The perceptual effects of learning object categories that predict perceptual goals

Category Learning Selectively Enhances Representations of Boundary-Adjacent Exemplars in Early Visual Cortex

Category learning and visual perception are fundamentally interactive processes, such that successful categorization often depends on the ability to make fine visual discriminations between stimuli that vary on continuously valued dimensions. Research suggests that category learning can improve perceptual discrimination along the stimulus dimensions that predict category membership and that these perceptual enhancements are a byproduct of functional plasticity in the visual system. However, the precise mechanisms underlying learning-dependent sensory modulation in categorization are not well understood. We hypothesized that category learning leads to a representational sharpening of underlying sensory populations tuned to values at or near the category boundary. Furthermore, such sharpening should occur largely during active learning of new categories. These hypotheses were tested using fMRI and a theoretically constrained model of vision to quantify changes in the shape of orientation representations while human adult subjects learned to categorize physically identical stimuli based on either an orientation rule (N = 12) or an orthogonal spatial frequency rule (N = 13). Consistent with our predictions, modeling results revealed relatively enhanced reconstructed representations of stimulus orientation in visual cortex (V1-V3) only for orientation rule learners. Moreover, these reconstructed representations varied as a function of distance from the category boundary, such that representations for challenging stimuli near the boundary were significantly sharper than those for stimuli at the category centers. These results support an efficient model of plasticity wherein only the sensory populations tuned to the most behaviorally relevant regions of feature space are enhanced during category learning.

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

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.

Training set coherence and set size effects on concept generalization and recognition

Building conceptual knowledge that generalizes to novel situations is a key function of human memory. Category-learning paradigms have long been used to understand the mechanisms of knowledge generalization. In the present study, we tested the conditions that promote formation of new concepts. Participants underwent 1 of 6 training conditions that differed in the number of examples per category (set size) and their relative similarity to the category average (set coherence). Performance metrics included rates of category learning, ability to generalize categories to new items of varying similarity to prototypes, and recognition memory for individual examples. In categorization, high set coherence led to faster learning and better generalization, while set size had little effect. Recognition did not differ reliably among conditions. We also tested the nature of memory representations used for categorization and recognition decisions using quantitative prototype and exemplar models fit to behavioral responses. Prototype models posit abstract category representations based on the category's central tendency, whereas exemplar models posit that categories are represented by individual category members. Prototype strategy use during categorization increased with increasing set coherence, suggesting that coherent training sets facilitate extraction of commonalities within a category. We conclude that learning from a coherent set of examples is an efficient means of forming abstract knowledge that generalizes broadly. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Category learning can alter perception and its neural correlates

Learned Categorical Perception (CP) occurs when the members of different categories come to look more dissimilar ("between-category separation") and/or members of the same category come to look more similar ("within-category compression") after a new category has been learned. To measure learned CP and its physiological correlates we compared dissimilarity judgments and Event Related Potentials (ERPs) before and after learning to sort multi-featured visual textures into two categories by trial and error with corrective feedback. With the same number of training trials and feedback, about half the subjects succeeded in learning the categories ("Learners": criterion 80% accuracy) and the rest did not ("Non-Learners"). At both lower and higher levels of difficulty, successful Learners showed significant between-category separation-and, to a lesser extent, within-category compression-in pairwise dissimilarity judgments after learning, compared to before; their late parietal ERP positivity (LPC, usually interpreted as decisional) also increased and their occipital N1 amplitude (usually interpreted as perceptual) decreased. LPC amplitude increased with response accuracy and N1 amplitude decreased with between-category separation for the Learners. Non-Learners showed no significant changes in dissimilarity judgments, LPC or N1, within or between categories. This is behavioral and physiological evidence that category learning can alter perception. We sketch a neural net model predictive of this effect.

Novel representations that support rule-based categorization are acquired on-the-fly during category learning

Humans learn categorization rules that are aligned with separable dimensions through a rule-based learning system, which makes learning faster and easier to generalize than categorization rules that require integration of information from different dimensions. Recent research suggests that learning to categorize objects along a completely novel dimension changes its perceptual representation, making it more separable and discriminable. Here, we asked whether such newly learned dimensions could support rule-based category learning. One group received extensive categorization training and a second group did not receive such training. Later, both groups were trained in a task that made use of the category-relevant dimension, and then tested in an analogical transfer task (Experiment 1) and a button-switch interference task (Experiment 2). We expected that only the group with extensive pre-training (with well-learned dimensional representations) would show evidence of rule-based behavior in these tasks. Surprisingly, both groups performed as expected from rule-based learning. A third experiment tested whether a single session (less than 1 h) of training in a categorization task would facilitate learning in a task requiring executive function. There was a substantial learning advantage for a group with brief pre-training with the relevant dimension. We hypothesize that extensive experience with separable dimensions is not required for rule-based category learning; rather, the rule-based system may learn representations "on the fly" that allow rule application. We discuss what kind of neurocomputational model might explain these data best.

Dimension-selective attention as a possible driver of dynamic, context-dependent re-weighting in speech processing

The contribution of acoustic dimensions to an auditory percept is dynamically adjusted and reweighted based on prior experience about how informative these dimensions are across the long-term and short-term environment. This is especially evident in speech perception, where listeners differentially weight information across multiple acoustic dimensions, and use this information selectively to update expectations about future sounds. The dynamic and selective adjustment of how acoustic input dimensions contribute to perception has made it tempting to conceive of this as a form of non-spatial auditory selective attention. Here, we review several human speech perception phenomena that might be consistent with auditory selective attention although, as of yet, the literature does not definitively support a mechanistic tie. We relate these human perceptual phenomena to illustrative nonhuman animal neurobiological findings that offer informative guideposts in how to test mechanistic connections. We next present a novel empirical approach that can serve as a methodological bridge from human research to animal neurobiological studies. Finally, we describe four preliminary results that demonstrate its utility in advancing understanding of human non-spatial dimension-based auditory selective attention.

Changing similarity: Stable and flexible modulations of psychological dimensions

Successfully categorizing objects requires discriminating between relevant and irrelevant dimensions (e.g., shape, color). Categorization can lead to changes in the visual system that stretch psychological space, making relevant dimensions more distinct and irrelevant dimensions more similar. These changes are known as dimensional modulation (DM) and they can be both stable and flexible in nature. The current study examined the interaction between stable DM and flexible DM, as well as the time course of relative changes in similarity. Using a two-dimensional space of cars, participants learned to categorize the space and then completed a target identification task during EEG recording. We found that attention, operationally defined as the selection negativity, was sensitive to category-relevance and appeared to selectively enhance previously irrelevant differences in the service of a target detection task. In contrast, we found that late decisional stages, operationally defined as the P3 b, were less sensitive to relevance and instead more sensitive to the number of morphsteps that separated targets from non-targets. Thus, it appears that relative similarity between targets and non-targets dynamically changed over the time course of individual decisions. Similarity between exemplars was greater along the irrelevant than the relevant dimension early on in the time course but a compensatory allocation of attention led to similarity being optimized among all dimensions for later stages. This finding is important because it 1) provides a new source of converging evidence for stable DM and 2) links a neural measure of attentional modulation with facilitation of an unpracticed, but task-relevant perceptual dimension.

Holistic processing from learned attention to parts

Attention helps us focus on what is most relevant to our goals, and prior work has shown that aspects of attention can be learned. Learned inattention to parts can abolish holistic processing of faces, but it is unknown whether learned attention to parts is sufficient to cause a change from part-based to holistic processing with objects. We trained subjects to individuate nonface objects (Greebles) from 2 categories: Ploks and Glips. Diagnostic information was in complementary halves for the 2 categories. Holistic processing was then tested with Plok-Glip composites that combined the kind of part that was diagnostic or nondiagnostic during training. Exposure to Greeble parts resulted in general failures of selective attention for nondiagnostic composites, but face-like holistic processing was only observed for diagnostic composites. These results demonstrated a novel link between learned attentional control and the acquisition of holistic processing.

Category Learning Stretches Neural Representations in Visual Cortex

We review recent work that shows how learning to categorize objects changes how those objects are represented in the mind and the brain. After category learning, visual perception of objects is enhanced along perceptual dimensions that were relevant to the learned categories, an effect we call dimensional modulation (DM). DM stretches object representations along category-relevant dimensions and shrinks them along category-irrelevant dimensions. The perceptual advantage for category-relevant dimensions extends beyond categorization and can be observed during visual discrimination and other tasks that do not depend on the learned categories. fMRI shows that category learning causes ventral stream neural populations in visual cortex representing objects along a category-relevant dimension to become more distinct. These results are consistent with a view that specific aspects of cognitive tasks associated with objects can account for how our visual system responds to objects.

Perceptual advantage for category-relevant perceptual dimensions: the case of shape and motion

Category learning facilitates perception along relevant stimulus dimensions, even when tested in a discrimination task that does not require categorization. While this general phenomenon has been demonstrated previously, perceptual facilitation along dimensions has been documented by measuring different specific phenomena in different studies using different kinds of objects. Across several object domains, there is support for acquired distinctiveness, the stretching of a perceptual dimension relevant to learned categories. Studies using faces and studies using simple separable visual dimensions have also found evidence of acquired equivalence, the shrinking of a perceptual dimension irrelevant to learned categories, and categorical perception, the local stretching across the category boundary. These later two effects are rarely observed with complex non-face objects. Failures to find these effects with complex non-face objects may have been because the dimensions tested previously were perceptually integrated. Here we tested effects of category learning with non-face objects categorized along dimensions that have been found to be processed by different areas of the brain, shape and motion. While we replicated acquired distinctiveness, we found no evidence for acquired equivalence or categorical perception.