Habits without values.

Abstract

Habits form a crucial component of behavior. In recent years, key computational models have conceptualized habits as arising from model-free reinforcement learning mechanisms, which typically select between available actions based on the future value expected to result from each. Traditionally, however, habits have been understood as behaviors that can be triggered directly by a stimulus, without requiring the animal to evaluate expected outcomes. Here, we develop a computational model instantiating this traditional view, in which habits develop through the direct strengthening of recently taken actions rather than through the encoding of outcomes. We demonstrate that this model accounts for key behavioral manifestations of habits, including insensitivity to outcome devaluation and contingency degradation, as well as the effects of reinforcement schedule on the rate of habit formation. The model also explains the prevalent observation of perseveration in repeated-choice tasks as an additional behavioral manifestation of the habit system. We suggest that mapping habitual behaviors onto value-free mechanisms provides a parsimonious account of existing behavioral and neural data. This mapping may provide a new foundation for building robust and comprehensive models of the interaction of habits with other, more goal-directed types of behaviors and help to better guide research into the neural mechanisms underlying control of instrumental behavior more generally.

Dopamine role in learning and action inference

“…The increased uncertainty of the goal-directed systems makes 14 it more likely to give in to the habit system. This property of the model parallels experimental 15 observation that habitual behaviour is easier to produce in an experimental paradigm (variable 16 interval schedule) (Dickinson, Nicholas, & Adams, 1983), which has more variable reward probability 17 (Miller et al, 2019). 18…”

“…is encoded by the habit system and given in blue Equation 8.1. The expected action intensity encoded 12 in the habit system has mean ̅ , and this notation highlights the analogy with a recent model of habit 13 formation (Miller et al, 2019) where a tendency to select action in state is also denoted by . 14 Additionally, we introduce another prior given in Equation 8.2, which ensures that only one action has 15 intensity significantly deviating from 0.…”

“…Methods section derives equations describing inference and learning for the above probabilistic 22 model, which for binary state and action vectors take a very similar form to those in standard models 23 of reinforcement learning and a recent model of habit formation (Miller et al, 2019). In particular, at 24 the start of the planning process the dynamics of action intensity is described by Equation 8.4, where 25 the action intensity changes proportionally to a sum of inputs form the goal-directed and habit 26 systems weighted by their confidence, analogously as in a previous model (Miller et al, 2019). The 27 action with a larger initial input is likely to win the competition, so the action for which the right hand 28 side of Equation 8.4 is highest is most likely to be selected.…”

“…and learning processes in a very similar way to a recent model of habit formation (Miller et al, 2019). 42 Both in that model and the DopAct framework, the parameters of the habit system are updated on 43 the basis of prediction errors that do not depend on reward, but rather encode the difference between 44 the chosen and habitual actions.…”

“…At this stage the habit system contributes little to the planning process as its uncertainty is high. As 46 1 system (Miller et al, 2019). If for a given state , the animal selects very similar actions over many 2 trials, the certainty of the habit system increases.…”