Psychological review最新文献

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).

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).

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).

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).

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).

Why do some mental activities feel harder than others? The answer to this question is surprisingly controversial. Current theories propose that cognitive effort affords a computational benefit, such as instigating a switch from an activity with low reward value to a different activity with higher reward value. By contrast, in this article, I relate cognitive effort to the fact that brain neuroanatomy and neurophysiology render some neural states more energy-efficient than others. I introduce the concept of the "controllosphere," an energy-inefficient region of neural state space associated with high control, which surrounds the better known "intrinsic manifold," an energy-efficient subspace associated with low control. Integration of control-theoretic principles with classic neurocomputational models of cognitive control suggests that dorsolateral prefrontal cortex (DLPFC) implements a controller that can drive the system state into the controllosphere, anterior cingulate cortex (ACC) implements an observer that monitors changes of state of the controlled system, and cognitive effort reflects a mismatch between DLPFC and ACC energies for control and observation. On this account, cognitive effort scales with the energetic demands of the DLPFC control signal, especially when the consequences of the control are unobservable by ACC. Further, I propose that neural transitions through the controllosphere lead to a buildup of neural waste. Cognitive effort therefore prevents against neural damage by discouraging extended periods of high control. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

[Correction Notice: An Erratum for this article was reported online in Psychological Review on Mar 14 2024 (see record 2024-63968-001). Incorrect italic formatting was removed throughout the article, and an unnecessary paragraph of text was removed from the "Levels of Analysis and the Branches of Psychology: What Is Needed for a Complete Explanation of a Behavior or Cognitive System?" section. These were editorial production errors. All versions of this article have been corrected.] Levels of analysis are crucial to the progress of science. They frame the epistemological boundaries of a discipline, chart its explanatory goals, help scientists to avoid needless conflict, and highlight knowledge gaps. Two frameworks in particular, Tinbergen's four questions from biology and Marr's three levels from cognitive science, hold immense potential for psychology. This article proposes ways to integrate the two frameworks and suggests that doing so helps resolve key confusions and unnecessary conflicts in psychology. Integrating these two frameworks clarifies what "mechanism" really means, sheds light on how to test evolutionary hypotheses in psychology, and specifies what is required for a comprehensive explanation of a behavior or cognitive system. Adopting and integrating these two theoretical frameworks has the capacity to spur progress in psychology and to clarify what is needed for a comprehensive science of the mind. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

When a circular coin is rotated in depth, is there any sense in which it comes to resemble an ellipse? While this question is at the center of a rich and divided philosophical tradition (with some scholars answering affirmatively and some negatively), Morales et al. (2020, 2021) took an empirical approach, reporting 10 experiments whose results favor such perspectival similarity. Recently, Burge and Burge (2022) offered a vigorous critique of this work, objecting to its approach and conclusions on both philosophical and empirical grounds. Here, we answer these objections on both fronts. We show that Burge and Burge's critique rests on misunderstandings of Morales et al.'s claims; of the relation between the data and conclusions; and of the philosophical context in which the work appears. Specifically, Burge and Burge attribute to us a much stronger (and stranger) view than we hold, involving the introduction of "a new entity" located "in some intermediate position(s) between the distal shape and the retinal image." We do not hold this view. Indeed, once properly understood, most of Burge and Burge's objections favor Morales et al.'s claims rather than oppose them. Finally, we discuss several questions that remain unanswered, and reflect on a productive path forward on these issues of foundational scientific and philosophical interest. (PsycInfo Database Record (c) 2024 APA, all rights reserved).