Pub Date : 2026-03-18DOI: 10.1177/09637214261417968
Chuyan Qu, Daniel Ansari
The place-value concept is fundamental to understanding the symbolic number system. It dictates that the value of a digit in a number is based on its position or place within the number (e.g., the “5” in “510” is five units of 100, whereas the “5” in “51” is five units of 10). Place value is central to understanding multidigit numbers, performing arithmetic, and learning more complex math. Despite its significance, relatively little research has systematically examined the developmental trajectory and cognitive underpinnings of the place-value concept. In this article, we synthesize prior findings and propose a conceptual framework that delineates the core properties of the place-value concept and characterizes its developmental trajectory. We also identify key cognitive factors that may underpin individual differences in its acquisition. This framework can guide future research to understand how children acquire the place-value concept and how best to support this learning. It also has broad implications for understanding the cognitive architecture of human compositional symbol systems.
{"title":"The Structure Beneath the Symbols: How Children Develop an Understanding of Place Value","authors":"Chuyan Qu, Daniel Ansari","doi":"10.1177/09637214261417968","DOIUrl":"https://doi.org/10.1177/09637214261417968","url":null,"abstract":"The place-value concept is fundamental to understanding the symbolic number system. It dictates that the value of a digit in a number is based on its position or place within the number (e.g., the “5” in “510” is five units of 100, whereas the “5” in “51” is five units of 10). Place value is central to understanding multidigit numbers, performing arithmetic, and learning more complex math. Despite its significance, relatively little research has systematically examined the developmental trajectory and cognitive underpinnings of the place-value concept. In this article, we synthesize prior findings and propose a conceptual framework that delineates the core properties of the place-value concept and characterizes its developmental trajectory. We also identify key cognitive factors that may underpin individual differences in its acquisition. This framework can guide future research to understand how children acquire the place-value concept and how best to support this learning. It also has broad implications for understanding the cognitive architecture of human compositional symbol systems.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"11 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147471156","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 : 2026-02-22DOI: 10.1177/09637214261416790
Judith E. Fan
Human behavior is fundamentally generative: People create pictures, write stories, compose music, and engage in conversation. Traditional approaches in psychology and cognitive science have not focused on this open-endedness, instead favoring more constrained task settings that admit a limited set of outcomes. Although those approaches have been fruitful, new approaches might be needed to develop a unified understanding of the generative, open-ended behaviors that are so emblematic of human cognition. This article demonstrates the value of generative behaviors as targets for cognitive modeling by providing rich behavioral data that reveal how multiple cognitive processes coordinate. Drawing production serves as a case study illustrating this approach, showing how perception, memory, social inference, and motor control coordinate flexibly on the basis of communicative context. Recent advances in generative artificial intelligence offer both new tools for modeling open-ended human behavior and new comparative targets for understanding similarities and differences between human and machine intelligence. However, applying these tools effectively might require new experimental paradigms, larger data sets, and careful consideration of what mechanistic correspondence between models and human cognition is necessary for scientific progress. Embracing the open-ended nature of human thought and behavior poses methodological challenges but offers a promising path toward understanding the most distinctive aspects of human intelligence.
{"title":"Generative Behaviors as Key Targets for Cognitive Models","authors":"Judith E. Fan","doi":"10.1177/09637214261416790","DOIUrl":"https://doi.org/10.1177/09637214261416790","url":null,"abstract":"Human behavior is fundamentally generative: People create pictures, write stories, compose music, and engage in conversation. Traditional approaches in psychology and cognitive science have not focused on this open-endedness, instead favoring more constrained task settings that admit a limited set of outcomes. Although those approaches have been fruitful, new approaches might be needed to develop a unified understanding of the generative, open-ended behaviors that are so emblematic of human cognition. This article demonstrates the value of generative behaviors as targets for cognitive modeling by providing rich behavioral data that reveal how multiple cognitive processes coordinate. Drawing production serves as a case study illustrating this approach, showing how perception, memory, social inference, and motor control coordinate flexibly on the basis of communicative context. Recent advances in generative artificial intelligence offer both new tools for modeling open-ended human behavior and new comparative targets for understanding similarities and differences between human and machine intelligence. However, applying these tools effectively might require new experimental paradigms, larger data sets, and careful consideration of what mechanistic correspondence between models and human cognition is necessary for scientific progress. Embracing the open-ended nature of human thought and behavior poses methodological challenges but offers a promising path toward understanding the most distinctive aspects of human intelligence.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"417 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778503","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 : 2026-02-19DOI: 10.1177/09637214261416505
Charisse B. Pickron, Laurie Bayet
Individual variations in face-perception expertise become apparent by the second year of life. We propose that infants’ “face diet”—the nature and quantity of their visual interactions with faces—provides a useful lens for understanding how individual differences in face perception arise. In this article, we discuss how the diversity of an infant’s face diet and their interactions with caregivers shape their face-perception and social-learning skills, how a masked face diet may influence infants’ face perception, and how neurodiversity may affect infants’ face diets and learning about faces. These components underscore how face perception develops through both shared and individual pathways, with implications for identifying early-emerging challenges and designing supportive interventions. Future research opportunities include incorporating diverse contexts, improving measurement tools, and examining developmental periods beyond infancy.
{"title":"From a Baby’s Point of View: How Infants’ Face Diets Shape Their Face Perception","authors":"Charisse B. Pickron, Laurie Bayet","doi":"10.1177/09637214261416505","DOIUrl":"https://doi.org/10.1177/09637214261416505","url":null,"abstract":"Individual variations in face-perception expertise become apparent by the second year of life. We propose that infants’ “face diet”—the nature and quantity of their visual interactions with faces—provides a useful lens for understanding how individual differences in face perception arise. In this article, we discuss how the diversity of an infant’s face diet and their interactions with caregivers shape their face-perception and social-learning skills, how a masked face diet may influence infants’ face perception, and how neurodiversity may affect infants’ face diets and learning about faces. These components underscore how face perception develops through both shared and individual pathways, with implications for identifying early-emerging challenges and designing supportive interventions. Future research opportunities include incorporating diverse contexts, improving measurement tools, and examining developmental periods beyond infancy.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"3 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146215704","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 : 2026-02-12DOI: 10.1177/09637214261417960
Gordon Pennycook, Thomas H. Costello, David G. Rand
A consistent pattern emerges from the history of psychology: Technological advances change the way that we understand ourselves. We argue that, in addition to various uses that are already common (e.g., qualitative coding), large language models can be integrated into survey software and act as a virtual research assistant that can generate tailored stimuli on the fly. This creates unprecedented flexibility in developing materials for psychological theory testing. We present an illustrative case study to show how a major lingering debate in the field—that is, whether people really change their mind according to evidence or, instead, rely on motivated reasoning—was pushed forward by using artificial intelligence (AI) to administer personalized experimental treatments. We discuss various potential uses of AI to test hypotheses in psychological science and argue that psychologists should seriously consider using AI to better understand human intelligence.
{"title":"Using Artificial Intelligence to Better Understand Human Intelligence","authors":"Gordon Pennycook, Thomas H. Costello, David G. Rand","doi":"10.1177/09637214261417960","DOIUrl":"https://doi.org/10.1177/09637214261417960","url":null,"abstract":"A consistent pattern emerges from the history of psychology: Technological advances change the way that we understand ourselves. We argue that, in addition to various uses that are already common (e.g., qualitative coding), large language models can be integrated into survey software and act as a virtual research assistant that can generate tailored stimuli on the fly. This creates unprecedented flexibility in developing materials for psychological theory testing. We present an illustrative case study to show how a major lingering debate in the field—that is, whether people really change their mind according to evidence or, instead, rely on motivated reasoning—was pushed forward by using artificial intelligence (AI) to administer personalized experimental treatments. We discuss various potential uses of AI to test hypotheses in psychological science and argue that psychologists should seriously consider using AI to better understand human intelligence.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"47 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160502","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 : 2026-02-07DOI: 10.1177/09637214251414021
Matthew K. Nock, Shirley B. Wang
Suicide is among the most perplexing of all human behaviors. It has been a leading cause of death for decades, and despite significant study it continues unabated. Over the past few years, the development of new digital and computational methods has provided tools that are helping to overcome many long-standing challenges to studying suicide. Here we review recent advances in the understanding, prediction, and prevention of suicidal behaviors using such methods. Examples include the use of mathematical and computational modeling to build and test more precise theories of suicidal thoughts and behaviors, large-scale electronic databases to better detect and predict those at risk for suicide (e.g., health-care networks, social media, and other web-based platforms), smartphones and wearable biosensors to identify person-specific high-risk periods, and digital devices and platforms to deliver and test just-in-time adaptive interventions. Although suicide is a long-standing problem, these advances are facilitating significant progress and hope for the future of suicide prevention.
{"title":"Understanding, Predicting, and Preventing Suicide: Recent Advances Using Digital and Computational Methods","authors":"Matthew K. Nock, Shirley B. Wang","doi":"10.1177/09637214251414021","DOIUrl":"https://doi.org/10.1177/09637214251414021","url":null,"abstract":"Suicide is among the most perplexing of all human behaviors. It has been a leading cause of death for decades, and despite significant study it continues unabated. Over the past few years, the development of new digital and computational methods has provided tools that are helping to overcome many long-standing challenges to studying suicide. Here we review recent advances in the understanding, prediction, and prevention of suicidal behaviors using such methods. Examples include the use of mathematical and computational modeling to build and test more precise theories of suicidal thoughts and behaviors, large-scale electronic databases to better detect and predict those at risk for suicide (e.g., health-care networks, social media, and other web-based platforms), smartphones and wearable biosensors to identify person-specific high-risk periods, and digital devices and platforms to deliver and test just-in-time adaptive interventions. Although suicide is a long-standing problem, these advances are facilitating significant progress and hope for the future of suicide prevention.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"6 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138489","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 : 2026-01-27DOI: 10.1177/09637214251401848
Julia A. Leonard, Reut Shachnai
Persistence is essential for learning, but children cannot and should not persist at everything. How do young children decide what is worth their effort? We build a theory of young children’s state persistence as the outcome of a socially guided decision-making process between children and caregivers. Integrating research from metacognition, decision-making, and social learning, we show how caregivers shape two key beliefs that guide children’s effort: What children think they are capable of and whether their effort is worthwhile. Caregivers’ actions, in turn, are guided by their own beliefs about children’s abilities and the value of tasks, creating a dynamic social system of effort calibration. By reframing persistence as a dynamic coconstructed process, we uncover how motivation is built—and where it can break down.
{"title":"Dyadic Decisions About Effort: How Caregivers Shape Young Children’s Persistence","authors":"Julia A. Leonard, Reut Shachnai","doi":"10.1177/09637214251401848","DOIUrl":"https://doi.org/10.1177/09637214251401848","url":null,"abstract":"Persistence is essential for learning, but children cannot and should not persist at everything. How do young children decide what is worth their effort? We build a theory of young children’s state persistence as the outcome of a socially guided decision-making process between children and caregivers. Integrating research from metacognition, decision-making, and social learning, we show how caregivers shape two key beliefs that guide children’s effort: What children think they are capable of and whether their effort is worthwhile. Caregivers’ actions, in turn, are guided by their own beliefs about children’s abilities and the value of tasks, creating a dynamic social system of effort calibration. By reframing persistence as a dynamic coconstructed process, we uncover how motivation is built—and where it can break down.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"7 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056186","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 : 2026-01-27DOI: 10.1177/09637214251407571
Cleotilde Gonzalez, Tailia Malloy
This article calls for complementary human-AI intelligence. Rather than redefining intelligence to fit machine capabilities, we argue for designing AI that complements and extends human cognition. We distinguish between cognitive AI , which is grounded in cognitive science to model human perception, learning, and decision-making, and machine AI , which achieves large-scale performance through data-driven optimization. Building on advances in machine learning alignment and human-AI complementarity, we propose an integrative framework that connects cognitive and machine AI across four routes: embedding integration , aligning human and machine representations; instruction encoding , using machine AI to translate goals into cognitive AI; training agents , using cognitive AI to guide and train machine AI through human-like data; and coevolving agents , enabling cognitive and machine AI to coadapt and improve together over time. These integration routes provide a foundation for complementary intelligence : systems that combine human interpretability with machine scalability and precision to enhance trust, adaptability, and human agency in complex sociotechnical environments.
{"title":"Toward Complementary Intelligence: Integrating Cognitive and Machine AI","authors":"Cleotilde Gonzalez, Tailia Malloy","doi":"10.1177/09637214251407571","DOIUrl":"https://doi.org/10.1177/09637214251407571","url":null,"abstract":"This article calls for complementary human-AI intelligence. Rather than redefining intelligence to fit machine capabilities, we argue for designing AI that complements and extends human cognition. We distinguish between <jats:italic toggle=\"yes\">cognitive AI</jats:italic> , which is grounded in cognitive science to model human perception, learning, and decision-making, and <jats:italic toggle=\"yes\">machine AI</jats:italic> , which achieves large-scale performance through data-driven optimization. Building on advances in machine learning alignment and human-AI complementarity, we propose an integrative framework that connects cognitive and machine AI across four routes: <jats:italic toggle=\"yes\">embedding integration</jats:italic> , aligning human and machine representations; <jats:italic toggle=\"yes\">instruction encoding</jats:italic> , using machine AI to translate goals into cognitive AI; <jats:italic toggle=\"yes\">training agents</jats:italic> , using cognitive AI to guide and train machine AI through human-like data; and <jats:italic toggle=\"yes\">coevolving agents</jats:italic> , enabling cognitive and machine AI to coadapt and improve together over time. These integration routes provide a foundation for <jats:italic toggle=\"yes\">complementary intelligence</jats:italic> : systems that combine human interpretability with machine scalability and precision to enhance trust, adaptability, and human agency in complex sociotechnical environments.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"38 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056187","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 : 2026-01-26DOI: 10.1177/09637214251410195
Frank J. Infurna, Yesenia Cruz-Carrillo, Nutifafa E. Y. Dey, Markus Wettstein, Margie E. Lachman, Denis Gerstorf
We summarize empirical evidence documenting that (a) U.S. middle-aged adults have displayed historical trends of elevations in loneliness and depressive symptoms and declining memory and physical health and (b) this pattern is largely confined to the United States and not observed in peer nations. A conceptual model is provided to detail possible explanations for these historical trends. We also discuss future directions to explore whether similar historical trends are transpiring across population subgroups and low- and middle-income nations, and we identify psychosocial resources for promoting resilience. This timely article sheds light on midlife development from a cross-national and historical perspective.
{"title":"Historical Change in Midlife Development From a Cross-National Perspective","authors":"Frank J. Infurna, Yesenia Cruz-Carrillo, Nutifafa E. Y. Dey, Markus Wettstein, Margie E. Lachman, Denis Gerstorf","doi":"10.1177/09637214251410195","DOIUrl":"https://doi.org/10.1177/09637214251410195","url":null,"abstract":"We summarize empirical evidence documenting that (a) U.S. middle-aged adults have displayed historical trends of elevations in loneliness and depressive symptoms and declining memory and physical health and (b) this pattern is largely confined to the United States and not observed in peer nations. A conceptual model is provided to detail possible explanations for these historical trends. We also discuss future directions to explore whether similar historical trends are transpiring across population subgroups and low- and middle-income nations, and we identify psychosocial resources for promoting resilience. This timely article sheds light on midlife development from a cross-national and historical perspective.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"291 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048517","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 : 2025-12-26DOI: 10.1177/09637214251382091
Stephanie L. Brown, R. Michael Brown, David Cavallino
This article provides an overview of the debate within social psychology concerning the possible existence of altruistic motivation. After presenting the social-psychological background, we describe selective investment theory , an evolutionary theory of altruistic motivation, and discuss the underlying neurobiology. We describe evidence of the theory’s generativity within health psychology and consider its implications for solving social problems in the areas of economics, overpopulation, peace negotiations, and environmental protection.
{"title":"Does Altruism Exist? Implications of Selective Investment Theory for Solving Social Problems","authors":"Stephanie L. Brown, R. Michael Brown, David Cavallino","doi":"10.1177/09637214251382091","DOIUrl":"https://doi.org/10.1177/09637214251382091","url":null,"abstract":"This article provides an overview of the debate within social psychology concerning the possible existence of altruistic motivation. After presenting the social-psychological background, we describe <jats:italic toggle=\"yes\">selective investment theory</jats:italic> , an evolutionary theory of altruistic motivation, and discuss the underlying neurobiology. We describe evidence of the theory’s generativity within health psychology and consider its implications for solving social problems in the areas of economics, overpopulation, peace negotiations, and environmental protection.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"8 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145829842","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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