Large language models surpass human experts in predicting neuroscience results.

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts.

A jsPsych touchscreen extension for behavioral research on touch-enabled interfaces.

Online experiments are increasingly gaining traction in the behavioral sciences. Despite this, behavioral researchers have largely continued to use keyboards as the primary input devices for such online studies, overlooking the ubiquity of touchscreens in everyday use. This paper presents an open-source touchscreen extension for jsPsych, a JavaScript framework designed for conducting online experiments.

Explore your experimental designs and theories before you exploit them!

In many areas of the social and behavioral sciences, the nature of the experiments and theories that best capture the underlying constructs are themselves areas of active inquiry. Integrative experiment design risks being prematurely exploitative, hindering exploration of experimental paradigms and of diverse theoretical accounts for target phenomena.

Task performance errors and rewards affect voluntary task choices.

Humans are remarkably flexible in adapting their behavior to current demands. It has been suggested that the decision which of multiple tasks to perform is based on a variety of factors pertaining to the rewards associated with each task as well as task performance (e.g.

Control adjustment costs limit goal flexibility: Empirical evidence and a computational account.

A cornerstone of human intelligence is the ability to flexibly adjust our cognition and behavior as our goals change. For instance, achieving some goals requires efficiency, while others require caution. Adapting to these changing goals require corresponding adjustments in cognitive control (e.

A Benchmark for Compositional Visual Reasoning.

A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks. Yet, there remains a major gap between humans and AI systems in terms of the sample efficiency with which they learn new visual reasoning tasks.

Assignment strategies modulate students' academic performance in an online learning environment during the first and second COVID-19 related school closures.

A growing number of studies seek to evaluate the impact of school closures during the ongoing COVID-19 pandemic. While most studies reported severe learning losses in students, some studies found positive effects of school closures on academic performance. However, it is still unclear which factors contribute to the differential effects observed in these studies.

Pushing the Bounds of Bounded Optimality and Rationality.

All forms of cognition, whether natural or artificial, are subject to constraints of their computing architecture. This assumption forms the tenet of virtually all general theories of cognition, including those deriving from bounded optimality and bounded rationality. In this letter, we highlight an unresolved puzzle related to this premise: what are these constraints, and why are cognitive architectures subject to cognitive constraints in the first place? First, we lay out some pieces along the puzzle edge, such as computational tradeoffs inherent to neural architectures that give rise to rational bounds of cognition.

An integrative effort: Bridging motivational intensity theory and recent neurocomputational and neuronal models of effort and control allocation.

An increasing number of cognitive, neurobiological, and computational models have been proposed in the last decade, seeking to explain how humans allocate physical or cognitive effort. Most models share conceptual similarities with motivational intensity theory (MIT), an influential classic psychological theory of motivation. Yet, little effort has been made to integrate such models, which remain confined within the explanatory level for which they were developed, that is, psychological, computational, neurobiological, and neuronal.

SweetPea: A standard language for factorial experimental design.

Experimental design is a key ingredient of reproducible empirical research. Yet, given the increasing complexity of experimental designs, researchers often struggle to implement ones that allow them to measure their variables of interest without confounds. SweetPea ( https://sweetpea-org.

Academic performance of K-12 students in an online-learning environment for mathematics increased during the shutdown of schools in wake of the COVID-19 pandemic.

The shutdown of schools in response to the rapid spread of COVID-19 poses risks to the education of young children, including a widening education gap. In the present article, we investigate how school closures in 2020 influenced the performance of German students in a curriculum-based online learning software for mathematics. We analyzed data from more than 2,500 K-12 students who computed over 124,000 mathematical problem sets before and during the shutdown, and found that students' performance increased during the shutdown of schools in 2020 relative to the year before.

Rationalizing constraints on the capacity for cognitive control.

Humans are remarkably limited in: (i) how many control-dependent tasks they can execute simultaneously, and (ii) how intensely they can focus on a single task. These limitations are universal assumptions of most theories of cognition. Yet, a rationale for why humans are subject to these constraints remains elusive.

Meta-control: From psychology to computational neuroscience.

Research in the past decades shed light on the different mechanisms that underlie our capacity for cognitive control. However, the meta-level processes that regulate cognitive control itself remain poorly understood. Following the terminology from artificial intelligence, meta-control can be defined as a collection of mechanisms that (a) monitor the progress of controlled processing and (b) regulate the underlying control parameters in the service of current task goals and in response to internal or external constraints.

Learning to Overexert Cognitive Control in a Stroop Task.

How do people learn when to allocate how much cognitive control to which task? According to the Learned Value of Control (LVOC) model, people learn to predict the value of alternative control allocations from features of a situation. This suggests that people may generalize the value of control learned in one situation to others with shared features, even when demands for control are different. This makes the intriguing prediction that what a person learned in one setting could cause them to misestimate the need for, and potentially overexert, control in another setting, even if this harms their performance.

A computational perspective on the roles of affect in cognitive control.

Previous work has demonstrated that cognitive control can be influenced by affect, both when it is tied to the anticipated outcomes for cognitive performance (integral affect) and when affect is induced independently of performance (incidental affect). However, the mechanisms through which such interactions occur remain debated, in part because they have yet to be formalized in a way that allows experimenters to test quantitative predictions of a putative mechanism. To generate such predictions, we leveraged a recent model that determines cognitive control allocation by weighing potential costs and benefits in order to determine the overall Expected Value of Control (EVC).

Motivation and cognitive control in depression.

Depression is linked to deficits in cognitive control and a host of other cognitive impairments arise as a consequence of these deficits. Despite of their important role in depression, there are no mechanistic models of cognitive control deficits in depression. In this paper we propose how these deficits can emerge from the interaction between motivational and cognitive processes.

Dissociable neural mechanisms track evidence accumulation for selection of attention versus action.

Decision-making is typically studied as a sequential process from the selection of what to attend (e.g., between possible tasks, stimuli, or stimulus attributes) to which actions to take based on the attended information.

Rational metareasoning and the plasticity of cognitive control.

The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert.

Toward a Rational and Mechanistic Account of Mental Effort.

In spite of its familiar phenomenology, the mechanistic basis for mental effort remains poorly understood. Although most researchers agree that mental effort is aversive and stems from limitations in our capacity to exercise cognitive control, it is unclear what gives rise to those limitations and why they result in an experience of control as costly. The presence of these control costs also raises further questions regarding how best to allocate mental effort to minimize those costs and maximize the attendant benefits.