Relevance Acquisition through Motivational Incentives: Modeling the time-course of associative learning and the role of visual features

The dataset comprises structured behavioral, psychophysiological, and computational modeling data collected to investigate how motivational relevance is acquired for symbolic stimuli through incentive learning and how visual features contribute to associative learning dynamics. It includes trial-level measures of participants’ response times, accuracy, and choice behaviors obtained during associative learning tasks where previously neutral symbolic visual stimuli were paired with varying monetary or motivational incentives. Concurrent psychophysiological recordings include time-resolved pupil dilation responses and event-related brain potentials (ERPs) that index sensory and cognitive processing at multiple temporal stages of stimulus evaluation.

The dataset also contains quantified visual feature descriptors for all stimuli (e.g., luminance, contrast, spatial frequency), condition labels identifying learning phases and incentive strengths, and parameters from computational models fitted to individual learning trajectories. Metadata encompass participant identifiers and demographics (e.g., age, gender), experimental session details (e.g., trial timing, block structure, counterbalancing variables), equipment specifications (eye-tracking and EEG systems, sampling rates), preprocessing pipelines, and versioned analysis code with environment dependencies.