Psychological review最新文献

Time is a central dimension against which perception, action, and cognition play out. From anticipating when future events will happen to recalling how long ago previous events occurred, humans and animals are exquisitely sensitive to temporal structure. Empirical evidence seems to suggest that estimating time prospectively (i.e., in passing) is qualitatively different from estimating time in retrospect (i.e., after the event is over). Indeed, computational models that attempt to explain both prospective and retrospective timing assume a fundamental separation of their underlying processes. We, in contrast, propose a new neurocomputational model of timing, the unified temporal coding (UTC) model that unifies prospective and retrospective timing through common principles. The UTC model assumes that both stimulus and timing information are represented inside the same rolling window of input history. As a consequence, the UTC model explains a wide range of phenomena typically covered by specialized models, such as conformity to and violations of the scalar property, one-shot learning of intervals, neural responses underlying timing, timing behavior under normal and distracting conditions, common capacity limits in timing and working memory, and how timing depends on attention. Strikingly, by assuming that prospective and retrospective timing rely on the same principles and are implemented in the same neural network, a simple attentional gain mechanism can resolve the apparently paradoxical effect of cognitive load on prospective and retrospective timing. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

We examined how elements are integrated into larger units in working memory (WM). Four contrasting conceptual models exist with regard to this question: (a) a unitization model, in which there is a single integrated representation which is retrieved in an all-or-none fashion; (b) a unitization-with-element-failure model, in which a single integrated representation is retrieved as a whole, but access to its elements can still fail individually; (c) a pairwise-binding model, in which elements of a unit are represented separately and are bound together in pairs; (d) a hybrid model that includes an integrated representation as well as pairwise bindings between element representations. We developed four multinomial process tree models to test these theories. In three experiments, participants memorized multiple units which were random combinations of three elements. They were given one element as a cue and prompted to report the other two elements. The model-comparison analysis revealed that the hybrid model provides the best quantitative fit to the data. We conclude that multielement units are represented on two levels, as an integrated unit retrieved in an all-or-none manner, and in addition through pairwise bindings between their elements. Moreover, the assumption that bindings of nonspatial elements are mediated through their shared spatial location-a special case of the pairwise-binding model-was not supported by the data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

A longstanding focus in the causal learning literature has been on inferring causal relations from contingencies, where these abstract away from time by collating independent instances or by aggregating over regularly demarcated trials. In contrast, individual causal learners encounter events in their daily lives that occur in a continuous temporal flow with no such demarcation. Consequently, the process of learning causal relationships in naturalistic environments is comparatively less understood. In this article, we lay out a rational framework that foregrounds the role of time in causal learning. We work within the Bayesian rational analysis tradition, starting by considering how causal relations induce dependence between events in continuous time and how this can be modeled by stochastic processes from the Poisson-Gamma distribution family. We derive the qualitative signatures of causal influence and the general computations needed to infer structure from temporal patterns. We show that this rational account can parsimoniously explain the human preference for causal models that invoke shorter, more reliable, and more predictable causal influences. Furthermore, we show this provides a unifying explanation for human judgments across a wide variety of tasks in the reanalysis of seven experimental data sets. We anticipate the framework will help researchers better understand the many manifestations of continuous-time causal learning across human cognition and the tasks that probe it, from explicit causal structure induction settings to implicit associative or reinforcement learning settings. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

A key role for attention is to continually focus visual processing to satisfy our goals. How does this work in computational terms? Here we introduce adaptive computation-a new computational mechanism of human attention that bridges the momentary application of perceptual computations with their impact on decision outcomes. Adaptive computation is a dynamic algorithm that rations perceptual computations across objects on-the-fly, enabled by a novel and general formulation of task relevance. We evaluate adaptive computation in a case study of multiple object tracking (MOT)-a paradigmatic example of selection as a dynamic process, where observers track a set of target objects moving amid visually identical distractors. Adaptive computation explains the attentional dynamics of object selection with unprecedented depth. It not only recapitulates several classic features of MOT (e.g., trial-level tracking accuracy and localization error of targets), but also captures properties that have not previously been measured or modeled-including both the subsecond patterns of attentional deployment between objects, and the resulting sense of subjective effort. Critically, this approach captures such data within a framework that is in-principle domain-general, and, unlike past models, without using any MOT-specific heuristic components. Beyond this case study, we also look to the future, discussing how adaptive computation may apply more generally, providing a new type of mechanistic model for the dynamic operation of many forms of visual attention. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Autism has historically been defined by the presence of differences in social communication and restricted, repetitive patterns of behavior, interests, or activities (RRBs). Since 2013 when the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders was published, sensory features were added as one of the polythetic restricted and repetitive behavior diagnostic criteria of autism, though it has remained understudied. Here, we summarize theory and research to provide support for the perspective that early sensory functions and experiences play a primary role in autism and have downstream effects on social communication and repetitive behavioral features of autism. The goals of this article are to provide an understanding of the current sensory research landscape over the early developmental period; to contextualize our knowledge autism within a developmental framework; to delineate a cascading developmental model that provides testable hypotheses; and to identify current gaps in research that would allow us to further our understanding of the role, and primacy of sensory differences in the development of the autistic phenotype. We close by offering a set of recommendations for the field. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Ostensive communication (Sperber & Wilson, 1986/1995) involves both an informative and a communicative intention: The communicator draws attention not only to the information she intends to convey but also to her intention to convey it. This elicits an expectation of relevance in addressees that guides them in identifying the information communicated. This notion of ostensive communication has been influential in pragmatics, developmental psychology, and comparative psychology but also raises many questions. In the light of much relevant research, elaboration, and criticism over the years, we put forward a revised, broadened, more explicit, and more explanatory account of ostensive communication and of the role played in it by cognitive expectations of relevance and social expectations of cooperativeness. We distinguish two forms of ostension: In basic ostension, communicators give evidence of the information they intend to communicate, and in mentalistic communication, they give evidence of their intention to communicate that information. We interpret relevant comparative psychology findings (such as Gómez, 1996) as suggesting that a basic, nonmentalistic form of ostension may have evolved in great apes as a solution to the problems and opportunities presented by intentional communication. We discuss Csibra and Gergely's (2009) "natural pedagogy theory" claim that ostension is specifically adapted for the transmission of general knowledge to children. Correcting earlier pragmatic theories inspired by Grice (1989) including our own, we argue that typical verbal communication makes use of both basic and mentalistic ostension. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Grief is a reaction to loss that is observed across human cultures and even in other species. While the particular expressions of grief vary significantly, universal aspects include experiences of emotional pain and frequent remembering of what was lost. Despite its prevalence, and its obvious nature, considering grief from a functional perspective is puzzling: Why do we grieve? Why is it painful? And why is it sometimes prolonged enough to be clinically impairing? Using the framework of reinforcement learning with memory replay, we offer answers to these questions and suggest, counterintuitively, that grief may function to maximize future reward. That is, grieving may help to unlearn old habits so that alternative sources of reward can be found. We additionally perform a set of simulations that identify and explore optimal grieving parameters and use our model to account for empirical phenomena such as individual differences in human grief trajectories. (PsycInfo Database Record (c) 2025 APA, all rights reserved).