The Center for Open Science (COS) has officially established an independent subsidiary organization in Brussels, Belgium. This presence gives COS flexibility to engage with European institutions, organizations, funders, and communities.

Cognitive flexibility allows us to adjust behavior when environmental rules change and to recombine prior knowledge in novel situations. However, the …

Itamar Ben-Gvir claims proposal for illegal forced displacement of 2 million people is ‘realistic’ and ‘concrete’

People are effort-averse in most situations but can exert effort for little reward when progressing towards goals. Here, the authors show that under deadline pressure people become effort-seeking whil...

Behaviour of human participants in a prey-pursuit task reflects dynamic blending of goal-specific control policies, with hippocampus estimating latent states, anterior cingulate cortex orchestrating p...

Response inhibition is a key control function that allows humans to perform safe, goal-directed behaviors. The dominant behavioral-computational model holds that response inhibition fails when the inh...

Levodopa restores the balance between exploration and exploitation in Parkinson’s disease.

Humans adapt learning rates on both a fast and a slow time scale, and the central orbitofrontal cortex may represent the slow time scale.

Fully funded PostDoc Position (m/f/d) for 4 years in Cognitive and Computational Neuroscience (Area: Functions of the Perirhinal Cortex) - Doeller Lab | Karriereportal Max-Planck-Institut für Kogniti...

Memory, Attention, & Cognitive Control Lab at Sabancı University - Eren Günseli. We study how memory and attention interact, how humans control what to remember, what to forget, what to attend to, and...

Extensive evidence suggests that cognitive performance is typically enhanced under increased motivation, often in the form of reward anticipation. Whi…

Two hallmarks of biological computation are its flexibility and efficiency. These features are often attributed to cognitive control processes that balance external utility against computational cost. However, how the brain could implement such adaptive control remains unknown. Here, we provide one possible answer by combining the computational theory of rational meta-reasoning with a meta-learning algorithm recently proposed as a model of prefrontal cortex. This yields a recurrent neural network model that learns to select computations. In simple choice tasks, the model approximates the algorithms and representations of optimal symbolic models and reproduces neural dynamics observed in macaque orbitofrontal cortex. In multi-step planning tasks, the model replicates key behavioral signatures of human planning strategies and captures human neural dynamics associated with step-by-step mental simulation. Our framework unifies meta-reasoning and meta-learning by showing that learning to reason can be understood as learning to learn from information generated by one’s own cognitive operations, providing a mechanistic account of how adaptive control of thought can be implemented in neural systems. ### Competing Interest Statement The authors have declared no competing interest.

This study presents a unified computational framework of meta-control that explains how people regulate cognitive control across different timescales and contexts, accounting for both rapid, short-liv...

The neuroscience of planning has long been analogized to search algorithms in artificial intelligence (AI), which simulate future actions to guide immediate choices. We argue that advances in both neu...

We are looking for a motivated PhD researcher to study the behavioral and neural dynamics between cognitive control and multitasking in young and aging populations.

We, the undersigned academics and researchers from around the world, express our profound concern over recent military strikes on Iran, the retaliatory responses, and the reported impact on civilian l...

The CAMP Lab at the University of Iowa uses computational, neuroimaging, pharmacological, and neuromodulation approaches to understand motivation, affect, and decision-making — and how these processes...

Analyzing more than 100k human ratings, we show that ambiguity resolution relies on high-level visual features. After disambiguation, the visual system shifts from top-down processing to bottom-up mat...

Jobs@UIOWA: The official place to search and apply for jobs at The University of Iowa.

Bruijns et al. present a modeling tool that enables the tracking of learning dynamics across subjects to reveal how behaviors emerge and adapt. Applying the tool to a decision-making task in mice unco...

The Reward Positivity (RewP) is an electroencephalogram (EEG) feature that emerges following performance feedback and is commonly understood to index both positive and negative reward-prediction error (RPE+ and RPE−, respectively) signals. In contrast to this dominant perspective, we argue that the RewP is an independent EEG feature that selectively responds to positive RPE and is superimposed on a common background signal. We further propose that the RewP signals a goal prediction error: it is elicited by abstract signals instead of by hedonic 'rewards'. This goal prediction error appears to be produced by a critic-like architecture that is associated with the actor–critic framework in reinforcement learning. This perspective emphasizes the role of the RewP in goal attainment and cognitive control as opposed to being a simple indicator of reward receipt.

Decision-making is a central function of adaptive behaviour in biological agents. However, strategies for adaptive decision-making can vary substantially across species. Here, we aim to extend the co...