How expectations influence scene gist recognition: A diffusion model analysis.

When we perceive the world, visual input follows a meaningful structure, which can be considered as “grammar” of the visual scene, as the relationships between scenes have been learnt implicitly from the rules of the world like learning a language. For example, we expect to see scenes of the hallway – rather than a parking lot – when leaving an office. In the present study, we aim to disentangle the effect of expectations on the scene gist recognition process. In each trail of the experiment, participants will see a sequence of visual scenes, where the previous pictures serve as primes for the latter ones. The experimental design comprises three within-subject factors: We manipulate whether there is scene category change at superordinate level (i.e., a change from indoor to outdoor), whether there is basic-level scene change (e.g., a change from office to classroom), and whether there is high or low expectancy for target scenes. In the high expectancy condition, the primes and target will be displayed in a spatial temporal coherent sequence, whereas in the low expectancy conditions, primes and the target will be shown in a randomized sequence. We hypothesize that high expectancy will facilitate gist extraction and thus speed up response times and accuracy of scene categorization. Further, we expect that the facilitation effects result from faster speed of evidence accumulation (i.e., drift rate) in high expectancy than low expectancy. Besides, we also test for an effect of scene category change on the starting point of the diffusion process. To test these hypotheses, we fit diffusion model with parameters depending on different conditions using a hierarchical Bayesian parameter estimation procedure.