Further tests of sequence-sensitive models in a modified garner task using separable dimensions

In the study of perceptual categorization, a key distinction is made between separable and integral dimensions. Separable dimensions are easy to attend in isolation, while integral dimensions are not. Little et al. (2016) showed that when trial-by-trial responses are analyzed, a consistent pattern of sequential effects was found in a modified Garner paradigm using integral-dimension stimuli. The present experiments investigated whether these pronounced sequential effects are also found with separable-dimension stimuli. Four experiments using two different types of separable dimensions were conducted. The results indicated that similar patterns of sequential effects were present for separable-dimension stimuli, but, unlike for integral dimensions, the effect of a change in the irrelevant dimension in the filtering task was not found. Further, for separable dimensions, the overall pattern of sequential effects did not vary between the Garner tasks (i.e., control, correlated, and filtering). To explain these results, we fit a sequence-sensitive exemplar model and compared the fits of this model to a novel sequence-sensitive feature model, in which only the relevant feature influences the categorization decision. We found that the full exemplar model provided a more compelling account of both our separable dimension data and the integral dimension data of Little et al. (2016). These findings provide a more complete understanding of perceptual categorization and add to the growing body of literature on the prevalence and critical implications of strong sequential effects in cognitive tasks. (PsycInfo Database Record (c) 2023 APA, all rights reserved).