Psychological review最新文献

Previous research suggests that the MINERVA2 model can capture basic Deese/Roediger/McDermott (DRM) false recognition findings with either randomized representations or distributional semantic representations. In the current article, we extended this line of research by showing that MINERVA2 can accommodate not only basic DRM recognition findings but also the effects of various theory-driven manipulations. Importantly, we incorporated two assumptions of fuzzy-trace theory into MINERVA2: the verbatim-gist distinction and hierarchies of gist. To implement the verbatim-gist distinction, we represented local gist traces with distributional semantic vectors and verbatim traces with holographic word-form vectors. With separate representations incorporated, MINERVA2 successfully simulated a wide range of empirical effects in the DRM illusion, as well as remember/know and source judgments. To incorporate hierarchies of gist into the framework, we added an assumption that an item's storage quality depends on its semantic similarity to the preceding item. This accommodated the effect of global gist beyond that of local gist and solved the problem of storage independence in multitrace models of episodic memory. Our findings provided extensive evidence that MINERVA2 is a viable candidate for scalable modeling of the DRM illusion and strengthened the connection between computational modeling and substantive theories of false memory. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Our premodern ancestors had perceptual, motoric, and cognitive functional domains that were modularly encapsulated. Some of these came to interact through a new type of cross-modular binding in our species. This allowed previously domain-dedicated, encapsulated motoric and sensory operators to operate on operands for which they had not evolved. Such operators could at times operate nonvolitionally, while at other times they could be governed volitionally. In particular, motoric operations that derive from the same circuits that compute hand motions for object manipulation could now be retooled for virtual manipulation in a mental workspace in the absence of any physical hand or other effector movements. I hypothesize that the creativity of human imagination and mental models is rooted in premotor simulation of sequential manipulations of objects and symbols in the mental workspace, in analogy with the premotor theory of attention, which argues that attention evolved from "internalized" eye movement circuitry. Overall, operator "disencapsulation" led to a bifurcation of consciousness in humans: a concrete form centered on perception of the body in the physical world and an abstract form focused on explanatory mental models. One of the consequences of these new abilities was the advent of psychotic disorders that do not exist in species possessed solely of the concrete type of consciousness. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The efficient representation of visual information is essential for learning and decision making due to the complexity and uncertainty of the world, as well as inherent constraints on the capacity of cognitive systems. We hypothesize that biological agents learn to efficiently represent visual information in a manner that balances performance across multiple potentially competing objectives. In this article, we examine two such objectives: storing information in a manner that supports accurate recollection (maximizing veridicality) and in a manner that facilitates utility-based decision making (maximizing behavioral utility). That these two objectives may be in conflict is not immediately obvious. Our hypothesis suggests that neither behavior nor representation formation can be fully understood by studying either in isolation, with information processing constraints exerting an overarching influence. Alongside this hypothesis we develop a computational model of representation formation and behavior motivated by recent methods in machine learning and neuroscience. The resulting model explains both the beneficial aspects of human visual learning, such as fast acquisition and high generalization, as well as the biases that result from information constraints. To test this model, we developed two experimental paradigms, in decision making and learning, to evaluate how well the model's predictions match human behavior. A key feature of the proposed model is that it predicts the occurrence of commonly found biases in human decision making, resulting from the desire to form efficient representations of visual information that are useful for behavioral goals in learning and decision making and optimized under an information processing constraint. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Cognitive scientists and neuroscientists are increasingly deploying computational models to develop testable theories of psychological functions and make quantitative predictions about cognition, brain activity, and behavior. Computational models are used to explain target phenomena such as experimental effects, individual, and/or population differences. They do so by relating these phenomena to the underlying components of the model that map onto distinct cognitive mechanisms. These components make up a "cognitive state space," where different positions correspond to different cognitive states that produce variation in behavior. We examine the rationale and practice of such model-based inferences and argue that model-based explanations typically miss a key ingredient: They fail to explain why and how agents occupy specific positions in this space. A critical insight is that the agent's position in the state space is not fixed, but that the behavior they produce is the result of a trajectory. Therefore, we discuss (a) the constraints that limit movement in the state space; (b) the reasons for moving around at all (i.e., agents' objectives); and (c) the information and cognitive mechanisms that guide these movements. We review existing research practices, from experimental design to the model-based analysis of data, and through simulations we demonstrate some of the inferential pitfalls that arise when we ignore these dynamics. By bringing the agent's perspective into sharp focus, we stand to gain better and more complete explanations of the variation in cognition and behavior over time, between different environmental conditions, and between different populations or individuals. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

One of the most important challenges in decision theory has been how to reconcile the normative expectations from Bayesian theory with the apparent fallacies that are common in probabilistic reasoning. Recently, Bayesian models have been driven by the insight that apparent fallacies are due to sampling errors or biases in estimating (Bayesian) probabilities. An alternative way to explain apparent fallacies is by invoking different probability rules, specifically the probability rules from quantum theory. Arguably, quantum cognitive models offer a more unified explanation for a large body of findings, problematic from a baseline classical perspective. This work addresses two major corresponding theoretical challenges: first, a framework is needed which incorporates both Bayesian and quantum influences, recognizing the fact that there is evidence for both in human behavior. Second, there is empirical evidence which goes beyond any current Bayesian and quantum model. We develop a model for probabilistic reasoning, seamlessly integrating both Bayesian and quantum models of reasoning and augmented by a sequential sampling process, which maps subjective probabilistic estimates to observable responses. Our model, called the Quantum Sequential Sampler, is compared to the currently leading Bayesian model, the Bayesian Sampler (J. Zhu et al., 2020) using a new experiment, producing one of the largest data sets in probabilistic reasoning to this day. The Quantum Sequential Sampler embodies several new components, which we argue offer a more theoretically accurate approach to probabilistic reasoning. Moreover, our empirical tests revealed a new, surprising systematic overestimation of probabilities. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Time is a central dimension against which perception, action, and cognition play out. From anticipating when future events will happen to recalling how long ago previous events occurred, humans and animals are exquisitely sensitive to temporal structure. Empirical evidence seems to suggest that estimating time prospectively (i.e., in passing) is qualitatively different from estimating time in retrospect (i.e., after the event is over). Indeed, computational models that attempt to explain both prospective and retrospective timing assume a fundamental separation of their underlying processes. We, in contrast, propose a new neurocomputational model of timing, the unified temporal coding (UTC) model that unifies prospective and retrospective timing through common principles. The UTC model assumes that both stimulus and timing information are represented inside the same rolling window of input history. As a consequence, the UTC model explains a wide range of phenomena typically covered by specialized models, such as conformity to and violations of the scalar property, one-shot learning of intervals, neural responses underlying timing, timing behavior under normal and distracting conditions, common capacity limits in timing and working memory, and how timing depends on attention. Strikingly, by assuming that prospective and retrospective timing rely on the same principles and are implemented in the same neural network, a simple attentional gain mechanism can resolve the apparently paradoxical effect of cognitive load on prospective and retrospective timing. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

We examined how elements are integrated into larger units in working memory (WM). Four contrasting conceptual models exist with regard to this question: (a) a unitization model, in which there is a single integrated representation which is retrieved in an all-or-none fashion; (b) a unitization-with-element-failure model, in which a single integrated representation is retrieved as a whole, but access to its elements can still fail individually; (c) a pairwise-binding model, in which elements of a unit are represented separately and are bound together in pairs; (d) a hybrid model that includes an integrated representation as well as pairwise bindings between element representations. We developed four multinomial process tree models to test these theories. In three experiments, participants memorized multiple units which were random combinations of three elements. They were given one element as a cue and prompted to report the other two elements. The model-comparison analysis revealed that the hybrid model provides the best quantitative fit to the data. We conclude that multielement units are represented on two levels, as an integrated unit retrieved in an all-or-none manner, and in addition through pairwise bindings between their elements. Moreover, the assumption that bindings of nonspatial elements are mediated through their shared spatial location-a special case of the pairwise-binding model-was not supported by the data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

A longstanding focus in the causal learning literature has been on inferring causal relations from contingencies, where these abstract away from time by collating independent instances or by aggregating over regularly demarcated trials. In contrast, individual causal learners encounter events in their daily lives that occur in a continuous temporal flow with no such demarcation. Consequently, the process of learning causal relationships in naturalistic environments is comparatively less understood. In this article, we lay out a rational framework that foregrounds the role of time in causal learning. We work within the Bayesian rational analysis tradition, starting by considering how causal relations induce dependence between events in continuous time and how this can be modeled by stochastic processes from the Poisson-Gamma distribution family. We derive the qualitative signatures of causal influence and the general computations needed to infer structure from temporal patterns. We show that this rational account can parsimoniously explain the human preference for causal models that invoke shorter, more reliable, and more predictable causal influences. Furthermore, we show this provides a unifying explanation for human judgments across a wide variety of tasks in the reanalysis of seven experimental data sets. We anticipate the framework will help researchers better understand the many manifestations of continuous-time causal learning across human cognition and the tasks that probe it, from explicit causal structure induction settings to implicit associative or reinforcement learning settings. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

A key role for attention is to continually focus visual processing to satisfy our goals. How does this work in computational terms? Here we introduce adaptive computation-a new computational mechanism of human attention that bridges the momentary application of perceptual computations with their impact on decision outcomes. Adaptive computation is a dynamic algorithm that rations perceptual computations across objects on-the-fly, enabled by a novel and general formulation of task relevance. We evaluate adaptive computation in a case study of multiple object tracking (MOT)-a paradigmatic example of selection as a dynamic process, where observers track a set of target objects moving amid visually identical distractors. Adaptive computation explains the attentional dynamics of object selection with unprecedented depth. It not only recapitulates several classic features of MOT (e.g., trial-level tracking accuracy and localization error of targets), but also captures properties that have not previously been measured or modeled-including both the subsecond patterns of attentional deployment between objects, and the resulting sense of subjective effort. Critically, this approach captures such data within a framework that is in-principle domain-general, and, unlike past models, without using any MOT-specific heuristic components. Beyond this case study, we also look to the future, discussing how adaptive computation may apply more generally, providing a new type of mechanistic model for the dynamic operation of many forms of visual attention. (PsycInfo Database Record (c) 2025 APA, all rights reserved).