Adaptation aftereffects reveal how categorization training changes the encoding of face identity

Abstract

Previous research suggests that learning to categorize faces along a novel dimension changes the perceptual representation of such dimension, increasing its discriminability, its invariance, and the information used to identify faces varying along the dimension. A common interpretation of these results is that categorization training promotes the creation of novel dimensions, rather than simply the enhancement of already-existing representations. Here, we trained a group of participants to categorize faces that varied along two morphing dimensions, one of them relevant to the categorization task and the other irrelevant to the task. An untrained group did not receive such categorization training. In three experiments, we used face adaptation aftereffects to explore how categorization training changes the encoding of face identities at the extremes of the category-relevant dimension, and whether such training produces encoding of the category-relevant dimension as a preferred direction in face space. The pattern of results suggests that categorization training enhances the already-existing norm-based coding of face identity, rather than creating novel category-relevant representations. We formalized this conclusion in a model that explains the most important results in our experiments and serves as a working hypothesis for future work in this area.

Changes within neural population codes can be inferred from psychophysical threshold studies

“…The tuning functions used by the brain to encode such dimensions might be different than what is represented by the standard population model used here. For example, face features are thought to be encoded through monotonic tuning functions (e.g., sigmoidal; see [5,45,43]). Using computational modeling and visual adaptation, it has been found that the effects of categorization on perception of face identities along the category-relevant dimension [59,60,61] can be best explained using a specific gain mechanism [45].…”

“…For example, face features are thought to be encoded through monotonic tuning functions (e.g., sigmoidal; see [5,45,43]). Using computational modeling and visual adaptation, it has been found that the effects of categorization on perception of face identities along the category-relevant dimension [59,60,61] can be best explained using a specific gain mechanism [45]. It is currently unknown exactly how the complex shape and object stimuli used in some studies are encoded, but encoding that is different from that of orientation might be at the heart of the results obtained with such dimensions.…”

“…Fourth, we assumed a bellshaped tuning function, which as far as we know is the only type of function used in research applying inverted encoding modeling [e.g., 9,38,31,15,13,11,12,10]. For other stimulus dimensions, such as those characterized by monotonically increasing or decreasing tuning functions (e.g., face dimensions; [44,5,45,43]), new simulations would be required. Finally, we assumed a homogeneous encoding population for the baseline condition.…”

“…For orientation stimuli, we chose to model channels as symmetrical Gaussians with Poisson noise based on previous literature, but there are other ways to characterize tuning functions that may be more appropriate for other types of stimuli. For example, face dimensions are often modeled using asymmetric sigmoidal tuning functions [44,5,45,43]. Also, another common assumption for neural channel noise is that it follows a Gaussian distribution [e.g., 6, 7], a common assumption implicit in inverted encoding modeling analyses of neuroimaging data [see 8,14].…”