Psychological review最新文献

Research on saccadic and pursuit eye movements led to great advances in our understanding of sensorimotor processing and human behavior. However, studies often have focused on isolated saccadic and pursuit eye movements measured with respect to different sensory information (static vs. dynamic targets). Here, we leveraged interindividual differences across a carefully balanced combination of different tasks to demonstrate that critical links in the control of oculomotor behavior were previously missed. We observed correlations in eye movement behavior across tasks, but only when compared with the same sensory information (e.g., pursuit gain and accuracy of saccades to moving targets). Within the same task, the coordination of saccadic and pursuit eye movements was tailored to the strengths of the individual: observers with more accurate saccades to moving targets rely on them more to catch up with moving targets. Our results have profound implications for the theoretical understanding of sensorimotor processing for oculomotor control. They necessitate a reevaluation of previous data used to map brain circuits for saccadic and pursuit eye movements measured with different types of relevant sensory information. Additionally, they underscore the importance of moving beyond average observations to embrace individual differences as a rich source of information. These individual differences not only reveal the strengths and weaknesses of observers. When combined across different tasks, they allow insights about why observers behave differently in a given task. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The speed-accuracy trade-off (SAT) is the tendency for fast decisions to come at the expense of accurate performance. Evidence accumulation models such as the drift diffusion model can reproduce a variety of behavioral data related to the SAT, and their parameters have been linked to neural activities in the brain. However, our understanding of how biological neural networks realize the associated cognitive operations remains incomplete, limiting our ability to unify neurological and computational accounts of the SAT. We address this gap by developing and analyzing a biologically plausible spiking neural network that extends the drift diffusion approach. We apply our model to both perceptual and nonperceptual tasks, investigate several contextual manipulations, and validate model performance using neural and behavioral data. Behaviorally, we find that our model (a) reproduces individual response time distributions; (b) generalizes across experimental contexts, including the number of choice alternatives, speed- or accuracy-emphasis, and task difficulty; and (c) predicts accuracy data, despite being fit only to response time data. Neurally, we show that our model (a) recreates observed patterns of spiking neural activity and (b) captures age-related deficits that are consistent with the behavioral data. More broadly, our model exhibits the SAT across a variety of tasks and contexts and explains how individual differences in speed and accuracy arise from synaptic weights within a spiking neural network. Our work showcases a method for translating mathematical models into functional neural networks and demonstrates that simulating such networks permits analyses and predictions that are outside the scope of purely mathematical models. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Although the importance of unsupervised learning has been recognized since William James's "blooming, buzzing confusion," it has received less attention in the literature than supervised learning. An important form of unsupervised learning is clustering, which involves determining the groups of distinct objects that belong together. Visual clustering is foundational for ensemble perception, numerosity judgments, spatial problem-solving, understanding information visualizations, and other forms of visual cognition, and yet surprisingly few researchers have directly investigated this human ability. In this study, participants freely clustered arrays that varied in the number of points (10-40) and cluster structure of the stimuli, which was defined based on the statistical distribution of points. We found that clustering is a reliable ability: Participants' clusterings of the same stimulus on two occasions were highly similar. With respect to the objective properties of the clusterings that people produce, points of individual clusters tend to follow a Gaussian distribution. With respect to processing, we identified five visual attributes that characterize the clusters that participants draw-cluster numerosity, area, density, and linearity and also percentage of points on the convex hull. We also discovered evidence for sequential strategies, with some attributes dominating when drawing the initial clusters of a stimulus and others guiding the final clusters. Collectively, these findings offer a comprehensive picture of human visual clustering and serve as a foundation for the development of new models of this important ability. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Food and eating are fundamental for survival but also have significant impacts on health, psychology, sociology, and economics. Understanding what motivates people to eat can provide insights into "adaptive" eating behavior, which is especially important due to the increasing prevalence of health-related conditions such as obesity. There has been considerable interest in developing theoretical models and associated constructs that explain individual differences in eating behavior. However, many of these models contain overlapping theories and shared theoretical mechanisms of action. Currently, there is no recognized standard framework that integrates psychological, physiological, and neurobiological theory to help explain human eating behavior. The aim of the current article was to review key psychological theories in relation to energy balance, homeostasis, energy intake, and motivation to eat and begin to develop a comprehensive framework of relevant factors that drive eating behavior. The key findings from this review suggest that eating behavior is conceptualized by elements of dual process models, which include conscious processing (reflective factors) and automatic responses to desires, environmental cues, habits, and associative learning. These processes are mediated by neurobiology and physiological signaling (homeostatic feedback) of energy balance, which is more tolerant of positive than negative energy balances. From a synthesis of available evidence, it is suggested that eating behavior constructs (traits) can be explained by three latent constructs: reflective, reactive, and homeostatic eating. By understanding the interplay between reflective, reactive, and homeostatic processes, interventions can be developed that tailor treatments to target key aspects of eating behavior. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Covert identity signals permit the communication of group membership to ingroup members while avoiding potentially costly detection by members of other groups. If individuals are incentivized to detect others' group memberships, however, covert signals may not remain covert for very long. We propose a theoretical extension to the literature on covert signaling in which conventionalized identity signals can become destabilized when learned by outgroup individuals to be replaced by the emergence of new signaling conventions. We formalize this idea with both analytical and agent-based modeling of ingroup and outgroup individuals who learn about signals of group membership. Depending on the risk and associated cost of detection by the outgroup, the model yields three dynamic classes: saturation, where all identity signals become stable conventions and never go extinct; cycling, in which new signals emerge to replace old ones as they are learned by the outgroup; and suppression, in which informative identity signals never emerge. Our analysis has implications for understanding identity signaling, the emergence of conventions, coded speech, and the ebb and flow of fashion cycles. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Understanding the mechanisms enabling the learning and flexible use of knowledge in context-appropriate ways has been a major focus of research in the study of both semantic cognition and cognitive control. We present a unified model of semantics and control that addresses these questions from both perspectives. The model provides a coherent view of how semantic knowledge, and the ability to flexibly access and deploy that knowledge to meet current task demands, arises from end-to-end learning of the statistics of the environment. We show that the model addresses unresolved issues from both literatures, including how control operates over features that covary with one another and how control representations themselves are structured and emerge through learning, through a series of behavioral experiments and simulations. We conclude by discussing the implications of our approach to other fundamental questions in cognitive science, machine learning, and artificial intelligence. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Social norms shape a vast range of human behaviors, from everyday interactions to major life choices. Yet, existing theories of norm emergence typically focus either on why certain norms arise (substantive properties) or on how they spread and persist (dynamical properties), often making conflicting assumptions. Here, we propose a unified account in which norms prescribing how one ought to act emerge naturally from the fundamental algorithms that guide learning-whether in social or nonsocial settings. Our account builds on recent advances in decision making and emotion research that have highlighted "actor-critic" models as a core mechanism of learning from feedback. We extend this mechanism to social settings by assuming that it is not only we who critique our actions; others critique our actions as well. By simulating this interindividual form of learning, we show that it uniquely produces group behavior that exhibits both substantive and dynamical properties of real-world social norms, including prosociality, ingroup bias, stickiness, S-shaped curves, and local conformity/global diversity. Our framework thus offers a uniquely parsimonious way to bridge the gap between individual learning and group behavior. (PsycInfo Database Record (c) 2026 APA, all rights reserved).