A. M. Wybo
- Department of Physiology, University of Bern, Bern, Switzerland
- Institute of Neuroscience and Medicine, Forschungszentrum Jülich, Jülich, Germany
| | | | - Walter Senn
- Department of Physiology, University of Bern, Bern, Switzerland
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31
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Kringelbach ML, Sanz Perl Y, Deco G. The Thermodynamics of Mind. Trends Cogn Sci 2024; 28:568-581. [PMID: 38677884 DOI: 10.1016/j.tics.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/16/2024] [Accepted: 03/18/2024] [Indexed: 04/29/2024]
Abstract
To not only survive, but also thrive, the brain must efficiently orchestrate distributed computation across space and time. This requires hierarchical organisation facilitating fast information transfer and processing at the lowest possible metabolic cost. Quantifying brain hierarchy is difficult but can be estimated from the asymmetry of information flow. Thermodynamics has successfully characterised hierarchy in many other complex systems. Here, we propose the 'Thermodynamics of Mind' framework as a natural way to quantify hierarchical brain orchestration and its underlying mechanisms. This has already provided novel insights into the orchestration of hierarchy in brain states including movie watching, where the hierarchy of the brain is flatter than during rest. Overall, this framework holds great promise for revealing the orchestration of cognition.
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Affiliation(s)
- Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; International Centre for Flourishing, Universities of Oxford, Aarhus, and Pompeu Fabra, Oxford, UK.
| | - Yonatan Sanz Perl
- International Centre for Flourishing, Universities of Oxford, Aarhus, and Pompeu Fabra, Oxford, UK; Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, Spain; Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
| | - Gustavo Deco
- International Centre for Flourishing, Universities of Oxford, Aarhus, and Pompeu Fabra, Oxford, UK; Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain.
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32
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De Ridder D, Siddiqi MA, Dauwels J, Serdijn WA, Strydis C. NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed? Neuromodulation 2024; 27:711-729. [PMID: 38639704 DOI: 10.1016/j.neurom.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/05/2023] [Accepted: 01/10/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVES Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model. MATERIALS AND METHODS A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation. RESULTS Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements. CONCLUSION The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.
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Affiliation(s)
- Dirk De Ridder
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
| | - Muhammad Ali Siddiqi
- Department of Electrical Engineering, Lahore University of Management Sciences, Lahore, Pakistan; Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
| | - Justin Dauwels
- Microelectronics Department, Delft University of Technology, Delft, The Netherlands
| | - Wouter A Serdijn
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Section Bioelectronics, Delft University of Technology, Delft, The Netherlands
| | - Christos Strydis
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
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33
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Muñoz-Caracuel M, Muñoz V, Ruiz-Martínez FJ, Vázquez Morejón AJ, Gómez CM. Systemic neurophysiological signals of auditory predictive coding. Psychophysiology 2024; 61:e14544. [PMID: 38351668 DOI: 10.1111/psyp.14544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/03/2024] [Accepted: 02/02/2024] [Indexed: 05/16/2024]
Abstract
Predictive coding framework posits that our brain continuously monitors changes in the environment and updates its predictive models, minimizing prediction errors to efficiently adapt to environmental demands. However, the underlying neurophysiological mechanisms of these predictive phenomena remain unclear. The present study aimed to explore the systemic neurophysiological correlates of predictive coding processes during passive and active auditory processing. Electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and autonomic nervous system (ANS) measures were analyzed using an auditory pattern-based novelty oddball paradigm. A sample of 32 healthy subjects was recruited. The results showed shared slow evoked potentials between passive and active conditions that could be interpreted as automatic predictive processes of anticipation and updating, independent of conscious attentional effort. A dissociated topography of the cortical hemodynamic activity and distinctive evoked potentials upon auditory pattern violation were also found between both conditions, whereas only conscious perception leading to imperative responses was accompanied by phasic ANS responses. These results suggest a systemic-level hierarchical reallocation of predictive coding neural resources as a function of contextual demands in the face of sensory stimulation. Principal component analysis permitted to associate the variability of some of the recorded signals.
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Affiliation(s)
- Manuel Muñoz-Caracuel
- Department of Experimental Psychology, University of Seville, Seville, Spain
- Mental Health Unit, Hospital Universitario Virgen del Rocio, Seville, Spain
| | - Vanesa Muñoz
- Department of Experimental Psychology, University of Seville, Seville, Spain
| | | | - Antonio J Vázquez Morejón
- Mental Health Unit, Hospital Universitario Virgen del Rocio, Seville, Spain
- Department of Personality, Evaluation and Psychological Treatments, University of Seville, Seville, Spain
| | - Carlos M Gómez
- Department of Experimental Psychology, University of Seville, Seville, Spain
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Poublan-Couzardot A, Talmi D. Pain perception as hierarchical Bayesian inference: A test case for the theory of constructed emotion. Ann N Y Acad Sci 2024; 1536:42-59. [PMID: 38837401 DOI: 10.1111/nyas.15141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
An intriguing perspective about human emotion, the theory of constructed emotion considers emotions as generative models according to the Bayesian brain hypothesis. This theory brings fresh insight to existing findings, but its complexity renders it challenging to test experimentally. We argue that laboratory studies of pain could support the theory because although some may not consider pain to be a genuine emotion, the theory must at minimum be able to explain pain perception and its dysfunction in pathology. We review emerging evidence that bear on this question. We cover behavioral and neural laboratory findings, computational models, placebo hyperalgesia, and chronic pain. We conclude that there is substantial evidence for a predictive processing account of painful experience, paving the way for a better understanding of neuronal and computational mechanisms of other emotions.
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Affiliation(s)
- Arnaud Poublan-Couzardot
- Université Claude Bernard Lyon 1, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL, Bron, France
| | - Deborah Talmi
- Department of Psychology, University of Cambridge, Cambridge, UK
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35
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Truman A, Kutas M. Flexible Conceptual Representations. Cogn Sci 2024; 48:e13475. [PMID: 38923016 DOI: 10.1111/cogs.13475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 05/14/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024]
Abstract
A view that has been gaining prevalence over the past decade is that the human conceptual system is malleable, dynamic, context-dependent, and task-dependent, that is, flexible. Within the flexible conceptual representation framework, conceptual representations are constructed ad hoc, forming a different, idiosyncratic instantiation upon each occurrence. In this review, we scrutinize the neurocognitive literature to better understand the nature of this flexibility. First, we identify some key characteristics of these representations. Next, we consider how these flexible representations are constructed by addressing some of the open questions in this framework: We review the age-old question of how to reconcile flexibility with the apparent need for shareable stable definitions to anchor meaning and come to mutual understanding, as well as some newer questions we find critical, namely, the nature of relations among flexible representations, the role of feature saliency in activation, and the viability of all-or-none feature activations. We suggest replacing the debate about the existence of a definitional stable core that is obligatorily activated with a question of the degree and probability of activation of the information constituting a conceptual representation. We rely on published works to suggest that (1) prior featural salience matters, (2) feature activation may be graded, and (3) Bayesian updating of prior information according to current demands offers a viable account of how flexible representations are constructed. This proposal provides a theoretical mechanism for incorporating a changing momentary context into a constructed representation, while still preserving some of the concept's constituent meaning.
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Affiliation(s)
- Alyssa Truman
- Department of Cognitive Science, University of California, San Diego
| | - Marta Kutas
- Department of Cognitive Science, University of California, San Diego
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36
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Ficco L, Li C, Kaufmann JM, Schweinberger SR, Kovács GZ. Investigating the neural effects of typicality and predictability for face and object stimuli. PLoS One 2024; 19:e0293781. [PMID: 38776350 PMCID: PMC11111078 DOI: 10.1371/journal.pone.0293781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/08/2024] [Indexed: 05/24/2024] Open
Abstract
The brain calibrates itself based on the past stimulus diet, which makes frequently observed stimuli appear as typical (as opposed to uncommon stimuli, which appear as distinctive). Based on predictive processing theory, the brain should be more "prepared" for typical exemplars, because these contain information that has been encountered frequently, allowing it to economically represent items of that category. Thus, one could ask whether predictability and typicality of visual stimuli interact, or rather act in an additive manner. We adapted the design by Egner and colleagues (2010), who used cues to induce expectations about stimulus category (face vs. chair) occurrence during an orthogonal inversion detection task. We measured BOLD responses with fMRI in 35 participants. First, distinctive stimuli always elicited stronger responses than typical ones in all ROIs, and our whole-brain directional contrasts for the effects of typicality and distinctiveness converge with previous findings. Second and importantly, we could not replicate the interaction between category and predictability reported by Egner et al. (2010), which casts doubt on whether cueing designs are ideal to elicit reliable predictability effects. Third, likely as a consequence of the lack of predictability effects, we found no interaction between predictability and typicality in any of the four tested regions (bilateral fusiform face areas, lateral occipital complexes) when considering both categories, nor in the whole brain. We discuss the issue of replicability in neuroscience and sketch an agenda for how future studies might address the same question.
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Affiliation(s)
- Linda Ficco
- Department of General Psychology and Cognitive Neuroscience, Friedrich Schiller University, Jena, Germany
- Department of Biological Psychology and Cognitive Neurosciences, Friedrich Schiller University, Jena, Germany
- International Max-Planck Research School for the Science of Human History, Jena, Germany
| | - Chenglin Li
- Department of Biological Psychology and Cognitive Neurosciences, Friedrich Schiller University, Jena, Germany
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Jürgen M. Kaufmann
- Department of General Psychology and Cognitive Neuroscience, Friedrich Schiller University, Jena, Germany
| | - Stefan R. Schweinberger
- Department of General Psychology and Cognitive Neuroscience, Friedrich Schiller University, Jena, Germany
- International Max-Planck Research School for the Science of Human History, Jena, Germany
| | - Gyula Z. Kovács
- Department of Biological Psychology and Cognitive Neurosciences, Friedrich Schiller University, Jena, Germany
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White O, Dehouck V, Boulanger N, Dierick F, Babič J, Goswami N, Buisseret F. Resonance tuning of rhythmic movements is disrupted at short time scales: A centrifuge study. iScience 2024; 27:109618. [PMID: 38650981 PMCID: PMC11033689 DOI: 10.1016/j.isci.2024.109618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/17/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
The human body exploits its neural mechanisms to optimize actions. Rhythmic movements are optimal when their frequency is close to the natural frequency of the system. In a pendulum, gravity modulates this spontaneous frequency. Participants unconsciously adjust their natural pace when cyclically moving the arm in altered gravity. However, the timescale of this adaptation is unexplored. Participants performed cyclic movements before, during, and after fast transitions between hypergravity levels (1g-3g and 3g-1g) induced by a human centrifuge. Movement periods were modulated with the average value of gravity during transitions. However, while participants increased movement pace on a cycle basis when gravity increased (1g-3g), they did not decrease pace when gravity decreased (3g-1g). We highlight asymmetric effects in the spontaneous adjustment of movement dynamics on short timescales, suggesting the involvement of cognitive factors, beyond standard dynamical models.
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Affiliation(s)
- Olivier White
- INSERM UMR1093-CAPS, Université de Bourgogne, UFR des Sciences du Sport, 21000 Dijon, France
| | - Victor Dehouck
- INSERM UMR1093-CAPS, Université de Bourgogne, UFR des Sciences du Sport, 21000 Dijon, France
| | - Nicolas Boulanger
- Service de Physique de l’Univers, Champs et Gravitation, UMONS Research Institute for Complex Systems, Université de Mons, 20 Place du Parc, 7000 Mons, Belgium
| | - Frédéric Dierick
- CeREF-Technique, Chaussée de Binche 159, 7000 Mons, Belgium
- Laboratoire d’Analyse du Mouvement et de la Posture (LAMP), Centre National de Rééducation Fonctionnelle et de Réadaptation—Rehazenter, Rue André Vésale 1, 2674 Luxembourg, Luxembourg
- Faculté des Sciences de la Motricité, UCLouvain, Place Pierre de Coubertin 2, 1348 Louvain-la-Neuve, Belgium
| | - Jan Babič
- Laboratory for Neuromechanics, and Biorobotics, Jožef Stefan Institute, Ljubljana, Slovenia
- Slovenia and also with the Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
| | - Nandu Goswami
- Gravitational Physiology and Medicine Research Unit, Otto Loewi Research Center of Vascular Biology, Immunity and Inflammation, Medical University of Graz, Graz, Austria
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Fabien Buisseret
- CeREF-Technique, Chaussée de Binche 159, 7000 Mons, Belgium
- Service de Physique Nucléaire et Subnucléaire, UMONS Research Institute for Complex Systems, Université de Mons, 20 Place du Parc, 7000 Mons, Belgium
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38
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Ota K, Maloney LT. Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample. PLoS Comput Biol 2024; 20:e1011999. [PMID: 38691544 PMCID: PMC11104641 DOI: 10.1371/journal.pcbi.1011999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 05/20/2024] [Accepted: 03/14/2024] [Indexed: 05/03/2024] Open
Abstract
Bayesian decision theory (BDT) is frequently used to model normative performance in perceptual, motor, and cognitive decision tasks where the possible outcomes of actions are associated with rewards or penalties. The resulting normative models specify how decision makers should encode and combine information about uncertainty and value-step by step-in order to maximize their expected reward. When prior, likelihood, and posterior are probabilities, the Bayesian computation requires only simple arithmetic operations: addition, etc. We focus on visual cognitive tasks where Bayesian computations are carried out not on probabilities but on (1) probability density functions and (2) these probability density functions are derived from samples. We break the BDT model into a series of computations and test human ability to carry out each of these computations in isolation. We test three necessary properties of normative use of pdf information derived from a sample-accuracy, additivity and influence. Influence measures allow us to assess how much weight each point in the sample is assigned in making decisions and allow us to compare normative use (weighting) of samples to actual, point by point. We find that human decision makers violate accuracy and additivity systematically but that the cost of failure in accuracy or additivity would be minor in common decision tasks. However, a comparison of measured influence for each sample point with normative influence measures demonstrates that the individual's use of sample information is markedly different from the predictions of BDT. We will show that the normative BDT model takes into account the geometric symmetries of the pdf while the human decision maker does not. An alternative model basing decisions on a single extreme sample point provided a better account for participants' data than the normative BDT model.
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Affiliation(s)
- Keiji Ota
- Department of Psychology, New York University, New York, New York, United States
- Center for Neural Science, New York University, New York, New York, United States
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
- Department of Psychology, School of Biologoical and Behavioural Sciences, Queen Mary University of London, London, United Kingdom
| | - Laurence T. Maloney
- Department of Psychology, New York University, New York, New York, United States
- Center for Neural Science, New York University, New York, New York, United States
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39
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Scheller M, Fang H, Sui J. Self as a prior: The malleability of Bayesian multisensory integration to social salience. Br J Psychol 2024; 115:185-205. [PMID: 37747452 DOI: 10.1111/bjop.12683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 08/26/2023] [Accepted: 09/11/2023] [Indexed: 09/26/2023]
Abstract
Our everyday perceptual experiences are grounded in the integration of information within and across our senses. Due to this direct behavioural relevance, cross-modal integration retains a certain degree of contextual flexibility, even to social relevance. However, how social relevance modulates cross-modal integration remains unclear. To investigate possible mechanisms, Experiment 1 tested the principles of audio-visual integration for numerosity estimation by deriving a Bayesian optimal observer model with perceptual prior from empirical data to explain perceptual biases. Such perceptual priors may shift towards locations of high salience in the stimulus space. Our results showed that the tendency to over- or underestimate numerosity, expressed in the frequency and strength of fission and fusion illusions, depended on the actual event numerosity. Experiment 2 replicated the effects of social relevance on multisensory integration from Scheller & Sui, 2022 JEP:HPP, using a lower number of events, thereby favouring the opposite illusion through enhanced influences of the prior. In line with the idea that the self acts like a prior, the more frequently observed illusion (more malleable to prior influences) was modulated by self-relevance. Our findings suggest that the self can influence perception by acting like a prior in cue integration, biasing perceptual estimates towards areas of high self-relevance.
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Affiliation(s)
- Meike Scheller
- Department of Psychology, University of Aberdeen, Aberdeen, UK
- Department of Psychology, Durham University, Durham, UK
| | - Huilin Fang
- Department of Psychology, University of Aberdeen, Aberdeen, UK
| | - Jie Sui
- Department of Psychology, University of Aberdeen, Aberdeen, UK
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40
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Wang W, Zhu C, Jia T, Zu M, Tang Y, Zhou L, Tian Y, Si B, Zhou K. Reviving Bistable Perception in Patients With Depression by Decreasing the Overestimation of Prior Precision. Cogn Sci 2024; 48:e13452. [PMID: 38742272 DOI: 10.1111/cogs.13452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/01/2024] [Accepted: 04/18/2024] [Indexed: 05/16/2024]
Abstract
Slower perceptual alternations, a notable perceptual effect observed in psychiatric disorders, can be alleviated by antidepressant therapies that affect serotonin levels in the brain. While these phenomena have been well documented, the underlying neurocognitive mechanisms remain to be elucidated. Our study bridges this gap by employing a computational cognitive approach within a Bayesian predictive coding framework to explore these mechanisms in depression. We fitted a prediction error (PE) model to behavioral data from a binocular rivalry task, uncovering that significantly higher initial prior precision and lower PE led to a slower switch rate in patients with depression. Furthermore, serotonin-targeting antidepressant treatments significantly decreased the prior precision and increased PE, both of which were predictive of improvements in the perceptual alternation rate of depression patients. These findings indicated that the substantially slower perception switch rate in patients with depression was caused by the greater reliance on top-down priors and that serotonin treatment's efficacy was in its recalibration of these priors and enhancement of PE. Our study not only elucidates the cognitive underpinnings of depression, but also suggests computational modeling as a potent tool for integrating cognitive science with clinical psychology, advancing our understanding and treatment of cognitive impairments in depression.
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Affiliation(s)
- Wenbo Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University
| | - Changbo Zhu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences
- University of Chinese Academy of Sciences, Beijing
| | | | - Meidan Zu
- Department of Psychology and Sleep Medicine, The Second Hospital of Anhui Medical University
| | - Yandong Tang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences
- University of Chinese Academy of Sciences, Beijing
| | - Liqin Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University
| | - Yanghua Tian
- Department of Neurology, The Second Hospital of Anhui Medical University
- Department of Neurology, The First Hospital of Anhui Medical University
| | - Bailu Si
- School of Systems Science, Beijing Normal University
| | - Ke Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University
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41
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Matsumura T, Esaki K, Yang S, Yoshimura C, Mizuno H. Active Inference With Empathy Mechanism for Socially Behaved Artificial Agents in Diverse Situations. ARTIFICIAL LIFE 2024; 30:277-297. [PMID: 38018026 DOI: 10.1162/artl_a_00416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
This article proposes a method for an artificial agent to behave in a social manner. Although defining proper social behavior is difficult because it differs from situation to situation, the agent following the proposed method adaptively behaves appropriately in each situation by empathizing with the surrounding others. The proposed method is achieved by incorporating empathy into active inference. We evaluated the proposed method regarding control of autonomous mobile robots in diverse situations. From the evaluation results, an agent controlled by the proposed method could behave more adaptively socially than an agent controlled by the standard active inference in the diverse situations. In the case of two agents, the agent controlled with the proposed method behaved in a social way that reduced the other agent's travel distance by 13.7% and increased the margin between the agents by 25.8%, even though it increased the agent's travel distance by 8.2%. Also, the agent controlled with the proposed method behaved more socially when it was surrounded by altruistic others but less socially when it was surrounded by selfish others.
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42
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Shaw S, Kilpatrick ZP. Representing stimulus motion with waves in adaptive neural fields. J Comput Neurosci 2024; 52:145-164. [PMID: 38607466 DOI: 10.1007/s10827-024-00869-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 04/13/2024]
Abstract
Traveling waves of neural activity emerge in cortical networks both spontaneously and in response to stimuli. The spatiotemporal structure of waves can indicate the information they encode and the physiological processes that sustain them. Here, we investigate the stimulus-response relationships of traveling waves emerging in adaptive neural fields as a model of visual motion processing. Neural field equations model the activity of cortical tissue as a continuum excitable medium, and adaptive processes provide negative feedback, generating localized activity patterns. Synaptic connectivity in our model is described by an integral kernel that weakens dynamically due to activity-dependent synaptic depression, leading to marginally stable traveling fronts (with attenuated backs) or pulses of a fixed speed. Our analysis quantifies how weak stimuli shift the relative position of these waves over time, characterized by a wave response function we obtain perturbatively. Persistent and continuously visible stimuli model moving visual objects. Intermittent flashes that hop across visual space can produce the experience of smooth apparent visual motion. Entrainment of waves to both kinds of moving stimuli are well characterized by our theory and numerical simulations, providing a mechanistic description of the perception of visual motion.
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Affiliation(s)
- Sage Shaw
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
| | - Zachary P Kilpatrick
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA.
- Institute for Cognitive Sciences, University of Colorado Boulder, Boulder, CO, USA.
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43
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Terada Y, Toyoizumi T. Chaotic neural dynamics facilitate probabilistic computations through sampling. Proc Natl Acad Sci U S A 2024; 121:e2312992121. [PMID: 38648479 PMCID: PMC11067032 DOI: 10.1073/pnas.2312992121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 02/13/2024] [Indexed: 04/25/2024] Open
Abstract
Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.
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Affiliation(s)
- Yu Terada
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Saitama351-0198, Japan
- Department of Neurobiology, University of California, San Diego, La Jolla, CA92093
- The Institute for Physics of Intelligence, The University of Tokyo, Tokyo113-0033, Japan
| | - Taro Toyoizumi
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Saitama351-0198, Japan
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo113-8656, Japan
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44
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Zhang Z, Xu F. An Overview of the Free Energy Principle and Related Research. Neural Comput 2024; 36:963-1021. [PMID: 38457757 DOI: 10.1162/neco_a_01642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/20/2023] [Indexed: 03/10/2024]
Abstract
The free energy principle and its corollary, the active inference framework, serve as theoretical foundations in the domain of neuroscience, explaining the genesis of intelligent behavior. This principle states that the processes of perception, learning, and decision making-within an agent-are all driven by the objective of "minimizing free energy," evincing the following behaviors: learning and employing a generative model of the environment to interpret observations, thereby achieving perception, and selecting actions to maintain a stable preferred state and minimize the uncertainty about the environment, thereby achieving decision making. This fundamental principle can be used to explain how the brain processes perceptual information, learns about the environment, and selects actions. Two pivotal tenets are that the agent employs a generative model for perception and planning and that interaction with the world (and other agents) enhances the performance of the generative model and augments perception. With the evolution of control theory and deep learning tools, agents based on the FEP have been instantiated in various ways across different domains, guiding the design of a multitude of generative models and decision-making algorithms. This letter first introduces the basic concepts of the FEP, followed by its historical development and connections with other theories of intelligence, and then delves into the specific application of the FEP to perception and decision making, encompassing both low-dimensional simple situations and high-dimensional complex situations. It compares the FEP with model-based reinforcement learning to show that the FEP provides a better objective function. We illustrate this using numerical studies of Dreamer3 by adding expected information gain into the standard objective function. In a complementary fashion, existing reinforcement learning, and deep learning algorithms can also help implement the FEP-based agents. Finally, we discuss the various capabilities that agents need to possess in complex environments and state that the FEP can aid agents in acquiring these capabilities.
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Affiliation(s)
- Zhengquan Zhang
- Key Laboratory of Information Science of Electromagnetic Waves, Fudan University, Shanghai, P.R.C.
| | - Feng Xu
- Key Laboratory of Information Science of Electromagnetic Waves, Fudan University, Shanghai, P.R.C.
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45
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Garlichs A, Blank H. Prediction error processing and sharpening of expected information across the face-processing hierarchy. Nat Commun 2024; 15:3407. [PMID: 38649694 PMCID: PMC11035707 DOI: 10.1038/s41467-024-47749-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024] Open
Abstract
The perception and neural processing of sensory information are strongly influenced by prior expectations. The integration of prior and sensory information can manifest through distinct underlying mechanisms: focusing on unexpected input, denoted as prediction error (PE) processing, or amplifying anticipated information via sharpened representation. In this study, we employed computational modeling using deep neural networks combined with representational similarity analyses of fMRI data to investigate these two processes during face perception. Participants were cued to see face images, some generated by morphing two faces, leading to ambiguity in face identity. We show that expected faces were identified faster and perception of ambiguous faces was shifted towards priors. Multivariate analyses uncovered evidence for PE processing across and beyond the face-processing hierarchy from the occipital face area (OFA), via the fusiform face area, to the anterior temporal lobe, and suggest sharpened representations in the OFA. Our findings support the proposition that the brain represents faces grounded in prior expectations.
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Affiliation(s)
- Annika Garlichs
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany.
| | - Helen Blank
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany.
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46
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Murphy CP, Runswick OR, Gredin NV, Broadbent DP. The effect of task load, information reliability and interdependency on anticipation performance. Cogn Res Princ Implic 2024; 9:22. [PMID: 38616234 PMCID: PMC11016527 DOI: 10.1186/s41235-024-00548-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 03/20/2024] [Indexed: 04/16/2024] Open
Abstract
In sport, coaches often explicitly provide athletes with stable contextual information related to opponent action preferences to enhance anticipation performance. This information can be dependent on, or independent of, dynamic contextual information that only emerges during the sequence of play (e.g. opponent positioning). The interdependency between contextual information sources, and the associated cognitive demands of integrating information sources during anticipation, has not yet been systematically examined. We used a temporal occlusion paradigm to alter the reliability of contextual and kinematic information during the early, mid- and final phases of a two-versus-two soccer anticipation task. A dual-task paradigm was incorporated to investigate the impact of task load on skilled soccer players' ability to integrate information and update their judgements in each phase. Across conditions, participants received no contextual information (control) or stable contextual information (opponent preferences) that was dependent on, or independent of, dynamic contextual information (opponent positioning). As predicted, participants used reliable contextual and kinematic information to enhance anticipation. Further exploratory analysis suggested that increased task load detrimentally affected anticipation accuracy but only when both reliable contextual and kinematic information were available for integration in the final phase. This effect was observed irrespective of whether the stable contextual information was dependent on, or independent of, dynamic contextual information. Findings suggest that updating anticipatory judgements in the final phase of a sequence of play based on the integration of reliable contextual and kinematic information requires cognitive resources.
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Affiliation(s)
- Colm P Murphy
- Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, CF23 6XD, UK.
- Faculty of Sport, Technology and Health Sciences, St. Mary's University, Twickenham, UK.
| | - Oliver R Runswick
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - N Viktor Gredin
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - David P Broadbent
- Centre for Cognitive Neuroscience, College of Health, Medicine and Life Sciences, Brunel University London, London, UK
- Centre for Sport Research, Institute for Physical Activity and Nutrition, Deakin University, Burwood, VIC, Australia
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47
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Lee K, Dora S, Mejias JF, Bohte SM, Pennartz CMA. Predictive coding with spiking neurons and feedforward gist signaling. Front Comput Neurosci 2024; 18:1338280. [PMID: 38680678 PMCID: PMC11045951 DOI: 10.3389/fncom.2024.1338280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/14/2024] [Indexed: 05/01/2024] Open
Abstract
Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neurons that approximate neural activity with firing rates in the continuous time domain and propagate signals synchronously. Therefore, we developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. Adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: (1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and (2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high baseline firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature.
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Affiliation(s)
- Kwangjun Lee
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands
| | - Shirin Dora
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands
- Department of Computer Science, School of Science, Loughborough University, Loughborough, United Kingdom
| | - Jorge F. Mejias
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands
| | - Sander M. Bohte
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands
- Machine Learning Group, Centre of Mathematics and Computer Science, Amsterdam, Netherlands
| | - Cyriel M. A. Pennartz
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands
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48
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Kolvoort IR, Fisher EL, van Rooij R, Schulz K, van Maanen L. Probabilistic causal reasoning under time pressure. PLoS One 2024; 19:e0297011. [PMID: 38603716 PMCID: PMC11008876 DOI: 10.1371/journal.pone.0297011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 12/26/2023] [Indexed: 04/13/2024] Open
Abstract
While causal reasoning is a core facet of our cognitive abilities, its time-course has not received proper attention. As the duration of reasoning might prove crucial in understanding the underlying cognitive processes, we asked participants in two experiments to make probabilistic causal inferences while manipulating time pressure. We found that participants are less accurate under time pressure, a speed-accuracy-tradeoff, and that they respond more conservatively. Surprisingly, two other persistent reasoning errors-Markov violations and failures to explain away-appeared insensitive to time pressure. These observations seem related to confidence: Conservative inferences were associated with low confidence, whereas Markov violations and failures to explain were not. These findings challenge existing theories that predict an association between time pressure and all causal reasoning errors including conservatism. Our findings suggest that these errors should not be attributed to a single cognitive mechanism and emphasize that causal judgements are the result of multiple processes.
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Affiliation(s)
- Ivar R. Kolvoort
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
- Institute for Logic, Language, and Computation, University of Amsterdam, Amsterdam, The Netherlands
- Department of Experimental Psychology, Utrecht University, Utrecht, The Netherlands
| | - Elizabeth L. Fisher
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Cognition & Philosophy Laboratory, Monash University, Clayton, Australia
| | - Robert van Rooij
- Institute for Logic, Language, and Computation, University of Amsterdam, Amsterdam, The Netherlands
| | - Katrin Schulz
- Institute for Logic, Language, and Computation, University of Amsterdam, Amsterdam, The Netherlands
| | - Leendert van Maanen
- Department of Experimental Psychology, Utrecht University, Utrecht, The Netherlands
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49
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Toussaint B, Heinzle J, Stephan KE. A computationally informed distinction of interoception and exteroception. Neurosci Biobehav Rev 2024; 159:105608. [PMID: 38432449 DOI: 10.1016/j.neubiorev.2024.105608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
While interoception is of major neuroscientific interest, its precise definition and delineation from exteroception continue to be debated. Here, we propose a functional distinction between interoception and exteroception based on computational concepts of sensor-effector loops. Under this view, the classification of sensory inputs as serving interoception or exteroception depends on the sensor-effector loop they feed into, for the control of either bodily (physiological and biochemical) or environmental states. We explain the utility of this perspective by examining the perception of skin temperature, one of the most challenging cases for distinguishing between interoception and exteroception. Specifically, we propose conceptualising thermoception as inference about the thermal state of the body (including the skin), which is directly coupled to thermoregulatory processes. This functional view emphasises the coupling to regulation (control) as a defining property of perception (inference) and connects the definition of interoception to contemporary computational theories of brain-body interactions.
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Affiliation(s)
- Birte Toussaint
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland.
| | - Jakob Heinzle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
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50
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Engström J, Liu SY, Dinparastdjadid A, Simoiu C. Modeling road user response timing in naturalistic traffic conflicts: A surprise-based framework. ACCIDENT; ANALYSIS AND PREVENTION 2024; 198:107460. [PMID: 38295653 DOI: 10.1016/j.aap.2024.107460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 12/07/2023] [Accepted: 01/03/2024] [Indexed: 02/20/2024]
Abstract
There is currently no established method for evaluating human response timing across a range of naturalistic traffic conflict types. Traditional notions derived from controlled experiments, such as perception-response time, fail to account for the situation-dependency of human responses and offer no clear way to define the stimulus in many common traffic conflict scenarios. As a result, they are not well suited for application in naturalistic settings. We present a novel framework for measuring and modeling response times in naturalistic traffic conflicts applicable to automated driving systems as well as other traffic safety domains. The framework suggests that response timing must be understood relative to the subject's current (prior) belief and is always embedded in, and dependent on, the dynamically evolving situation. The response process is modeled as a belief update process driven by perceived violations to this prior belief, that is, by surprising stimuli. The framework resolves two key limitations with traditional notions of response time when applied in naturalistic scenarios: (1) The strong situation dependence of response timing and (2) how to unambiguously define the stimulus. Resolving these issues is a challenge that must be addressed by any response timing model intended to be applied in naturalistic traffic conflicts. We show how the framework can be implemented by means of a relatively simple heuristic model fit to naturalistic human response data from real crashes and near crashes from the SHRP2 dataset and discuss how it is, in principle, generalizable to any traffic conflict scenario. We also discuss how the response timing framework can be implemented computationally based on evidence accumulation enhanced by machine learning-based generative models and the information-theoretic concept of surprise.
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Affiliation(s)
- Johan Engström
- Waymo LLC, 1600 Amphitheatre Parkway, Mountain View, 94043, CA, USA.
| | - Shu-Yuan Liu
- Waymo LLC, 1600 Amphitheatre Parkway, Mountain View, 94043, CA, USA
| | | | - Camelia Simoiu
- Waymo LLC, 1600 Amphitheatre Parkway, Mountain View, 94043, CA, USA
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