Motivated by generative AI, Samiei et al. argue against classical models that treat schemas as distinct memory structures. Instead, they propose that schemas are merely a conceptual tool describing ho...

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Nature - Experiments with human volunteers and macaques show that expectations produced by probabilistic cueing of future sensory inputs shape motor circuit dynamics in order to increase the...

The ubiquity of closed-weight language models with public-facing APIs has generated interest in forensic methods, both for extracting hidden model details (e.g., parameters) and for identifying...

Multi-step planning is a challenge for LLMs. Here, the authors introduce a brain-inspired Modular Agentic Planner that decomposes planning into specialized LLM modules, improving performance across tasks and highlighting the value of cognitive neuroscience for LLM design.

A core strength of computational psychiatry is its focus on theory-driven research, in which cognitive processes are precisely quantified using computational models that formalize specific theoretical mechanisms. However, the data used in these studies often come from traditional laboratory-based cognitive tasks, which have unclear ecological validity. In this review we propose that the same theoretical frameworks and computational models can be applied to real-world data such as experience sampling, passive data, and digital-behavior data (e.g., online activity such as on social media). In turn, modeling real-world data can benefit from a theory-driven computational approach to move from purely predictive to explanatory power. We illustrate these points using emerging studies and discuss the challenges and opportunities of using real-world data in computational psychiatry.

Computational cognitive models, which formalize theories of cognition, enable researchers to quantify cognitive processes and arbitrate between competing theories by fitting models to behavioral data....

Theories of the evolution of cooperation through reciprocity explain how unrelated self-interested individuals can accomplish more together than th...

A language for mental models

Modelling biological decision-making with tiny recurrent neural networks enables more accurate predictions of animal choices than classical cognitive models and offers insights into the underlying cog...

It has been argued that social processes are relevant to belief formation and maintenance and thence to persecutory delusions – the fixed false beliefs that others intend harm. We call this the social...

Rewards play a crucial role in sculpting all motivated behavior. Traditionally, research on reinforcement learning has centered on how rewards guide learning and decision-making. Here, we examine the ...

Attractor dynamics are a hallmark of many complex systems, including the brain. Understanding how such self-organizing dynamics emerge from first principles is crucial for advancing our understanding ...

Abstract. Knowing when to trust and incorporate the advice from artificially intelligent (AI) systems is of increasing importance in the modern world. Rese

The capacity to prevent unwanted thoughts is important for cognitive function and mental health. Anderson et al. describe insights into the neural mechanisms of the inhibitory control of thought that ...

Nature Reviews Cardiology - The physiological phenomenon whereby heart rate varies in phase with breathing in vertebrates has been known as ‘respiratory sinus arrhythmia’. In this...

An intriguing aspect of the human mind is our knowledge that our perceptions may be false. Our frequent exposure to non-veridical perceptions such as those found in dreams, illusions and hallucination...

The conversational diagnostic artificial intelligence system AMIE (Articulate Medical Intelligence Explorer) has potential as a real-world tool for clinical history-taking and diagnostic dialogue, based on its performance in simulated consultations.

Recent work proposes using world models to generate controlled virtual environments in which AI agents can be tested before deployment to ensure their reliability and safety. However, accurate world m...

A general reinforcement-learning algorithm, called Dreamer, outperforms specialized expert algorithms across diverse tasks by learning a model of the environment and improving its behaviour by imagining future scenarios.

We seek to understand if there is interest in being able to attend NeurIPS in Europe, i.e. without travelling to San Diego, US. In the following, assume that it is possible to present accepted papers ...

Vacancy at School of Culture and Society - Center for Humanities Computing Aarhus, Aarhus University