Psychological review最新文献

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).

The N-back task is often considered to be a canonical example of a task that relies on working memory (WM), requiring both maintenance of representations of previously presented stimuli and also processing of these representations. In particular, the set-size effect in this task (e.g., poorer performance on three-back than two-back judgments), as in others, is often interpreted as indicating that the task relies on retention and processing of information in a limited-capacity WM system. Here, we consider an alternative possibility: that retention in episodic memory (EM) rather than WM can account for both set-size and lure effects in the N-back task. Accordingly, performance in the N-back task may reflect engagement of the processing ("working") function of WM but not necessarily limits in either that processing ability nor in retention ("memory"). To demonstrate this point, we constructed a neural network model that was augmented with an EM component, but lacked any capacity to retain information across trials in WM, and trained it to perform the N-back task. We show that this model can account for the set-size and lure effects obtained in an N-back study by M. J. Kane et al. (2007), and that it does so as a result of the well-understood effects of temporal distinctiveness on EM retrieval, and the processing of this information in WM. These findings help illuminate the ways in which WM may interact with EM in the service of cognitive function and add to a growing body of evidence that tasks commonly assumed to rely on WM may alternatively (or additionally) rely on EM. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Two of the main impediments to learning complex tasks are that relationships between different stimuli, including rewards, can be uncertain and context-dependent. Reinforcement learning (RL) provides a framework for learning, by predicting total future reward directly (model-free RL), or via predictions of future states (model-based RL). Within this framework, "successor representation" (SR) predicts total future occupancy of all states. A recent theoretical proposal suggests that the hippocampus encodes the SR in order to facilitate prediction of future reward. However, this proposal does not take into account how learning should adapt under uncertainty and switches of context. Here, we introduce a theory of learning SRs using prediction errors which includes optimally balancing uncertainty in new observations versus existing knowledge. We then generalize that approach to a multicontext setting, allowing the model to learn and maintain multiple task-specific SRs and infer which one to use at any moment based on the accuracy of its predictions. Thus, the context used for predictions can be determined by both the contents of the states themselves and the distribution of transitions between them. This probabilistic SR model captures animal behavior in tasks which require contextual memory and generalization, and unifies previous SR theory with hippocampal-dependent contextual decision-making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The multicomponent nature of executive functions (EF) has long been recognized, pushing for a better understanding of both the commonalities and the diversity between EF components. Despite the advances made, the operationalization of performance in EF tasks remains rather heterogeneous, and the structure of EF as modeled by confirmatory factor analyses (CFA) is still a topic of debate (Karr et al., 2018). The present work demonstrates these two issues are related, showing how different operationalizations in task-based performance indicators impact the resulting models of EF structure with CFA. Using bootstrapped data from 294 children (8-12 years old) and nine EF tasks (tapping inhibition, working memory, and cognitive flexibility), we first show improved model convergence and acceptance when operationalizing EF through single tasks' scores (e.g., incongruent trials, Flanker task) relative to difference scores (e.g., incongruent minus congruent trials, Flanker task). Furthermore, we show that response times exhibit poor model convergence and acceptance compared not only to accuracy but also drift rate. The latter, a well-known indicator in drift-diffusion models, is found to present the best trade-off between convergence and acceptance to model EF with CFA. Finally, we examine how various operationalizations of performance in EF tasks impact CFA model comparison in the assessment of EF structure and discuss the theoretical foundations for these results. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

When faced with distraction, we can focus more on goal-relevant information (targets) or focus less on goal-conflicting information (distractors). How people use cognitive control to distribute attention across targets and distractors remains unclear. We address this question by developing a novel Parametric Attentional Control Task that can "tag" participants' sensitivity to target and distractor information. We use these precise measures of attention to develop a novel process model that can explain how participants control attention toward targets and distractors. Across three experiments, we find that participants met the demands of this task by independently controlling their processing of target and distractor information, exhibiting distinct adaptations to manipulations of incentives and conflict. Whereas incentives preferentially led to target enhancement, conflict in the previous trial preferentially led to distractor suppression. These distinct drivers of control altered sensitivity to targets and distractors early in the trial, promptly followed by reactive reconfiguration toward task-appropriate feature sensitivity. To provide a process-level account of these empirical findings, we develop a novel neural network model of evidence accumulation with attractor dynamics over feature weights that reconfigure target and distractor processing. These results provide a computational account of control reconfiguration that provides new insights into how multivariate attentional signals are optimized to achieve task goals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Normative models of decision-making that optimally transform noisy (sensory) information into categorical decisions qualitatively mismatch human behavior. Indeed, leading computational models have only achieved high empirical corroboration by adding task-specific assumptions that deviate from normative principles. In response, we offer a Bayesian approach that implicitly produces a posterior distribution of possible answers (hypotheses) in response to sensory information. But we assume that the brain has no direct access to this posterior, but can only sample hypotheses according to their posterior probabilities. Accordingly, we argue that the primary problem of normative concern in decision-making is integrating stochastic hypotheses, rather than stochastic sensory information, to make categorical decisions. This implies that human response variability arises mainly from posterior sampling rather than sensory noise. Because human hypothesis generation is serially correlated, hypothesis samples will be autocorrelated. Guided by this new problem formulation, we develop a new process, the Autocorrelated Bayesian Sampler (ABS), which grounds autocorrelated hypothesis generation in a sophisticated sampling algorithm. The ABS provides a single mechanism that qualitatively explains many empirical effects of probability judgments, estimates, confidence intervals, choice, confidence judgments, response times, and their relationships. Our analysis demonstrates the unifying power of a perspective shift in the exploration of normative models. It also exemplifies the proposal that the "Bayesian brain" operates using samples not probabilities, and that variability in human behavior may primarily reflect computational rather than sensory noise. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Decades of findings in psychology suggest that human belief is thoroughly irrational. At best, beliefs might be formed by heuristic processes that predictably lead to suboptimal outcomes. At worst, they are slaves to motivated reasoning, which allows people to come to whichever conclusions they prefer. In this article, we suggest that belief updating, narrowly construed, may be a rational process that is uniquely sensitive to evidence and cognitively impenetrable to desires or incentives. Before any updating can occur, however, a series of processes mediate between information in the world and subjectively compelling evidence. We distinguish between updating proper and processes of evidence search, acceptance, hypothesis specification, integration of relevant information, and reasoning. We review research highlighting the computational difficulty inherent to each of these problems and conclude that solutions must be heuristic and fallible. Beyond incidental failures, evidence evaluation processes-unlike updating-are penetrable to motivation and as such, may be biased by people's desires and goals. In light of this distinction, we propose a theoretical framework for integrating research on belief which divides the cognitive processes involved in belief into two distinct levels. At Level 1, updating is suggested to be approximately Bayesian and impenetrable to desires and goals. In contrast, Level 2 processes, which search for and evaluate evidence, are cognitively penetrable. In addition, we emphasize that Level 2 processes are necessarily heuristic and exhibit bounded rationality (Simon, 1956) given the difficulty of the problems they have to solve. Finally, we specify an additional set of relatively invariant characteristics, which influence how Level 2 processes are employed by making different methods of information processing available. Our framework offers a more nuanced understanding of belief, permits a granular localization of irrationality, and may help reconcile extant debates in the literature. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Orthographic processing is an open problem. Decades of visual word recognition research have fueled the development of various theoretical frameworks. Although these frameworks have had good explanatory power, various recent results cannot be satisfactorily captured in any model. In order to account for old and new phenomena alike, here I present a new theory of how the brain computes letter positions. According to PONG (which describes the Positional Ordering of N-Grams), each hemisphere of the brain comprises a set of mono- and multigram detectors. The crux is that the detectors for a given N-gram are activated to different extents in their respective hemispheres, depending on where in the visual field the N-gram is located. This differential activity allows the brain to estimate the leftness or rightness of that N-gram, whereby word activation is a function of the N-gram's identity plus its laterality relative to that of other activated N-grams. Simulations with PONG suggest that the framework effectively accounts for classic phenomena, as well as newer phenomena and cross-linguistic differences that cannot be explained by other models. I also reflect on the neurophysiological plausibility of the model and avenues for future inquiry. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The present research develops a predictive model of prejudice. For nearly a century, psychology and other fields have sought to scientifically understand and describe the causes of prejudice. Numerous theories of prejudice now exist. Yet these theories are overwhelmingly defined verbally and thus lack the ability to precisely predict when and to what extent prejudice will emerge. The abundance of theory also raises the possibility of undetected overlap between constructs theorized to cause prejudice. Predictive models enable falsification and provide a way for the field to move forward. To this end, here we present 18 studies with ∼5,000 participants in seven phases of model development. After initially identifying major theorized causes of prejudice in the literature, we used a model selection approach to winnow constructs into a parsimonious predictive model of prejudice (Phases I and II). We confirm this model in a preregistered out-of-sample test (Phase III), test variations in operationalizations and boundary conditions (Phases IV and V), and test generalizability on a U.S. representative sample, an Indian sample, and a U.K. sample (Phase VI). Finally, we consulted the predictions of experts in the field to examine how well they align with our results (Phase VII). We believe this initial predictive model is limited and bad, but by developing a model that makes highly specific predictions, drawing on the state of the art, we hope to provide a foundation from which research can build to improve science of prejudice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

We are all constantly going in and out of sync with the people we meet in our lives: significant others, incidental encounters, and strangers. Synchrony is a ubiquitous phenomenon, considered an evolution-based mechanism of survival. In recent years, technological development has made it possible to collect much data on synchrony across disciplines. The collected data show great potential to shed light on the benefits of this universal phenomenon. At the same time, mixed results emerged, stressing the need for a theory to navigate research inquiries and discoveries. It is proposed here that synchrony serves as an individual-specific mechanism for making relationships curative in all life circumstances, especially therapeutic ones-hence its special relevance for psychotherapy. A synthesis of the majority of the literature across disciplines reveals two implicit assumptions about synchrony, resulting in two separate bodies of knowledge: (a) synchrony is a trait-like signature characterizing individuals; and (b) synchrony is a state-like phenomenon that can be manipulated in the lab. It is proposed here to personalize synchrony research by integrating the two assumptions into a comprehensive theory according to which individuals have a trait-like signature for getting in sync, which determines their physical and mental health, and that this deterministic reality can be subject to state-like manipulation. Individuals can deviate from their trait-like signature. When the deviation is toward normative activation, mental health improves, and the state-like changes are defined as therapeutic. This article calls for research to investigate how trait-like signature of synchrony develops and how it can be therapeutically changed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).