Stefan J. Kiebel

The paper investigates how task-specific preferences influence forward planning in sequential decision-making, finding that preferences guide decisions even when forward planning is not used.

This study finds that individuals with alcohol and tobacco use disorders show reduced planning depth and stronger probability discounting in decision-making tasks compared to healthy controls.

The paper introduces NP-BCC, a model that integrates automatisation and prior knowledge to achieve stable adaptive behaviour in dynamic environments through simulations.

This study investigates forward planning in individuals with mild-to-moderate alcohol use disorder compared to healthy controls, finding no evidence of planning impairments.

The paper presents a computational model showing repetition bias in human sequential decision-making, finding that choice frequency influences decisions alongside reward seeking.

This study identifies context-dependent repetition as a driver of decision biases and demonstrates a computational model based on reward learning and decision repetition that outperforms existing models.

The paper investigates how goal-directed and habitual control interact under time pressure, finding that habitual control adapts contextually through proactive and reactive mechanisms.

The paper introduces a joint model of choices and reaction times that integrates Bayesian decision making with sampling procedures to explain decision processes in cognitive tasks.

The paper identifies network instability dynamics as a driver of transient bursting activity in the developing hippocampus, revealing a mechanism for input-discrimination learning before environmental exploration.

This study investigates how humans represent and use temporal structure in decision-making using a dynamic probabilistic learning task and computational modeling.

The paper investigates how the brain decides when to use forward planning by analyzing context-dependent conflict processing in the anterior cingulate cortex using fMRI.

This paper shows that probabilistic context inference affects motor adaptation and control by influencing switching-task behavior and action selection in simulated experiments.

This study shows that NKCC1 deletion in developing mice has minimal impact on hippocampal network development and behavior, challenging its essential role in these processes.

The paper presents a computational model of probabilistic inference over a hierarchy of time scales to solve the exploration-exploitation dilemma in cognitive control.

The paper shows that left ventral MGB modulation increases during speech recognition in noisy conditions compared to clear speech or speaker identity tasks.

This paper introduces a hierarchical Bayesian model to explain how the brain balances habitual and goal-directed behavior through context-specific learning and probabilistic inference.

The paper models dynamic effort allocation in a sequential task using discounting mechanisms and shows that inferred parameters explain effort dynamics and forward planning.

This study investigates how decision evidence is represented in human brain signals using magnetoencephalography, identifying three distinct phases of processing in different brain regions.