Despite its importance in different occupational and everyday contexts, vigilance, typically defined as the capacity to sustain attention over time, is remarkably limited. What explains these limits? Two theories have been proposed. The Overload Theory states that being vigilant consumes limited information‐processing resources; when depleted, task performance degrades. The Underload Theory states that motivation to perform vigilance tasks declines over time, thereby prompting attentional shifts and hindering performance. We highlight some conceptual and empirical problems for both theories and propose an alternative: the Strategic Allocation Theory. For the Strategic Allocation Theory, performance on vigilance tasks optimizes as a function of intrinsic and extrinsic motivations, including metacognitive factors such as the expected value of effort and the expected value of planning. Limited capacities must be deployed across task sets to maximize expected reward. The observed limits of vigilance reflect changes in the perceived value of, among other things, sustaining attention to a task rather than attending to something else. Drawing from recent computational theories of cognitive control and meta‐reasoning, we argue that the Strategic Allocation Theory explains more phenomena related to vigilance behavior than other theories, including self‐report data. Finally, we outline some of the testable predictions the theory makes across several experimental paradigms.This article is categorized under:Philosophy > Foundations of Cognitive SciencePsychology > Attention
{"title":"The strategic allocation theory of vigilance","authors":"Samuel Murray, Santiago Amaya","doi":"10.1002/wcs.1693","DOIUrl":"https://doi.org/10.1002/wcs.1693","url":null,"abstract":"Despite its importance in different occupational and everyday contexts, vigilance, typically defined as the capacity to sustain attention over time, is remarkably limited. What explains these limits? Two theories have been proposed. The Overload Theory states that being vigilant consumes limited information‐processing resources; when depleted, task performance degrades. The Underload Theory states that motivation to perform vigilance tasks declines over time, thereby prompting attentional shifts and hindering performance. We highlight some conceptual and empirical problems for both theories and propose an alternative: the <jats:italic>Strategic Allocation Theory</jats:italic>. For the Strategic Allocation Theory, performance on vigilance tasks optimizes as a function of intrinsic and extrinsic motivations, including metacognitive factors such as the expected value of effort and the expected value of planning. Limited capacities must be deployed across task sets to maximize expected reward. The observed limits of vigilance reflect changes in the perceived value of, among other things, sustaining attention to a task rather than attending to something else. Drawing from recent computational theories of cognitive control and meta‐reasoning, we argue that the Strategic Allocation Theory explains more phenomena related to vigilance behavior than other theories, including self‐report data. Finally, we outline some of the testable predictions the theory makes across several experimental paradigms.This article is categorized under:<jats:list list-type=\"simple\"> <jats:list-item>Philosophy > Foundations of Cognitive Science</jats:list-item> <jats:list-item>Psychology > Attention</jats:list-item> </jats:list>","PeriodicalId":501132,"journal":{"name":"WIREs Cognitive Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xize Jia, Mengting Li, Chunjie Wang, Collins Opoku Antwi, Adjei Peter Darko, Baojing Zhang, Jun Ren
Emotional disorders inflict an enormous burden on society. Research on brain abnormalities implicated in emotional disorders has witnessed great progress over the past decades. Using cross‐sectional and longitudinal designs, resting state functional magnetic resonance imaging (rs‐fMRI) and its analytic approaches have been applied to characterize the local properties of patients with emotional disorders. Additionally, brain activity alterations of emotional disorders have shown frequency‐specific. Despite the gains in understanding the roles of brain abnormalities in emotional disorders, the limitation of the small sample size needs to be highlighted. Lastly, we proposed that evidence from the positive psychology research stream presents it as a viable discipline, whose suggestions could be developed in future emotional disorders research. Such interdisciplinary research may produce novel treatments and intervention options.This article is categorized under:Psychology > Brain Function and Dysfunction
{"title":"Local brain abnormalities in emotional disorders: Evidence from resting state fMRI studies","authors":"Xize Jia, Mengting Li, Chunjie Wang, Collins Opoku Antwi, Adjei Peter Darko, Baojing Zhang, Jun Ren","doi":"10.1002/wcs.1694","DOIUrl":"https://doi.org/10.1002/wcs.1694","url":null,"abstract":"Emotional disorders inflict an enormous burden on society. Research on brain abnormalities implicated in emotional disorders has witnessed great progress over the past decades. Using cross‐sectional and longitudinal designs, resting state functional magnetic resonance imaging (rs‐fMRI) and its analytic approaches have been applied to characterize the local properties of patients with emotional disorders. Additionally, brain activity alterations of emotional disorders have shown frequency‐specific. Despite the gains in understanding the roles of brain abnormalities in emotional disorders, the limitation of the small sample size needs to be highlighted. Lastly, we proposed that evidence from the positive psychology research stream presents it as a viable discipline, whose suggestions could be developed in future emotional disorders research. Such interdisciplinary research may produce novel treatments and intervention options.This article is categorized under:<jats:list list-type=\"simple\"> <jats:list-item>Psychology > Brain Function and Dysfunction</jats:list-item> </jats:list>","PeriodicalId":501132,"journal":{"name":"WIREs Cognitive Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This review article examines the extant literature on animism and anthropomorphism in infants and young children. A substantial body of work indicates that both infants and young children have a broad concept of what constitutes a sentient agent and react to inanimate objects as they do to people in the same context. The literature has also revealed a developmental pattern in which anthropomorphism decreases with age, but social robots appear to be an exception to this pattern. Additionally, the review shows that children attribute psychological properties to social robots less so than people but still anthropomorphize them. Importantly, some research suggests that anthropomorphism of social robots is dependent upon their morphology and human‐like behaviors. The extent to which children anthropomorphize robots is dependent on their exposure to them and the presence of human‐like features. Based on the existing literature, we conclude that in infancy, a large range of inanimate objects (e.g., boxes, geometric figures) that display animate motion patterns trigger the same behaviors observed in child‐adult interactions, suggesting some implicit form of anthropomorphism. The review concludes that additional research is needed to understand what infants and children judge as social agents and how the perception of inanimate agents changes over the lifespan. As exposure to robots and virtual assistants increases, future research must focus on better understanding the full impact that regular interactions with such partners will have on children's anthropomorphizing.This article is categorized under:Psychology > LearningCognitive Biology > Cognitive DevelopmentComputer Science and Robotics > Robotics
{"title":"Children's anthropomorphism of inanimate agents","authors":"Elizabeth J. Goldman, Diane Poulin‐Dubois","doi":"10.1002/wcs.1676","DOIUrl":"https://doi.org/10.1002/wcs.1676","url":null,"abstract":"This review article examines the extant literature on animism and anthropomorphism in infants and young children. A substantial body of work indicates that both infants and young children have a broad concept of what constitutes a sentient agent and react to inanimate objects as they do to people in the same context. The literature has also revealed a developmental pattern in which anthropomorphism decreases with age, but social robots appear to be an exception to this pattern. Additionally, the review shows that children attribute psychological properties to social robots less so than people but still anthropomorphize them. Importantly, some research suggests that anthropomorphism of social robots is dependent upon their morphology and human‐like behaviors. The extent to which children anthropomorphize robots is dependent on their exposure to them and the presence of human‐like features. Based on the existing literature, we conclude that in infancy, a large range of inanimate objects (e.g., boxes, geometric figures) that display animate motion patterns trigger the same behaviors observed in child‐adult interactions, suggesting some implicit form of anthropomorphism. The review concludes that additional research is needed to understand what infants and children judge as social agents and how the perception of inanimate agents changes over the lifespan. As exposure to robots and virtual assistants increases, future research must focus on better understanding the full impact that regular interactions with such partners will have on children's anthropomorphizing.This article is categorized under:<jats:list list-type=\"simple\"> <jats:list-item>Psychology > Learning</jats:list-item> <jats:list-item>Cognitive Biology > Cognitive Development</jats:list-item> <jats:list-item>Computer Science and Robotics > Robotics</jats:list-item> </jats:list>","PeriodicalId":501132,"journal":{"name":"WIREs Cognitive Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cognitive science was founded on the idea that the mind/brain can be understood in computational terms. While computational modeling in science is ubiquitous, cognitive science takes the stronger stance that the mind/brain literally performs computations. Moreover, performing computations is crucial to explaining what the mind/brain does, qua mind/brain. Unfortunately, most scientists fail to consider analog computation as a legitimate and theoretically useful type of computation in addition to digital computation; to the extent that analog computation is acknowledged, it is mostly based on a simplistic and incomplete understanding. Taking computation to consist of only one type (i.e., digital) while ignoring another, interestingly distinct type (i.e., analog) leads to an impoverished understanding of what it could mean for minds/brains to compute. A full appreciation and understanding of analog computation—particularly in relation to digital computation—allows researchers to develop computational frameworks and hypotheses in new and exciting ways. Thus, somewhat counterintuitively, looking to the once‐dominant computing paradigm of yesteryear can provide novel computational ways of thinking about the mind and brain.This article is categorized under:Philosophy > Foundations of Cognitive Science
{"title":"Computation for cognitive science: Analog versus digital","authors":"Corey J. Maley","doi":"10.1002/wcs.1679","DOIUrl":"https://doi.org/10.1002/wcs.1679","url":null,"abstract":"Cognitive science was founded on the idea that the mind/brain can be understood in computational terms. While computational <jats:italic>modeling</jats:italic> in science is ubiquitous, cognitive science takes the stronger stance that the mind/brain literally performs computations. Moreover, performing computations is crucial to explaining what the mind/brain does, qua mind/brain. Unfortunately, most scientists fail to consider analog computation as a legitimate and theoretically useful type of computation in addition to digital computation; to the extent that analog computation <jats:italic>is</jats:italic> acknowledged, it is mostly based on a simplistic and incomplete understanding. Taking computation to consist of only one type (i.e., digital) while ignoring another, interestingly distinct type (i.e., analog) leads to an impoverished understanding of what it could mean for minds/brains to compute. A full appreciation and understanding of analog computation—particularly in relation to digital computation—allows researchers to develop computational frameworks and hypotheses in new and exciting ways. Thus, somewhat counterintuitively, looking to the once‐dominant computing paradigm of yesteryear can provide novel computational ways of thinking about the mind and brain.This article is categorized under:<jats:list list-type=\"simple\"> <jats:list-item>Philosophy > Foundations of Cognitive Science</jats:list-item> </jats:list>","PeriodicalId":501132,"journal":{"name":"WIREs Cognitive Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The evolution of cognition can be understood in terms of a few major transitions—changes in the computational architecture of nervous systems that changed what cognitive capacities could be evolved by downstream lineages. We demonstrate how the idea of a major cognitive transition can be modeled in terms of where a system's effective computational architecture falls on the well‐studied hierarchy of formal automata (HFA). We then use recent work connecting artificial neural networks to the HFA, which provides a way to make the structure‐architecture link in natural systems. We conclude with reflections on the power and the challenges of traditional thinking when applied to neural architectures.This article is categorized under:Cognitive Biology > Evolutionary Roots of CognitionPsychology > ComparativePhilosophy > Foundations of Cognitive Science
{"title":"Comparing cognition across major transitions using the hierarchy of formal automata","authors":"Colin Klein, Andrew B. Barron","doi":"10.1002/wcs.1680","DOIUrl":"https://doi.org/10.1002/wcs.1680","url":null,"abstract":"The evolution of cognition can be understood in terms of a few <jats:italic>major transitions</jats:italic>—changes in the computational architecture of nervous systems that changed what cognitive capacities could be evolved by downstream lineages. We demonstrate how the idea of a major cognitive transition can be modeled in terms of where a system's effective computational architecture falls on the well‐studied hierarchy of formal automata (HFA). We then use recent work connecting artificial neural networks to the HFA, which provides a way to make the structure‐architecture link in natural systems. We conclude with reflections on the power and the challenges of traditional thinking when applied to neural architectures.This article is categorized under:<jats:list list-type=\"simple\"> <jats:list-item>Cognitive Biology > Evolutionary Roots of Cognition</jats:list-item> <jats:list-item>Psychology > Comparative</jats:list-item> <jats:list-item>Philosophy > Foundations of Cognitive Science</jats:list-item> </jats:list>","PeriodicalId":501132,"journal":{"name":"WIREs Cognitive Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Causal reasoning—the ability to reason about causal relations between events—is fundamental to understanding how the world works. This paper reviews two prominent theories on early causal learning and offers possibilities for theory bridging. Both theories grow out of computational modeling and have significant areas of overlap while differing in several respects. Explanation‐Based Learning (EBL) focuses on young infants' learning about causal concepts of physical objects and events, whereas Bayesian models have been used to describe causal reasoning beyond infancy across various concept domains. Connecting the two models offers a more integrated approach to clarifying the developmental processes in causal reasoning from early infancy through later childhood. We further suggest that everyday language practices offer a promising space for theory bridging. We provide a review of selective work on caregiver–child conversations, in particular, on the use of scaffolding language including causal talk and pedagogical questions. Linking the research on language practices to the two cognitive theories, we point out directions for further research to integrate EBL and Bayesian models and clarify how causal learning unfolds in real life.This article is categorized under:Psychology > LearningCognitive Biology > Cognitive Development
{"title":"Causal learning by infants and young children: From computational theories to language practices","authors":"Samantha Basch, Su‐hua Wang","doi":"10.1002/wcs.1678","DOIUrl":"https://doi.org/10.1002/wcs.1678","url":null,"abstract":"Causal reasoning—the ability to reason about causal relations between events—is fundamental to understanding how the world works. This paper reviews two prominent theories on early causal learning and offers possibilities for theory bridging. Both theories grow out of computational modeling and have significant areas of overlap while differing in several respects. Explanation‐Based Learning (EBL) focuses on young infants' learning about causal concepts of physical objects and events, whereas Bayesian models have been used to describe causal reasoning beyond infancy across various concept domains. Connecting the two models offers a more integrated approach to clarifying the developmental processes in causal reasoning from early infancy through later childhood. We further suggest that everyday language practices offer a promising space for theory bridging. We provide a review of selective work on caregiver–child conversations, in particular, on the use of scaffolding language including causal talk and pedagogical questions. Linking the research on language practices to the two cognitive theories, we point out directions for further research to integrate EBL and Bayesian models and clarify how causal learning unfolds in real life.This article is categorized under:<jats:list list-type=\"simple\"> <jats:list-item>Psychology > Learning</jats:list-item> <jats:list-item>Cognitive Biology > Cognitive Development</jats:list-item> </jats:list>","PeriodicalId":501132,"journal":{"name":"WIREs Cognitive Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Our understanding of implicit bias and how to measure it has yet to be settled. Various debates between cognitive scientists are unresolved. Moreover, the public's understanding of implicit bias tests continues to lag behind cognitive scientists'. These discrepancies pose potential problems. After all, a great deal of implicit bias research has been publicly funded. Further, implicit bias tests continue to feature in discourse about public- and private-sector policies surrounding discrimination, inequality, and even the purpose of science. We aim to do our part by reconstructing some of the recent arguments in ordinary language and then revealing some of the operative norms or values that are often hidden beneath the surface of these arguments. This may help the public learn more about the science of implicit bias. It may also help both laypeople and scientists reflect on the values, interests, and stakeholders involved in establishing, justifying, and communicating scientific research.
{"title":"Testing for implicit bias: Values, psychometrics, and science communication","authors":"Nick Byrd, Morgan Thompson","doi":"10.1002/wcs.1612","DOIUrl":"https://doi.org/10.1002/wcs.1612","url":null,"abstract":"Our understanding of implicit bias and how to measure it has yet to be settled. Various debates <i>between</i> cognitive scientists are unresolved. Moreover, the public's understanding of implicit bias tests continues to lag behind cognitive scientists'. These discrepancies pose potential problems. After all, a great deal of implicit bias research has been publicly funded. Further, implicit bias tests continue to feature in discourse about public- and private-sector policies surrounding discrimination, inequality, and even the purpose of science. We aim to do our part by reconstructing some of the recent arguments in ordinary language and then revealing some of the operative norms or values that are often hidden beneath the surface of these arguments. This may help the public learn more about the science of implicit bias. It may also help both laypeople and scientists reflect on the values, interests, and stakeholders involved in establishing, justifying, and communicating scientific research.","PeriodicalId":501132,"journal":{"name":"WIREs Cognitive Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138510667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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