As the proverbial creatures of habit, people tend to repeat the same behaviors in recurring contexts. This review characterizes habits in terms of their cognitive, motivational, and neurobiological properties. In so doing, we identify three ways that habits interface with deliberate goal pursuit: First, habits form as people pursue goals by repeating the same responses in a given context. Second, as outlined in computational models, habits and deliberate goal pursuit guide actions synergistically, although habits are the efficient, default mode of (...) response. Third, people tend to infer from the frequency of habit performance that the behavior must have been intended. We conclude by applying insights from habit research to understand stress and addiction as well as the design of effective interventions to change health and consumer behaviors. (shrink)

Prominent models of human long-term memory distinguish between memory systems on the basis of whether learning and retrieval occur consciously or unconsciously. Episodic memory formation requires the rapid encoding of associations between different aspects of an event which, according to these models, depends on the hippocampus and on consciousness. However, recent evidence indicates that the hippocampus mediates rapid associative learning with and without consciousness in humans and animals, for long-term and short-term retention. Consciousness seems to be a poor criterion for (...) differentiating between declarative (or explicit) and nondeclarative (or implicit) types of memory. A new model is therefore required in which memory systems are distinguished based on the processing operations involved rather than by consciousness. (shrink)

The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could (...) then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making. (shrink)