Recognizing and responding to threat cues is essential to survival. Freezing is a predominant threat behavior in rats. We have recently shown that a threat cue can organize diverse behaviors beyond freezing, including locomotion (Chu et al., 2024). However, that experimental design was complex, required many sessions, and had rats receive many foot shock presentations. Moreover, the findings were descriptive. Here, we gave female and male Long Evans rats cue light illumination paired or unpaired with foot shock (8 total) in a conditioned suppression setting, using a range of shock intensities (0.15, 0.25, 0.35, or 0.50 mA). We found that conditioned suppression was only observed at higher foot shock intensities (0.35 mA and 0.50 mA). We constructed comprehensive temporal ethograms by scoring 22,272 frames across 12 behavior categories in 200-ms intervals around cue light illumination. The 0.50 mA and 0.35 mA shock-paired visual cues suppressed reward seeking, rearing, and scaling, as well as light-directed rearing and light-directed scaling. The shock-paired visual cue further elicited locomotion and freezing. Linear discriminant analyses showed that ethogram data could accurately classify rats into paired and unpaired groups. Using complete ethogram data produced superior classification compared to behavior subsets, including an Immobility subset featuring freezing. The results demonstrate diverse threat behaviors – in a short and simple procedure – containing sufficient information to distinguish the visual fear conditioning status of individual rats.

Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAMs are jointly fitted to trial-level RTs and raw (pixel-space) visual stimuli from individual subjects in a unified Bayesian framework. Models fitted to large-scale cognitive training data from a stylized flanker task captured individual differences in congruency effects, RTs, and accuracy. We find evidence that the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations, demonstrating how our framework can be used to relate visual representations to behavioral outputs. Together, our work provides a probabilistic framework for both constraining neural network models of vision with behavioral data and studying how the visual system extracts representations that guide decisions.