Psychological review最新文献

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).

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).

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).

People use language to influence others' beliefs and actions. Yet models of communication have diverged along these lines, formalizing the speaker's objective in terms of either the listener's beliefs or actions. We argue that this divergence lies at the root of a longstanding controversy over the Gricean maxims of truthfulness and relevance. We first bridge the divide by introducing a speaker model which considers both the listener's beliefs (epistemic utility) and their actions (decision-theoretic utility). We show that formalizing truthfulness as an epistemic utility and relevance as a decision-theoretic utility reconciles the tension between them, readily explaining puzzles such as context-dependent standards of truthfulness. We then test a set of novel predictions generated by our model. We introduce a new signaling game which decouples utterances' truthfulness and relevance, then use it to conduct a pair of experiments. Our first experiment demonstrates that participants jointly maximize epistemic and decision-theoretic utility, rather than either alone. Our second experiment shows that when the two conflict, participants make a graded tradeoff rather than prioritizing one over the other. These results demonstrate that human communication cannot be reduced to influencing beliefs or actions alone. Taken together, our work provides a new foundation for grounding rational communication not only in what we believe, but in what those beliefs lead us to do. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Inhibition can be defined as a phenomenon in which an agent prevents or suppresses a behavioral state that would otherwise occur. Associative learning studies have extensively examined how experiences shape the acquisition of inhibitory behavioral tendencies across many species and situations. Associative inhibitory phenomena can be studied at various levels of analysis. One could focus on the trajectory of behavioral change involved in learning from negative statistical associations between discrete events (inhibitory learning). Alternatively, one could be interested in the effects of accumulated experience with those negative associations (conditioned inhibition). One could rather be interested in how organisms implement what they learn through experiences involving negative associations (response inhibition). Yet, one could inquire into how the capacity of learning negative associations and performing accordingly varies between individuals and along time for the same individual (inhibitory control). This article presents a tentative taxonomy addressing different levels of analysis of associative inhibitory phenomena by using different terms for each. In addition, recent evidence and certain unresolved issues at each level are thoroughly scrutinized and contrasted with prior findings. The empirical and theoretical advances made by modeling inhibition as an associative learning phenomenon have provided scaffolds for the current knowledge and emerging accounts of the topic. Some of those emerging accounts have the potential to bridge different levels of analysis and foster "cross-pollination" of ideas among broad fields beyond associative learning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Hunger refers to (1) the meaning of certain bodily sensations; (2) a mental state of anticipation that food will be good to eat; and (3) an organizing principal, which prioritizes feeding. Definitions (1) and (2) are the focus here, as (3) can be considered their consequent. Definition (1) has been linked to energy-depletion models of hunger, but these are no longer thought viable. Definition (2) has been linked to learning and memory (L&M) models of hunger, but these apply just to palatable foods. Nonetheless, L&M probably forms the basis for hunger generally, as damage to declarative memory can eradicate the experience of hunger. Currently, there is no general L&M model of hunger, little understanding of how physiology intersects with a L&M approach, and no understanding of how Definitions (1) and (2) are related. We present a new L&M model of human hunger. People learn associations between internal (e.g., tummy rumbles) and external cues (e.g., brand names) and food. These associations can be to specific foods (episodic memories) or food-related categories (semantic memories). When a cue is encountered, it may lead to food-related memory retrieval. If retrieval occurs, the memory's affective content allows one to know if food will be good to eat now-hunger-a cognitive operation learned in childhood. These memory processes are acutely inhibited during satiety, and chronically by multiple biological parameters, allowing physiology to modulate hunger. Implications are considered for the process of making hunger judgments, thirst, the cephalic phase response, and motivational and lay theories of hunger. (PsycInfo Database Record (c) 2024 APA, all rights reserved).