Psychological review最新文献

I propose a flexible threshold theory of change perception in self and social judgment. Traditionally, change perception is viewed as a basic cognitive process entailing the act of discriminating informational differences. This article takes a more dynamic view of change perception, highlighting people's motivations in interpreting those differences. Specifically, I propose people's change perceptions depend not only on the salience and quality of the evidence for change but they also depend on the adaptation implications of the change, as people are sensitive to whether their prompted response would be worth it. Variables that exacerbate perceived adaptation implications should thus lead people to contract their change perception thresholds (people should become less open to concluding things have changed and so less likely to act), while variables that alleviate perceived adaptation implications should thus lead people to expand their change perception thresholds (people should become more open to concluding things have changed and so more likely to act), all else equal in the evidence. Moreover, these effects should emerge for perceiving declines and improvements alike so long as change bears on adaptation implications. I review support for these proposals and use the theory to generate novel predictions, contributions, and applications. The theory can explain anew why people respond (or fail to respond) to changing climates and economies, worsening personal health, growing social progress, and many other self and social phenomena. Change perception is more than an act of discriminating differences-it also entails people's threshold judgments of whether and how these differences matter. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

We present a theory of belief dynamics that explains the interplay between internal beliefs in people's minds and beliefs of others in their external social environments. The networks of belief theory goes beyond existing theories of belief dynamics in three ways. First, it provides an explicit connection between belief networks in individual minds and belief dynamics on social networks. The connection, absent from most previous theories, is established through people's social beliefs or perceived beliefs of others. Second, the theory recognizes that the correspondence between social beliefs and others' actual beliefs can be imperfect, because social beliefs are affected by personal beliefs as well as by the actual beliefs of others. Past theories of belief dynamics on social networks do not distinguish between perceived and actual beliefs of others. Third, the theory explains diverse belief dynamics phenomena parsimoniously through the differences in attention and the resulting felt dissonances in personal, social, and external parts of belief networks. We implement our theoretical assumptions in a computational model within a statistical physics framework and derive model predictions. We find support for our theoretical assumptions and model predictions in two large survey studies (N₁ = 973, N₂ = 669). We then derive insights about diverse phenomena related to belief dynamics, including group consensus and polarization, group radicalization, minority influence, and different empirically observed belief distributions. We discuss how the theory goes beyond different existing models of belief dynamics and outline promising directions for future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dynamic models of choice typically describe the decision-making process in terms of the degree or balance of support for available response options. However, these alternative-specific representations of support are liable to fail when the available options change during the course of a decision. We suggest that people may use alternative-general representations, where stimulus feature information-rather than option-specific support-is accumulated over time and mapped onto support for available options as they appear. We tested alternative-specific and alternative-general models of choice in two perceptual experiments where the available options could change during a trial. In the first study, we showed that changing the choice options partway through a trial resulted in no substantial difference in performance relative to a condition where the final options were always onscreen. This was supported by a quantitative model comparison that strongly favored an alternative-general (geometric) model over two alternative-specific models (diffusion and racing accumulator models). In the second study, the stimulus primed specific unavailable responses to test whether irrelevant support for unavailable options was integrated into the decision process. This study elicited a pattern of accuracy that could not have occurred unless participants accumulated support for options that were not yet available. Together, these experiments and modeling results indicate that the majority of participants rely on alternative-general representations of evidence during dynamic decisions among options that can change over time. Future work on decision behavior and its neural antecedents should explore the predictions of these alternative-general theories of choice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Memory theories distinguish between item and associative information, which are engaged by different tasks: item recognition uses item information to decide whether an event occurred in a particular context; associative recognition uses associative information to decide whether two events occurred together. Associative recognition is slower and less accurate than item recognition, suggesting that item and associative information may be represented in different forms and retrieved using different processes. Instead, I show how a dynamic model (Cox & Criss, 2020; Cox & Shiffrin, 2017) accounts for accuracy and response time distributions in both item and associative recognition with the same set of representations and processes. Item and associative information are both represented as vectors of features. Item and associative recognition both depend on comparing traces in memory with probes of memory in which item and associative features gradually accumulate. Associative features are slower to accumulate, but largely because they emerge from conjunctions of already-accumulated item features. I apply the model to data from 453 participants, each of whom performed an item and performed associative recognition following identical study conditions (Cox et al., 2018). Comparisons among restricted versions of the model show that its account of associative feature formation, coupled with limits on the rate at which features accumulate from multiple items, explains how and why the dynamics of associative recognition differ from those of item recognition even while both tasks rely on the same underlying representations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Partisan animosity has been growing in the United States and around the world over the past few decades, fueling efforts by researchers and practitioners to help heal the divide. Many studies have been conducted to test interventions that aim to promote open-mindedness; however, these studies have been conducted in disparate literatures that do not always use the same terminology. In this review, we integrate research on open-mindedness in order to facilitate cross-talk and collaboration between disciplines. We review various concepts related to open-mindedness and then offer a conceptual model to help guide the further development of interventions and research to understand open-mindedness. We propose that open-mindedness is multifaceted and dynamic, such that interventions should focus on targeting multiple psychological pathways in order to maximize and sustain their effects. Specifically, we propose that interventions that target cognitive and/or motivational pathways can induce open-mindedness initially. Then, training in emotion regulation and/or social skills can help to sustain and build on open-mindedness once individuals enter into a situation where their beliefs are challenged. We conclude with a discussion of potential future directions for research on open-mindedness interventions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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).

Some neural representations gradually change across multiple timescales. Here we argue that modeling this "drift" could help explain the spacing effect (the long-term benefit of distributed learning), whereby differences between stored and current temporal context activity patterns produce greater error-driven learning. We trained a neurobiologically realistic model of the entorhinal cortex and hippocampus to learn paired associates alongside temporal context vectors that drifted between learning episodes and/or before final retention intervals. In line with spacing effects, greater drift led to better model recall after longer retention intervals. Dissecting model mechanisms revealed that greater drift increased error-driven learning, strengthened weights in slower drifting temporal context neurons (temporal abstraction), and improved direct cue-target associations (decontextualization). Intriguingly, these results suggest that decontextualization-generally ascribed only to the neocortex-can occur within the hippocampus itself. Altogether, our findings provide a mechanistic formalization for established learning concepts such as spacing effects and errors during learning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).