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) 2025 APA, all rights reserved).

Episodic memory is a core function that allows us to remember the events of our lives. Given that many events in our life contain overlapping elements (e.g., similar people and places), it is critical to understand how well we can remember the specific events of our lives versus how susceptible we are to interference between similar memories. Several prominent theories converged on the notion that pattern separation in the hippocampus causes it to play a greater role in processes such as recollection, associative memory, and memory for specific details, while overlapping distributed representations in the neocortex cause it to play a stronger role in domain-specific memory. We propose that studying memory performance on tasks with targets and similar lures provides a critical test bed for comparing the extent to which human memory is driven by pattern separation (e.g., hippocampus) versus more overlapping representations (e.g., neocortex). We generated predictions from several computational models and tested these predictions in a large sample of human participants. We found a linear relationship between memory performance and target-lure pattern similarity within a neural network simulation of inferior temporal cortex, an object-processing region. We also observed strong effects of test format on performance and consistent relationships between test formats. Altogether, our results were better accounted for by distributed memory models at the more linear end of a representational continuum than pattern-separated representations; therefore, our results provide important insight into prominent memory theories by suggesting that recognition memory performance is primarily driven by overlapping representations (e.g., neocortex). (PsycInfo Database Record (c) 2025 APA, all rights reserved).

According to chunking theories, children discover their first words by extracting subsequences embedded in their continuous input. However, the mechanisms proposed in these accounts are often incompatible with data from other areas of language development. We present a new theory to connect the chunking accounts of word discovery with the broader developmental literature. We argue that (a) children build a diverse collection of chunks, including words, multiword phrases, and sublexical units; (b) these chunks have different processing times determined by how often each chunk is used to recode the input; and (c) these processing times interact with short-term memory limitations and incremental processing to constrain learning. We implemented this theory as a computational modeling architecture called Chunk-Based Incremental Processing and Learning (CIPAL). Across nine studies, we demonstrate that CIPAL can model word discovery in different contexts. First, we trained the model with 70 child-directed speech corpora from 15 languages. CIPAL gradually discovered words in each language, with cross-linguistic variation in performance. The model's average processing time also improved with experience, resembling the developmental changes observed in children's speed of processing. Second, we showed that CIPAL could simulate seven influential effects reported in statistical learning experiments with artificial languages. This included a preference for words over nonwords, part words, frequency-matched part words, phantom words, and sublexical units. On this basis, we argue that incremental chunking is an effective implicit statistical learning mechanism that may be central to children's vocabulary development. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

In this article, we compare two theories of habituation: the standard operating processes (SOP) and the multiple time scales (MTS) models. Both theories propose that habituation is due to a reduction in the difference between actual and remembered stimulation. Although the two approaches explain short-term habituation using a similar nonassociative mechanism based on a time-decaying memory of recent stimulus presentations, their understanding of retention of habituation or long-term habituation differs. SOP suggests that retention of habituation happens through associative retrieval from a long-term memory store, while MTS relies on the differential decay rate of a series of memory units. This essential difference implies that spontaneous recovery, which refers to the return of the response to levels above those reached during habituation, is predominantly a consequence of a mixture of decay and loss of association for SOP and exclusively of decay for MTS. We analyze these mechanisms conceptually and mathematically and demonstrate their functioning with computer simulations of conceptual and published experiments. We evaluate both theories regarding parsimony and explanatory power and propose potential experiments to evaluate their predictions. We provide MATLAB-Simulink and Python codes for the simulations. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Experiencing stressful or traumatic events can lead to a range of responses, from mild disruptions to severe and persistent mental health issues. Understanding the various trajectories of response to adversity is crucial for developing effective interventions and support systems. Researchers have identified four commonly observed response trajectories to adversity, from which the resilient is the most common one. Resilience refers to the maintenance of healthy psychological functioning despite facing adversity. However, it remains an open question how to understand and anticipate resilience, due to its dynamic and multifactorial nature. This article presents a novel formalized framework to conceptualize resilience from a complex systems perspective. We use the network theory of psychopathology, which states that mental disorders are self-sustaining endpoints of direct symptom-symptom interactions organized in a network system. The internal structure of the network determines the most likely trajectory of symptom development. We introduce the resilience quadrant, which organizes the state of symptom networks on two domains: (1) healthy versus dysfunctional and (2) stable versus unstable. The quadrant captures the four commonly observed response trajectories to adversity along those dimensions: resilient trajectories in the face of adversity, as well as persistent symptoms despite treatment interventions. Subsequently, an empirical illustration, by means of a proof-of-principle, shows how simulated observations from four different network architectures lead to the four commonly observed responses to adversity. As such, we present a novel outlook on resilience by combining existing statistical symptom network models with simulation techniques. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Traditional models of rational choice assume that preferences are complete, but the completeness axiom is neither normatively compelling nor psychologically plausible. Building on recent work in economics, we develop a rational analysis of decision making with incomplete preferences. The analysis sheds surprising light on a range of well-known behavioral "anomalies," including the endowment effect, status quo maintenance, the sunk cost effect, and coherent arbitrariness. We propose a two-part division of rational choice theory-into preference theory and "implementation theory"-and show how conservative and coherently arbitrary policies can effectively implement incomplete preferences. The two-part normative framework motivates a psychological distinction between evaluation and implementation phases in decision making. We argue that the endowment effect and related phenomena, which have usually been attributed to loss aversion in the evaluation phase, are better explained by conservatism in the implementation phase. The rational analysis challenges the normative adequacy of expected utility theory and raises questions about the explanatory scope of prospect theory. It illustrates the rich interplay between psychological models of value structure and normative models of rational choice. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Why do humans believe in moralizing gods? Leading accounts argue that these beliefs evolved because they help societies grow and promote group cooperation. Yet recent evidence suggests that beliefs in moralizing gods are not limited to large societies and might not have strong effects on cooperation. Here, we propose that beliefs in moralizing gods develop because individuals shape supernatural beliefs to achieve strategic goals in within-group interactions. People have a strategic interest in controlling others' cooperation, either to extort benefits from them or to gain reputational benefits for protecting the public good. Moreover, they believe, based on their folk-psychology, that others would be less likely to cheat if they feared supernatural punishment. Thus, people endorse beliefs in moralizing gods to manipulate others into cooperating. Prosocial religions emerge from a dynamic of mutual monitoring, in which each individual, lacking confidence in the cooperativeness of conspecifics, attempts to incentivize others' cooperation by endorsing beliefs in supernatural punishment. We show how variations of this incentive structure explain the variety of cultural attractors toward which supernatural punishment converges, including extractive religions that extort benefits from exploited individuals, prosocial religions geared toward mutual benefit, and forms of prosocial religion where belief in moralizing gods is itself a moral duty. We review evidence for nine predictions of this account and use it to explain the decline of prosocial religions in modern societies. Supernatural punishment beliefs seem endorsed as long as people believe them necessary to ensure others' cooperation, regardless of their objective effectiveness in doing so. (PsycInfo Database Record (c) 2025 APA, all rights reserved).