Despite good intentions, people often fail to cross the “intention–behavior gap,” especially when goal achievement requires repeated action. To bridge this gap, the formation of automatized routines may be crucial. However, people may differ in the tendency to switch from goal-directed toward habitual control. To shed light on why some people succeed in forming routines while others struggle, the present study related the automatization of a novel, daily routine to individual differences in white-matter connectivity in corticostriatal networks that have been implicated in goal-directed and habitual control. Seventy-seven participants underwent diffusion-weighted imaging and formed the daily routine of taking a (placebo) pill for 3 weeks. Pill intake was measured by electronic pill boxes, and participants filled out a daily online questionnaire on the subjective automaticity of this behavior. Automatization of pill intake was negatively related to striatal (mainly caudate) connectivity with frontal goal-directed and cognitive control regions, namely, ventromedial pFC and anterior cingulate gyrus. Furthermore, daily pill intake was positively related to individual differences in striatal (mainly caudate) connectivity with cognitive control regions, including dorsolateral and anterior pFC. Therefore, strong control networks may be relevant for implementing a new routine but may not benefit its automatization. We also show that habit tendency (assessed with an outcome-devaluation task), conscientiousness, and daily life regularity were positively related to routine automatization. This translational study moves the field of habit research forward by relating self-reported routine automatization to individual differences in performance on an experimental habit measure and to brain connectivity.

Frontal oscillatory dynamics in the theta (4–8 Hz) and beta (20–30 Hz) frequency bands have been implicated in cognitive control processes. Here we investigated the changes in coordinated activity within and between frontal brain areas during feedback-based response learning. In a time estimation task, participants learned to press a button after specific, randomly selected time intervals (300–2000 msec) using the feedback after each button press (correct, too fast, too slow). Consistent with previous findings, theta-band activity over medial frontal scalp sites (presumably reflecting medial frontal cortex activity) was stronger after negative feedback, whereas beta-band activity was stronger after positive feedback. Theta-band power predicted learning only after negative feedback, and beta-band power predicted learning after positive and negative feedback. Furthermore, negative feedback increased theta-band intersite phase synchrony (a millisecond resolution measure of functional connectivity) among right lateral prefrontal, medial frontal, and sensorimotor sites. These results demonstrate the importance of frontal theta- and beta-band oscillations and intersite communication in the realization of reinforcement learning.