Multiple timescales of learning indicated by changes in evidence-accumulation processes during perceptual decision-making.

Evidence accumulation models have enabled strong advances in our understanding of decision-making, yet their application to examining learning has not been common. Using data from participants completing a dynamic random dot-motion direction discrimination task across four days, we characterized alterations in two components of perceptual decision-making (Drift Diffusion Model drift rate and response boundary). Continuous-time learning models were applied to characterize trajectories of performance change, with different models allowing for varying dynamics. The best-fitting model included drift rate changing as a continuous, exponential function of cumulative trial number. In contrast, response boundary changed within each daily session, but in an independent manner across daily sessions. Our results highlight two different processes underlying the pattern of behavior observed across the entire learning trajectory, one involving a continuous tuning of perceptual sensitivity, and another more variable process describing participants' threshold of when enough evidence is present to act.

Rumination Derails Reinforcement Learning with Possible Implications for Ineffective Behavior.

How does rumination affect reinforcement learning-the ubiquitous process by which we adjust behavior after error in order to behave more effectively in the future? In a within-subject design (n=49), we tested whether experimentally manipulated rumination disrupts reinforcement learning in a multidimensional learning task previously shown to rely on selective attention. Rumination impaired performance, yet unexpectedly this impairment could not be attributed to decreased attentional breadth (quantified using a "decay" parameter in a computational model). Instead, trait rumination (between subjects) was associated with higher decay rates (implying narrower attention), yet not with impaired performance. Our task-performance results accord with the possibility that state rumination promotes stress-generating behavior in part by disrupting reinforcement learning. The trait-rumination finding accords with the predictions of a prominent model of trait rumination (the attentional-scope model). More work is needed to understand the specific mechanisms by which state rumination disrupts reinforcement learning.

Conceptual knowledge shapes visual working memory for complex visual information.

Human visual working memory (VWM) is a memory store people use to maintain the visual features of objects and scenes. Although it is obvious that bottom-up information influences VWM, the extent to which top-down conceptual information influences VWM is largely unknown. We report an experiment in which groups of participants were trained in one of two different categories of geologic faults (left/right lateral, or normal/reverse faults), or received no category training. Following training, participants performed a visual change detection task in which category knowledge was irrelevant to the task. Participants were more likely to detect a change in geologic scenes when the changes crossed a trained categorical distinction (e.g., the left/right lateral fault boundary), compared to within-category changes. In addition, participants trained to distinguish left/right lateral faults were more likely to detect changes when the scenes were mirror images along the left/right dimension. Similarly, participants trained to distinguish normal/reverse faults were more likely to detect changes when scenes were mirror images along the normal/reverse dimension. Our results provide direct empirical evidence that conceptual knowledge influences VWM performance for complex visual information. An implication of our results is that cognitive scientists may need to reconceptualize VWM so that it is closer to "conceptual short-term memory".

Emotion perception in habitual players of action video games.

Action video game players (AVGPs) display superior performance in various aspects of cognition, especially in perception and top-down attention. The existing literature has examined these performance almost exclusively with stimuli and tasks devoid of any emotional content. Thus, whether the superior performance documented in the cognitive domain extend to the emotional domain remains unknown. We present 2 cross-sectional studies contrasting AVGPs and nonvideo game players (NVGPs) in their ability to perceive facial emotions. Under an enhanced perception account, AVGPs should outperform NVGPs when processing facial emotion. Yet, alternative accounts exist. For instance, under some social accounts, exposure to action video games, which often contain violence, may lower sensitivity for empathy-related expressions such as sadness, happiness, and pain while increasing sensitivity to aggression signals. Finally, under the view that AVGPs excel at learning new tasks (in contrast to the view that they are immediately better at all new tasks), the use of stimuli that participants are already experts at predicts little to no group differences. Study 1 uses drift-diffusion modeling and establishes that AVGPs are comparable to NVGPs in every decision-making stage mediating the discrimination of facial emotions, despite showing group difference in aggressive behavior. Study 2 uses the reverse inference technique to assess the mental representation of facial emotion expressions, and again documents no group differences. These results indicate that the perceptual benefits associated with action video game play do not extend to overlearned stimuli such as facial emotion, and rather indicate equivalent facial emotion skills in AVGPs and NVGPs. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The importance of constraints on constraints.

The "resource-rational" approach is ambitious and worthwhile. A shortcoming of the proposed approach is that it fails to constrain what counts as a constraint. As a result, constraints used in different cognitive domains often have nothing in common. We describe an alternative framework that satisfies many of the desiderata of the resource-rational approach, but in a more disciplined manner.

Adaptive allocation of human visual working memory capacity during statistical and categorical learning.

Human brains are finite, and thus have bounded capacity. An efficient strategy for a capacity-limited agent is to continuously adapt by dynamically reallocating capacity in a task-dependent manner. Here we study this strategy in the context of visual working memory (VWM). People use their VWM stores to remember visual information over seconds or minutes. However, their memory performances are often error-prone, presumably due to VWM capacity limits. We hypothesize that people attempt to be flexible and robust by strategically reallocating their limited VWM capacity based on two factors: (a) the statistical regularities (e.g., stimulus feature means and variances) of the to-be-remembered items, and (b) the requirements of the task that they are attempting to perform. The latter specifies, for example, which types of errors are costly versus irrelevant for task performance. These hypotheses are formalized within a normative computational modeling framework based on rate-distortion theory, an extension of conventional Bayesian approaches that uses information theory to study rate-limited (or capacity-limited) processes. Using images of plants that are naturalistic and precisely controlled, we carried out two sets of experiments. Experiment 1 found that when a stimulus dimension (the widths of plants' leaves) was assigned a distribution, subjects adapted their VWM performances based on this distribution. Experiment 2 found that when one stimulus dimension (e.g., leaf width) was relevant for distinguishing plant categories but another dimension (leaf angle) was irrelevant, subjects' responses in a memory task became relatively more sensitive to the relevant stimulus dimension. Together, these results illustrate the task-dependent robustness of VWM, thereby highlighting the dependence of memory on learning.

Efficient coding explains the universal law of generalization in human perception.

Perceptual generalization and discrimination are fundamental cognitive abilities. For example, if a bird eats a poisonous butterfly, it will learn to avoid preying on that species again by generalizing its past experience to new perceptual stimuli. In cognitive science, the "universal law of generalization" seeks to explain this ability and states that generalization between stimuli will follow an exponential function of their distance in "psychological space." Here, I challenge existing theoretical explanations for the universal law and offer an alternative account based on the principle of efficient coding. I show that the universal law emerges inevitably from any information processing system (whether biological or artificial) that minimizes the cost of perceptual error subject to constraints on the ability to process or transmit information.

Rate-distortion theory and human perception.

The fundamental goal of perception is to aid in the achievement of behavioral objectives. This requires extracting and communicating useful information from noisy and uncertain sensory signals. At the same time, given the complexity of sensory information and the limitations of biological information processing, it is necessary that some information must be lost or discarded in the act of perception. Under these circumstances, what constitutes an 'optimal' perceptual system? This paper describes the mathematical framework of rate-distortion theory as the optimal solution to the problem of minimizing the costs of perceptual error subject to strong constraints on the ability to communicate or transmit information. Rate-distortion theory offers a general and principled theoretical framework for developing computational-level models of human perception (Marr, 1982). Models developed in this framework are capable of producing quantitatively precise explanations for human perceptual performance, while yielding new insights regarding the nature and goals of perception. This paper demonstrates the application of rate-distortion theory to two benchmark domains where capacity limits are especially salient in human perception: discrete categorization of stimuli (also known as absolute identification) and visual working memory. A software package written for the R statistical programming language is described that aids in the development of models based on rate-distortion theory.

Decision theory, motor planning, and visual memory: deciding where to reach when memory errors are costly.

Limitations in visual working memory (VWM) have been extensively studied in psychophysical tasks, but not well understood in terms of how these memory limits translate to performance in more natural domains. For example, in reaching to grasp an object based on a spatial memory representation, overshooting the intended target may be more costly than undershooting, such as when reaching for a cup of hot coffee. The current body of literature lacks a detailed account of how the costs or consequences of memory error influence what we encode in visual memory and how we act on the basis of remembered information. Here, we study how externally imposed monetary costs influence behavior in a motor decision task that involves reach planning based on recalled information from VWM. We approach this from a decision theoretic perspective, viewing decisions of where to aim in relation to the utility of their outcomes given the uncertainty of memory representations. Our results indicate that subjects accounted for the uncertainty in their visual memory, showing a significant difference in their reach planning when monetary costs were imposed for memory errors. However, our findings indicate that subjects memory representations per se were not biased by the imposed costs, but rather subjects adopted a near-optimal post-mnemonic decision strategy in their motor planning.

The cost of misremembering: Inferring the loss function in visual working memory.

Visual working memory (VWM) is a highly limited storage system. A basic consequence of this fact is that visual memories cannot perfectly encode or represent the veridical structure of the world. However, in natural tasks, some memory errors might be more costly than others. This raises the intriguing possibility that the nature of memory error reflects the costs of committing different kinds of errors. Many existing theories assume that visual memories are noise-corrupted versions of afferent perceptual signals. However, this additive noise assumption oversimplifies the problem. Implicit in the behavioral phenomena of visual working memory is the concept of a loss function: a mathematical entity that describes the relative cost to the organism of making different types of memory errors. An optimally efficient memory system is one that minimizes the expected loss according to a particular loss function, while subject to a constraint on memory capacity. This paper describes a novel theoretical framework for characterizing visual working memory in terms of its implicit loss function. Using inverse decision theory, the empirical loss function is estimated from the results of a standard delayed recall visual memory experiment. These results are compared to the predicted behavior of a visual working memory system that is optimally efficient for a previously identified natural task, gaze correction following saccadic error. Finally, the approach is compared to alternative models of visual working memory, and shown to offer a superior account of the empirical data across a range of experimental datasets.

Melioration as rational choice: sequential decision making in uncertain environments.

Melioration-defined as choosing a lesser, local gain over a greater longer term gain-is a behavioral tendency that people and pigeons share. As such, the empirical occurrence of meliorating behavior has frequently been interpreted as evidence that the mechanisms of human choice violate the norms of economic rationality. In some environments, the relationship between actions and outcomes is known. In this case, the rationality of choice behavior can be evaluated in terms of how successfully it maximizes utility given knowledge of the environmental contingencies. In most complex environments, however, the relationship between actions and future outcomes is uncertain and must be learned from experience. When the difficulty of this learning challenge is taken into account, it is not evident that melioration represents suboptimal choice behavior. In the present article, we examine human performance in a sequential decision-making experiment that is known to induce meliorating behavior. In keeping with previous results using this paradigm, we find that the majority of participants in the experiment fail to adopt the optimal decision strategy and instead demonstrate a significant bias toward melioration. To explore the origins of this behavior, we develop a rational analysis (Anderson, 1990) of the learning problem facing individuals in uncertain decision environments. Our analysis demonstrates that an unbiased learner would adopt melioration as the optimal response strategy for maximizing long-term gain. We suggest that many documented cases of melioration can be reinterpreted not as irrational choice but rather as globally optimal choice under uncertainty.

An ideal observer analysis of visual working memory.

Limits in visual working memory (VWM) strongly constrain human performance across many tasks. However, the nature of these limits is not well understood. In this article we develop an ideal observer analysis of human VWM by deriving the expected behavior of an optimally performing but limited-capacity memory system. This analysis is framed around rate-distortion theory, a branch of information theory that provides optimal bounds on the accuracy of information transmission subject to a fixed information capacity. The result of the ideal observer analysis is a theoretical framework that provides a task-independent and quantitative definition of visual memory capacity and yields novel predictions regarding human performance. These predictions are subsequently evaluated and confirmed in 2 empirical studies. Further, the framework is general enough to allow the specification and testing of alternative models of visual memory (e.g., how capacity is distributed across multiple items). We demonstrate that a simple model developed on the basis of the ideal observer analysis-one that allows variability in the number of stored memory representations but does not assume the presence of a fixed item limit-provides an excellent account of the empirical data and further offers a principled reinterpretation of existing models of VWM.

Adaptive allocation of vision under competing task demands.

Human behavior in natural tasks consists of an intricately coordinated dance of cognitive, perceptual, and motor activities. Although much research has progressed in understanding the nature of cognitive, perceptual, or motor processing in isolation or in highly constrained settings, few studies have sought to examine how these systems are coordinated in the context of executing complex behavior. Previous research has suggested that, in the course of visually guided reaching movements, the eye and hand are yoked, or linked in a nonadaptive manner. In this work, we report an experiment that manipulated the demands that a task placed on the motor and visual systems, and then examined in detail the resulting changes in visuomotor coordination. We develop an ideal actor model that predicts the optimal coordination of vision and motor control in our task. On the basis of the predictions of our model, we demonstrate that human performance in our experiment reflects an adaptive response to the varying costs imposed by our experimental manipulations. Our results stand in contrast to previous theories that have assumed a fixed control mechanism for coordinating vision and motor control in reaching behavior.

The soft constraints hypothesis: a rational analysis approach to resource allocation for interactive behavior.

Soft constraints hypothesis (SCH) is a rational analysis approach that holds that the mixture of perceptual-motor and cognitive resources allocated for interactive behavior is adjusted based on temporal cost-benefit tradeoffs. Alternative approaches maintain that cognitive resources are in some sense protected or conserved in that greater amounts of perceptual-motor effort will be expended to conserve lesser amounts of cognitive effort. One alternative, the minimum memory hypothesis (MMH), holds that people favor strategies that minimize the use of memory. SCH is compared with MMH across 3 experiments and with predictions of an Ideal Performer Model that uses ACT-R's memory system in a reinforcement learning approach that maximizes expected utility by minimizing time. Model and data support the SCH view of resource allocation; at the under 1000-ms level of analysis, mixtures of cognitive and perceptual-motor resources are adjusted based on their cost-benefit tradeoffs for interactive behavior.