Psychological review最新文献

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) 2024 APA, all rights reserved).

The explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains a phenomenon. We address this shortcoming by proposing a productive account of explanation: a theory explains a phenomenon to some degree if and only if a formal model of the theory produces the statistical pattern representing the phenomenon. Using this account, we outline a workable methodology of explanation: (a) explicating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we provide three major criteria for evaluating the goodness of an explanation (precision, robustness, and empirical relevance), and examine some cases of explanatory breakdowns. Finally, we situate our framework within existing theories of explanation from philosophy of science and discuss how our approach contributes to constructing and developing better psychological theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Within psychology, the underachievement of students from working-class backgrounds has often been explained as a product of individual characteristics such as a lack of intelligence or motivation. Here, we propose an integrated model illustrating how educational contexts contribute to social class disparities in education over and beyond individual characteristics. According to this new Social Class-Academic Contexts Mismatch model, social class disparities in education are due to several mismatches between the experiences that students from working-class backgrounds bring with them to the classroom and those valued in academic contexts-specifically, mismatches between (a) academic contexts' culture of independence and the working-class orientation to interdependence, (b) academic contexts' culture of competition and the working-class orientation toward cooperation, (c) the knowledge valued in academic contexts and the knowledge developed through working-class socialization, and (d) the social identities valued in academic contexts and the negatively stereotyped social identities of students from working-class backgrounds. Because of these mismatches, students from working-class backgrounds are likely to experience discomfort and difficulty in the classroom. We further propose that, when attempting to make sense of these first-order effects, students and teachers rely on inherent characteristics (e.g., ability, motivation) more often than warranted; conversely, they overlook extrinsic, contextual factors. In turn, this explanatory bias toward inherent features leads (a) students from working-class backgrounds to experience self-threat and (b) their teachers to treat them unfairly. These second-order effects magnify social class disparities in education. This integrated model has the potential to reshape research and discourse on social class and education. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Generative models of decision now permeate all subfields of psychology, cognitive, and clinical neuroscience. To successfully investigate decision mechanisms from behavior, it is necessary to assume the presence of delays prior and after the decision process itself. However, directly observing this "non-decision time (NDT)" from behavior long appeared beyond reach, the field mainly relying on models to estimate it. Here, we propose a biological definition of decision that includes perceptual discrimination and action selection, and in turn, explicitly equates NDT with the minimum sensorimotor delay, or "deadtime." We show how this delay is directly observable in behavioral data, without modeling assumptions, using the visual interference approach. We apply this approach to 11 novel and archival data sets from humans and monkeys gathered from multiple labs. We validate the method by showing that visual properties (brightness, color, size) consistently affect empirically measured visuomotor deadtime (VMDT), as predicted by neurophysiology. We then show that endogenous factors (strategic slowing, attention) do not affect VMDT. Therefore, VMDT consistently satisfies widespread selective influence assumptions, in contrast to NDT parameters from model fits. Last, contrasting empirically observed VMDT with NDT estimates from the EZ, drift diffusion, and linear ballistic accumulator models, we conclude that NDT parameters from these models are unlikely to consistently reflect visuomotor delays, neither at a group level nor for individual differences, in contrast to a widely held assumption. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

This article introduces an integrated and biologically inspired theory of decision making, motor preparation, and motor execution. The theory is formalized as an extension of the diffusion model, in which diffusive accumulated evidence from the decision-making process is continuously conveyed to motor areas of the brain that prepare the response, where it is smoothed by a mechanism that approximates a Kalman-Bucy filter. The resulting motor preparation variable is gated prior to reaching agonist muscles until it exceeds a particular level of activation. We tested this gated cascade diffusion model by continuously probing the electrical activity of the response agonists through electromyography in four choice tasks that span a variety of domains in cognitive sciences, namely motion perception, numerical cognition, recognition memory, and lexical knowledge. The model provided a good quantitative account of behavioral and electromyographic data and systematically outperformed previous models. This work represents an advance in the integration of processes involved in simple decisions and sheds new light on the interplay between decision and motor systems. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Cognitive scientists have become increasingly interested in understanding how natural minds represent and reason about possible ways the world could be. However, there is currently little agreement on how to understand this remarkable capacity for modal thought. We argue that the capacity for modal thought is built from a set of relatively simple component parts, centrally involving an ability to consider possible extensions of a part of the actual world. Natural minds can productively combine this ability with a range of other capacities, eventually allowing for the observed suite of increasingly more sophisticated ways of modal reasoning. We demonstrate how our (de)compositional account is supported by both the trajectory of children's developing capacity for reasoning about possible ways the world could be and by what we know about how such modal thought is expressed within and across natural languages. Our approach makes new predictions about which kinds of capacities are required by which kinds of experimental tasks and, as a result, contributes to settling currently open theoretical questions about the development of modal thought and the acquisition of modal vocabulary in children. Our work also provides a more systematic way of understanding possible variation in modal thought and talk, and, more generally, paves the way toward a unified theory that will ultimately allow researchers across disciplines to relate their findings to each other within a framework of shared assumptions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Humans selectively attend to task-relevant information in order to make accurate decisions. However, selective attention incurs consequences if the learning environment changes unexpectedly. This trade-off has been underscored by studies that compare learning behaviors between adults and young children: broad sampling during learning comes with a breadth of information in memory, often allowing children to notice details of the environment that are missed by their more selective adult counterparts. The current work extends the exemplar-similarity account of object discrimination to consider both the intentional and consequential aspects of selective attention when predicting choice. In a novel direct input approach, we used trial-level eye-tracking data from training and test to replace the otherwise freely estimated attention dynamics of the model. We demonstrate that only a model imbued with gaze correlates of memory precision in addition to decision weights can accurately predict key behaviors associated with (a) selective attention to a relevant dimension, (b) distributed attention across dimensions, and (c) flexibly shifting strategies between tasks. Although humans engage in selective attention with the intention of being accurate in the moment, our findings suggest that its consequences on memory constrain the information that is available for making decisions in the future. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Sensory perception is widely considered an inference process that reflects the best guess of a stimulus feature based on uncertain sensory information. Here we challenge this reductionist view and propose that perception is rather a holistic inference process that operates not only at the feature but jointly across all levels of the representational hierarchy. We test this hypothesis in the context of a commonly used psychophysical matching task in which subjects are asked to report their perceived orientation of a test stimulus by adjusting a probe stimulus (method-of-adjustment). We introduce a holistic matching model that assumes that subjects' reports reflect an optimal match between the test and probe stimulus, both in terms of their inferred feature (orientation) and also their higher level representation (orientation category). Validation against several existing data sets demonstrates that the model accurately and comprehensively predicts subjects' response behavior and outperforms previous models both qualitatively and quantitatively. Moreover, the model generalizes to other feature domains and offers an alternative account for categorical color perception. Our results suggest that categorical effects in sensory perception are ubiquitous and can be parsimoniously explained as optimal behavior based on holistic sensory representations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Pavlovian conditioning is widely used to study the substrates of learning and memory in the mammalian brain. In a standard protocol, subjects are exposed to pairings of a conditioned stimulus (CS; e.g., a tone) with an unconditioned stimulus (US; e.g., an electric shock). Subsequent presentations of the CS elicit a range of behaviors that relate to the US (e.g., freezing) showing that animals learned the CS-US relation. However, it is still unclear how neuronal activity pertaining to the CS comes to excite a representation of the US, and thereby, conditioned responses. The current analysis of this problem, based on neurophysiological evidence, views Pavlovian conditioning as a process of facilitating the disinhibition, rather than the excitation, of neuronal responses representing the US. Conversely, Pavlovian extinction is viewed as a process of relearning to inhibit neuronal responses representing the US. We propose a mathematical equation that confirms the predictions made by this novel perspective on Pavlovian conditioning when applied to conditioning phenomena that fall beyond classic associative learning theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).