The problem of consciousness in habitual decision making

An amygdala-cingulate network underpins changes in effort-based decision making after a fitness program

When humans make decisions, objective rewards are mainly discounted by delay, risk and effort. Whereas recent research has demonstrated that several brain areas process costs and code subjective value in effort-based decision making, it remains obscure how neural activity patterns change when effort costs are reduced due to the acquisition of healthy habits, such as moving from sedentary to active lifestyles. Here, a sample of sedentary volunteers was behaviorally assessed and fMRI-scanned before and after completing a 3-month fitness plan. The impact of effort cost on decisions, measured as the constant defining a hyperbolic decaying function, was reduced after the plan. A logistic mixed model demonstrated that the explanatory power of effort decreased with time. At a neural level, there was a marginally significant disruption of effort-cost related functional activity in the anterior cingulate after the plan. Functional connectivity between the amygdala and anterior cingulate cortex was strengthened after habit acquisition. In turn, this interaction was stronger in those participants with lower effort discounting. Thus, we show for the first time changes in value-based decision making after moving from a sedentary to an active lifestyle, which points to the relevance of the amygdala-cingulate interplay when the impact of effort on decisions fades away.

The Phenomenology of Habits: Integrating First-Person and Neuropsychological Studies of Memory

There is an ongoing debate how one can integrate the subjective (first-person) dimension of experiences more thoroughly into neuropsychological research. In cognitive experimental memory research, for instance, cognitive psychology begins by separating the act of recollection from the context where recollections occur, so as to make memory research suitable for study in the experimental conditions of the laboratory. It is the claim of this article that the challenge for memory research consists not merely in the (possible) loss of meaning entailed by transforming embedded recollected experiences into operationalized cognitive functions. Rather, from the outset, the first-person experiential basis of the entire research procedure is often insufficiently elaborated and hence risks neglecting or misrepresenting significant dimensions of the phenomena it studies. I demonstrate this with regard to habits understood as procedural memories. Research based on the paradigm of embodied cognition and phenomenology has shown that procedural memory-based skills and habits are not necessarily confined to sub-personal (unconscious) processing mechanisms. This paradigm states that some cognitive processes involve not only the brain but also the pre-reflectively experienced lived-body. The key idea is that we have experiential access to bodily processes that are not yet conceptualized or reflexively mediated. In the final part of my paper, I delineate how such experiences can be integrated into the neuropsychological study of habits via the method of ‘front-loaded phenomenology.’

The Aristotelian conception of habit and its contribution to human neuroscience

The notion of habit used in neuroscience is an inheritance from a particular theoretical origin, whose main source is William James. Thus, habits have been characterized as rigid, automatic, unconscious, and opposed to goal-directed actions. This analysis leaves unexplained several aspects of human behavior and cognition where habits are of great importance. We intend to demonstrate the utility that another philosophical conception of habit, the Aristotelian, may have for neuroscientific research. We first summarize the current notion of habit in neuroscience, its philosophical inspiration and the problems that arise from it, mostly centered on the sharp distinction between goal-directed actions and habitual behavior. We then introduce the Aristotelian view and we compare it with that of William James. For Aristotle, a habit is an acquired disposition to perform certain types of action. If this disposition involves an enhanced cognitive control of actions, it can be considered a “habit-as-learning”. The current view of habit in neuroscience, which lacks cognitive control and we term “habit-as-routine”, is also covered by the Aristotelian conception. He classifies habits into three categories: (1) theoretical, or the retention of learning understood as “knowing that x is so”; (2) behavioral, through which the agent achieves a rational control of emotion-permeated behavior (“knowing how to behave”); and (3) technical or learned skills (“knowing how to make or to do”). Finally, we propose new areas of research where this “novel” conception of habit could serve as a framework concept, from the cognitive enrichment of actions to the role of habits in pathological conditions. In all, this contribution may shed light on the understanding of habits as an important feature of human action. Habits, viewed as a cognitive enrichment of behavior, are a crucial resource for understanding human learning and behavioral plasticity.

Model averaging, optimal inference, and habit formation

Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge-that of determining which model or models of their environment are the best for guiding behavior. Bayesian model averaging-which says that an agent should weight the predictions of different models according to their evidence-provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent's behavior should show an equivalent balance. We hypothesize that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realizable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behavior. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded) Bayesian inference, focusing particularly upon the relationship between goal-directed and habitual behavior.