1
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Liu C, Yu R. Neural mechanisms underpinning metacognitive shifts driven by non-informative predictions. Neuroimage 2024; 296:120670. [PMID: 38848980 DOI: 10.1016/j.neuroimage.2024.120670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 05/10/2024] [Accepted: 06/05/2024] [Indexed: 06/09/2024] Open
Abstract
Humans constantly make predictions and such predictions allow us to prepare for future events. Yet, such benefits may come with drawbacks as premature predictions may potentially bias subsequent judgments. Here we examined how prediction influences our perceptual decisions and subsequent confidence judgments, on scenarios where the predictions were arbitrary and independent of the identity of the upcoming stimuli. We defined them as invalid and non-informative predictions. Behavioral results showed that, such non-informative predictions biased perceptual decisions in favor of the predicted choice, and such prediction-induced perceptual bias further increased the metacognitive efficiency. The functional MRI results showed that activities in the medial prefrontal cortex (mPFC) and subgenual anterior cingulate cortex (sgACC) encoded the response consistency between predictions and perceptual decisions. Activity in mPFC predicted the strength of this congruency bias across individuals. Moreover, the parametric encoding of confidence in putamen was modulated by prediction-choice consistency, such that activity in putamen was negatively correlated with confidence rating after inconsistent responses. These findings suggest that predictions, while made arbitrarily, orchestrate the neural representations of choice and confidence judgment.
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Affiliation(s)
- Cuizhen Liu
- School of Psychology, Shaanxi Normal University, Xi'an 710062, PR China
| | - Rongjun Yu
- Department of Management, Marketing, and Information Systems, Hong Kong Baptist University, Hong Kong 999077, PR China.
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2
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Guppy F, Muniz-Pardos B, Angeloudis K, Grivas GV, Pitsiladis A, Bundy R, Zelenkova I, Tanisawa K, Akiyama H, Keramitsoglou I, Miller M, Knopp M, Schweizer F, Luckfiel T, Ruiz D, Racinais S, Pitsiladis Y. Technology Innovation and Guardrails in Elite Sport: The Future is Now. Sports Med 2023; 53:97-113. [PMID: 37787844 PMCID: PMC10721698 DOI: 10.1007/s40279-023-01913-1] [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] [Accepted: 08/15/2023] [Indexed: 10/04/2023]
Abstract
A growing number of companies are developing or using wearable sensor technologies that can monitor, analyse and transmit data from humans in real time that can be used by the sporting, biomedical and media industries. To explore this phenomenon, we describe and review two high-profile sporting events where innovations in wearable technologies were trialled: the Tokyo 2020 Summer Olympic Games (Tokyo 2020, Japan) and the 2022 adidas Road to Records (Germany). These two major sporting events were the first time academic and industry partners came together to implement real-time wearable solutions during major competition, to protect the health of athletes competing in hot and humid environments, as well as to better understand how these metrics can be used moving forwards. Despite the undoubted benefits of such wearables, there are well-founded concerns regarding their use including: (1) limited evidence quantifying the potential beneficial effects of analysing specific parameters, (2) the quality of hardware and provided data, (3) information overload, (4) data security and (5) exaggerated marketing claims. Employment and sporting rules and regulations also need to evolve to facilitate the use of wearable devices. There is also the potential to obtain real-time data that will oblige medical personnel to make crucial decisions around whether their athletes should continue competing or withdraw for health reasons. To protect athletes, the urgent need is to overcome these ethical/data protection concerns and develop wearable technologies that are backed by quality science. The fields of sport and exercise science and medicine provide an excellent platform to understand the impact of wearable sensors on performance, wellness, health, and disease.
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Affiliation(s)
- Fergus Guppy
- Institute of Life and Earth Sciences, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, UK
| | - Borja Muniz-Pardos
- GENUD (Growth, Exercise, Nutrition and Development) Research Group, Faculty of Health and Sport Sciences, University of Zaragoza, Saragossa, Spain
| | - Konstantinos Angeloudis
- Institute of Life and Earth Sciences, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, UK
| | - Gerasimos V Grivas
- Physical Education and Sports, Division of Humanities and Political Sciences, Hellenic Naval Academy, Piraeus, Athens, Greece
| | | | | | - Irina Zelenkova
- GENUD (Growth, Exercise, Nutrition and Development) Research Group, Faculty of Health and Sport Sciences, University of Zaragoza, Saragossa, Spain
| | - Kumpei Tanisawa
- Faculty of Sport Sciences, Waseda University, Tokorozawa, Japan
| | - Hiroshi Akiyama
- Graduate School of Sport Sciences, Waseda University, Tokorozawa, Japan
| | | | - Mike Miller
- Human Telemetrics, London, UK
- World Olympians Association, Lausanne, Switzerland
| | - Melanie Knopp
- adidas Innovation, adidas AG, Herzogenaurach, Germany
| | | | | | - Daniel Ruiz
- adidas Innovation, adidas AG, Herzogenaurach, Germany
| | - Sebastien Racinais
- Environmental Stress Unit, CREPS Montpellier - Font Romeu, Montpellier, France
| | - Yannis Pitsiladis
- Human Telemetrics, London, UK.
- Department of Sport, Physical Education and Health, Hong Kong Baptist University, Hong Kong SAR, Hong Kong.
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3
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Lu Y, Guo X, Weng X, Jiang H, Yan H, Shen X, Feng Z, Zhao X, Li L, Zheng L, Liu Z, Men W, Gao JH. Theta Signal Transfer from Parietal to Prefrontal Cortex Ignites Conscious Awareness of Implicit Knowledge during Sequence Learning. J Neurosci 2023; 43:6760-6778. [PMID: 37607820 PMCID: PMC10552945 DOI: 10.1523/jneurosci.2172-22.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 08/08/2023] [Accepted: 08/13/2023] [Indexed: 08/24/2023] Open
Abstract
Unconscious acquisition of sequence structure from experienced events can lead to explicit awareness of the pattern through extended practice. Although the implicit-to-explicit transition has been extensively studied in humans using the serial reaction time (SRT) task, the subtle neural activity supporting this transition remains unclear. Here, we investigated whether frequency-specific neural signal transfer contributes to this transition. A total of 208 participants (107 females) learned a sequence pattern through a multisession SRT task, allowing us to observe the transitions. Session-by-session measures of participants' awareness for sequence knowledge were conducted during the SRT task to identify the session when the transition occurred. By analyzing time course RT data using switchpoint modeling, we identified an increase in learning benefit specifically at the transition session. Electroencephalogram (EEG)/magnetoencephalogram (MEG) recordings revealed increased theta power in parietal (precuneus) regions one session before the transition (pretransition) and a prefrontal (superior frontal gyrus; SFG) one at the transition session. Phase transfer entropy (PTE) analysis confirmed that directional theta transfer from precuneus → SFG occurred at the pretransition session and its strength positively predicted learning improvement at the subsequent transition session. Furthermore, repetitive transcranial magnetic stimulation (TMS) modulated precuneus theta power and altered transfer strength from precuneus to SFG, resulting in changes in both transition rate and learning benefit at that specific point of transition. Our brain-stimulation evidence supports a role for parietal → prefrontal theta signal transfer in igniting conscious awareness of implicitly acquired knowledge.SIGNIFICANCE STATEMENT There exists a pervasive phenomenon wherein individuals unconsciously acquire sequence patterns from their environment, gradually becoming aware of the underlying regularities through repeated practice. While previous studies have established the robustness of this implicit-to-explicit transition in humans, the refined neural mechanisms facilitating conscious access to implicit knowledge remain poorly understood. Here, we demonstrate that prefrontal activity, known to be crucial for conscious awareness, is triggered by neural signal transfer originating from the posterior brain region, specifically the precuneus. By employing brain stimulation techniques, we establish a causal link between neural signal transfer and the occurrence of awareness. Our findings unveil a mechanism by which implicit knowledge becomes consciously accessible in human cognition.
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Affiliation(s)
- Yang Lu
- Fudan Institute on Ageing, Fudan University, Shanghai, China, 200433
- Ministry of education (MOE) Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China, 200433
- School of Psychology and cognitive science, East China Normal University, Shanghai, China, 200062
| | - Xiuyan Guo
- Fudan Institute on Ageing, Fudan University, Shanghai, China, 200433
- Ministry of education (MOE) Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China, 200433
| | - Xue Weng
- School of Psychology and cognitive science, East China Normal University, Shanghai, China, 200062
| | - Haoran Jiang
- Fudan Institute on Ageing, Fudan University, Shanghai, China, 200433
- Ministry of education (MOE) Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China, 200433
| | - Huidan Yan
- School of Psychology and cognitive science, East China Normal University, Shanghai, China, 200062
| | - Xianting Shen
- Fudan Institute on Ageing, Fudan University, Shanghai, China, 200433
- Department of Psychology, Fudan University, Shanghai, China, 200433
| | - Zhengning Feng
- School of Psychology and cognitive science, East China Normal University, Shanghai, China, 200062
| | - Xinyue Zhao
- School of Psychology and cognitive science, East China Normal University, Shanghai, China, 200062
| | - Lin Li
- School of Psychology and cognitive science, East China Normal University, Shanghai, China, 200062
| | - Li Zheng
- Fudan Institute on Ageing, Fudan University, Shanghai, China, 200433
- Ministry of education (MOE) Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China, 200433
| | - Zhiyuan Liu
- Shaanxi Key Laboratory of Behavior and Cognitive Neuroscience, School of Psychology, Shaanxi Normal University, Xi'an, China, 710062
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China, 100871
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China, 100871
| | - Jia-Hong Gao
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China, 100871
- Center for MRI Research and McGovern Institute for Brain Research, Peking University, Beijing, China, 100871
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4
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Flanagin VL, Klinkowski S, Brodt S, Graetsch M, Roselli C, Glasauer S, Gais S. The precuneus as a central node in declarative memory retrieval. Cereb Cortex 2023; 33:5981-5990. [PMID: 36610736 DOI: 10.1093/cercor/bhac476] [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: 06/03/2022] [Revised: 11/11/2022] [Accepted: 11/17/2022] [Indexed: 01/09/2023] Open
Abstract
Both, the hippocampal formation and the neocortex are contributing to declarative memory, but their functional specialization remains unclear. We investigated the differential contribution of both memory systems during free recall of word lists. In total, 21 women and 17 men studied the same list but with the help of different encoding associations. Participants associated the words either sequentially with the previous word on the list, with spatial locations on a well-known path, or with unique autobiographical events. After intensive rehearsal, subjects recalled the words during functional magnetic resonance imaging (fMRI). Common activity to all three types of encoding associations was identified in the posterior parietal cortex, in particular in the precuneus. Additionally, when associating spatial or autobiographical material, retrosplenial cortex activity was elicited during word list recall, while hippocampal activity emerged only for autobiographically associated words. These findings support a general, critical function of the precuneus in episodic memory storage and retrieval. The encoding-retrieval repetitions during learning seem to have accelerated hippocampus-independence and lead to direct neocortical integration in the sequentially associated and spatially associated word list tasks. During recall of words associated with autobiographical memories, the hippocampus might add spatiotemporal information supporting detailed scenic and contextual memories.
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Affiliation(s)
- Virginia L Flanagin
- Bernstein Center for Computational Neuroscience, Großhaderner Str. 2, 82152 Planegg-Martinsried, Germany.,IFB-LMU, Dept. of Neurology, Marchioninistr. 15, 81377 München, Germany
| | - Svenja Klinkowski
- Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstr. 5, 72076 Tübingen, Germany
| | - Svenja Brodt
- Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstr. 5, 72076 Tübingen, Germany
| | - Melanie Graetsch
- General and Experimental Psychology, Ludwig Maximilians University München, Leopoldstr. 13, 80802 München, Germany
| | - Carolina Roselli
- General and Experimental Psychology, Ludwig Maximilians University München, Leopoldstr. 13, 80802 München, Germany
| | - Stefan Glasauer
- Bernstein Center for Computational Neuroscience, Großhaderner Str. 2, 82152 Planegg-Martinsried, Germany.,Computational Neuroscience, Brandenburg University of Technology Cottbus-Senftenberg, Universitätsplatz 1, 01968 Senftenberg, Germany
| | - Steffen Gais
- Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstr. 5, 72076 Tübingen, Germany
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5
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Kotler S, Mannino M, Kelso S, Huskey R. First few seconds for flow: A comprehensive proposal of the neurobiology and neurodynamics of state onset. Neurosci Biobehav Rev 2022; 143:104956. [PMID: 36368525 DOI: 10.1016/j.neubiorev.2022.104956] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 10/22/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022]
Abstract
Flow is a cognitive state that manifests when there is complete attentional absorption while performing a task. Flow occurs when certain internal as well as external conditions are present, including intense concentration, a sense of control, feedback, and a balance between the challenge of the task and the relevant skillset. Phenomenologically, flow is accompanied by a loss of self-consciousness, seamless integration of action and awareness, and acute changes in time perception. Research has begun to uncover some of the neurophysiological correlates of flow, as well as some of the state's neuromodulatory processes. We comprehensively review this work and consider the neurodynamics of the onset of the state, considering large-scale brain networks, as well as dopaminergic, noradrenergic, and endocannabinoid systems. To accomplish this, we outline an evidence-based hypothetical situation, and consider the flow state in a broader context including other profound alterations in consciousness, such as the psychedelic state and the state of traumatic stress that can induce PTSD. We present a broad theoretical framework which may motivate future testable hypotheses.
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Affiliation(s)
| | | | - Scott Kelso
- Human Brain & Behavior Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, United States; Intelligent Systems Research Centre, Ulster University, Derry∼Londonderry, North Ireland
| | - Richard Huskey
- Cognitive Communication Science Lab, Department of Communication, University of California Davis, United States; Cognitive Science Program, University of California Davis, United States; Center for Mind and Brain, University of California Davis, United States.
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6
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Eckstein MK, Master SL, Xia L, Dahl RE, Wilbrecht L, Collins AGE. The interpretation of computational model parameters depends on the context. eLife 2022; 11:e75474. [PMID: 36331872 PMCID: PMC9635876 DOI: 10.7554/elife.75474] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 09/09/2022] [Indexed: 11/06/2022] Open
Abstract
Reinforcement Learning (RL) models have revolutionized the cognitive and brain sciences, promising to explain behavior from simple conditioning to complex problem solving, to shed light on developmental and individual differences, and to anchor cognitive processes in specific brain mechanisms. However, the RL literature increasingly reveals contradictory results, which might cast doubt on these claims. We hypothesized that many contradictions arise from two commonly-held assumptions about computational model parameters that are actually often invalid: That parameters generalize between contexts (e.g. tasks, models) and that they capture interpretable (i.e. unique, distinctive) neurocognitive processes. To test this, we asked 291 participants aged 8-30 years to complete three learning tasks in one experimental session, and fitted RL models to each. We found that some parameters (exploration / decision noise) showed significant generalization: they followed similar developmental trajectories, and were reciprocally predictive between tasks. Still, generalization was significantly below the methodological ceiling. Furthermore, other parameters (learning rates, forgetting) did not show evidence of generalization, and sometimes even opposite developmental trajectories. Interpretability was low for all parameters. We conclude that the systematic study of context factors (e.g. reward stochasticity; task volatility) will be necessary to enhance the generalizability and interpretability of computational cognitive models.
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Affiliation(s)
| | - Sarah L Master
- Department of Psychology, University of California, BerkeleyBerkeleyUnited States
- Department of Psychology, New York UniversityNew YorkUnited States
| | - Liyu Xia
- Department of Psychology, University of California, BerkeleyBerkeleyUnited States
- Department of Mathematics, University of California, BerkeleyBerkeleyUnited States
| | - Ronald E Dahl
- Institute of Human Development, University of California, BerkeleyBerkeleyUnited States
| | - Linda Wilbrecht
- Department of Psychology, University of California, BerkeleyBerkeleyUnited States
- Helen Wills Neuroscience Institute, University of California, BerkeleyBerkeleyUnited States
| | - Anne GE Collins
- Department of Psychology, University of California, BerkeleyBerkeleyUnited States
- Helen Wills Neuroscience Institute, University of California, BerkeleyBerkeleyUnited States
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7
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de Smet MD, Haim-Langford D, Neumann R, Kramer M, Cunningham E, Deutsch L, Milman Z. Tarsier Anterior Chamber Cell Grading: Improving the SUN Grading Scheme with a Visual Analog Scale. Ocul Immunol Inflamm 2022; 30:1686-1691. [PMID: 34232824 DOI: 10.1080/09273948.2021.1934036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
PURPOSE To compare an analog visual scale in grading anterior chamber cells (ACC) to a modified Standardization of Uveitis Nomenclature (SUN) ACC scale. METHOD A graphical representation of anterior chamber cells as a reference and a test set was created and shown to two groups of experienced uveitis experts. Group 1 was given the analog scale in written format, while group two was given the reference images for comparison. Each test subject was asked to provide the best approximation for each grade. RESULTS Eleven graders participated in phase 1. Correct grading occurred in 87.4% of cases. Discrepancies were seen at all grades. Only 3 of 11 graders were able to achieve a perfect score. Seven graders participated in phase 2. Agreement was 95.2% with 4/7 graders achieving a perfect score. Discrepancies were seen at higher grades only. CONCLUSIONS ACC grading is improved by a visual grading scale, and interobserver variability is reduced.
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Affiliation(s)
- Marc D de Smet
- MIOS Sa, Lausanne, Switzerland; Department of Ophthalmology, Leiden Medical Center, University of Leiden, Leiden, The Netherlands
| | | | - Ron Neumann
- Department of Ophthalmology, Maccabi Sherutei Briut, Ramat Hasharon, Israel
| | - Michal Kramer
- Department of Ophthalmology, Rabin Medical Center, Petach-Tikva, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Emmett Cunningham
- Department of Ophthalmology, California Pacific Medical Center, San Francisco, California; the Department of Ophthalmology, Stanford University School of Medicine, Stanford, California; the Francis I Proctor Foundation, UCSF School of Medicine, San Francisco, California; and West Coast Retina Medical Group, San Francisco, California, USA
| | - Lisa Deutsch
- BioStats, Statistical Consulting Ltd, Modiin, Israel
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8
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Strategic complexity and cognitive skills affect brain response in interactive decision-making. Sci Rep 2022; 12:15896. [PMID: 36151117 PMCID: PMC9508177 DOI: 10.1038/s41598-022-17951-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/03/2022] [Indexed: 11/30/2022] Open
Abstract
Deciding the best action in social settings requires decision-makers to consider their and others’ preferences, since the outcome depends on the actions of both. Numerous empirical investigations have demonstrated variability of behavior across individuals in strategic situations. While prosocial, moral, and emotional factors have been intensively investigated to explain this diversity, neuro-cognitive determinants of strategic decision-making and their relation with intelligence remain mostly unknown. This study presents a new model of the process of strategic decision-making in repeated interactions, first providing a precise measure of the environment’s complexity, and then analyzing how this complexity affects subjects’ performance and neural response. The results confirm the theoretical predictions of the model. The frequency of deviations from optimal behavior is explained by a combination of higher complexity of the strategic environment and cognitive skills of the individuals. Brain response correlates with strategic complexity, but only in the subgroups with higher cognitive skills. Furthermore, neural effects were only observed in a fronto-parietal network typically involved in single-agent tasks (the Multiple Demand Network), thus suggesting that neural processes dealing with cognitively demanding individual tasks also have a central role in interactive decision-making. Our findings contribute to understanding how cognitive factors shape strategic decision-making and may provide the neural pathway of the reported association between strategic sophistication and fluid intelligence.
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9
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Neuroeconomics in Cooperatives: Hierarchy of Emotional Patterns in the Collective Decision-Making Process for Sustainable Development. SUSTAINABILITY 2022. [DOI: 10.3390/su14127321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
The goal of this study is to determine the level of adaptation of agro-industrial cooperatives of small producers of alternative crops, and it considers the hierarchy of patterns to evaluate their systemic responses to accelerated change following the COVID-19 pandemic by evaluating the risk of their structures adapting to the digital environment. With a total of (n = 90) volunteer responders, the study is experimental, transactional, descriptive, and correlational, with a control group (CENFROCAFE) and an experimental group (ACEPAT) (24 producer partners, 14 producer managers, and 7 employees for each cooperative). In Step 1 (SOFT aspect), it measures the organizational memory (OM) of Y0 = 0.32 in the (control group) and Y1 = 0.59 in the (experimental group) by measuring hidden plots in the formal and informal interrelations of its members with the correlation of the holistic competencies of innovation. In Stage 2 (HARD aspect), the impact of the digital operational risk (DOR) is measured in the adaptation of the organization structure, which results in the control group with a Digital Operational Risk (DOR) = (3.4), which is “High” and greater than the experimental group with DOR = (3.3), which is “Moderate”. In conclusion, Hypothesis 1 is met with a greater adaptation of the experimental group, greater organizational memory, and lower digital operational risk, which reflects that the memory of the organization would reflect the temporal memories of the human brains of its members, and that, in the same way, its behavior could be predicted linearly.
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10
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Rational arbitration between statistics and rules in human sequence processing. Nat Hum Behav 2022; 6:1087-1103. [PMID: 35501360 DOI: 10.1038/s41562-021-01259-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 11/17/2021] [Indexed: 01/29/2023]
Abstract
Detecting and learning temporal regularities is essential to accurately predict the future. A long-standing debate in cognitive science concerns the existence in humans of a dissociation between two systems, one for handling statistical regularities governing the probabilities of individual items and their transitions, and another for handling deterministic rules. Here, to address this issue, we used finger tracking to continuously monitor the online build-up of evidence, confidence, false alarms and changes-of-mind during sequence processing. All these aspects of behaviour conformed tightly to a hierarchical Bayesian inference model with distinct hypothesis spaces for statistics and rules, yet linked by a single probabilistic currency. Alternative models based either on a single statistical mechanism or on two non-commensurable systems were rejected. Our results indicate that a hierarchical Bayesian inference mechanism, capable of operating over distinct hypothesis spaces for statistics and rules, underlies the human capability for sequence processing.
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11
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Manjili MH, Khazaie K. Pattern recognition of tumor dormancy and relapse beyond cell-intrinsic and cell-extrinsic pathways. Semin Cancer Biol 2022; 78:1-4. [PMID: 34990835 DOI: 10.1016/j.semcancer.2021.12.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In this thematic issue, several mechanisms of tumor dormancy and relapse are discussed. The reviews suggest mutual interactions and communications between malignant cells and other cells in their niche during tumor dormancy. Nevertheless, a complete understanding of tumor dormancy remains elusive. This is because we are getting lost in details of cell-intrinsic and cell-extrinsic molecular pathways without being able to discover the pattern of tumor dormancy. Here, we discuss some conceptual frameworks and methodological approaches that facilitate pattern recognition of tumor dormancy, and propose that settling on certain biological scale such as mitochondria would be the key to discover the pattern of tumor dormancy and relapse.
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Affiliation(s)
- Masoud H Manjili
- Department of Microbiology & Immunology, VCU School of Medicine, Massey Cancer Center, 401 College Street, Box 980035, Richmond, VA, 23298, United States.
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12
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Konovalov A, Hill C, Daunizeau J, Ruff CC. Dissecting functional contributions of the social brain to strategic behavior. Neuron 2021; 109:3323-3337.e5. [PMID: 34407389 DOI: 10.1016/j.neuron.2021.07.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 06/21/2021] [Accepted: 07/27/2021] [Indexed: 10/20/2022]
Abstract
Social interactions routinely lead to neural activity in a "social brain network" comprising, among other regions, the temporoparietal junction (TPJ) and the dorsomedial prefrontal cortex (dmPFC). But what is the function of these areas? Are they specialized for behavior in social contexts or do they implement computations required for dealing with any reactive process, even non-living entities? Here, we use fMRI and a game paradigm separating the need for these two aspects of cognition. We find that most social-brain areas respond to both social and non-social reactivity rather than just to human opponents. However, the TPJ shows a dissociation from the dmPFC: its activity and connectivity primarily reflect context-dependent outcome processing and reactivity detection, while dmPFC engagement is linked to implementation of a behavioral strategy. Our results characterize an overarching computational property of the social brain but also suggest specialized roles for subregions of this network.
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Affiliation(s)
- Arkady Konovalov
- Zurich Center for Neuroeconomics (ZNE), Department of Economics, University of Zurich, Zurich 8006, Switzerland.
| | - Christopher Hill
- Zurich Center for Neuroeconomics (ZNE), Department of Economics, University of Zurich, Zurich 8006, Switzerland
| | - Jean Daunizeau
- Université Pierre et Marie Curie, Paris, France; Institut du Cerveau et de la Moelle épinière, Paris, France; INSERM UMR S975, Paris, France
| | - Christian C Ruff
- Zurich Center for Neuroeconomics (ZNE), Department of Economics, University of Zurich, Zurich 8006, Switzerland.
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13
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Eckstein MK, Wilbrecht L, Collins AGE. What do Reinforcement Learning Models Measure? Interpreting Model Parameters in Cognition and Neuroscience. Curr Opin Behav Sci 2021; 41:128-137. [PMID: 34984213 PMCID: PMC8722372 DOI: 10.1016/j.cobeha.2021.06.004] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Reinforcement learning (RL) is a concept that has been invaluable to fields including machine learning, neuroscience, and cognitive science. However, what RL entails differs between fields, leading to difficulties when interpreting and translating findings. After laying out these differences, this paper focuses on cognitive (neuro)science to discuss how we as a field might over-interpret RL modeling results. We too often assume-implicitly-that modeling results generalize between tasks, models, and participant populations, despite negative empirical evidence for this assumption. We also often assume that parameters measure specific, unique (neuro)cognitive processes, a concept we call interpretability, when evidence suggests that they capture different functions across studies and tasks. We conclude that future computational research needs to pay increased attention to implicit assumptions when using RL models, and suggest that a more systematic understanding of contextual factors will help address issues and improve the ability of RL to explain brain and behavior.
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Affiliation(s)
- Maria K Eckstein
- Department of Psychology, UC Berkeley, 2121 Berkeley Way West, Berkeley, 94720, CA, USA
| | - Linda Wilbrecht
- Department of Psychology, UC Berkeley, 2121 Berkeley Way West, Berkeley, 94720, CA, USA
- Helen Wills Neuroscience Institute, UC Berkeley, 175 Li Ka Shing Center, Berkeley, 94720, CA, USA
| | - Anne G E Collins
- Department of Psychology, UC Berkeley, 2121 Berkeley Way West, Berkeley, 94720, CA, USA
- Helen Wills Neuroscience Institute, UC Berkeley, 175 Li Ka Shing Center, Berkeley, 94720, CA, USA
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Konovalov A, Ruff CC. Enhancing models of social and strategic decision making with process tracing and neural data. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2021; 13:e1559. [PMID: 33880846 DOI: 10.1002/wcs.1559] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 02/26/2021] [Accepted: 03/24/2021] [Indexed: 11/11/2022]
Abstract
Every decision we take is accompanied by a characteristic pattern of response delay, gaze position, pupil dilation, and neural activity. Nevertheless, many models of social decision making neglect the corresponding process tracing data and focus exclusively on the final choice outcome. Here, we argue that this is a mistake, as the use of process data can help to build better models of human behavior, create better experiments, and improve policy interventions. Specifically, such data allow us to unlock the "black box" of the decision process and evaluate the mechanisms underlying our social choices. Using these data, we can directly validate latent model variables, arbitrate between competing personal motives, and capture information processing strategies. These benefits are especially valuable in social science, where models must predict multi-faceted decisions that are taken in varying contexts and are based on many different types of information. This article is categorized under: Economics > Interactive Decision-Making Neuroscience > Cognition Psychology > Reasoning and Decision Making.
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Affiliation(s)
- Arkady Konovalov
- Department of Economics, Zurich Center for Neuroeconomics (ZNE), University of Zurich
| | - Christian C Ruff
- Department of Economics, Zurich Center for Neuroeconomics (ZNE), University of Zurich
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15
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Recanatesi S, Farrell M, Lajoie G, Deneve S, Rigotti M, Shea-Brown E. Predictive learning as a network mechanism for extracting low-dimensional latent space representations. Nat Commun 2021; 12:1417. [PMID: 33658520 PMCID: PMC7930246 DOI: 10.1038/s41467-021-21696-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 01/22/2021] [Indexed: 01/02/2023] Open
Abstract
Artificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the task’s low-dimensional latent structure in the network activity – i.e., in the learned neural representations. Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful predictive representations emerge. We focus throughout on how our results can aid the analysis and interpretation of experimental data. Neural networks trained using predictive models generate representations that recover the underlying low-dimensional latent structure in the data. Here, the authors demonstrate that a network trained on a spatial navigation task generates place-related neural activations similar to those observed in the hippocampus and show that these are related to the latent structure.
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Affiliation(s)
- Stefano Recanatesi
- University of Washington Center for Computational Neuroscience and Swartz Center for Theoretical Neuroscience, Seattle, WA, USA.
| | - Matthew Farrell
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Guillaume Lajoie
- Department of Mathematics and Statistics, Université de Montréal, Montreal, QC, Canada.,Mila-Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Sophie Deneve
- Group for Neural Theory, Ecole Normal Superieur, Paris, France
| | | | - Eric Shea-Brown
- University of Washington Center for Computational Neuroscience and Swartz Center for Theoretical Neuroscience, Seattle, WA, USA.,Department of Applied Mathematics, University of Washington, Seattle, WA, USA.,Allen Institute for Brain Science, Seattle, WA, USA
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16
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Hernández N, Duarte A, Ost G, Fraiman R, Galves A, Vargas CD. Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data. Sci Rep 2021; 11:3520. [PMID: 33568773 PMCID: PMC7875997 DOI: 10.1038/s41598-021-83119-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 01/29/2021] [Indexed: 11/09/2022] Open
Abstract
Using a new probabilistic approach we model the relationship between sequences of auditory stimuli generated by stochastic chains and the electroencephalographic (EEG) data acquired while 19 participants were exposed to those stimuli. The structure of the chains generating the stimuli are characterized by rooted and labeled trees whose leaves, henceforth called contexts, represent the sequences of past stimuli governing the choice of the next stimulus. A classical conjecture claims that the brain assigns probabilistic models to samples of stimuli. If this is true, then the context tree generating the sequence of stimuli should be encoded in the brain activity. Using an innovative statistical procedure we show that this context tree can effectively be extracted from the EEG data, thus giving support to the classical conjecture.
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Affiliation(s)
- Noslen Hernández
- Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Brazil
| | - Aline Duarte
- Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Brazil
| | - Guilherme Ost
- Instituto de Matemática, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Ricardo Fraiman
- Centro de Matemática, Universidad de la República, Montevideo, Uruguay
| | - Antonio Galves
- Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Brazil
| | - Claudia D Vargas
- Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
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17
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Liakoni V, Modirshanechi A, Gerstner W, Brea J. Learning in Volatile Environments With the Bayes Factor Surprise. Neural Comput 2021; 33:269-340. [PMID: 33400898 DOI: 10.1162/neco_a_01352] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting old observations and integrating them with the new ones. The modulation depends on a probability ratio, which we call the Bayes Factor Surprise, that tests the prior belief against the current belief. We demonstrate that in several existing approximate algorithms, the Bayes Factor Surprise modulates the rate of adaptation to new observations. We derive three novel surprise-based algorithms, one in the family of particle filters, one in the family of variational learning, and one in the family of message passing, that have constant scaling in observation sequence length and particularly simple update dynamics for any distribution in the exponential family. Empirical results show that these surprise-based algorithms estimate parameters better than alternative approximate approaches and reach levels of performance comparable to computationally more expensive algorithms. The Bayes Factor Surprise is related to but different from the Shannon Surprise. In two hypothetical experiments, we make testable predictions for physiological indicators that dissociate the Bayes Factor Surprise from the Shannon Surprise. The theoretical insight of casting various approaches as surprise-based learning, as well as the proposed online algorithms, may be applied to the analysis of animal and human behavior and to reinforcement learning in nonstationary environments.
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Affiliation(s)
- Vasiliki Liakoni
- École Polytechnique Fédérale de Lausanne, School of Computer and Communication Sciences and School of Life Sciences, 1015 Lausanne, Switzerland
| | - Alireza Modirshanechi
- École Polytechnique Fédérale de Lausanne, School of Computer and Communication Sciences and School of Life Sciences, 1015 Lausanne, Switzerland
| | - Wulfram Gerstner
- École Polytechnique Fédérale de Lausanne, School of Computer and Communication Sciences and School of Life Sciences, 1015 Lausanne, Switzerland
| | - Johanni Brea
- École Polytechnique Fédérale de Lausanne, School of Computer and Communication Sciences and School of Life Sciences, 1015 Lausanne, Switzerland
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18
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Mouse tracking reveals structure knowledge in the absence of model-based choice. Nat Commun 2020; 11:1893. [PMID: 32312966 PMCID: PMC7170897 DOI: 10.1038/s41467-020-15696-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 03/24/2020] [Indexed: 11/28/2022] Open
Abstract
Converging evidence has demonstrated that humans exhibit two distinct strategies when learning in complex environments. One is model-free learning, i.e., simple reinforcement of rewarded actions, and the other is model-based learning, which considers the structure of the environment. Recent work has argued that people exhibit little model-based behavior unless it leads to higher rewards. Here we use mouse tracking to study model-based learning in stochastic and deterministic (pattern-based) environments of varying difficulty. In both tasks participants’ mouse movements reveal that they learned the structures of their environments, despite the fact that standard behavior-based estimates suggested no such learning in the stochastic task. Thus, we argue that mouse tracking can reveal whether subjects have structure knowledge, which is necessary but not sufficient for model-based choice. Mouse tracking can reveal people’s subjective beliefs and whether they understand the structure of a task. These data demonstrate that people often do not use this information to make good choices.
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Zhang L, Redžepović S, Rose M, Gläscher J. Zen and the Art of Making a Bayesian Espresso. Neuron 2019; 98:1066-1068. [PMID: 29953869 DOI: 10.1016/j.neuron.2018.06.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure.
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Affiliation(s)
- Lei Zhang
- Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Saša Redžepović
- Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Michael Rose
- Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Jan Gläscher
- Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany.
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