Weight bias, the negative attitude toward overweight and/or obese individuals, has become a pressing concern amid the global rise in overweight and obesity prevalence, highlighting the need to understand the mechanisms underlying this bias. In the present study, we therefore employed an affective priming paradigm to assess the underlying processes of implicit weight bias. Participants (N = 164) completed an online experiment in which primes depicting four gender-matched body types (underweight, normal weight, overweight, and muscular) were presented, followed by target stimuli (positive and negative adjectives) that participants had to classify as either positive or negative. We analyzed the reaction time distributions for both correct and incorrect responses using a Bayesian hierarchical implementation of the diffusion model, which allowed us to disentangle distinct cognitive processes, such as decision thresholds, processing speeds, and a priori biases. Preliminary results of these analyses indicate that exposure to the overweight prime was associated with a higher decision threshold compared to the underweight prime. This suggests participants exhibited greater caution or deliberation after seeing an overweight prime compared to an underweight prime, which could potentially reflect an internal conflict or hesitation. Our findings shed light on the implicit mechanisms of weight bias, particularly how certain body types subconsciously influence decision-making processes. Our findings thereby offer a new basis for the development of future interventions aiming at reducing implicit weight biases.