Psychological review最新文献

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 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) 2024 APA, all rights reserved).

Everything that happens has a multitude of causes, but people make causal judgments effortlessly. How do people select one particular cause (e.g., the lightning bolt that set the forest ablaze) out of the set of factors that contributed to the event (the oxygen in the air, the dry weather … )? Cognitive scientists have suggested that people make causal judgments about an event by simulating alternative ways things could have happened. We argue that this counterfactual theory explains many features of human causal intuitions, given two simple assumptions. First, people tend to imagine counterfactual possibilities that are both a priori likely and similar to what actually happened. Second, people judge that a factor C caused effect E if C and E are highly correlated across these counterfactual possibilities. In a reanalysis of existing empirical data, and a set of new experiments, we find that this theory uniquely accounts for people's causal intuitions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The ecological approach to psychology has been a main antecedent of embodied and situated approaches to cognition. The concept of affordances in particular has gained currency throughout psychological science. Yet, contemporary ecological psychology has seemed inaccessible to outsiders and protective of its legacy. Indeed, some prominent ecological psychologists have presented their approach as a "package deal"-a principled and unified perspective on perception and action. Looking at the history of the field, however, we argue that ecological psychology has developed in rich and pluriform ways. Aiming to open the field to critical engagement and productive exchange, we identify three major strands of thought within ecological psychology, each of which emerged in the 20 years after Gibson's death: physical, biological, and social ecological psychology. Each of these strands develop ecological ideas in quite different directions, making different use of some of its central concepts, adopting different explanatory principles, and embodying different philosophical worldviews. Proponents of the ecological approach have been arguing for pluralism within cognitive science to make room for ecological psychology. Given the diversity of the strands, we extend this plea to within ecological psychology itself; the field is better off aiming for a productive pluralism in which the different strands are in dialogue with each other. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

People often form polarized beliefs, imbuing objects (e.g., themselves or others) with unambiguously positive or negative qualities. In clinical settings, this is referred to as dichotomous thinking or "splitting" and is a feature of several psychiatric disorders. Here, we introduce a Bayesian model of splitting that parameterizes a tendency to rigidly categorize objects as either entirely "Bad" or "Good," rather than to flexibly learn dispositions along a continuous scale. Distinct from the previous descriptive theories, the model makes quantitative predictions about how dichotomous beliefs emerge and are updated in light of new information. Specifically, the model addresses how splitting is context-dependent, yet exhibits stability across time. A key model feature is that phases of devaluation and/or idealization are consolidated by rationally attributing counter-evidence to external factors. For example, when another person is idealized, their less-than-perfect behavior is attributed to unfavorable external circumstances. However, sufficient counter-evidence can trigger switches of polarity, producing bistable dynamics. We show that the model can be fitted to empirical data, to measure individual susceptibility to relational instability. For example, we find that a latent categorical belief that others are "Good" accounts for less changeable, and more certain, character impressions of benevolent as opposed to malevolent others among healthy participants. By comparison, character impressions made by participants with borderline personality disorder reveal significantly higher and more symmetric splitting. The generative framework proposed invites applications for modeling oscillatory relational and affective dynamics in psychotherapeutic contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Face matching consists of the ability to decide whether two face images (or more) belong to the same person or to different identities. Face matching is crucial for efficient face recognition and plays an important role in applied settings such as passport control and eyewitness memory. However, despite extensive research, the mechanisms that govern face-matching performance are still not well understood. Moreover, to date, many researchers hold on to the belief that match and mismatch conditions are governed by two separate systems, an assumption that likely thwarted the development of a unified model of face matching. The present study outlines a unified unequal variance confidence-similarity signal detection-based model of face-matching performance, one that facilitates the use of receiver operating characteristics (ROC) and confidence-accuracy plots to better understand the relations between match and mismatch conditions, and their relations to factors of confidence and similarity. A binomial feature-matching mechanism is developed to support this signal detection model. The model can account for the presence of both within-identities and between-identities sources of variation in face recognition and explains a myriad of face-matching phenomena, including the match-mismatch dissociation. The model is also capable of generating new predictions concerning the role of confidence and similarity and their intricate relations with accuracy. The new model was tested against six alternative competing models (some postulate discrete rather than continuous representations) in three experiments. Data analyses consisted of hierarchically nested model fitting, ROC curve analyses, and confidence-accuracy plots analyses. All of these provided substantial support in the signal detection-based confidence-similarity model. The model suggests that the accuracy of face-matching performance can be predicted by the degree of similarity/dissimilarity of the depicted faces and the level of confidence in the decision. Moreover, according to the model, confidence and similarity ratings are strongly correlated. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Individual differences in processing speed and executive attention have both been proposed as explanations for individual differences in cognitive ability, particularly general and fluid intelligence (Engle et al., 1999; Kail & Salthouse, 1994). Both constructs have long intellectual histories in scientific psychology. This article attempts to describe the historical development of these constructs, particularly as they pertain to intelligence. It also aims to determine the degree to which speed and executive attention are theoretical competitors in explaining individual differences in intelligence. We suggest that attention is the more fundamental mechanism in explaining variation in human intelligence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Auditory perceptual deficits are widely observed among children with developmental language disorder (DLD). Yet, the nature of these deficits and the extent to which they explain speech and language problems remain controversial. In this study, we hypothesize that disruption to the maturation of the basilar membrane may impede the optimization of the auditory pathway from brainstem to cortex, curtailing high-resolution frequency sensitivity and the efficient spectral decomposition and encoding of natural speech. A series of computational simulations involving deep convolutional neural networks that were trained to encode, recognize, and retrieve naturalistic speech are presented to demonstrate the strength of this account. These neural networks were built on top of biologically truthful inner ear models developed to model human cochlea function, which-in the key innovation of the present study-were scheduled to mature at different rates over time. Delaying cochlea maturation qualitatively replicated the linguistic behavior and neurophysiology of individuals with language learning difficulties in a number of ways, resulting in (a) delayed language acquisition profiles, (b) lower spoken word recognition accuracy, (c) word finding and retrieval difficulties, (d) "fuzzy" and intersecting speech encodings and signatures of immature neural optimization, and (e) emergent working memory and attentional deficits. These simulations illustrate many negative cascading effects that a primary maturational frequency discrimination deficit may have on early language development and generate precise and testable hypotheses for future research into the nature and cost of auditory processing deficits in children with language learning difficulties. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

This article describes UMA (Unified Model of Arithmetic), a theory of children's arithmetic implemented as a computational model. UMA builds on FARRA (Fraction Arithmetic Reflects Rules and Associations; Braithwaite et al., 2017), a model of children's fraction arithmetic. Whereas FARRA-like all previous models of arithmetic-focused on arithmetic with only one type of number, UMA simulates arithmetic with whole numbers, fractions, and decimals. The model was trained on arithmetic problems from the first to sixth grade volumes of a math textbook series; its performance on tests administered at the end of each grade was compared to the performance of children in prior empirical research. In whole number arithmetic (Study 1), fraction arithmetic (Study 2), and decimal arithmetic (Study 3), UMA displayed types of errors, effects of problem features on error rates, and individual differences in strategy use that resembled those documented in the previous studies of children. Further, UMA generated correlations between individual differences in basic and advanced arithmetic skills similar to those observed in longitudinal studies of arithmetic development (Study 4). The results support UMA's main theoretical assumptions regarding arithmetic development: (a) most errors reflect small deviations from standard procedures via two mechanisms, overgeneralization and omission; (b) between-problem variations in error rates reflect effects of intrinsic difficulty and differential amounts of practice; and (c) individual differences in strategy use reflect underlying variation in parameters governing learning and decision making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Whether working memory reflects a thresholded recollection process whereby only a limited number of items are maintained in memory, or a signal detection process in which each studied item is increased in familiarity strength, is a topic of considerable debate. A review of visual working memory studies that have examined receiver operating characteristics (ROCs) across a broad set of materials and test conditions indicates that both signal detection and threshold processes contribute to working memory. In addition, the role that these two processes play varies systematically across conditions, such that a threshold process plays a particularly critical role when binary old/new judgments are required, when changes are relatively discrete, and when the hippocampus does not contribute to performance. In contrast, a signal detection process plays a greater role when confidence judgments are required, when the materials or the changes are global in nature, and when the hippocampus contributes to performance. In addition, the ROC results indicate that in standard single-probe tests of working memory, items that are maintained in an active recollected state support both recall-to-accept and recall-to-reject responses; whereas in complex-probe tests, recollection preferentially supports recall-to-reject; and in item-recognition tests it preferentially supports recall-to-accept. Moreover, there is growing evidence that these threshold and strength-based processes are related to distinct states of conscious awareness whereby they support perceiving- and sensing-based responses, respectively. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Where does arithmetic come from, and why are addition and multiplication its fundamental operations? Although we know that arithmetic is true, no explanation that meets standards of scientific rigor is available from philosophy, mathematical logic, or the cognitive sciences. We propose a new approach based on the assumption that arithmetic has a biological origin: Many examples of adaptive behavior such as spatial navigation suggest that organisms can perform arithmetic-like operations on represented magnitudes. If so, these operations-nonsymbolic precursors of addition and multiplication-might be optimal due to evolution and thus identifiable according to an appropriate criterion. We frame this as a metamathematical question, and using an order-theoretic criterion, prove that four qualitative conditions-monotonicity, convexity, continuity, and isomorphism-are sufficient to identify addition and multiplication over the real numbers uniquely from the uncountably infinite class of possible operations. Our results show that numbers and algebraic structure emerge from purely qualitative conditions, and as a construction of arithmetic, provide a rigorous explanation for why addition and multiplication are its fundamental operations. We argue that these conditions are preverbal psychological intuitions or principles of perceptual organization that are biologically based and shape how humans and nonhumans alike perceive the world. This is a Kantian view and suggests that arithmetic need not be regarded as an immutable truth of the universe but rather as a natural consequence of our perception. Algebraic structure may be inherent in the representations of the world formed by our perceptual system. (PsycInfo Database Record (c) 2024 APA, all rights reserved).