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The Editors at Communications Psychology, Nature Communications, and Scientific Reports invite research that studies the role of habits in decision-making.
How habits influence actions is a longstanding topic in cognitive neuroscience. Computational accounts model the role of habits across decision-making domains, from motor control to value-based decision making.
The aim of this curated Collection is to bring together high-quality publications spanning the range of fields interested habit as a component of decision-making to foster cross-disciplinary exchange. Relevant work may come from a range of fields, including but not limited to behavioural neuroscience, behavioural economics, public policy and studies consumer behaviour.
The call for papers is accordingly open to research reflecting a broad range of questions including research that investigates how habits act as an input signal into the decision-making process to studies targeting habits for interventions.
The journals will consider submissions of research Articles, Registered Reports, and Resources on the topic. More information on the different formats can be found here. If you are interested in contributing a review, primer, or opinion piece, please email the Editors directly. We will highlight relevant publications in this Collection.
Using a sequential decision making task and cognitive modeling, we show that human decisions are best explained by a combination of repetition bias and goal directed reward-based behavior.
Choice bias - the tendency to prefer one option over another for no apparent reason - is stable for at least 22 months in perceptual tasks. While feedback can induce choice bias, its effect diminishes within weeks, suggesting a different underlying mechanism.
Reinforcement learning strategies and motor performance are linked. Participants show poorer motor performance when they adopt or shift towards a model-free strategy under threat of electric shocks than when they use a model-based strategy.
Intelligent agents can perform two types of behavior, habitual and goal-directed. The authors propose a deep learning framework using a variational Bayes approach, which computationally explains many aspects of the interaction between the two types of behaviors in sensorimotor tasks.