<p>Cravings contribute to eating behaviours and health, yet how they emerge and translate into consumption remains unclear. Individual biases in how we learn to assign value to cues and actions, through Pavlovian conditioning (sign-tracking and goal-tracking) and reinforcement learning (model-free and model-based), provide a framework for exploring these behaviours. Cravings, emerging from associations between environmental cues and physiological responses, may be amplified by learning biases enhancing the cues’ salience (i.e., sign-tracking bias) and habitual behaviour (i.e., model-free). Learning biases may also moderate the relationship between cravings and consumption by intensifying automatic and habitual processes. Lastly, approach bias, which measures automatic processes involved in craving formation, may be associated with learning biases. To test these hypotheses, we conducted a two-week longitudinal study in 81 participants (minimum post-exclusion sample of 65) using ecological momentary assessments via a smartphone-based app to measure high-caloric food cravings, food intake, and approach-avoidance tendencies in daily life. Learning biases were assessed using Pavlovian conditioning and sequential learning tasks. Results indicate that sign-tracking bias was not related to craving intensity or variability, whereas model-free bias was associated with higher mean cravings but not lower day-to-day craving variability. The hypothesis linking cravings to consumption was not tested due to a consumption floor effect, and neither learning bias was associated with approach bias. The healthy-weight sample may explain our null findings, as they may show limited expression of high-caloric food cravings and consumption. Overall, these findings suggest that reinforcement learning tendencies may contribute to everyday craving dynamics.</p>