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Yan X, Ebitz RB, Grissom N, Darrow DP, Herman AB. Distinct computational mechanisms of uncertainty processing explain opposing exploratory behaviors in anxiety and apathy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.597412. [PMID: 38895240 PMCID: PMC11185698 DOI: 10.1101/2024.06.04.597412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Decision-making in uncertain environments often leads to varied outcomes. Understanding how individuals interpret the causes of unexpected feedback is crucial for adaptive behavior and mental well-being. Uncertainty can be broadly categorized into two components: volatility and stochasticity. Volatility is about how quickly conditions change, impacting results. Stochasticity, on the other hand, refers to outcomes affected by random chance or "luck". Understanding these factors enables individuals to have more effective environmental analysis and strategy implementation (explore or exploit) for future decisions. This study investigates how anxiety and apathy, two prevalent affective states, influence the perceptions of uncertainty and exploratory behavior. Participants (N = 1001) completed a restless three-armed bandit task that was analyzed using latent state models. Anxious individuals perceived uncertainty as more volatile, leading to increased exploration and learning rates, especially after reward omission. Conversely, apathetic individuals viewed uncertainty as more stochastic, resulting in decreased exploration and learning rates. The perceived volatility-to-stochasticity ratio mediated the anxiety-exploration relationship post-adverse outcomes. Dimensionality reduction showed exploration and uncertainty estimation to be distinct but related latent factors shaping a manifold of adaptive behavior that is modulated by anxiety and apathy. These findings reveal distinct computational mechanisms for how anxiety and apathy influence decision-making, providing a framework for understanding cognitive and affective processes in neuropsychiatric disorders.
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Affiliation(s)
- Xinyuan Yan
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - R. Becket Ebitz
- Department of Neuroscience, Universite de Montreal, 2900 Edouard Montpetit Blvd, Montreal, Quebec H3T 1J4, Canada
| | - Nicola Grissom
- Department of Psychology, University of Minnesota, 75 E River Rd, Minneapolis, MN 55455, USA
| | - David P. Darrow
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, USA
| | - Alexander B. Herman
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
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2
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Kim S, Kang U, Gu J, Kim J, Park J, Hwang GW, Park S, Jang HJ, Seong TY, Lee S. Artificial Multimodal Neuron with Associative Learning Capabilities: Acquisition, Extinction, and Spontaneous Recovery. ACS APPLIED MATERIALS & INTERFACES 2024; 16:36519-36526. [PMID: 38950119 DOI: 10.1021/acsami.4c02343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Associative multimodal artificial intelligence (AMAI) has gained significant attention across various fields, yet its implementation poses challenges due to the burden on computing and memory resources. To address these challenges, researchers have paid increasing attention to neuromorphic devices based on novel materials and structures, which can implement classical conditioning behaviors with simplified circuitry. Herein, we introduce an artificial multimodal neuron device that shows not only the acquisition behavior but also the extinction and the spontaneous recovery behaviors for the first time. Being composed of an ovonic threshold switch (OTS)-based neuron device, a conductive bridge memristor (CBM)-based synapse device, and a few passive electrical elements, such observed behaviors of this neuron device are explained in terms of the electroforming and the diffusion of metallic ions in the CBM. We believe that the proposed associative learning neuron device will shed light on the way of developing large-scale AMAI systems by providing inspiration to devise an associative learning network with improved energy efficiency.
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Affiliation(s)
- Sangheon Kim
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea
- Department of Materials Science and Engineering, Korea University, Seoul 02841, Rep. of Korea
| | - Unhyeon Kang
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea
- Materials Science & Engineering, Seoul National University, Seoul 08826, Rep. of Korea
| | - Jiyoung Gu
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea
- Department of Materials Science & Engineering, Seoul National University of Science and Technology, Seoul 01811, Rep. of Korea
| | - Jaewook Kim
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea
| | - Jongkil Park
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea
| | - Gyu Weon Hwang
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea
| | - Seongsik Park
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea
| | - Hyun Jae Jang
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea
| | - Tae-Yeon Seong
- Department of Materials Science and Engineering, Korea University, Seoul 02841, Rep. of Korea
| | - Suyoun Lee
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Rep. of Korea
- Division of Nano & Information Technology, Korea University of Science and Technology, Daejon 34316, Rep. of Korea
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3
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Vázquez D, Maulhardt SR, Stalnaker TA, Solway A, Charpentier CJ, Roesch MR. Optogenetic Inhibition of Rat Anterior Cingulate Cortex Impairs the Ability to Initiate and Stay on Task. J Neurosci 2024; 44:e1850232024. [PMID: 38569923 PMCID: PMC11097287 DOI: 10.1523/jneurosci.1850-23.2024] [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: 09/29/2023] [Revised: 01/16/2024] [Accepted: 01/20/2024] [Indexed: 04/05/2024] Open
Abstract
Our prior research has identified neural correlates of cognitive control in the anterior cingulate cortex (ACC), leading us to hypothesize that the ACC is necessary for increasing attention as rats flexibly learn new contingencies during a complex reward-guided decision-making task. Here, we tested this hypothesis by using optogenetics to transiently inhibit the ACC, while rats of either sex performed the same two-choice task. ACC inhibition had a profound impact on behavior that extended beyond deficits in attention during learning when expected outcomes were uncertain. We found that ACC inactivation slowed and reduced the number of trials rats initiated and impaired both their accuracy and their ability to complete sessions. Furthermore, drift-diffusion model analysis suggested that free-choice performance and evidence accumulation (i.e., reduced drift rates) were degraded during initial learning-leading to weaker associations that were more easily overridden in later trial blocks (i.e., stronger bias). Together, these results suggest that in addition to attention-related functions, the ACC contributes to the ability to initiate trials and generally stay on task.
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Affiliation(s)
- Daniela Vázquez
- Department of Psychology, University of Maryland, College Park, Maryland 20742
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20742
| | - Sean R Maulhardt
- Department of Psychology, University of Maryland, College Park, Maryland 20742
| | - Thomas A Stalnaker
- Intramural Research Program, National Institute on Drug Abuse, Baltimore, Maryland 21224
| | - Alec Solway
- Department of Psychology, University of Maryland, College Park, Maryland 20742
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20742
| | - Caroline J Charpentier
- Department of Psychology, University of Maryland, College Park, Maryland 20742
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20742
| | - Matthew R Roesch
- Department of Psychology, University of Maryland, College Park, Maryland 20742
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20742
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4
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Kang P, Tobler PN, Dayan P. Bayesian reinforcement learning: A basic overview. Neurobiol Learn Mem 2024; 211:107924. [PMID: 38579896 DOI: 10.1016/j.nlm.2024.107924] [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: 11/20/2023] [Revised: 03/21/2024] [Accepted: 04/02/2024] [Indexed: 04/07/2024]
Abstract
We and other animals learn because there is some aspect of the world about which we are uncertain. This uncertainty arises from initial ignorance, and from changes in the world that we do not perfectly know; the uncertainty often becomes evident when our predictions about the world are found to be erroneous. The Rescorla-Wagner learning rule, which specifies one way that prediction errors can occasion learning, has been hugely influential as a characterization of Pavlovian conditioning and, through its equivalence to the delta rule in engineering, in a much wider class of learning problems. Here, we review the embedding of the Rescorla-Wagner rule in a Bayesian context that is precise about the link between uncertainty and learning, and thereby discuss extensions to such suggestions as the Kalman filter, structure learning, and beyond, that collectively encompass a wider range of uncertainties and accommodate a wider assortment of phenomena in conditioning.
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Affiliation(s)
- Pyungwon Kang
- University of Zurich, Department of Economics, Laboratory for Social and Neural Systems Research, Zurich, Switzerland.
| | - Philippe N Tobler
- University of Zurich, Department of Economics, Laboratory for Social and Neural Systems Research, Zurich, Switzerland.
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany; University of Tübingen, Tübingen Germany.
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5
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Xu Y, Han S, Wang D, Wang G, Maltz JS, Yu H. Hybrid U-Net and Swin-transformer network for limited-angle cardiac computed tomography. Phys Med Biol 2024; 69:105012. [PMID: 38604178 PMCID: PMC11059034 DOI: 10.1088/1361-6560/ad3db9] [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: 12/04/2023] [Revised: 03/22/2024] [Accepted: 04/11/2024] [Indexed: 04/13/2024]
Abstract
Objective.Cardiac computed tomography (CT) is widely used for diagnosis of cardiovascular disease, the leading cause of morbidity and mortality in the world. Diagnostic performance depends strongly on the temporal resolution of the CT images. To image the beating heart, one can reduce the scanning time by acquiring limited-angle projections. However, this leads to increased image noise and limited-angle-related artifacts. The goal of this paper is to reconstruct high quality cardiac CT images from limited-angle projections.Approach. The ability to reconstruct high quality images from limited-angle projections is highly desirable and remains a major challenge. With the development of deep learning networks, such as U-Net and transformer networks, progresses have been reached on image reconstruction and processing. Here we propose a hybrid model based on the U-Net and Swin-transformer (U-Swin) networks. The U-Net has the potential to restore structural information due to missing projection data and related artifacts, then the Swin-transformer can gather a detailed global feature distribution.Main results. Using synthetic XCAT and clinical cardiac COCA datasets, we demonstrate that our proposed method outperforms the state-of-the-art deep learning-based methods.Significance. It has a great potential to freeze the beating heart with a higher temporal resolution.
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Affiliation(s)
- Yongshun Xu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, United States of America
| | - Shuo Han
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, United States of America
| | - Dayang Wang
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, United States of America
| | - Ge Wang
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, United States of America
| | - Jonathan S Maltz
- GE Healthcare, 3000 N Grandview Boulevard, Waukesha, WI, 53188, United States of America
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, United States of America
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6
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Rujoie A, Andersen OK, Frahm KS. Investigation of directional discrimination in the nociceptive system using temperature-controlled laser stimuli. Eur J Pain 2024. [PMID: 38440936 DOI: 10.1002/ejp.2259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 01/30/2024] [Accepted: 02/26/2024] [Indexed: 03/06/2024]
Abstract
BACKGROUND Cutaneous laser stimulation has commonly been employed to investigate the thermal properties of the nociceptive system. The aim of this study was to investigate how a temperature-controlled laser system improves the assessment of directional discrimination in the nociceptive system. METHODS In total, twenty healthy volunteers participated in this study. To determine the directional discrimination threshold (stimulation length 50% correct, expressed in mm), thermal stimuli were delivered using a diode laser and the laser beam was perpendicularly displaced across the skin to give a linear stimulation in four different directions (distal, proximal, lateral and medial) and displacement lengths (3 for lateral-medial and 5 for distal-proximal). Two temperature control modes were used in the stimulation system, open-loop and closed-loop control. The subjects had to report the perceived stimulus direction, the degree of certainty regarding the perceived direction and the intensity of the perceived stimulus (0-10 numerical rating scale, 3: pain threshold). RESULTS During closed-loop control, the orientation of stimuli was discriminated significantly more accurately than during open-loop control. During closed-loop control, the directional discrimination threshold was 31.9 and 26.1 mm for distal-proximal and lateral-medial directed stimuli, respectively. A numerical rating scale was significantly higher for the lateral/medial directions. Moreover, the variability of the discrimination threshold is reduced in the closed-loop control system. CONCLUSIONS The findings show that discrimination ability is better in the lateral-medial directions compared to the distal-proximal directions. This study indicates that using a system enabling closed-loop temperature control, allows more robust probing of the temporo-spatial mechanisms in the nociceptive system. SIGNIFICANCE This study shows that a newly developed temperature-controlled laser stimulation system enhances the possibilities to investigate the nociceptive temporo-spatial integration, as shown by a less variable directional discrimination threshold. The results also show that different orthogonal directions are discriminated differently. This new method allows a better investigation of the combined temporal and spatial mechanisms in the nociceptive system.
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Affiliation(s)
- Ahmad Rujoie
- Integrative Neuroscience Group, Center for Neuroplasticity and Pain (CNAP), Department of Health Science & Technology, Aalborg University, Aalborg, Denmark
| | - Ole Kaeseler Andersen
- Integrative Neuroscience Group, Center for Neuroplasticity and Pain (CNAP), Department of Health Science & Technology, Aalborg University, Aalborg, Denmark
| | - Ken Steffen Frahm
- Integrative Neuroscience Group, Center for Neuroplasticity and Pain (CNAP), Department of Health Science & Technology, Aalborg University, Aalborg, Denmark
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7
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Simoens J, Verguts T, Braem S. Learning environment-specific learning rates. PLoS Comput Biol 2024; 20:e1011978. [PMID: 38517916 PMCID: PMC10990245 DOI: 10.1371/journal.pcbi.1011978] [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: 06/22/2023] [Revised: 04/03/2024] [Accepted: 03/09/2024] [Indexed: 03/24/2024] Open
Abstract
People often have to switch back and forth between different environments that come with different problems and volatilities. While volatile environments require fast learning (i.e., high learning rates), stable environments call for lower learning rates. Previous studies have shown that people adapt their learning rates, but it remains unclear whether they can also learn about environment-specific learning rates, and instantaneously retrieve them when revisiting environments. Here, using optimality simulations and hierarchical Bayesian analyses across three experiments, we show that people can learn to use different learning rates when switching back and forth between two different environments. We even observe a signature of these environment-specific learning rates when the volatility of both environments is suddenly the same. We conclude that humans can flexibly adapt and learn to associate different learning rates to different environments, offering important insights for developing theories of meta-learning and context-specific control.
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Affiliation(s)
- Jonas Simoens
- Department of Experimental Psychology, Ghent University, Belgium
| | - Tom Verguts
- Department of Experimental Psychology, Ghent University, Belgium
| | - Senne Braem
- Department of Experimental Psychology, Ghent University, Belgium
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8
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Zhou M, Zhu S, Xu T, Wang J, Zhuang Q, Zhang Y, Becker B, Kendrick KM, Yao S. Neural and behavioral evidence for oxytocin's facilitatory effects on learning in volatile and stable environments. Commun Biol 2024; 7:109. [PMID: 38242969 PMCID: PMC10799007 DOI: 10.1038/s42003-024-05792-8] [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: 06/29/2023] [Accepted: 01/08/2024] [Indexed: 01/21/2024] Open
Abstract
Outcomes of past decisions profoundly shape our behavior. However, choice-outcome associations can become volatile and adaption to such changes is of importance. The present study combines pharmaco-electroencephalography with computational modeling to examine whether intranasal oxytocin can modulate reinforcement learning under a volatile vs. a stable association. Results show that oxytocin increases choice accuracy independent of learning context, which is paralleled by a larger N2pc and a smaller P300. Model-based analyses reveal that while oxytocin promotes learning by accelerating value update of outcomes in the volatile context, in the stable context it does so by improving choice consistency. These findings suggest that oxytocin's facilitatory effects on learning may be exerted via improving early attentional selection and late neural processing efficiency, although at the computational level oxytocin's actions are highly adaptive between learning contexts. Our findings provide proof of concept for oxytocin's therapeutic potential in mental disorders with adaptive learning dysfunction.
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Affiliation(s)
- Menghan Zhou
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
- The MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Siyu Zhu
- School of Sport Training, Chengdu Sport University, Chengdu, 610041, Sichuan, China
| | - Ting Xu
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
- The MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiayuan Wang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
- The MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qian Zhuang
- The MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China
| | - Yuan Zhang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
- The MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, Pokfulam, China
- Department of Psychology, The University of Hong Kong, Hong Kong, Pokfulam, China
| | - Keith M Kendrick
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
- The MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuxia Yao
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- The MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
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9
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Leimar O, Quiñones AE, Bshary R. Flexible learning in complex worlds. Behav Ecol 2024; 35:arad109. [PMID: 38162692 PMCID: PMC10756056 DOI: 10.1093/beheco/arad109] [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: 06/13/2023] [Revised: 10/23/2023] [Accepted: 12/03/2023] [Indexed: 01/03/2024] Open
Abstract
Cognitive flexibility can enhance the ability to adjust to changing environments. Here, we use learning simulations to investigate the possible advantages of flexible learning in volatile (changing) environments. We compare two established learning mechanisms, one with constant learning rates and one with rates that adjust to volatility. We study an ecologically relevant case of volatility, based on observations of developing cleaner fish Labroides dimidiatus that experience a transition from a simpler to a more complex foraging environment. There are other similar transitions in nature, such as migrating to a new and different habitat. We also examine two traditional approaches to volatile environments in experimental psychology and behavioral ecology: reversal learning, and learning set formation (consisting of a sequence of different discrimination tasks). These provide experimental measures of cognitive flexibility. Concerning transitions to a complex world, we show that both constant and flexible learning rates perform well, losing only a small proportion of available rewards in the period after a transition, but flexible rates perform better than constant rates. For reversal learning, flexible rates improve the performance with each successive reversal because of increasing learning rates, but this does not happen for constant rates. For learning set formation, we find no improvement in performance with successive shifts to new stimuli to discriminate for either flexible or constant learning rates. Flexible learning rates might thus explain increasing performance in reversal learning but not in learning set formation, and this can shed light on the nature of cognitive flexibility in a given system.
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Affiliation(s)
- Olof Leimar
- Department of Zoology, Stockholm University, 106 91 Stockholm, Sweden and
| | - Andrés E Quiñones
- Institute of Biology, University of Neuchâtel, Emile-Argand 11, 2000 Neuchâtel, Switzerland
| | - Redouan Bshary
- Institute of Biology, University of Neuchâtel, Emile-Argand 11, 2000 Neuchâtel, Switzerland
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10
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Danskin BP, Hattori R, Zhang YE, Babic Z, Aoi M, Komiyama T. Exponential history integration with diverse temporal scales in retrosplenial cortex supports hyperbolic behavior. SCIENCE ADVANCES 2023; 9:eadj4897. [PMID: 38019904 PMCID: PMC10686558 DOI: 10.1126/sciadv.adj4897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023]
Abstract
Animals use past experience to guide future choices. The integration of experiences typically follows a hyperbolic, rather than exponential, decay pattern with a heavy tail for distant history. Hyperbolic integration affords sensitivity to both recent environmental dynamics and long-term trends. However, it is unknown how the brain implements hyperbolic integration. We found that mouse behavior in a foraging task showed hyperbolic decay of past experience, but the activity of cortical neurons showed exponential decay. We resolved this apparent mismatch by observing that cortical neurons encode history information with heterogeneous exponential time constants that vary across neurons. A model combining these diverse timescales recreated the heavy-tailed, hyperbolic history integration observed in behavior. In particular, the time constants of retrosplenial cortex (RSC) neurons best matched the behavior, and optogenetic inactivation of RSC uniquely reduced behavioral history dependence. These results indicate that behavior-relevant history information is maintained across multiple timescales in parallel and that RSC is a critical reservoir of information guiding decision-making.
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Affiliation(s)
- Bethanny P. Danskin
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Ryoma Hattori
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Yu E. Zhang
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Zeljana Babic
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Mikio Aoi
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Takaki Komiyama
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
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11
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Zou J, Zhang Y, Li J, Tian X, Ding N. Human attention during goal-directed reading comprehension relies on task optimization. eLife 2023; 12:RP87197. [PMID: 38032825 PMCID: PMC10688971 DOI: 10.7554/elife.87197] [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: 12/02/2023] Open
Abstract
The computational principles underlying attention allocation in complex goal-directed tasks remain elusive. Goal-directed reading, that is, reading a passage to answer a question in mind, is a common real-world task that strongly engages attention. Here, we investigate what computational models can explain attention distribution in this complex task. We show that the reading time on each word is predicted by the attention weights in transformer-based deep neural networks (DNNs) optimized to perform the same reading task. Eye tracking further reveals that readers separately attend to basic text features and question-relevant information during first-pass reading and rereading, respectively. Similarly, text features and question relevance separately modulate attention weights in shallow and deep DNN layers. Furthermore, when readers scan a passage without a question in mind, their reading time is predicted by DNNs optimized for a word prediction task. Therefore, we offer a computational account of how task optimization modulates attention distribution during real-world reading.
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Affiliation(s)
- Jiajie Zou
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Sciences, Zhejiang UniversityHangzhouChina
- Nanhu Brain-computer Interface InstituteHangzhouChina
| | - Yuran Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Sciences, Zhejiang UniversityHangzhouChina
| | - Jialu Li
- Division of Arts and Sciences, New York University ShanghaiShanghaiChina
| | - Xing Tian
- Division of Arts and Sciences, New York University ShanghaiShanghaiChina
| | - Nai Ding
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Sciences, Zhejiang UniversityHangzhouChina
- Nanhu Brain-computer Interface InstituteHangzhouChina
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12
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Giron AP, Ciranka S, Schulz E, van den Bos W, Ruggeri A, Meder B, Wu CM. Developmental changes in exploration resemble stochastic optimization. Nat Hum Behav 2023; 7:1955-1967. [PMID: 37591981 PMCID: PMC10663152 DOI: 10.1038/s41562-023-01662-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 06/21/2023] [Indexed: 08/19/2023]
Abstract
Human development is often described as a 'cooling off' process, analogous to stochastic optimization algorithms that implement a gradual reduction in randomness over time. Yet there is ambiguity in how to interpret this analogy, due to a lack of concrete empirical comparisons. Using data from n = 281 participants ages 5 to 55, we show that cooling off does not only apply to the single dimension of randomness. Rather, human development resembles an optimization process of multiple learning parameters, for example, reward generalization, uncertainty-directed exploration and random temperature. Rapid changes in parameters occur during childhood, but these changes plateau and converge to efficient values in adulthood. We show that while the developmental trajectory of human parameters is strikingly similar to several stochastic optimization algorithms, there are important differences in convergence. None of the optimization algorithms tested were able to discover reliably better regions of the strategy space than adult participants on this task.
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Affiliation(s)
- Anna P Giron
- Human and Machine Cognition Lab, University of Tübingen, Tübingen, Germany
- Attention and Affect Lab, University of Tübingen, Tübingen, Germany
| | - Simon Ciranka
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Eric Schulz
- MPRG Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Wouter van den Bos
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands
| | - Azzurra Ruggeri
- MPRG iSearch, Max Planck Institute for Human Development, Berlin, Germany
- School of Social Sciences and Technology, Technical University Munich, Munich, Germany
- Central European University, Vienna, Austria
| | - Björn Meder
- MPRG iSearch, Max Planck Institute for Human Development, Berlin, Germany
- Institute for Mind, Brain and Behavior, Health and Medical University, Potsdam, Germany
| | - Charley M Wu
- Human and Machine Cognition Lab, University of Tübingen, Tübingen, Germany.
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany.
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13
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Teichroeb JA, Smeltzer EA, Mathur V, Anderson KA, Fowler EJ, Adams FV, Vasey EN, Tamara Kumpan L, Stead SM, Arseneau-Robar TJM. How can we apply decision-making theories to wild animal behavior? Predictions arising from dual process theory and Bayesian decision theory. Am J Primatol 2023:e23565. [PMID: 37839050 DOI: 10.1002/ajp.23565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 09/19/2023] [Accepted: 10/03/2023] [Indexed: 10/17/2023]
Abstract
Our understanding of decision-making processes and cognitive biases is ever increasing, thanks to an accumulation of testable models and a large body of research over the last several decades. The vast majority of this work has been done in humans and laboratory animals because these study subjects and situations allow for tightly controlled experiments. However, it raises questions about how this knowledge can be applied to wild animals in their complex environments. Here, we review two prominent decision-making theories, dual process theory and Bayesian decision theory, to assess the similarities in these approaches and consider how they may apply to wild animals living in heterogenous environments within complicated social groupings. In particular, we wanted to assess when wild animals are likely to respond to a situation with a quick heuristic decision and when they are likely to spend more time and energy on the decision-making process. Based on the literature and evidence from our multi-destination routing experiments on primates, we find that individuals are likely to make quick, heuristic decisions when they encounter routine situations, or signals/cues that accurately predict a certain outcome, or easy problems that experience or evolutionary history has prepared them for. Conversely, effortful decision-making is likely in novel or surprising situations, when signals and cues have unpredictable or uncertain relationships to an outcome, and when problems are computationally complex. Though if problems are overly complex, satisficing via heuristics is likely, to avoid costly mental effort. We present hypotheses for how animals with different socio-ecologies may have to distribute their cognitive effort. Finally, we examine the conservation implications and potential cognitive overload for animals experiencing increasingly novel situations caused by current human-induced rapid environmental change.
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Affiliation(s)
- Julie A Teichroeb
- Department of Anthropology, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Anthropology, University of Toronto, Toronto, Ontario, Canada
| | - Eve A Smeltzer
- Department of Anthropology, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Anthropology, University of Toronto, Toronto, Ontario, Canada
| | - Virendra Mathur
- Department of Anthropology, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Anthropology, University of Toronto, Toronto, Ontario, Canada
| | - Karyn A Anderson
- Department of Anthropology, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Anthropology, University of Toronto, Toronto, Ontario, Canada
| | - Erica J Fowler
- Department of Anthropology, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Anthropology, University of Toronto, Toronto, Ontario, Canada
| | - Frances V Adams
- Department of Anthropology, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Anthropology, University of Toronto, Toronto, Ontario, Canada
| | - Eric N Vasey
- Department of Anthropology, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Anthropology, University of Toronto, Toronto, Ontario, Canada
| | - Ludmila Tamara Kumpan
- Department of Anthropology, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Anthropology, University of Toronto, Toronto, Ontario, Canada
| | - Samantha M Stead
- Department of Anthropology, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Anthropology, University of Toronto, Toronto, Ontario, Canada
| | - T Jean M Arseneau-Robar
- Department of Anthropology, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Biology, Concordia University, Montréal, Quebec, Canada
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14
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Cervantes Constantino F, Sánchez-Costa T, Cipriani GA, Carboni A. Visuospatial attention revamps cortical processing of sound amid audiovisual uncertainty. Psychophysiology 2023; 60:e14329. [PMID: 37166096 DOI: 10.1111/psyp.14329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 04/13/2023] [Accepted: 04/25/2023] [Indexed: 05/12/2023]
Abstract
Selective attentional biases arising from one sensory modality manifest in others. The effects of visuospatial attention, important in visual object perception, are unclear in the auditory domain during audiovisual (AV) scene processing. We investigate temporal and spatial factors that underlie such transfer neurally. Auditory encoding of random tone pips in AV scenes was addressed via a temporal response function model (TRF) of participants' electroencephalogram (N = 30). The spatially uninformative pips were associated with spatially distributed visual contrast reversals ("flips"), through asynchronous probabilistic AV temporal onset distributions. Participants deployed visuospatial selection on these AV stimuli to perform a task. A late (~300 ms) cross-modal influence over the neural representation of pips was found in the original and a replication study (N = 21). Transfer depended on selected visual input being (i) presented during or shortly after a related sound, in relatively limited temporal distributions (<165 ms); (ii) positioned across limited (1:4) visual foreground to background ratios. Neural encoding of auditory input, as a function of visual input, was largest at visual foreground quadrant sectors and lowest at locations opposite to the target. The results indicate that ongoing neural representations of sounds incorporate visuospatial attributes for auditory stream segregation, as cross-modal transfer conveys information that specifies the identity of multisensory signals. A potential mechanism is by enhancing or recalibrating the tuning properties of the auditory populations that represent them as objects. The results account for the dynamic evolution under visual attention of multisensory integration, specifying critical latencies at which relevant cortical networks operate.
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Affiliation(s)
- Francisco Cervantes Constantino
- Centro de Investigación Básica en Psicología, Facultad de Psicología, Universidad de la República, Montevideo, Uruguay
- Instituto de Fundamentos y Métodos en Psicología, Facultad de Psicología, Universidad de la República, Montevideo, Uruguay
- Instituto de Investigaciones Biológicas "Clemente Estable", Montevideo, Uruguay
| | - Thaiz Sánchez-Costa
- Centro de Investigación Básica en Psicología, Facultad de Psicología, Universidad de la República, Montevideo, Uruguay
| | - Germán A Cipriani
- Centro de Investigación Básica en Psicología, Facultad de Psicología, Universidad de la República, Montevideo, Uruguay
| | - Alejandra Carboni
- Centro de Investigación Básica en Psicología, Facultad de Psicología, Universidad de la República, Montevideo, Uruguay
- Instituto de Fundamentos y Métodos en Psicología, Facultad de Psicología, Universidad de la República, Montevideo, Uruguay
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15
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Christie ST, Johnson HR, Schrater PR. Information-Theoretic Neural Decoding Reproduces Several Laws of Human Behavior. Open Mind (Camb) 2023; 7:675-690. [PMID: 37840757 PMCID: PMC10575563 DOI: 10.1162/opmi_a_00101] [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: 02/28/2023] [Accepted: 08/01/2023] [Indexed: 10/17/2023] Open
Abstract
Human response times conform to several regularities including the Hick-Hyman law, the power law of practice, speed-accuracy trade-offs, and the Stroop effect. Each of these has been thoroughly modeled in isolation, but no account describes these phenomena as predictions of a unified framework. We provide such a framework and show that the phenomena arise as decoding times in a simple neural rate code with an entropy stopping threshold. Whereas traditional information-theoretic encoding systems exploit task statistics to optimize encoding strategies, we move this optimization to the decoder, treating it as a Bayesian ideal observer that can track transmission statistics as prior information during decoding. Our approach allays prominent concerns that applying information-theoretic perspectives to modeling brain and behavior requires complex encoding schemes that are incommensurate with neural encoding.
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16
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Wang R, Gates V, Shen Y, Tino P, Kourtzi Z. Flexible structure learning under uncertainty. Front Neurosci 2023; 17:1195388. [PMID: 37599995 PMCID: PMC10437075 DOI: 10.3389/fnins.2023.1195388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/18/2023] [Indexed: 08/22/2023] Open
Abstract
Experience is known to facilitate our ability to interpret sequences of events and make predictions about the future by extracting temporal regularities in our environments. Here, we ask whether uncertainty in dynamic environments affects our ability to learn predictive structures. We exposed participants to sequences of symbols determined by first-order Markov models and asked them to indicate which symbol they expected to follow each sequence. We introduced uncertainty in this prediction task by manipulating the: (a) probability of symbol co-occurrence, (b) stimulus presentation rate. Further, we manipulated feedback, as it is known to play a key role in resolving uncertainty. Our results demonstrate that increasing the similarity in the probabilities of symbol co-occurrence impaired performance on the prediction task. In contrast, increasing uncertainty in stimulus presentation rate by introducing temporal jitter resulted in participants adopting a strategy closer to probability maximization than matching and improving in the prediction tasks. Next, we show that feedback plays a key role in learning predictive statistics. Trial-by-trial feedback yielded stronger improvement than block feedback or no feedback; that is, participants adopted a strategy closer to probability maximization and showed stronger improvement when trained with trial-by-trial feedback. Further, correlating individual strategy with learning performance showed better performance in structure learning for observers who adopted a strategy closer to maximization. Our results indicate that executive cognitive functions (i.e., selective attention) may account for this individual variability in strategy and structure learning ability. Taken together, our results provide evidence for flexible structure learning; individuals adapt their decision strategy closer to probability maximization, reducing uncertainty in temporal sequences and improving their ability to learn predictive statistics in variable environments.
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Affiliation(s)
- Rui Wang
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Vael Gates
- Institute for Human-Centered AI, Stanford University, Stanford, CA, United States
| | - Yuan Shen
- School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom
| | - Peter Tino
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Zoe Kourtzi
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
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17
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Yu K, Tuerlinckx F, Vanpaemel W, Zaman J. Humans display interindividual differences in the latent mechanisms underlying fear generalization behaviour. COMMUNICATIONS PSYCHOLOGY 2023; 1:5. [PMID: 39242719 PMCID: PMC11290606 DOI: 10.1038/s44271-023-00005-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/13/2023] [Indexed: 09/09/2024]
Abstract
Human generalization research aims to understand the processes underlying the transfer of prior experiences to new contexts. Generalization research predominantly relies on descriptive statistics, assumes a single generalization mechanism, interprets generalization from mono-source data, and disregards individual differences. Unfortunately, such an approach fails to disentangle various mechanisms underlying generalization behaviour and can readily result in biased conclusions regarding generalization tendencies. Therefore, we combined a computational model with multi-source data to mechanistically investigate human generalization behaviour. By simultaneously modelling learning, perceptual and generalization data at the individual level, we revealed meaningful variations in how different mechanisms contribute to generalization behaviour. The current research suggests the need for revising the theoretical and analytic foundations in the field to shift the attention away from forecasting group-level generalization behaviour and toward understanding how such phenomena emerge at the individual level. This raises the question for future research whether a mechanism-specific differential diagnosis may be beneficial for generalization-related psychiatric disorders.
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Affiliation(s)
| | | | | | - Jonas Zaman
- KU Leuven, Leuven, Belgium
- University of Hasselt, Hasselt, Belgium
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18
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Alsharif AH, Salleh NZM, Alrawad M, Lutfi A. Exploring global trends and future directions in advertising research: A focus on consumer behavior. CURRENT PSYCHOLOGY 2023:1-24. [PMID: 37359681 PMCID: PMC10239056 DOI: 10.1007/s12144-023-04812-w] [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] [Accepted: 05/25/2023] [Indexed: 06/28/2023]
Abstract
This study aims to select the physiological and neurophysiological studies utilized in advertising and to address the fragmented comprehension of consumers' mental responses to advertising held by marketers and advertisers. To fill the gap, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was employed to select relevant articles, and bibliometric analysis was conducted to determine global trends and advancements in advertising and neuromarketing. The study selected and analyzed forty-one papers from the Web of Science (WoS) database from 2009-2020. The results indicated that Spain, particularly the Complutense University of Madrid, was the most productive country and institution, respectively, with 11 and 3 articles. The journal Frontiers in Psychology was the most prolific, with eight articles. The article "Neuromarketing: The New Science of Consumer Behavior" had the most citations (152 T.Cs). Additionally, the researchers discovered that the inferior frontal and middle temporal gyri were associated with pleasant and unpleasant emotions, respectively, while the right superior temporal and right middle frontal gyrus was connected to high and low arousal. Furthermore, the right prefrontal cortex (PFC) and left PFC were linked to withdrawal and approach behaviors. In terms of the reward system, the ventral striatum played a critical role, while the orbitofrontal cortex and ventromedial PFC were connected to perception. As far as we know, this is the first paper that focused on the global academic trends and developments of neurophysiological and physiological instruments used in advertising in the new millennium, emphasizing the significance of intrinsic and extrinsic emotional processes, endogenous and exogenous attentional processes, memory, reward, motivational attitude, and perception in advertising campaigns.
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Affiliation(s)
- Ahmed H. Alsharif
- Azman Hashim International Business School, Universiti Teknologi Malaysia, 81310 Skudai, Johor Malaysia
| | - Nor Zafir Md Salleh
- Azman Hashim International Business School, Universiti Teknologi Malaysia, 81310 Skudai, Johor Malaysia
| | - Mahmaod Alrawad
- Department of Quantitative Methods, College of Business Administration, King Faisal University, Al-Ahsa, 31982 Saudi Arabia
- College of Business Administration and Economics, Al-Hussein Bin Talal University, Ma’an, 71111 Jordan
| | - Abdalwali Lutfi
- Department of Accounting, College of Business, King Faisal University, Al-Ahsa, 31982 Saudi Arabia
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19
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Bounmy T, Eger E, Meyniel F. A characterization of the neural representation of confidence during probabilistic learning. Neuroimage 2023; 268:119849. [PMID: 36640947 DOI: 10.1016/j.neuroimage.2022.119849] [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: 09/20/2022] [Revised: 12/09/2022] [Accepted: 12/29/2022] [Indexed: 01/13/2023] Open
Abstract
Learning in a stochastic and changing environment is a difficult task. Models of learning typically postulate that observations that deviate from the learned predictions are surprising and used to update those predictions. Bayesian accounts further posit the existence of a confidence-weighting mechanism: learning should be modulated by the confidence level that accompanies those predictions. However, the neural bases of this confidence are much less known than the ones of surprise. Here, we used a dynamic probability learning task and high-field MRI to identify putative cortical regions involved in the representation of confidence about predictions during human learning. We devised a stringent test based on the conjunction of four criteria. We localized several regions in parietal and frontal cortices whose activity is sensitive to the confidence of an ideal observer, specifically so with respect to potential confounds (surprise and predictability), and in a way that is invariant to which item is predicted. We also tested for functionality in two ways. First, we localized regions whose activity patterns at the subject level showed an effect of both confidence and surprise in qualitative agreement with the confidence-weighting principle. Second, we found neural representations of ideal confidence that also accounted for subjective confidence. Taken together, those results identify a set of cortical regions potentially implicated in the confidence-weighting of learning.
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Affiliation(s)
- Tiffany Bounmy
- Cognitive Neuroimaging Unit, CEA DRF/Joliot, INSERM, Université Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette, France; Université de Paris, Paris, France.
| | - Evelyn Eger
- Cognitive Neuroimaging Unit, CEA DRF/Joliot, INSERM, Université Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette, France
| | - Florent Meyniel
- Cognitive Neuroimaging Unit, CEA DRF/Joliot, INSERM, Université Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette, France.
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20
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Jiang Y, Mi Q, Zhu L. Neurocomputational mechanism of real-time distributed learning on social networks. Nat Neurosci 2023; 26:506-516. [PMID: 36797365 DOI: 10.1038/s41593-023-01258-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 01/17/2023] [Indexed: 02/18/2023]
Abstract
Social networks shape our decisions by constraining what information we learn and from whom. Yet, the mechanisms by which network structures affect individual learning and decision-making remain unclear. Here, by combining a real-time distributed learning task with functional magnetic resonance imaging, computational modeling and social network analysis, we studied how humans learn from observing others' decisions on seven-node networks with varying topological structures. We show that learning on social networks can be approximated by a well-established error-driven process for observational learning, supported by an action prediction error encoded in the lateral prefrontal cortex. Importantly, learning is flexibly weighted toward well-connected neighbors, according to activity in the dorsal anterior cingulate cortex, but only insofar as social observations contain secondhand, potentially intertwining, information. These data suggest a neurocomputational mechanism of network-based filtering on the sources of information, which may give rise to biased learning and the spread of misinformation in an interconnected society.
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Affiliation(s)
- Yaomin Jiang
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.,IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Qingtian Mi
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.,IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Lusha Zhu
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China. .,IDG/McGovern Institute for Brain Research, Peking University, Beijing, China. .,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
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21
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Aversion, interpretation and determinability: Three factors of uncertainty that may play a role in psychopathology. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023:10.3758/s13415-023-01068-6. [PMID: 36792816 PMCID: PMC10390353 DOI: 10.3758/s13415-023-01068-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/16/2023] [Indexed: 02/17/2023]
Abstract
This opinion piece considers the construct of tolerance of uncertainty and suggests that it should be viewed in the context of three psychological factors: uncertainty aversion, uncertainty interpretation, and uncertainty determinability. Uncertainty aversion refers to a dislike of situations in which the outcomes are not deterministic and is similar to conventional conceptions of (in)tolerance of uncertainty. Uncertainty interpretation refers to the extent to which variability in an observed outcome is interpreted as random fluctuation around a relatively stable base-rate versus frequent and rapid changes in the base-rate. Uncertainty determinability refers to the (actual or perceived) capacity of the individual to generate any meaningful expectancy of the uncertain outcome, which may be undeterminable if predictions are updated too quickly. We argue that uncertainty interpretation and determinability are psychological responses to the experience of probabilistic events that vary among individuals and can moderate negative affect experienced in response to uncertainty. We describe how individual differences in basic parameters of associative learning (modelled by a simple learning window) could lead to this variation. To explain these hypotheses, we utilise the distinction between aleatory uncertainty (the inherent unpredictability of individual stochastic events) and epistemic uncertainty (obtainable knowledge that the individual lacks or perceives to be lacking). We argue that when expectancies are updated quickly, epistemic uncertainty will dominate the individual's representation of the events around them, leading to a subjective experience of the world as one that is volatile and unpredictable.
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22
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Heald JB, Lengyel M, Wolpert DM. Contextual inference in learning and memory. Trends Cogn Sci 2023; 27:43-64. [PMID: 36435674 PMCID: PMC9789331 DOI: 10.1016/j.tics.2022.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 11/25/2022]
Abstract
Context is widely regarded as a major determinant of learning and memory across numerous domains, including classical and instrumental conditioning, episodic memory, economic decision-making, and motor learning. However, studies across these domains remain disconnected due to the lack of a unifying framework formalizing the concept of context and its role in learning. Here, we develop a unified vernacular allowing direct comparisons between different domains of contextual learning. This leads to a Bayesian model positing that context is unobserved and needs to be inferred. Contextual inference then controls the creation, expression, and updating of memories. This theoretical approach reveals two distinct components that underlie adaptation, proper and apparent learning, respectively referring to the creation and updating of memories versus time-varying adjustments in their expression. We review a number of extensions of the basic Bayesian model that allow it to account for increasingly complex forms of contextual learning.
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Affiliation(s)
- James B Heald
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary.
| | - Daniel M Wolpert
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.
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23
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Alsharif AH, Salleh NZM, Al-Zahrani SA, Khraiwish A. Consumer Behaviour to Be Considered in Advertising: A Systematic Analysis and Future Agenda. Behav Sci (Basel) 2022; 12:bs12120472. [PMID: 36546955 PMCID: PMC9774318 DOI: 10.3390/bs12120472] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/28/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2022] Open
Abstract
In the past decade, neurophysiological and physiological tools have been used to explore consumer behaviour toward advertising. The studies into brain processes (e.g., emotions, motivation, reward, attention, perception, and memory) toward advertising are scant, and remain unclear in the academic literature. To fill the gap in the literature, this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to extract relevant articles. It extracted and analysed 76 empirical articles from the Web of Science (WoS) database from 2009-2020. The findings revealed that the inferior frontal gyrus was associated with pleasure, while the middle temporal gyrus correlated with displeasure of advertising. Meanwhile, the right superior-temporal is related to high arousal and the right middle-frontal-gyrus is linked to low arousal toward advertisement campaigns. The right prefrontal-cortex (PFC) is correlated with withdrawal behaviour, and the left PFC is linked to approach behaviour. For the reward system, the ventral striatum has a main role in the reward system. It has also been found that perception is connected to the orbitofrontal cortex (OFC) and ventromedial (Vm) PFC. The study's findings provide a profound overview of the importance of brain processes such as emotional processes, reward, motivation, cognitive processes, and perception in advertising campaigns such as commercial, social initiative, and public health.
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Affiliation(s)
- Ahmed H. Alsharif
- Azman Hashim International Business School, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
- Correspondence:
| | - Nor Zafir Md Salleh
- Azman Hashim International Business School, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
| | - Shaymah Ahmed Al-Zahrani
- Department of Economic & Finance, College of Business Administration, Taif University, Taif 21944, Saudi Arabia
| | - Ahmad Khraiwish
- Department of Marketing, Faculty of Business, Applied Science Private University (ASU), Amman 11931, Jordan
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24
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Rajpal H, Mediano PAM, Rosas FE, Timmermann CB, Brugger S, Muthukumaraswamy S, Seth AK, Bor D, Carhart-Harris RL, Jensen HJ. Psychedelics and schizophrenia: Distinct alterations to Bayesian inference. Neuroimage 2022; 263:119624. [PMID: 36108798 DOI: 10.1016/j.neuroimage.2022.119624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/11/2022] [Accepted: 09/10/2022] [Indexed: 11/28/2022] Open
Abstract
Schizophrenia and states induced by certain psychotomimetic drugs may share some physiological and phenomenological properties, but they differ in fundamental ways: one is a crippling chronic mental disease, while the others are temporary, pharmacologically-induced states presently being explored as treatments for mental illnesses. Building towards a deeper understanding of these different alterations of normal consciousness, here we compare the changes in neural dynamics induced by LSD and ketamine (in healthy volunteers) against those associated with schizophrenia, as observed in resting-state M/EEG recordings. While both conditions exhibit increased neural signal diversity, our findings reveal that this is accompanied by an increased transfer entropy from the front to the back of the brain in schizophrenia, versus an overall reduction under the two drugs. Furthermore, we show that these effects can be reproduced via different alterations of standard Bayesian inference applied on a computational model based on the predictive processing framework. In particular, the effects observed under the drugs are modelled as a reduction of the precision of the priors, while the effects of schizophrenia correspond to an increased precision of sensory information. These findings shed new light on the similarities and differences between schizophrenia and two psychotomimetic drug states, and have potential implications for the study of consciousness and future mental health treatments.
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Affiliation(s)
- Hardik Rajpal
- Centre for Complexity Science, Imperial College London, South Kensington, London, United Kingdom; Department of Mathematics, Imperial College London, South Kensington, London, United Kingdom; Public Policy Program, The Alan Turing Institute, London, United Kingdom.
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, South Kensington, London, United Kingdom; Department of Psychology, University of Cambridge, Cambridge, United Kingdom; Department of Psychology, Queen Mary University of London, London, United Kingdom.
| | - Fernando E Rosas
- Centre for Complexity Science, Imperial College London, South Kensington, London, United Kingdom; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, United Kingdom; Data Science Institute, Imperial College London, London, United Kingdom; Department of Informatics, University of Sussex, Brighton, United Kingdom
| | - Christopher B Timmermann
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Stefan Brugger
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom; Centre for Academic Mental Health, Bristol Medical School, University of Bristol, United Kingdom
| | | | - Anil K Seth
- School of Engineering and Informatics, University of Sussex, United Kingdom; CIFAR Program on Brain, Mind, and Consciousness, Toronto, Canada
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom; Department of Psychology, Queen Mary University of London, London, United Kingdom
| | - Robin L Carhart-Harris
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, United Kingdom; Psychedelics Division, Neuroscape, Department of Neurology, University of California San Francisco, US
| | - Henrik J Jensen
- Centre for Complexity Science, Imperial College London, South Kensington, London, United Kingdom; Department of Mathematics, Imperial College London, South Kensington, London, United Kingdom; Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
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25
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Speekenbrink M. Chasing Unknown Bandits: Uncertainty Guidance in Learning and Decision Making. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 2022. [DOI: 10.1177/09637214221105051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In repeated decision problems for which it is possible to learn from experience, people should actively seek out uncertain options, rather than avoid ambiguity or uncertainty, in order to learn and improve future decisions. Research on human behavior in a variety of multiarmed-bandit tasks supports this prediction. Multiarmed-bandit tasks involve repeated decisions between options with initially unknown reward distributions and require a careful balance between learning about relatively unknown options (exploration) and obtaining high immediate rewards (exploitation). Resolving this exploration-exploitation dilemma optimally requires considering not only the estimated value of each option, but also the uncertainty in these estimations. Bayesian learning naturally quantifies uncertainty and hence provides a principled framework to study how humans resolve this dilemma. On the basis of computational modeling and behavioral results in bandit tasks, I argue that human learning, attention, and exploration are guided by uncertainty. These results support Bayesian theories of cognition and underpin the fundamental role of subjective uncertainty in both learning and decision making.
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Affiliation(s)
- Maarten Speekenbrink
- Department of Experimental Psychology, University College London, and The Alan Turing Institute, London, England
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26
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Vázquez D, Schneider KN, Roesch MR. Neural signals implicated in the processing of appetitive and aversive events in social and non-social contexts. Front Syst Neurosci 2022; 16:926388. [PMID: 35993086 PMCID: PMC9381696 DOI: 10.3389/fnsys.2022.926388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
In 2014, we participated in a special issue of Frontiers examining the neural processing of appetitive and aversive events. Specifically, we reviewed brain areas that contribute to the encoding of prediction errors and value versus salience, attention and motivation. Further, we described how we disambiguated these cognitive processes and their neural substrates by using paradigms that incorporate both appetitive and aversive stimuli. We described a circuit in which the orbitofrontal cortex (OFC) signals expected value and the basolateral amygdala (BLA) encodes the salience and valence of both appetitive and aversive events. This information is integrated by the nucleus accumbens (NAc) and dopaminergic (DA) signaling in order to generate prediction and prediction error signals, which guide decision-making and learning via the dorsal striatum (DS). Lastly, the anterior cingulate cortex (ACC) is monitoring actions and outcomes, and signals the need to engage attentional control in order to optimize behavioral output. Here, we expand upon this framework, and review our recent work in which within-task manipulations of both appetitive and aversive stimuli allow us to uncover the neural processes that contribute to the detection of outcomes delivered to a conspecific and behaviors in social contexts. Specifically, we discuss the involvement of single-unit firing in the ACC and DA signals in the NAc during the processing of appetitive and aversive events in both social and non-social contexts.
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Affiliation(s)
- Daniela Vázquez
- Department of Psychology, University of Maryland, College Park, College Park, MD, United States
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, College Park, MD, United States
| | - Kevin N. Schneider
- Department of Psychology, University of Maryland, College Park, College Park, MD, United States
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, College Park, MD, United States
| | - Matthew R. Roesch
- Department of Psychology, University of Maryland, College Park, College Park, MD, United States
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, College Park, MD, United States
- *Correspondence: Matthew R. Roesch,
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27
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Freezing revisited: coordinated autonomic and central optimization of threat coping. Nat Rev Neurosci 2022; 23:568-580. [PMID: 35760906 DOI: 10.1038/s41583-022-00608-2] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2022] [Indexed: 12/16/2022]
Abstract
Animals have sophisticated mechanisms for coping with danger. Freezing is a unique state that, upon threat detection, allows evidence to be gathered, response possibilities to be previsioned and preparations to be made for worst-case fight or flight. We propose that - rather than reflecting a passive fear state - the particular somatic and cognitive characteristics of freezing help to conceal overt responses, while optimizing sensory processing and action preparation. Critical for these functions are the neurotransmitters noradrenaline and acetylcholine, which modulate neural information processing and also control the sympathetic and parasympathetic branches of the autonomic nervous system. However, the interactions between autonomic systems and the brain during freezing, and the way in which they jointly coordinate responses, remain incompletely explored. We review the joint actions of these systems and offer a novel computational framework to describe their temporally harmonized integration. This reconceptualization of freezing has implications for its role in decision-making under threat and for psychopathology.
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A Single Session of SMR-Neurofeedback Training Improves Selective Attention Emerging from a Dynamic Structuring of Brain-Heart Interplay. Brain Sci 2022; 12:brainsci12060794. [PMID: 35741679 PMCID: PMC9221475 DOI: 10.3390/brainsci12060794] [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: 05/13/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/17/2022] Open
Abstract
Research on sensorimotor rhythms (SMR) based on neurofeedback (NFb) emphasizes improvements in selective attention associated with SMR amplification. However, the long-term training proposed in most studies posed the question of acceptability, which led to the evaluation of the potential of a single NFb session. Based on cognitive and autonomic controls interfering with attention processes, we hypothesized changes in selective attention after a single SMR-NFb session, along with changes in brain-heart interplay, which are reflected in the multifractality of heartbeat dynamics. Here, young healthy participants (n = 35, 20 females, 21 ± 3 years) were randomly assigned either to a control group (Ctrl) watching a movie or to a neurofeedback (NFb) group performing a single session of SMR-NFb. A headset with EEG electrodes (positioned on C3 and C4) connected to a smartphone app served to guide and to evaluate NFb training efficacy. A Stroop task was performed for 8 min by each group before and after the intervention (movie vs. SMR-NFb) while collecting heart rate variability and C4-EEG for 20 min. When compared to Ctrl, the NFb group exhibited better Stroop performance, especially when facing incongruent trials. The multifractality and NFb training efficacy were identified as strong predictors of the gain in global Stroop performance, while multifractality was the only predictor regarding incongruent trials. We conclude that a single session of SMR-NFb improves selective attention in healthy individuals through the specific reorganization of brain-heart interplay, which is reflected in multifractal heartbeat dynamics.
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29
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Leone G, Postel C, Mary A, Fraisse F, Vallée T, Viader F, de La Sayette V, Peschanski D, Dayan J, Eustache F, Gagnepain P. Altered predictive control during memory suppression in PTSD. Nat Commun 2022; 13:3300. [PMID: 35676268 PMCID: PMC9177681 DOI: 10.1038/s41467-022-30855-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 05/20/2022] [Indexed: 11/09/2022] Open
Abstract
Aberrant predictions of future threat lead to maladaptive avoidance in individuals with post-traumatic stress disorder (PTSD). How this disruption in prediction influences the control of memory states orchestrated by the dorsolateral prefrontal cortex is unknown. We combined computational modeling and brain connectivity analyses to reveal how individuals exposed and nonexposed to the 2015 Paris terrorist attacks formed and controlled beliefs about future intrusive re-experiencing implemented in the laboratory during a memory suppression task. Exposed individuals with PTSD used beliefs excessively to control hippocampal activity during the task. When this predictive control failed, the prediction-error associated with unwanted intrusions was poorly downregulated by reactive mechanisms. This imbalance was linked to higher severity of avoidance symptoms, but not to general disturbances such as anxiety or negative affect. Conversely, trauma-exposed participants without PTSD and nonexposed individuals were able to optimally balance predictive and reactive control during the memory suppression task. These findings highlight a potential pathological mechanism occurring in individuals with PTSD rooted in the relationship between the brain’s predictive and control mechanisms. It remains unclear how predictions of future threat affect memory recall, specifically in the case of post-traumatic stress disorder (PTSD). Here, the authors combined computational modeling and brain connectivity analyses to show that individuals with PTSD have exaggerated predictive control and reduced reactive control in a memory suppression task.
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Affiliation(s)
- Giovanni Leone
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Charlotte Postel
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Alison Mary
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.,Neuropsychology and Functional Imaging Research Group (UR2NF), Centre for Research in Cognition and Neurosciences (CRCN), UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Florence Fraisse
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Thomas Vallée
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Fausto Viader
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Vincent de La Sayette
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Denis Peschanski
- Université Paris I Panthéon Sorbonne, HESAM Université, EHESS, CNRS, UMR8209, Paris, France
| | - Jaques Dayan
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.,Pôle Hospitalo-Universitaire de Psychiatrie de l'Enfant et de l'Adolescent, Centre Hospitalier Guillaume Régnier, Université Rennes 1, 35700, Rennes, France
| | - Francis Eustache
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Pierre Gagnepain
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.
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30
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Morris RW, Dezfouli A, Griffiths KR, Le Pelley ME, Balleine BW. The Neural Bases of Action-Outcome Learning in Humans. J Neurosci 2022; 42:3636-3647. [PMID: 35296548 PMCID: PMC9053851 DOI: 10.1523/jneurosci.1079-21.2022] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 02/17/2022] [Accepted: 02/22/2022] [Indexed: 11/21/2022] Open
Abstract
From an associative perspective the acquisition of new goal-directed actions requires the encoding of specific action-outcome (AO) associations and, therefore, sensitivity to the validity of an action as a predictor of a specific outcome relative to other events. Although competitive architectures have been proposed within associative learning theory to achieve this kind of identity-based selection, whether and how these architectures are implemented by the brain is still a matter of conjecture. To investigate this issue, we trained human participants to encode various AO associations while undergoing functional neuroimaging (fMRI). We then degraded one AO contingency by increasing the probability of the outcome in the absence of its associated action while keeping other AO contingencies intact. We found that this treatment selectively reduced performance of the degraded action. Furthermore, when a signal predicted the unpaired outcome, performance of the action was restored, suggesting that the degradation effect reflects competition between the action and the context for prediction of the specific outcome. We used a Kalman filter to model the contribution of different causal variables to AO learning and found that activity in the medial prefrontal cortex (mPFC) and the dorsal anterior cingulate cortex (dACC) tracked changes in the association of the action and context, respectively, with regard to the specific outcome. Furthermore, we found the mPFC participated in a network with the striatum and posterior parietal cortex to segregate the influence of the various competing predictors to establish specific AO associations.SIGNIFICANCE STATEMENT Humans and other animals learn the consequences of their actions, allowing them to control their environment in a goal-directed manner. Nevertheless, it is unknown how we parse environmental causes from the effects of our own actions to establish these specific action-outcome (AO) relationships. Here, we show that the brain learns the causal structure of the environment by segregating the unique influence of actions from other causes in the medial prefrontal and anterior cingulate cortices and, through a network of structures, including the caudate nucleus and posterior parietal cortex, establishes the distinct causal relationships from which specific AO associations are formed.
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Affiliation(s)
- Richard W Morris
- Centre for Translational Data Science, University of Sydney, Sydney, NSW 2006, Australia
| | - Amir Dezfouli
- Data61, Commonwealth Scientific and Industrial Research Organisation, Sydney, NSW 2015, Australia
| | - Kristi R Griffiths
- Brain Dynamics Centre, Westmead Institute for Medical Research, University of Sydney, Sydney, NSW 2145, Australia
| | - Mike E Le Pelley
- School of Psychology, University of New South Wales Sydney, Sydney, NSW 2052, Australia
| | - Bernard W Balleine
- School of Psychology, University of New South Wales Sydney, Sydney, NSW 2052, Australia
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31
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Labrenz F, Spisák T, Ernst TM, Gomes CA, Quick HH, Axmacher N, Elsenbruch S, Timmann D. Temporal dynamics of fMRI signal changes during conditioned interoceptive pain-related fear and safety acquisition and extinction. Behav Brain Res 2022; 427:113868. [PMID: 35364111 DOI: 10.1016/j.bbr.2022.113868] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 03/14/2022] [Accepted: 03/28/2022] [Indexed: 12/18/2022]
Abstract
Associative learning and memory mechanisms drive interoceptive signaling along the gut-brain axis, thus shaping affective-emotional reactions and behavior. Specifically, learning to predict potentially harmful, visceral pain is assumed to succeed within very few trials. However, the temporal dynamics of cerebellar and cerebral fMRI signal changes underlying early acquisition and extinction of learned fear signals and the concomitant evolvement of safety learning remain incompletely understood. 3T fMRI data of healthy individuals from three studies were uniformly processed across the whole brain and the cerebellum including an advanced normalizing method of the cerebellum. All studies employed differential delay conditioning (N=94) with one visual cue (CS+) being repeatedly paired with visceral pain as unconditioned stimulus (US) while a second cue remained unpaired (CS-). During subsequent extinction (N=51), all CS were presented without US. Behavioral results revealed increased CS+-aversiveness and CS--pleasantness after conditioning and diminished valence ratings for both CS following extinction. During early acquisition, the CS- induced linearly increasing neural activation in the insula, midcingulate cortex, hippocampus, precuneus as well as cerebral and cerebellar somatomotor regions. The comparison between acquisition and extinction phases yielded a CS--induced linear increase in the posterior cingulate cortex and precuneus during early acquisition, while there was no evidence for linear fMRI signal changes for the CS+ during acquisition and for both CS during extinction. Based on theoretical accounts of discrimination and temporal difference learning, these results suggest a gradual evolvement of learned safety cues that engage emotional arousal, memory, and cortical modulatory networks. As safety signals are presumably more difficult to learn and to discriminate from learned threat cues, the underlying temporal dynamics may reflect enhanced salience and prediction processing as well as increasing demands for attentional resources and the integration of multisensory information. Maladaptive responses to learned safety signals are a clinically relevant phenotype in multiple conditions, including chronic visceral pain, and can be exceptionally resistant to modification or extinction. Through sustained hypervigilance, safety seeking constitutes one key component in pain and stress-related avoidance behavior, calling for future studies targeting the mechanisms of safety learning and extinction to advance current cognitive-behavioral treatment approaches.
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Affiliation(s)
- Franziska Labrenz
- Department of Medical Psychology and Medical Sociology, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany; Department of Neurology, Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
| | - Tamás Spisák
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Thomas M Ernst
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Carlos A Gomes
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Harald H Quick
- High-Field and Hybrid Magnetic Resonance Imaging, University Hospital Essen, Essen, Germany; Erwin L. Hahn Institute for MR Imaging, University of Duisburg-Essen, Essen, Germany
| | - Nikolai Axmacher
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Sigrid Elsenbruch
- Department of Medical Psychology and Medical Sociology, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany; Department of Neurology, Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Dagmar Timmann
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
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Time pressure changes how people explore and respond to uncertainty. Sci Rep 2022; 12:4122. [PMID: 35260717 PMCID: PMC8904509 DOI: 10.1038/s41598-022-07901-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 02/28/2022] [Indexed: 12/25/2022] Open
Abstract
How does time pressure influence exploration and decision-making? We investigated this question with several four-armed bandit tasks manipulating (within subjects) expected reward, uncertainty, and time pressure (limited vs. unlimited). With limited time, people have less opportunity to perform costly computations, thus shifting the cost-benefit balance of different exploration strategies. Through behavioral, reinforcement learning (RL), reaction time (RT), and evidence accumulation analyses, we show that time pressure changes how people explore and respond to uncertainty. Specifically, participants reduced their uncertainty-directed exploration under time pressure, were less value-directed, and repeated choices more often. Since our analyses relate uncertainty to slower responses and dampened evidence accumulation (i.e., drift rates), this demonstrates a resource-rational shift towards simpler, lower-cost strategies under time pressure. These results shed light on how people adapt their exploration and decision-making strategies to externally imposed cognitive constraints.
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33
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Grossman CD, Bari BA, Cohen JY. Serotonin neurons modulate learning rate through uncertainty. Curr Biol 2022; 32:586-599.e7. [PMID: 34936883 PMCID: PMC8825708 DOI: 10.1016/j.cub.2021.12.006] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 10/11/2021] [Accepted: 12/03/2021] [Indexed: 12/20/2022]
Abstract
Regulating how fast to learn is critical for flexible behavior. Learning about the consequences of actions should be slow in stable environments, but accelerate when that environment changes. Recognizing stability and detecting change are difficult in environments with noisy relationships between actions and outcomes. Under these conditions, theories propose that uncertainty can be used to modulate learning rates ("meta-learning"). We show that mice behaving in a dynamic foraging task exhibit choice behavior that varied as a function of two forms of uncertainty estimated from a meta-learning model. The activity of dorsal raphe serotonin neurons tracked both types of uncertainty in the foraging task as well as in a dynamic Pavlovian task. Reversible inhibition of serotonin neurons in the foraging task reproduced changes in learning predicted by a simulated lesion of meta-learning in the model. We thus provide a quantitative link between serotonin neuron activity, learning, and decision making.
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Affiliation(s)
- Cooper D Grossman
- The Solomon H. Snyder Department of Neuroscience, Brain Science Institute, Kavli Neuroscience Discovery Institute, The Johns Hopkins University School of Medicine, 725 N. Wolfe Street, Baltimore, MD 21205, USA
| | - Bilal A Bari
- The Solomon H. Snyder Department of Neuroscience, Brain Science Institute, Kavli Neuroscience Discovery Institute, The Johns Hopkins University School of Medicine, 725 N. Wolfe Street, Baltimore, MD 21205, USA
| | - Jeremiah Y Cohen
- The Solomon H. Snyder Department of Neuroscience, Brain Science Institute, Kavli Neuroscience Discovery Institute, The Johns Hopkins University School of Medicine, 725 N. Wolfe Street, Baltimore, MD 21205, USA.
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34
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Rosenbaum GM, Grassie HL, Hartley CA. Valence biases in reinforcement learning shift across adolescence and modulate subsequent memory. eLife 2022; 11:e64620. [PMID: 35072624 PMCID: PMC8786311 DOI: 10.7554/elife.64620] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 12/24/2021] [Indexed: 12/12/2022] Open
Abstract
As individuals learn through trial and error, some are more influenced by good outcomes, while others weight bad outcomes more heavily. Such valence biases may also influence memory for past experiences. Here, we examined whether valence asymmetries in reinforcement learning change across adolescence, and whether individual learning asymmetries bias the content of subsequent memory. Participants ages 8-27 learned the values of 'point machines,' after which their memory for trial-unique images presented with choice outcomes was assessed. Relative to children and adults, adolescents overweighted worse-than-expected outcomes during learning. Individuals' valence biases modulated incidental memory, such that those who prioritized worse- (or better-) than-expected outcomes during learning were also more likely to remember images paired with these outcomes, an effect reproduced in an independent dataset. Collectively, these results highlight age-related changes in the computation of subjective value and demonstrate that a valence-asymmetric valuation process influences how information is prioritized in episodic memory.
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Affiliation(s)
- Gail M Rosenbaum
- Department of Psychology, New York UniversityNew YorkUnited States
| | - Hannah L Grassie
- Department of Psychology, New York UniversityNew YorkUnited States
| | - Catherine A Hartley
- Department of Psychology, New York UniversityNew YorkUnited States
- Center for Neural Science, New York UniversityNew YorkUnited States
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35
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Sörensen LKA, Zambrano D, Slagter HA, Bohté SM, Scholte HS. Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention. J Cogn Neurosci 2022; 34:655-674. [DOI: 10.1162/jocn_a_01819] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Abstract
Spatial attention enhances sensory processing of goal-relevant information and improves perceptual sensitivity. Yet, the specific neural mechanisms underlying the effects of spatial attention on performance are still contested. Here, we examine different attention mechanisms in spiking deep convolutional neural networks. We directly contrast effects of precision (internal noise suppression) and two different gain modulation mechanisms on performance on a visual search task with complex real-world images. Unlike standard artificial neurons, biological neurons have saturating activation functions, permitting implementation of attentional gain as gain on a neuron's input or on its outgoing connection. We show that modulating the connection is most effective in selectively enhancing information processing by redistributing spiking activity and by introducing additional task-relevant information, as shown by representational similarity analyses. Precision only produced minor attentional effects in performance. Our results, which mirror empirical findings, show that it is possible to adjudicate between attention mechanisms using more biologically realistic models and natural stimuli.
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Affiliation(s)
| | - Davide Zambrano
- Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- École Polytechnique Fédérale de Lausanne, Switzerland
| | | | - Sander M. Bohté
- University of Amsterdam, The Netherlands
- Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- Rijksuniversiteit Groningen, The Netherlands
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Soltani A, Koechlin E. Computational models of adaptive behavior and prefrontal cortex. Neuropsychopharmacology 2022; 47:58-71. [PMID: 34389808 PMCID: PMC8617006 DOI: 10.1038/s41386-021-01123-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 02/07/2023]
Abstract
The real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus-action associations through rewards; (2) predictive models learning stimulus- and/or action-outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.
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Affiliation(s)
- Alireza Soltani
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Etienne Koechlin
- Institut National de la Sante et de la Recherche Medicale, Universite Pierre et Marie Curie, Ecole Normale Superieure, Paris, France.
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Short breaks at school: effects of a physical activity and a mindfulness intervention on children's attention, reading comprehension, and self-esteem. Trends Neurosci Educ 2021; 25:100160. [PMID: 34844692 DOI: 10.1016/j.tine.2021.100160] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 09/10/2021] [Accepted: 09/10/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Although breaks are essential to restoring cognitive and psychological conditions for learning, short breaks within school lessons are not established and the specificity of effects has not often been investigated. Therefore, the effects of a physical activity (Study 1) and a mindfulness intervention (Study 2) were investigated. PROCEDURE By an intervention-control group design, the effects of daily 10-min physical activity (Study 1: N = 162, 4th grade) and mindfulness breaks (Study 2: N = 79, 5th grade) were implemented within regular school lessons over a 2-week time period to research the impact on attention, reading comprehension, and self-esteem. RESULTS In the physical activity intervention children's attention improved (attention-processing speed: p < .004, ηp2 = .05, attention-performance: p < .025, ηp2 = .03), and in the mindfulness intervention reading comprehension improved (p < .012, ηp2 = .08) compared to the controls. Results further indicated that self-esteem moderated the relationship between groups and attention improvement in study 1. CONCLUSION Classroom-based short physical and mindfulness breaks could support attention and reading comprehension, which are known to support overall academic success.
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Piray P, Daw ND. A model for learning based on the joint estimation of stochasticity and volatility. Nat Commun 2021; 12:6587. [PMID: 34782597 PMCID: PMC8592992 DOI: 10.1038/s41467-021-26731-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 10/08/2021] [Indexed: 02/08/2023] Open
Abstract
Previous research has stressed the importance of uncertainty for controlling the speed of learning, and how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we introduce a learning model, in which both factors are learned simultaneously from experience, and use the model to simulate human and animal data across many seemingly disparate neuroscientific and behavioral phenomena. By considering the full problem of joint estimation, we highlight a set of previously unappreciated issues, arising from the mutual interdependence of inference about volatility and stochasticity. This interdependence complicates and enriches the interpretation of previous results, such as pathological learning in individuals with anxiety and following amygdala damage.
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Affiliation(s)
- Payam Piray
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA.
| | - Nathaniel D Daw
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
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39
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Wang S, Feng SF, Bornstein AM. Mixing memory and desire: How memory reactivation supports deliberative decision-making. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2021; 13:e1581. [PMID: 34665529 DOI: 10.1002/wcs.1581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 08/24/2021] [Accepted: 09/16/2021] [Indexed: 11/09/2022]
Abstract
Memories affect nearly every aspect of our mental life. They allow us to both resolve uncertainty in the present and to construct plans for the future. Recently, renewed interest in the role memory plays in adaptive behavior has led to new theoretical advances and empirical observations. We review key findings, with particular emphasis on how the retrieval of many kinds of memories affects deliberative action selection. These results are interpreted in a sequential inference framework, in which reinstatements from memory serve as "samples" of potential action outcomes. The resulting model suggests a central role for the dynamics of memory reactivation in determining the influence of different kinds of memory in decisions. We propose that representation-specific dynamics can implement a bottom-up "product of experts" rule that integrates multiple sets of action-outcome predictions weighted based on their uncertainty. We close by reviewing related findings and identifying areas for further research. This article is categorized under: Psychology > Reasoning and Decision Making Neuroscience > Cognition Neuroscience > Computation.
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Affiliation(s)
- Shaoming Wang
- Department of Psychology, New York University, New York, New York, USA
| | - Samuel F Feng
- Department of Mathematics, Khalifa University of Science and Technology, Abu Dhabi, UAE.,Khalifa University Centre for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Aaron M Bornstein
- Department of Cognitive Sciences, University of California-Irvine, Irvine, California, USA.,Center for the Neurobiology of Learning & Memory, University of California-Irvine, Irvine, California, USA.,Institute for Mathematical Behavioral Sciences, University of California-Irvine, Irvine, California, USA
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40
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Caamaño-Navarrete F, Latorre-Román PÁ, Párraga-Montilla J, Jerez-Mayorga D, Delgado-Floody P. Selective Attention and Concentration Are Related to Lifestyle in Chilean Schoolchildren. CHILDREN-BASEL 2021; 8:children8100856. [PMID: 34682121 PMCID: PMC8534889 DOI: 10.3390/children8100856] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/20/2021] [Accepted: 09/21/2021] [Indexed: 12/27/2022]
Abstract
The objective of this investigation was to determine the association between selective attention and concentration with physical fitness (i.e., cardiorespiratory fitness (CRF), V˙O2max, the standing long jump test (SLJ) and handgrip muscle strength (HGS)), lifestyle parameters (i.e., physical activity (PA) level, screen time (ST), sleep duration and food habits) and anthropometric measures (i.e., body mass index (BMI) and waist circumference (WC)) among Chilean schoolchildren. Two hundred and forty-eight schoolchildren (137 boys, 111 girls, 11.80 ± 1.17 and 11.58 ± 1.09 years, respectively) participated. Selective attention, concentration and lifestyle (PA, ST, sleep duration and Mediterranean diet (MD) adherence) were determined using a standard questionnaire. CRF, SLJ, HGS and anthropometric indicators (BMI and WC) were also measured. Selective attention showed a positive association with MD adherence score (β; 5.012, p = p < 0.05). Concentration was linked inversely to ST (β; −5.498, p = p < 0.05). Likewise, concentration presented a positive association with MD adherence (β; 2.904, p = p < 0.05). In conclusion, children’s lifestyles are related to the selective attention and concentration of children; therefore, promoting healthy habits could be a cost-effective strategy in the promotion of cognitive development, as it relates to selective attention and concentration.
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Affiliation(s)
| | - Pedro Ángel Latorre-Román
- Department of Didactics of Music, Plastic and Corporal Expression, University of Jaen, 27301 Jaen, Spain; (P.Á.L.-R.); (J.P.-M.)
| | - Juan Párraga-Montilla
- Department of Didactics of Music, Plastic and Corporal Expression, University of Jaen, 27301 Jaen, Spain; (P.Á.L.-R.); (J.P.-M.)
| | - Daniel Jerez-Mayorga
- Faculty of Rehabilitation Sciences, Universidad Andres Bello, Santiago 7591538, Chile;
| | - Pedro Delgado-Floody
- Department of Physical Education, Sport and Recreation, Universidad de La Frontera, Temuco 4780000, Chile
- Correspondence: ; Tel.: +56-45-2-325200
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41
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Hamedi SM, Pishghadam R. Visual Attention and Lexical Involvement in L1 and L2 Word Processing: Emotional Stroop Effect. JOURNAL OF PSYCHOLINGUISTIC RESEARCH 2021; 50:585-602. [PMID: 32529535 DOI: 10.1007/s10936-020-09709-4] [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: 06/11/2023]
Abstract
Given the fact that the process of engaging and interacting with the text is not only the function of a reader but also the text itself, the current study attempts to examine the role of the type of the word in the attentional engagement. More specifically, the present investigation aims to verify the interplay of sensorimotor information, emotions, and the linguistic information in the word processing. In so doing, for the scale validation, a sample of 220 Iranian English as a Foreign Language (EFL) learners from different language institutes were requested to complete the newly designed Persian and English lexical involvement scales. The results of Structural Equation Modeling (SEM) supported the factor structure and the reliability of the measures. Moreover, using Emotional Stroop task in the experimental set up, the results revealed that there is a strong positive relationship between lexical involvement and visual attentional engagement in L1(Persian). The association was conversely negative in L2 (English). Finally, the statistical analysis indicates that the lexical stimuli differ regarding their magnitude of lexical involvement in L1 and L2.
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Affiliation(s)
| | - Reza Pishghadam
- Language Education, Ferdowsi University of Mashhad, Mashad, Iran.
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42
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Taylor BK, Eastman JA, Frenzel MR, Embury CM, Wang YP, Calhoun VD, Stephen JM, Wilson TW. Neural oscillations underlying selective attention follow sexually divergent developmental trajectories during adolescence. Dev Cogn Neurosci 2021; 49:100961. [PMID: 33984667 PMCID: PMC8131898 DOI: 10.1016/j.dcn.2021.100961] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 03/02/2021] [Accepted: 04/15/2021] [Indexed: 01/06/2023] Open
Abstract
A cohort of 9- to 16-year-olds completed a classic flanker task during MEG. There were developmentally-sensitive interference effects in key attention regions. Youth showed sexually-divergent patterns of age-related interference activity. Maturational differences among males supported improved task behavior.
Selective attention processes are critical to everyday functioning and are known to develop through at least young adulthood. Although numerous investigations have studied the maturation of attention systems in the brain, these studies have largely focused on the spatial configuration of these systems; there is a paucity of research on the neural oscillatory dynamics serving selective attention, particularly among youth. Herein, we examined the developmental trajectory of neural oscillatory activity serving selective attention in 53 typically developing youth age 9-to-16 years-old. Participants completed the classic arrow-based flanker task during magnetoencephalography, and the resulting data were imaged in the time-frequency domain. Flanker interference significantly modulated theta and alpha/beta oscillations within prefrontal, mid-cingulate, cuneus, and occipital regions. Interference-related neural activity also increased with age in the temporoparietal junction and the rostral anterior cingulate. Sex-specific effects indicated that females had greater theta interference activity in the anterior insula, whereas males showed differential effects in theta and alpha/beta oscillations across frontoparietal regions. Finally, males showed age-related changes in alpha/beta interference in the cuneus and middle frontal gyrus, which predicted improved behavioral performance. Taken together, these data suggest sexually-divergent developmental trajectories underlying selective attention in youth.
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Affiliation(s)
- Brittany K Taylor
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Jacob A Eastman
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Michaela R Frenzel
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Christine M Embury
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; Department of Psychology, University of Nebraska Omaha, Omaha, NE, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Vince D Calhoun
- Mind Research Network, Albuquerque, NM, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | | | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA.
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43
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Kalm K, Norris D. Sequence learning recodes cortical representations instead of strengthening initial ones. PLoS Comput Biol 2021; 17:e1008969. [PMID: 34029315 PMCID: PMC8177667 DOI: 10.1371/journal.pcbi.1008969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 06/04/2021] [Accepted: 04/16/2021] [Indexed: 11/18/2022] Open
Abstract
We contrast two computational models of sequence learning. The associative learner posits that learning proceeds by strengthening existing association weights. Alternatively, recoding posits that learning creates new and more efficient representations of the learned sequences. Importantly, both models propose that humans act as optimal learners but capture different statistics of the stimuli in their internal model. Furthermore, these models make dissociable predictions as to how learning changes the neural representation of sequences. We tested these predictions by using fMRI to extract neural activity patterns from the dorsal visual processing stream during a sequence recall task. We observed that only the recoding account can explain the similarity of neural activity patterns, suggesting that participants recode the learned sequences using chunks. We show that associative learning can theoretically store only very limited number of overlapping sequences, such as common in ecological working memory tasks, and hence an efficient learner should recode initial sequence representations.
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Affiliation(s)
- Kristjan Kalm
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Dennis Norris
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
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44
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Abstract
Adaptive behavior in a complex, dynamic, and multisensory world poses some of the most fundamental computational challenges for the brain, notably inference, decision-making, learning, binding, and attention. We first discuss how the brain integrates sensory signals from the same source to support perceptual inference and decision-making by weighting them according to their momentary sensory uncertainties. We then show how observers solve the binding or causal inference problem-deciding whether signals come from common causes and should hence be integrated or else be treated independently. Next, we describe the multifarious interplay between multisensory processing and attention. We argue that attentional mechanisms are crucial to compute approximate solutions to the binding problem in naturalistic environments when complex time-varying signals arise from myriad causes. Finally, we review how the brain dynamically adapts multisensory processing to a changing world across multiple timescales.
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Affiliation(s)
- Uta Noppeney
- Donders Institute for Brain, Cognition and Behavior, Radboud University, 6525 AJ Nijmegen, The Netherlands;
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45
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Kudryavitskaya E, Marom E, Shani-Narkiss H, Pash D, Mizrahi A. Flexible categorization in the mouse olfactory bulb. Curr Biol 2021; 31:1616-1631.e4. [DOI: 10.1016/j.cub.2021.01.063] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 12/11/2020] [Accepted: 01/19/2021] [Indexed: 11/30/2022]
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46
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Abstract
The central theme of this review is the dynamic interaction between information selection and learning. We pose a fundamental question about this interaction: How do we learn what features of our experiences are worth learning about? In humans, this process depends on attention and memory, two cognitive functions that together constrain representations of the world to features that are relevant for goal attainment. Recent evidence suggests that the representations shaped by attention and memory are themselves inferred from experience with each task. We review this evidence and place it in the context of work that has explicitly characterized representation learning as statistical inference. We discuss how inference can be scaled to real-world decisions by approximating beliefs based on a small number of experiences. Finally, we highlight some implications of this inference process for human decision-making in social environments.
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Affiliation(s)
- Angela Radulescu
- Department of Psychology, Princeton University, Princeton, New Jersey 08544, USA; .,Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA
| | - Yeon Soon Shin
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA
| | - Yael Niv
- Department of Psychology, Princeton University, Princeton, New Jersey 08544, USA; .,Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA
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47
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Corsi MC, Chavez M, Schwartz D, George N, Hugueville L, Kahn AE, Dupont S, Bassett DS, De Vico Fallani F. BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks. J Neural Eng 2021; 18. [PMID: 33725682 DOI: 10.1088/1741-2552/abef39] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 03/16/2021] [Indexed: 11/11/2022]
Abstract
Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is difficult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood. To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic (EEG) and magnetoencephalographic (MEG) data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time. We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the α band was paralleled by a decrease of the integration of visual processing and working memory areas in the β band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the α2 band: positively in somatosensory and decision-making related areas and negatively in associative areas. Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.
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Affiliation(s)
| | - Mario Chavez
- UMR-7225, CNRS, 47, boulevard de l'Hôpital, Paris, 75013, FRANCE
| | - Denis Schwartz
- INSERM, 47, boulevard de l'Hôpital, Paris, Île-de-France, 75013, FRANCE
| | - Nathalie George
- UMR-7225, CNRS, 47, boulevard de l'Hôpital, Paris, Île-de-France, 75013, FRANCE
| | - Laurent Hugueville
- Institut du Cerveau et de la Moelle Epiniere, 47, boulevard de l'Hôpital, Paris, Île-de-France, 75013, FRANCE
| | - Ari E Kahn
- Department of Neuroscience, University of Pennsylvania, 210 S. 33rd Street 240 Skirkanich Hall, Philadelphia, Pennsylvania, 19104-6321, UNITED STATES
| | - Sophie Dupont
- Institut du Cerveau et de la Moelle Epiniere, 47, boulevard de l'Hôpital, Paris, Île-de-France, 75013, FRANCE
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, 210 S. 33rd Street 240 Skirkanich Hall, USA, Philadelphia, Pennsylvania, 19104-6321, UNITED STATES
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48
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Inglis JB, Valentin VV, Ashby FG. Modulation of Dopamine for Adaptive Learning: A Neurocomputational Model. COMPUTATIONAL BRAIN & BEHAVIOR 2021; 4:34-52. [PMID: 34151186 PMCID: PMC8210637 DOI: 10.1007/s42113-020-00083-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
There have been many proposals that learning rates in the brain are adaptive, in the sense that they increase or decrease depending on environmental conditions. The majority of these models are abstract and make no attempt to describe the neural circuitry that implements the proposed computations. This article describes a biologically detailed computational model that overcomes this shortcoming. Specifically, we propose a neural circuit that implements adaptive learning rates by modulating the gain on the dopamine response to reward prediction errors, and we model activity within this circuit at the level of spiking neurons. The model generates a dopamine signal that depends on the size of the tonically active dopamine neuron population and the phasic spike rate. The model was tested successfully against results from two single-neuron recording studies and a fast-scan cyclic voltammetry study. We conclude by discussing the general applicability of the model to dopamine mediated tasks that transcend the experimental phenomena it was initially designed to address.
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Affiliation(s)
- Jeffrey B Inglis
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara
| | - Vivian V Valentin
- Department of Psychological & Brain Sciences, University of California, Santa Barbara
| | - F Gregory Ashby
- Department of Psychological & Brain Sciences, University of California, Santa Barbara
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49
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Levy I, Schiller D. Neural Computations of Threat. Trends Cogn Sci 2021; 25:151-171. [PMID: 33384214 PMCID: PMC8084636 DOI: 10.1016/j.tics.2020.11.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 11/16/2020] [Accepted: 11/18/2020] [Indexed: 12/26/2022]
Abstract
A host of learning, memory, and decision-making processes form the individual's response to threat and may be disrupted in anxiety and post-trauma psychopathology. Here we review the neural computations of threat, from the first encounter with a dangerous situation, through learning, storing, and updating cues that predict it, to making decisions about the optimal course of action. The overview highlights the interconnected nature of these processes and their reliance on shared neural and computational mechanisms. We propose an integrative approach to the study of threat-related processes, in which specific computations are studied across the various stages of threat experience rather than in isolation. This approach can generate new insights about the evolution, diagnosis, and treatment of threat-related psychopathology.
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Affiliation(s)
- Ifat Levy
- Departments of Comparative Medicine, Neuroscience, and Psychology, Yale University, New Haven, CT, USA.
| | - Daniela Schiller
- Department of Psychiatry, Department of Neuroscience, and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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50
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Guo CCG, He T, Grandjean J, Homberg J. Knockout serotonin transporter in rats moderates outcome and stimulus generalization. Transl Psychiatry 2021; 11:25. [PMID: 33414390 PMCID: PMC7791109 DOI: 10.1038/s41398-020-01162-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 12/07/2020] [Accepted: 12/10/2020] [Indexed: 11/30/2022] Open
Abstract
Understanding the common dimension of mental disorders (such as anxiety, depression, and drug addiction) might contribute to the construction of biological frameworks (Research Domain Criteria, RDoC) for novel ways of treatment. One common dimension at the behavioral level observed across these disorders is a generalization. Testing generalization in serotonin transporter (5-HTT) knockout (KO) rats, an animal model showing depression/anxiety-like behaviors and drug addiction-like behaviors, could therefore provide more insights into this framework. We tested the outcome and stimulus generalization in wild-type (WT) and 5-HTT KO rats. Using a newly established touchscreen-based task, subjects directly responded to visual stimuli (Gabor patch images). We measured the response time and outcome in a precise manner. We found that 5-HTT KO rats processed visual information faster than WT rats during outcome generalization. Interestingly, during stimulus generalization, WT rats gradually responded faster to the stimuli as the sessions progressed, while 5-HTT KO rats responded faster than WT in the initial sessions and did not change significantly as the sessions progressed. This observation suggests that KO rats, compared to WT rats, may be less able to update changes in information. Taken together, KO 5-HTT modulates information processing when the environment changes.
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Affiliation(s)
- Chao Ciu-Gwok Guo
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands.
| | - Tao He
- grid.11135.370000 0001 2256 9319School of Psychological and Cognitive Sciences, Peking University, Beijing, China
| | - Joanes Grandjean
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands ,grid.10417.330000 0004 0444 9382Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Judith Homberg
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands.
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