Simulating object recognition with the Standard Operating Procedures (SOP) model.

Object recognition is the ability to discriminate previously encountered objects from novel ones, typically inferred from exploratory preferences. It is usually treated as a form of recognition memory. A central idea is that both nonassociative and associative processes contribute, organized around priming: a stimulus is processed less, and therefore explored less, when its representation is already primed at the moment of encounter. Priming can arise from the stimulus's own recent activation (self-priming) or from associative cueing by context or other stimuli (associative priming). Allan Wagner's Standard Operating Procedures (SOP) model provides a quantitative framework for these two components, yet prior evaluations of this model have remained qualitative. Here, we specify a numerical implementation of SOP for the principal object-recognition procedures and their key manipulations (retention and interstimulus intervals, and spatial displacement). We then simulate three widely replicated experiments: spontaneous object recognition, relative recency, and object-in-place. The model quantitatively captures the canonical preferences (novel > familiar, remote > recent, displaced > nondisplaced) and aligns with the core features of recent datasets. Beyond fitting existing results, SOP has substantial heuristic value. Our analysis points to concrete future experiments that manipulate object locations, interstimulus spacing, distractor load, and arousal to dissociate associative from nonassociative influences.