Psychological review最新文献

A review of Pavlovian conditioning in animals and humans reveals a critical role for expectancy in the learning of an association between a conditioned stimulus (CS) and an unconditioned stimulus (US), as well as in the expression of this association in a conditioned response (CR). The automatic and involuntary nature of CRs has traditionally been explained in terms of the formation of excitatory or inhibitory links between representations of the CS and US. However, this view has difficulty accounting for the variety of CRs that are observed, some qualitatively different from those elicited by the US, depending on the imminence of the predicted US and the nature of the CS. Furthermore, in humans, the same anticipatory responses are seen when the CS-US relationship is instructed rather than experienced and when the imminent occurrence of the US is directly instructed, without a mediating CS. These findings suggest an alternative explanation in which CRs are anticipatory responses elicited automatically by a specific state of expectancy of the US. The similarity between Pavlovian conditioning in animals and humans in turn suggests a continuity of core mechanisms for learning and performance. We conclude that research and theory in Pavlovian conditioning should go beyond the search for direct CS-US connections and seek to understand the mechanisms that underlie CS-US contingency knowledge, expectancy states, and the generation of anticipatory responses. (PsycInfo Database Record (c) 2024 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) 2024 APA, all rights reserved).

Nonlinguistic external representations, such as diagrams, animations, or puppet shows, involve local relations between a perceptually available object (a symbol) and an entity that is relevant in the current communicative context (a discourse referent). By analyzing the empirical evidence on early pretend play, I argue that object substitution pretense can be fully accounted for if it is conceived of as a subspecies of external representation. This implies that the capacity to interpret objects as symbols emerges early and reliably in human ontogeny. I discuss several accounts of pretend play and related phenomena and argue that the current proposal provides a better and more general account of early symbolic understanding than alternative views. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Object individuation studies have been a valuable tool in understanding the development of kind concepts. In this article, we review evidence from object individuation paradigms to argue that by their first birthday, infants represent at least three superordinate-level sortal kinds: OBJECT, ANIMATE, and AGENT (possibly also ARTIFACT). These superordinate sortal-kind concepts share key characteristics of adult kind concepts, such as prioritizing causal properties and having inductive potential. We then discuss the implications of this body of research. First, we discuss how the early development of these sortal-kind concepts (i.e., OBJECT, ANIMATE, and AGENT) relate to the two major theories of concepts: core knowledge and psychological essentialism. Second, we suggest that superordinate kind concepts set the stage for later development of basic-level kind concepts and present evidence that human communication, either in the form of language or pedagogical demonstration, plays a key role in constructing basic-level kinds. Third, we compare feature-based versus kind-based object individuation studies and put forth the hypothesis that they may reflect two modes of construal theory. Last, we discuss several open theoretical and empirical questions about sortal-kind concepts and suggest directions for future research. Overall, our review underscores the importance of object individuation methods as a powerful research tool for investigating the development of kind concepts, mechanisms of learning, and the relationship between language and thoughts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Psychological measures frequently show trait-like properties, and the ontological status of stable psychological traits has been discussed for decades. We argue that these properties can emerge from causal dynamics of time-varying processes, which are omitted from the analysis model, potentially leading to the estimation of traits that are, at least in part, illusory. Theories positing the importance of a large set of dynamic psychological causes across development are consistent with the existence of illusory traits. We show via simulation that even a linear system with many processes can generate a covariance matrix with trait-like properties. We then attempt to examine how illusory traits affect our conclusions drawn from a common statistical model, which assumes stable traits to analyze longitudinal panel data-a random-intercept cross-lagged panel model (RI-CLPM). We find that the RI-CLPM sometimes falsely detects the existence of traits in the presence of omitted processes, even when the data-generating model does not include any traits. However, in this scenario, the RI-CLPM estimates less causally biased autoregressive and cross-lagged effects than an analysis model, which does not assume traits (i.e., the cross-lagged panel model). The results indicate that the detection of trait variance should not be inferred as strong evidence for the existence of time-invariant trait causes. On the other hand, even when traits are illusory, statistical models assuming stable traits may sometimes be useful for causal inference. (PsycInfo Database Record (c) 2024 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) 2024 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) 2024 APA, all rights reserved).

Group creative ideation, the capacity of group to produce novel and useful ideas, is essential for navigating challenges and embracing opportunities. Despite its significance, research to decode its neurocognitive underpinnings utilizing interpersonal neuroscience paradigm has just commenced, linking group creative ideation to interpersonal neural coupling. In this perspective, we propose an interpersonal neural coupling in group creative ideation framework, which suggests that group creative ideation is supported by interpersonal neural coupling within three interrelated systems: cognitive, affective, and physical alignments. The cognitive alignment system is considered as the core system that determines the outcome of group creative ideation. Variations in cognitive alignment spanning shared intention, joint attention, shared comprehension, and idea convergence interact with an individual decision making in selecting any of three creative ideation pathways, including flexibility, persistence, and convergence, which collectively determine the final creative performance. The interpersonal neural coupling in group creative ideation framework enhances our understanding of the neurocognitive underpinnings of group creative ideation and outlines promising avenues for future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

When it comes to body movements in external space, people are experts in learning fine-grained voluntary control, for example, when manipulating tiny objects. Voluntarily controlling actions in the internal body (e.g., decreasing heart rate), however, is far more difficult and requires dedicated training, for example, in meditation or yoga. Not much is currently known about the learning mechanism underlying the acquisition of voluntary control over internal visceromotor actions (i.e., interoaction) or why it is so difficult compared to controlling our external somatomotor actions (i.e., exteroaction). We propose the ideomotor hypothesis of voluntary interoaction in this article, which asserts that voluntary exteroactions and interoactions are governed by the same general principle, namely, the anticipation of sensory feedback. We apply this hypothesis to two techniques that can be used to acquire voluntary control over interoactions, that is, autogenic training and biofeedback training. As the afferent signal we receive from interoaction (i.e., interoceptive signals from the internal body) is of lower sensory quality than the afferent signal that we receive from exteroaction (i.e., exteroceptive signals from the external environment), this hypothesis explains why learning to control interoactions is more difficult. We propose ways in which to test predictions from this hypothesis and show its theoretical value by comparing it to other frameworks in the literature. We hope that this work motivates future empirical studies directly examining voluntary interoaction and its clinical applications, such as autogenic and biofeedback training, and mind-body practices more generally. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Depression is vastly heterogeneous in its symptoms, neuroimaging data, and treatment responses. As such, describing how it develops at the network level has been notoriously difficult. In an attempt to overcome this issue, a theoretical "negative prediction mechanism" is proposed. Here, eight key brain regions are connected in a transient, state-dependent, core network of pathological communication that could facilitate the development of depressive cognition. In the context of predictive processing, it is suggested that this mechanism is activated as a response to negative/adverse stimuli in the external and/or internal environment that exceed a vulnerable individual's capacity for cognitive appraisal. Specifically, repeated activation across this network is proposed to update an individual's brain so that it increasingly predicts and reinforces negative experiences over time-pushing an individual at-risk for or suffering from depression deeper into mental illness. Within this, the negative prediction mechanism is poised to explain various aspects of prognostic outcome, describing how depression might ebb and flow over multiple timescales in a dynamically changing, complex environment. (PsycInfo Database Record (c) 2025 APA, all rights reserved).