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Neville V, Mendl M, Paul ES, Seriès P, Dayan P. A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2024; 24:370-383. [PMID: 38036937 PMCID: PMC11039423 DOI: 10.3758/s13415-023-01137-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/30/2023] [Indexed: 12/02/2023]
Abstract
Objective measures of animal emotion-like and mood-like states are essential for preclinical studies of affective disorders and for assessing the welfare of laboratory and other animals. However, the development and validation of measures of these affective states poses a challenge partly because the relationships between affect and its behavioural, physiological and cognitive signatures are complex. Here, we suggest that the crisp characterisations offered by computational modelling of the underlying, but unobservable, processes that mediate these signatures should provide better insights. Although this computational psychiatry approach has been widely used in human research in both health and disease, translational computational psychiatry studies remain few and far between. We explain how building computational models with data from animal studies could play a pivotal role in furthering our understanding of the aetiology of affective disorders, associated affective states and the likely underlying cognitive processes involved. We end by outlining the basic steps involved in a simple computational analysis.
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Affiliation(s)
- Vikki Neville
- Bristol Veterinary School, University of Bristol, Langford, UK.
| | - Michael Mendl
- Bristol Veterinary School, University of Bristol, Langford, UK
| | | | - Peggy Seriès
- Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics & University of Tübingen, Tübingen, Germany
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2
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Gregorová K, Eldar E, Deserno L, Reiter AMF. A cognitive-computational account of mood swings in adolescence. Trends Cogn Sci 2024; 28:290-303. [PMID: 38503636 DOI: 10.1016/j.tics.2024.02.006] [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: 11/22/2022] [Revised: 02/06/2024] [Accepted: 02/12/2024] [Indexed: 03/21/2024]
Abstract
Teenagers have a reputation for being fickle, in both their choices and their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology. Here, we consider adolescents' mood swings from a novel computational perspective, grounded in reinforcement learning (RL). This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that mood biases learning and choice in a bidirectional manner. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents' mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.
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Affiliation(s)
- Klára Gregorová
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Würzburg 97080, Germany; Department of Psychology, Julius-Maximilians-Universität, Würzburg 97070, Germany; German Center of Prevention Research on Mental Health, Würzburg 97080, Germany
| | - Eran Eldar
- Department of Psychology, Hebrew University of Jerusalem, Jerusalem 9190501, Israel; Department of Cognitive & Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190501, Israel
| | - Lorenz Deserno
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Würzburg 97080, Germany; Department of Psychology, Julius-Maximilians-Universität, Würzburg 97070, Germany; Department of Cognitive & Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190501, Israel; Department of Psychiatry and Psychotherapy, Technical University of Dresden, Dresden 01069, Germany
| | - Andrea M F Reiter
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Würzburg 97080, Germany; Department of Psychology, Julius-Maximilians-Universität, Würzburg 97070, Germany; German Center of Prevention Research on Mental Health, Würzburg 97080, Germany; Collaborative Research Centre 940 Volition and Cognitive Control, Technical University of Dresden, Dresden 01069, Germany.
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3
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Villano WJ, Heller AS. Depression is associated with blunted affective responses to naturalistic reward prediction errors. Psychol Med 2024:1-9. [PMID: 38305099 DOI: 10.1017/s0033291724000047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
BACKGROUND Depression is characterized by abnormalities in emotional processing, but the specific drivers of such emotional abnormalities are unknown. Computational work indicates that both surprising outcomes (prediction errors; PEs) and outcomes (values) themselves drive emotional responses, but neither has been consistently linked to affective disturbances in depression. As a result, the computational mechanisms driving emotional abnormalities in depression remain unknown. METHODS Here, in 687 individuals, one-third of whom qualify as depressed via a standard self-report measure (the PHQ-9), we use high-stakes, naturalistic events - the reveal of midterm exam grades - to test whether individuals with heightened depression display a specific reduction in emotional response to positive PEs. RESULTS Using Bayesian mixed effects models, we find that individuals with heightened depression do not affectively benefit from surprising, good outcomes - that is, they display reduced affective responses to positive PEs. These results were highly specific: effects were not observed to negative PEs, value signals (grades), and were not related to generalized anxiety. This suggests that the computational drivers of abnormalities in emotion in depression may be specifically due to positive PE-based emotional responding. CONCLUSIONS Affective abnormalities are core depression symptoms, but the computational mechanisms underlying such differences are unknown. This work suggests that blunted affective reactions to positive PEs are likely mechanistic drivers of emotional dysregulation in depression.
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Affiliation(s)
- William J Villano
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Aaron S Heller
- Department of Psychology, University of Miami, Coral Gables, FL, USA
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4
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Johns BT. Determining the Relativity of Word Meanings Through the Construction of Individualized Models of Semantic Memory. Cogn Sci 2024; 48:e13413. [PMID: 38402448 DOI: 10.1111/cogs.13413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/11/2023] [Accepted: 01/27/2024] [Indexed: 02/26/2024]
Abstract
Distributional models of lexical semantics are capable of acquiring sophisticated representations of word meanings. The main theoretical insight provided by these models is that they demonstrate the systematic connection between the knowledge that people acquire and the experience that they have with the natural language environment. However, linguistic experience is inherently variable and differs radically across people due to demographic and cultural variables. Recently, distributional models have been used to examine how word meanings vary across languages and it was found that there is considerable variability in the meanings of words across languages for most semantic categories. The goal of this article is to examine how variable word meanings are across individual language users within a single language. This was accomplished by assembling 500 individual user corpora attained from the online forum Reddit. Each user corpus ranged between 3.8 and 32.3 million words each, and a count-based distributional framework was used to extract word meanings for each user. These representations were then used to estimate the semantic alignment of word meanings across individual language users. It was found that there are significant levels of relativity in word meanings across individuals, and these differences are partially explained by other psycholinguistic factors, such as concreteness, semantic diversity, and social aspects of language usage. These results point to word meanings being fundamentally relative and contextually fluid, with this relativeness being related to the individualized nature of linguistic experience.
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5
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Antony JW, Van Dam J, Massey JR, Barnett AJ, Bennion KA. Long-term, multi-event surprise correlates with enhanced autobiographical memory. Nat Hum Behav 2023; 7:2152-2168. [PMID: 37322234 DOI: 10.1038/s41562-023-01631-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 05/16/2023] [Indexed: 06/17/2023]
Abstract
Neurobiological and psychological models of learning emphasize the importance of prediction errors (surprises) for memory formation. This relationship has been shown for individual momentary surprising events; however, it is less clear whether surprise that unfolds across multiple events and timescales is also linked with better memory of those events. We asked basketball fans about their most positive and negative autobiographical memories of individual plays, games and seasons, allowing surprise measurements spanning seconds, hours and months. We used advanced analytics on National Basketball Association play-by-play data and betting odds spanning 17 seasons, more than 22,000 games and more than 5.6 million plays to compute and align the estimated surprise value of each memory. We found that surprising events were associated with better recall of positive memories on the scale of seconds and months and negative memories across all three timescales. Game and season memories could not be explained by surprise at shorter timescales, suggesting that long-term, multi-event surprise correlates with memory. These results expand notions of surprise in models of learning and reinforce its relevance in real-world domains.
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Affiliation(s)
- James W Antony
- Department of Psychology and Child Development, California Polytechnic State University, San Luis Obispo, CA, USA.
| | - Jacob Van Dam
- Department of Psychology and Child Development, California Polytechnic State University, San Luis Obispo, CA, USA
| | - Jarett R Massey
- Department of Psychology and Child Development, California Polytechnic State University, San Luis Obispo, CA, USA
| | | | - Kelly A Bennion
- Department of Psychology and Child Development, California Polytechnic State University, San Luis Obispo, CA, USA
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6
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Paxton A. The Dynamical Hypothesis in Situ: Challenges and Opportunities for a Dynamical Social Approach to Interpersonal Coordination. Top Cogn Sci 2023. [PMID: 38029348 DOI: 10.1111/tops.12712] [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: 03/24/2023] [Revised: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 12/01/2023]
Abstract
Over the past three decades, Van Gelder's dynamical hypothesis has been instrumental in reconceptualizing the ways in which perception-action-cognition unfolds over time and in context. Here, I examine how the dynamical approach has enriched the theoretical understanding of social dynamics within cognitive science, with a particular focus on interpersonal coordination. I frame this review around seven principles in dynamical systems: three that are well-represented in interpersonal coordination research to date (emergent behavior, context-sensitive behavior, and attractors) and four that could be useful opportunities for future growth (hysteresis, sensitivity to initial conditions, equifinality, and reciprocal compensation). In addition to identifying specific promising lines of theoretical inquiry, I focus on the significant potential afforded by computationally intensive science-especially in naturally occurring data or trace data. Building on the foundation laid over the past three decades, I argue that looking to increasingly situated and naturalistic settings (and data) is not only necessary to realize the full commitment to the dynamical hypothesis but is also critical to building parsimonious and principled theories of social phenomena.
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Affiliation(s)
- Alexandra Paxton
- Department of Psychological Sciences, University of Connecticut
- Center for the Ecological Study of Perception and Action, University of Connecticut
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7
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Heerema R, Carrillo P, Daunizeau J, Vinckier F, Pessiglione M. Mood fluctuations shift cost-benefit tradeoffs in economic decisions. Sci Rep 2023; 13:18173. [PMID: 37875525 PMCID: PMC10598198 DOI: 10.1038/s41598-023-45217-w] [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: 03/13/2023] [Accepted: 10/17/2023] [Indexed: 10/26/2023] Open
Abstract
Mood effects on economic choice seem blatantly irrational, but might rise from mechanisms adapted to natural environments. We have proposed a theory in which mood helps adapting the behaviour to statistical dependencies in the environment, by biasing the expected value of foraging actions (which involve taking risk, spending time and making effort to get more reward). Here, we tested the existence of this mechanism, using an established mood induction paradigm combined with independent economic choices that opposed small but uncostly rewards to larger but costly rewards (involving either risk, delay or effort). To maximise the sensitivity to mood fluctuations, we developed an algorithm ensuring that choice options were continuously adjusted to subjective indifference points. In 102 participants tested twice, we found that during episodes of positive mood (relative to negative mood), choices were biased towards better rewarded but costly options, irrespective of the cost type. Computational modelling confirmed that the incidental mood effect was best explained by a bias added to the expected value of costly options, prior to decision making. This bias is therefore automatically applied even in artificial environments where it is not adaptive, allowing mood to spill over many sorts of decisions and generate irrational behaviours.
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Affiliation(s)
- Roeland Heerema
- Motivation, Brain and Behavior (MBB) Lab, Paris Brain Institute (ICM), Pitié-Salpêtrière Hospital, 75013, Paris, France.
- Sorbonne Université, Inserm U1127, CNRS U7225, 75013, Paris, France.
| | - Pablo Carrillo
- Motivation, Brain and Behavior (MBB) Lab, Paris Brain Institute (ICM), Pitié-Salpêtrière Hospital, 75013, Paris, France
- Sorbonne Université, Inserm U1127, CNRS U7225, 75013, Paris, France
| | - Jean Daunizeau
- Motivation, Brain and Behavior (MBB) Lab, Paris Brain Institute (ICM), Pitié-Salpêtrière Hospital, 75013, Paris, France
- Sorbonne Université, Inserm U1127, CNRS U7225, 75013, Paris, France
| | - Fabien Vinckier
- Motivation, Brain and Behavior (MBB) Lab, Paris Brain Institute (ICM), Pitié-Salpêtrière Hospital, 75013, Paris, France
- Sorbonne Université, Inserm U1127, CNRS U7225, 75013, Paris, France
- Université Paris Cité, 75006, Paris, France
- Department of Psychiatry, Service Hospitalo-Universitaire, GHU Paris Psychiatrie and Neurosciences, 75014, Paris, France
| | - Mathias Pessiglione
- Motivation, Brain and Behavior (MBB) Lab, Paris Brain Institute (ICM), Pitié-Salpêtrière Hospital, 75013, Paris, France.
- Sorbonne Université, Inserm U1127, CNRS U7225, 75013, Paris, France.
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8
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Johns BT. Computing the Relativity of Word Meanings through the Construction of Individualized Models of Semantic Memory. COGN SYST RES 2023. [DOI: 10.1016/j.cogsys.2023.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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9
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Kao CH, Feng GW, Hur JK, Jarvis H, Rutledge RB. Computational models of subjective feelings in psychiatry. Neurosci Biobehav Rev 2023; 145:105008. [PMID: 36549378 PMCID: PMC9990828 DOI: 10.1016/j.neubiorev.2022.105008] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 12/02/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Research in computational psychiatry is dominated by models of behavior. Subjective experience during behavioral tasks is not well understood, even though it should be relevant to understanding the symptoms of psychiatric disorders. Here, we bridge this gap and review recent progress in computational models for subjective feelings. For example, happiness reflects not how well people are doing, but whether they are doing better than expected. This dependence on recent reward prediction errors is intact in major depression, although depressive symptoms lower happiness during tasks. Uncertainty predicts subjective feelings of stress in volatile environments. Social prediction errors influence feelings of self-worth more in individuals with low self-esteem despite a reduced willingness to change beliefs due to social feedback. Measuring affective state during behavioral tasks provides a tool for understanding psychiatric symptoms that can be dissociable from behavior. When smartphone tasks are collected longitudinally, subjective feelings provide a potential means to bridge the gap between lab-based behavioral tasks and real-life behavior, emotion, and psychiatric symptoms.
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Affiliation(s)
- Chang-Hao Kao
- Department of Psychology, Yale University, New Haven, CT, USA.
| | - Gloria W Feng
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Jihyun K Hur
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Huw Jarvis
- Department of Psychology, Yale University, New Haven, CT, USA; Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia; School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
| | - Robb B Rutledge
- Department of Psychology, Yale University, New Haven, CT, USA; Wellcome Centre for Human Neuroimaging, University College London, London, UK.
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10
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Villano WJ, Kraus NI, Reneau TR, Jaso BA, Otto AR, Heller AS. Individual differences in naturalistic learning link negative emotionality to the development of anxiety. SCIENCE ADVANCES 2023; 9:eadd2976. [PMID: 36598977 PMCID: PMC9812386 DOI: 10.1126/sciadv.add2976] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
Organisms learn from prediction errors (PEs) to predict the future. Laboratory studies using small financial outcomes find that humans use PEs to update expectations and link individual differences in PE-based learning to internalizing disorders. Because of the low-stakes outcomes in most tasks, it is unclear whether PE learning emerges in naturalistic, high-stakes contexts and whether individual differences in PE learning predict psychopathology risk. Using experience sampling to assess 625 college students' expected exam grades, we found evidence of PE-based learning and a general tendency to discount negative PEs, an "optimism bias." However, individuals with elevated negative emotionality, a personality trait linked to the development of anxiety disorders, displayed a global pessimism and learning differences that impeded accurate expectations and predicted future anxiety symptoms. A sensitivity to PEs combined with an aversion to negative PEs may result in a pessimistic and inaccurate model of the world, leading to anxiety.
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Affiliation(s)
| | - Noah I. Kraus
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Travis R. Reneau
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Brittany A. Jaso
- Center for Anxiety and Related Disorders, Boston University, Boston, MA, USA
| | - A. Ross Otto
- Department of Psychology, McGill University, Montreal, Canada
| | - Aaron S. Heller
- Department of Psychology, University of Miami, Coral Gables, FL, USA
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11
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Emanuel A, Eldar E. Emotions as computations. Neurosci Biobehav Rev 2023; 144:104977. [PMID: 36435390 PMCID: PMC9805532 DOI: 10.1016/j.neubiorev.2022.104977] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/26/2022] [Accepted: 11/22/2022] [Indexed: 11/26/2022]
Abstract
Emotions ubiquitously impact action, learning, and perception, yet their essence and role remain widely debated. Computational accounts of emotion aspire to answer these questions with greater conceptual precision informed by normative principles and neurobiological data. We examine recent progress in this regard and find that emotions may implement three classes of computations, which serve to evaluate states, actions, and uncertain prospects. For each of these, we use the formalism of reinforcement learning to offer a new formulation that better accounts for existing evidence. We then consider how these distinct computations may map onto distinct emotions and moods. Integrating extensive research on the causes and consequences of different emotions suggests a parsimonious one-to-one mapping, according to which emotions are integral to how we evaluate outcomes (pleasure & pain), learn to predict them (happiness & sadness), use them to inform our (frustration & content) and others' (anger & gratitude) actions, and plan in order to realize (desire & hope) or avoid (fear & anxiety) uncertain outcomes.
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Affiliation(s)
- Aviv Emanuel
- Department of Psychology, Hebrew University of Jerusalem, Jerusalem 9190501, Israel; Department of Cognitive and Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190501, Israel.
| | - Eran Eldar
- Department of Psychology, Hebrew University of Jerusalem, Jerusalem 9190501, Israel; Department of Cognitive and Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190501, Israel.
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12
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Michely J, Eldar E, Erdman A, Martin IM, Dolan RJ. Serotonin modulates asymmetric learning from reward and punishment in healthy human volunteers. Commun Biol 2022; 5:812. [PMID: 35962142 PMCID: PMC9374781 DOI: 10.1038/s42003-022-03690-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 07/08/2022] [Indexed: 11/15/2022] Open
Abstract
Instrumental learning is driven by a history of outcome success and failure. Here, we examined the impact of serotonin on learning from positive and negative outcomes. Healthy human volunteers were assessed twice, once after acute (single-dose), and once after prolonged (week-long) daily administration of the SSRI citalopram or placebo. Using computational modelling, we show that prolonged boosting of serotonin enhances learning from punishment and reduces learning from reward. This valence-dependent learning asymmetry increases subjects’ tendency to avoid actions as a function of cumulative failure without leading to detrimental, or advantageous, outcomes. By contrast, no significant modulation of learning was observed following acute SSRI administration. However, differences between the effects of acute and prolonged administration were not significant. Overall, these findings may help explain how serotonergic agents impact on mood disorders. Two factors can drive learning: punishment of failures and reward of successes. Serotonin induces a valence-dependent learning asymmetry, as revealed by prolonged administering of SSRIs to healthy participants in a gambling task.
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Affiliation(s)
- Jochen Michely
- Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany. .,Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Charité Clinician Scientist Program, Berlin, Germany. .,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK. .,Wellcome Centre for Human Neuroimaging, University College London, London, UK.
| | - Eran Eldar
- Psychology and Cognitive Sciences Departments, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alon Erdman
- Psychology and Cognitive Sciences Departments, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ingrid M Martin
- Wellcome Centre for Human Neuroimaging, University College London, London, UK.,Institute of Cognitive Neuroscience, University College London, London, UK
| | - Raymond J Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.,Wellcome Centre for Human Neuroimaging, University College London, London, UK
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13
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Galbraith E, Li J, Rio-Vilas VJD, Convertino M. In.To. COVID-19 socio-epidemiological co-causality. Sci Rep 2022; 12:5831. [PMID: 35388071 PMCID: PMC8986029 DOI: 10.1038/s41598-022-09656-1] [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: 06/10/2021] [Accepted: 03/11/2022] [Indexed: 11/09/2022] Open
Abstract
Social media can forecast disease dynamics, but infoveillance remains focused on infection spread, with little consideration of media content reliability and its relationship to behavior-driven epidemiological outcomes. Sentiment-encoded social media indicators have been poorly developed for expressed text to forecast healthcare pressure and infer population risk-perception patterns. Here we introduce Infodemic Tomography (InTo) as the first web-based interactive infoveillance cybertechnology that forecasts and visualizes spatio-temporal sentiments and healthcare pressure as a function of social media positivity (i.e., Twitter here), considering both epidemic information and potential misinformation. Information spread is measured on volume and retweets, and the Value of Misinformation (VoMi) is introduced as the impact on forecast accuracy where misinformation has the highest dissimilarity in information dynamics. We validated InTo for COVID-19 in New Delhi and Mumbai by inferring distinct socio-epidemiological risk-perception patterns. We forecast weekly hospitalization and cases using ARIMA models and interpolate spatial hospitalization using geostatistical kriging on inferred risk perception curves between tweet positivity and epidemiological outcomes. Geospatial tweet positivity tracks accurately [Formula: see text]60[Formula: see text] of hospitalizations and forecasts hospitalization risk hotspots along risk aversion gradients. VoMi is higher for risk-prone areas and time periods, where misinformation has the highest non-linear predictability, with high incidence and positivity manifesting popularity-seeking social dynamics. Hospitalization gradients, VoMi, effective healthcare pressure and spatial model-data gaps can be used to predict hospitalization fluxes, misinformation, healthcare capacity gaps and surveillance uncertainty. Thus, InTo is a participatory instrument to better prepare and respond to public health crises by extracting and combining salient epidemiological and social surveillance at any desired space-time scale.
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Affiliation(s)
- Elroy Galbraith
- Nexus Group, Faculty and Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Jie Li
- Nexus Group, Faculty and Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.,Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
| | | | - Matteo Convertino
- fuTuRE EcoSystems Lab, Institute of Environment and Ecology, Tsinghua SIGS, Tsinghua University, Shenzhen, China. .,Tsinghua Shenzhen International Graduate School, University Town of Shenzhen, Tsinghua Park, Nanshan District, Shenzhen, 518055, China.
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14
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Neville V, Dayan P, Gilchrist ID, Paul ES, Mendl M. Using Primary Reinforcement to Enhance Translatability of a Human Affect and Decision-Making Judgment Bias Task. J Cogn Neurosci 2021; 33:2523-2535. [PMID: 34477879 DOI: 10.1162/jocn_a_01776] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Good translatability of behavioral measures of affect (emotion) between human and nonhuman animals is core to comparative studies. The judgment bias (JB) task, which measures "optimistic" and "pessimistic" decision-making under ambiguity as indicators of positive and negative affective valence, has been used in both human and nonhuman animals. However, one key disparity between human and nonhuman studies is that the former typically use secondary reinforcers (e.g., money) whereas the latter typically use primary reinforcers (e.g., food). To address this deficiency and shed further light on JB as a measure of affect, we developed a novel version of a JB task for humans using primary reinforcers. Data on decision-making and reported affective state during the JB task were analyzed using computational modeling. Overall, participants grasped the task well, and as anticipated, their reported affective valence correlated with trial-by-trial variation in offered volume of juice. In addition, previous findings from monetary versions of the task were replicated: More positive prediction errors were associated with more positive affective valence, a higher lapse rate was associated with lower affective arousal, and affective arousal decreased as a function of number of trials completed. There was no evidence that more positive valence was associated with greater "optimism," but instead, there was evidence that affective valence influenced the participants' decision stochasticity, whereas affective arousal tended to influence their propensity for errors. This novel version of the JB task provides a useful tool for investigation of the links between primary reward and punisher experience, affect, and decision-making, especially from a comparative perspective.
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Affiliation(s)
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics.,University of Tübingen
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15
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16
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Fu HN, Monson E, Otto AR. Relationships between socio-economic status and lottery gambling across lottery types: neighborhood-level evidence from a large city. Addiction 2021; 116:1256-1261. [PMID: 32924215 DOI: 10.1111/add.15252] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 06/17/2020] [Accepted: 09/09/2020] [Indexed: 11/27/2022]
Abstract
BACKGROUND AND AIMS Lottery gambling participation tends to be higher among lower socio-economic status (SES) individuals, but it is unclear how this relationship differs as a function of lottery type. We estimated how the relationship between SES and lottery gambling rates varies across different types of lottery gambling: fixed-prize, progressive-prize (jackpot) and instant-win (scratch card) lottery tickets in a large Canadian city. DESIGN Neighborhood-level lottery purchase data obtained from the Ontario Lottery and Gaming Commission were analysed in conjunction with demographic data. Mixed-effects regression was used to assess simultaneously how neighborhood-level SES predicts per-person lottery gambling rates across fixed-prize, progressive-prize lottery and instant-win lotteries. SETTING AND PARTICIPANTS Neighborhoods in Toronto, Ontario, Canada in the years 2012-15. MEASUREMENTS Per-capita sales in dollars (CAD) of fixed-prize lottery, progressive-prize lottery and instant-win tickets in Toronto postal codes. SES was estimated as a composite of income, years of education and white-collar employment. FINDINGS Lower-SES neighborhoods engaged in higher rates of lottery gambling overall [β = -0.084, standard error (SE) = 0.24, P = 0.0007]. The predictive effect of SES varied significantly by lottery type (fixed-prize: β = -0.105, SE = 0.004, P < 0.0001, instant-win: β = -0.054, SE = 0.004, P < 0.0001; relative to progressive-prize). The predictive effect of SES was strongest for fixed-prize lotteries and weakest for progressive-prize lotteries, such that we did not observe a significant predictive effect of SES for progressive-prize lotteries (β = -0.031, SE = 0.024, P = 0.198). CONCLUSIONS People in lower socio-economic status neighborhoods in Toronto, Canada appear to engage in more lottery gambling than those in higher socio-economic status neighborhoods, with the difference being largest for fixed prize lotteries followed by instant win lotteries, and no clear difference for progressive prize lotteries.
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Affiliation(s)
- Hin-Ngai Fu
- Department of Psychology, McGill University, Montréal, QC, Canada
| | - Eva Monson
- Department of Community Health Sciences, Université de Sherbrooke, Longueuil, QC, Canada
| | - A Ross Otto
- Department of Psychology, McGill University, Montréal, QC, Canada
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Sznelwar LI, Zilbovicius M, Brunoro CM, de Andrade BLR, Piqueira JRC. Brumadinho: between prudence and probability, tragedy. Rev Bras Med Trab 2020; 17:4-12. [PMID: 32270098 DOI: 10.5327/z1679443520190414] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 03/20/2019] [Indexed: 11/05/2022] Open
Abstract
Will it be possible, sometime in the future, to exactly explain what happened in the dam failure in Brumadinho? Although word "exactness" is often used within the world of engineering, it is often known to be an euphemism. The engineering art consists in projecting, building, implementing and managing different types of systems which might have both positive and negative consequences for workers, society and the environment. Some event or series of events culminated in the dam failure. Is tailings dam engineering aware of and able to control all possible events which together might cause a failure? There are two possible paths: one involves absolute knowledge - engineering has absolute knowledge of everything and is able to design projects in a way to avoid any harmful event. According to the other, while engineering does not have absolute knowledge of all the phenomena, its traditional know-how (empirical knowledge) and wide margins of safety make the odds of dam failure come close to zero. No one projects a dam just to fail. But dams are projected without absolute control of all possible events. When the entire situation is known, all that should be done to avoid failures is 100% known - and the price fixed. However, this never happens, the probability of the occurence of events are never completely known, they are not deterministic and uncertainty is always a fact.
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Affiliation(s)
| | - Mauro Zilbovicius
- Polytechnic School, Universidade de São Paulo (USP) - São Paulo (SP), Brazil
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