Pub Date : 2024-10-07DOI: 10.1177/09637214241279539
Jessica L. Alquist, Roy F. Baumeister
Uncertainty has a negative reputation. Not knowing what has happened or is going to happen is typically depicted as undesirable, and people often seek to minimize and avoid it. Research has shown that having a negative attitude toward uncertainty is associated with poor mental health and that certainty seeking can lead to accepting meager rewards and low-quality information. As a remedy for negative views of uncertainty, the present review discusses the functions of some typical responses to uncertainty as well as research on circumstances in which uncertainty can be leveraged to improve well-being. Uncertainty can focus attention, increase effort, and increase the intensity and duration of positive effect. Recognizing that there are situations in which uncertainty is desirable may be a first step toward improving attitudes toward uncertainty.
{"title":"Learning to Love Uncertainty","authors":"Jessica L. Alquist, Roy F. Baumeister","doi":"10.1177/09637214241279539","DOIUrl":"https://doi.org/10.1177/09637214241279539","url":null,"abstract":"Uncertainty has a negative reputation. Not knowing what has happened or is going to happen is typically depicted as undesirable, and people often seek to minimize and avoid it. Research has shown that having a negative attitude toward uncertainty is associated with poor mental health and that certainty seeking can lead to accepting meager rewards and low-quality information. As a remedy for negative views of uncertainty, the present review discusses the functions of some typical responses to uncertainty as well as research on circumstances in which uncertainty can be leveraged to improve well-being. Uncertainty can focus attention, increase effort, and increase the intensity and duration of positive effect. Recognizing that there are situations in which uncertainty is desirable may be a first step toward improving attitudes toward uncertainty.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"10 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142384528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-21DOI: 10.1177/09637214241262329
Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy
The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case and that these systems in fact offer new opportunities for Bayesian modeling. Specifically, we argue that artificial neural networks and Bayesian models of cognition lie at different levels of analysis and are complementary modeling approaches, together offering a way to understand human cognition that spans these levels. We also argue that the same perspective can be applied to intelligent machines, in which a Bayesian approach may be uniquely valuable in understanding the behavior of large, opaque artificial neural networks that are trained on proprietary data.
{"title":"Bayes in the Age of Intelligent Machines","authors":"Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy","doi":"10.1177/09637214241262329","DOIUrl":"https://doi.org/10.1177/09637214241262329","url":null,"abstract":"The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case and that these systems in fact offer new opportunities for Bayesian modeling. Specifically, we argue that artificial neural networks and Bayesian models of cognition lie at different levels of analysis and are complementary modeling approaches, together offering a way to understand human cognition that spans these levels. We also argue that the same perspective can be applied to intelligent machines, in which a Bayesian approach may be uniquely valuable in understanding the behavior of large, opaque artificial neural networks that are trained on proprietary data.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"1 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142306213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1177/09637214241275570
Leah S. Richmond-Rakerd, Kallisse R. Dent, Signe Hald Andersen, Stephanie D’Souza, Barry J. Milne
Population-level administrative data—data on individuals’ interactions with administrative systems, such as health-care, social-welfare, criminal-justice, and education systems—are a fruitful resource for research into behavior, development, and well-being. However, administrative data are underutilized in psychological science. Here, we review advantages of population-level administrative data for psychological research and provide examples of advances in psychological theory arising from administrative data studies. We focus on advantages in three areas: the collection and recording of population-level administrative data, the data’s large scale, and unique data linkages. We also describe ethical issues as well as methodological considerations and limitations in population administrative data research and outline future directions to enable psychological scientists to more fully capitalize on administrative data resources.
{"title":"Population-Level Administrative Data: A Resource to Advance Psychological Science","authors":"Leah S. Richmond-Rakerd, Kallisse R. Dent, Signe Hald Andersen, Stephanie D’Souza, Barry J. Milne","doi":"10.1177/09637214241275570","DOIUrl":"https://doi.org/10.1177/09637214241275570","url":null,"abstract":"Population-level administrative data—data on individuals’ interactions with administrative systems, such as health-care, social-welfare, criminal-justice, and education systems—are a fruitful resource for research into behavior, development, and well-being. However, administrative data are underutilized in psychological science. Here, we review advantages of population-level administrative data for psychological research and provide examples of advances in psychological theory arising from administrative data studies. We focus on advantages in three areas: the collection and recording of population-level administrative data, the data’s large scale, and unique data linkages. We also describe ethical issues as well as methodological considerations and limitations in population administrative data research and outline future directions to enable psychological scientists to more fully capitalize on administrative data resources.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"11 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1177/09637214241268145
Julian Jara-Ettinger, Adena Schachner
How do humans build and navigate their complex social world? Standard theoretical frameworks often attribute this success to a foundational capacity to analyze other people’s appearance and behavior to make inferences about their unobservable mental states. Here we argue that this picture is incomplete. Human behavior leaves traces in our physical environment that reveal our presence, our goals, and even our beliefs and knowledge. A new body of research shows that, from early in life, humans easily detect these traces—sometimes spontaneously—and readily extract social information from the physical world. From the features and placement of inanimate objects, people make inferences about past events and how people have shaped the physical world. This capacity develops early and helps explain how people have such a rich understanding of others: by drawing not only on how others act but also on the environments they have shaped. Overall, social cognition is crucial not only to our reasoning about people and actions but also to our everyday reasoning about the inanimate world.
{"title":"Traces of Our Past: The Social Representation of the Physical World","authors":"Julian Jara-Ettinger, Adena Schachner","doi":"10.1177/09637214241268145","DOIUrl":"https://doi.org/10.1177/09637214241268145","url":null,"abstract":"How do humans build and navigate their complex social world? Standard theoretical frameworks often attribute this success to a foundational capacity to analyze other people’s appearance and behavior to make inferences about their unobservable mental states. Here we argue that this picture is incomplete. Human behavior leaves traces in our physical environment that reveal our presence, our goals, and even our beliefs and knowledge. A new body of research shows that, from early in life, humans easily detect these traces—sometimes spontaneously—and readily extract social information from the physical world. From the features and placement of inanimate objects, people make inferences about past events and how people have shaped the physical world. This capacity develops early and helps explain how people have such a rich understanding of others: by drawing not only on how others act but also on the environments they have shaped. Overall, social cognition is crucial not only to our reasoning about people and actions but also to our everyday reasoning about the inanimate world.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"12 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1177/09637214241268098
Sam Whitman McGrath, Jacob Russin, Ellie Pavlick, Roman Feiman
Over the last decade, deep neural networks (DNNs) have transformed the state of the art in artificial intelligence. In domains such as language production and reasoning, long considered uniquely human abilities, contemporary models have proven capable of strikingly human-like performance. However, in contrast to classical symbolic models, neural networks can be inscrutable even to their designers, making it unclear what significance, if any, they have for theories of human cognition. Two extreme reactions are common. Neural network enthusiasts argue that, because the inner workings of DNNs do not seem to resemble any of the traditional constructs of psychological or linguistic theory, their success renders these theories obsolete and motivates a radical paradigm shift. Neural network skeptics instead take this inability to interpret DNNs in psychological terms to mean that their success is irrelevant to psychological science. In this article, we review recent work that suggests that the internal mechanisms of DNNs can, in fact, be interpreted in the functional terms characteristic of psychological explanations. We argue that this undermines the shared assumption of both extremes and opens the door for DNNs to inform theories of cognition and its development.
{"title":"How Can Deep Neural Networks Inform Theory in Psychological Science?","authors":"Sam Whitman McGrath, Jacob Russin, Ellie Pavlick, Roman Feiman","doi":"10.1177/09637214241268098","DOIUrl":"https://doi.org/10.1177/09637214241268098","url":null,"abstract":"Over the last decade, deep neural networks (DNNs) have transformed the state of the art in artificial intelligence. In domains such as language production and reasoning, long considered uniquely human abilities, contemporary models have proven capable of strikingly human-like performance. However, in contrast to classical symbolic models, neural networks can be inscrutable even to their designers, making it unclear what significance, if any, they have for theories of human cognition. Two extreme reactions are common. Neural network enthusiasts argue that, because the inner workings of DNNs do not seem to resemble any of the traditional constructs of psychological or linguistic theory, their success renders these theories obsolete and motivates a radical paradigm shift. Neural network skeptics instead take this inability to interpret DNNs in psychological terms to mean that their success is irrelevant to psychological science. In this article, we review recent work that suggests that the internal mechanisms of DNNs can, in fact, be interpreted in the functional terms characteristic of psychological explanations. We argue that this undermines the shared assumption of both extremes and opens the door for DNNs to inform theories of cognition and its development.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"8 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1177/09637214241268083
Diane M. Beck, Evan G. Center, Zhenan Shao
Multiple models of vision propose that perception involves a process of prediction and verification. Here we argue that real-world statistical regularities—representations that, on average, more quickly make contact with meaning—serve as the basis of these predictions. We show that statistically regular images—those, we argue, that more closely match perceptual predictions—are more readily perceived and more efficiently processed than statistically irregular images.
{"title":"The Role of Real-World Statistical Regularities in Visual Perception","authors":"Diane M. Beck, Evan G. Center, Zhenan Shao","doi":"10.1177/09637214241268083","DOIUrl":"https://doi.org/10.1177/09637214241268083","url":null,"abstract":"Multiple models of vision propose that perception involves a process of prediction and verification. Here we argue that real-world statistical regularities—representations that, on average, more quickly make contact with meaning—serve as the basis of these predictions. We show that statistically regular images—those, we argue, that more closely match perceptual predictions—are more readily perceived and more efficiently processed than statistically irregular images.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"68 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1177/09637214241264292
Michael D. Lee
The wisdom of the crowd is the finding that aggregating the judgments of many people can lead to surprisingly accurate group judgments. Usually statistical methods are used to aggregate people’s judgments, but there are advantages to using cognitive models instead. Crowd judgments based on cognitive modeling can (a) identify experts and amplify their judgments, (b) provide a representational structure for aggregating complicated multidimensional judgments, (c) debias judgments that are affected by heuristic cognitive processes or competitive social situations, and (d) diversify the crowd by incorporating predictions about judgments that have not been observed. Demonstrations of these advantages are provided in case studies involving ranking, probability estimation, and categorization problems.
{"title":"Using Cognitive Models to Improve the Wisdom of the Crowd","authors":"Michael D. Lee","doi":"10.1177/09637214241264292","DOIUrl":"https://doi.org/10.1177/09637214241264292","url":null,"abstract":"The wisdom of the crowd is the finding that aggregating the judgments of many people can lead to surprisingly accurate group judgments. Usually statistical methods are used to aggregate people’s judgments, but there are advantages to using cognitive models instead. Crowd judgments based on cognitive modeling can (a) identify experts and amplify their judgments, (b) provide a representational structure for aggregating complicated multidimensional judgments, (c) debias judgments that are affected by heuristic cognitive processes or competitive social situations, and (d) diversify the crowd by incorporating predictions about judgments that have not been observed. Demonstrations of these advantages are provided in case studies involving ranking, probability estimation, and categorization problems.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"49 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1177/09637214241262334
Yong Hoon Chung, Timothy F. Brady, Viola S. Störmer
Visual working memory is traditionally studied using abstract, meaningless stimuli. Although studies using such simplified stimuli have been insightful in understanding the mechanisms of visual working memory, they also potentially limit our ability to understand how people encode and store conceptually rich and meaningful stimuli in the real world. Recent studies have demonstrated that meaningful and familiar visual stimuli that connect to existing knowledge are better remembered than abstract colors or shapes, indicating that meaning can unlock additional working memory capacity. These findings challenge current models of visual working memory and suggest that its capacity is not fixed but depends on the type of information that is being remembered and, in particular, how that information connects to preexisting knowledge.
{"title":"Meaningfulness and Familiarity Expand Visual Working Memory Capacity","authors":"Yong Hoon Chung, Timothy F. Brady, Viola S. Störmer","doi":"10.1177/09637214241262334","DOIUrl":"https://doi.org/10.1177/09637214241262334","url":null,"abstract":"Visual working memory is traditionally studied using abstract, meaningless stimuli. Although studies using such simplified stimuli have been insightful in understanding the mechanisms of visual working memory, they also potentially limit our ability to understand how people encode and store conceptually rich and meaningful stimuli in the real world. Recent studies have demonstrated that meaningful and familiar visual stimuli that connect to existing knowledge are better remembered than abstract colors or shapes, indicating that meaning can unlock additional working memory capacity. These findings challenge current models of visual working memory and suggest that its capacity is not fixed but depends on the type of information that is being remembered and, in particular, how that information connects to preexisting knowledge.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"11 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1177/09637214241263020
Karen L. Campbell, Emily E. Davis
Associative memory declines with age, and this decline is thought to stem from a decreased ability to form new associations or bind information together. However, a growing body of work suggests that (a) the binding process itself remains relatively intact with age when tested implicitly and (b) older adults form excessive associations (or “hyper-bind”) because of a decreased ability to control attention. In this article, we review evidence for the hyper-binding hypothesis. This work shows that older adults form more nontarget associations than younger adults, which leads to increased interference at retrieval and forgetting, an effect that may extend to others with poor attentional control, such as children and people with attention-deficit disorder. We discuss why hyper-binding is apparent only under implicit test conditions and how it affects memory for everyday events. Although hyper-binding likely contributes to forgetting, it may also confer certain advantages when seemingly irrelevant associations later become relevant.
{"title":"Hyper-Binding: Older Adults Form Too Many Associations, Not Too Few","authors":"Karen L. Campbell, Emily E. Davis","doi":"10.1177/09637214241263020","DOIUrl":"https://doi.org/10.1177/09637214241263020","url":null,"abstract":"Associative memory declines with age, and this decline is thought to stem from a decreased ability to form new associations or bind information together. However, a growing body of work suggests that (a) the binding process itself remains relatively intact with age when tested implicitly and (b) older adults form excessive associations (or “hyper-bind”) because of a decreased ability to control attention. In this article, we review evidence for the hyper-binding hypothesis. This work shows that older adults form more nontarget associations than younger adults, which leads to increased interference at retrieval and forgetting, an effect that may extend to others with poor attentional control, such as children and people with attention-deficit disorder. We discuss why hyper-binding is apparent only under implicit test conditions and how it affects memory for everyday events. Although hyper-binding likely contributes to forgetting, it may also confer certain advantages when seemingly irrelevant associations later become relevant.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"28 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1177/09637214241266818
Carol D. Ryff
This article provides an overview of a model of psychological well-being put forth over 30 years ago. The intent was to advance new dimensions of positive functioning based on integration of clinical, developmental, existential, and humanistic thinking along with Aristotle’s writings about eudaimonia. The operationalization and validation of the model are briefly described, followed by an overview of scientific findings organized around (a) demographic and experiential predictors of well-being, (b) well-being as predictors of health and biomedical outcomes, (c) pathway studies that examine intervening processes (moderators, mediators), and (d) underlying mechanistic processes (neuroscience, genomics). Much prior work underscores the benefits of well-being, including for longevity. Widening socioeconomic inequality is, however, increasingly compromising the well-being of disadvantaged segments of the population. These problems have been exacerbated by recent historical stressors (Great Recession, COVID-19 pandemic). Cumulative hardships from these events and their implications for health are critical targets for future science and practice.
{"title":"The Privilege of Well-Being in an Increasingly Unequal Society","authors":"Carol D. Ryff","doi":"10.1177/09637214241266818","DOIUrl":"https://doi.org/10.1177/09637214241266818","url":null,"abstract":"This article provides an overview of a model of psychological well-being put forth over 30 years ago. The intent was to advance new dimensions of positive functioning based on integration of clinical, developmental, existential, and humanistic thinking along with Aristotle’s writings about eudaimonia. The operationalization and validation of the model are briefly described, followed by an overview of scientific findings organized around (a) demographic and experiential predictors of well-being, (b) well-being as predictors of health and biomedical outcomes, (c) pathway studies that examine intervening processes (moderators, mediators), and (d) underlying mechanistic processes (neuroscience, genomics). Much prior work underscores the benefits of well-being, including for longevity. Widening socioeconomic inequality is, however, increasingly compromising the well-being of disadvantaged segments of the population. These problems have been exacerbated by recent historical stressors (Great Recession, COVID-19 pandemic). Cumulative hardships from these events and their implications for health are critical targets for future science and practice.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"4 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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