Less is More: Stimulus Feedback Co-Occurence in Perceptual Category Learning - eScholarship

Less is More: Stimulus-Feedback Co-Occurrence in Perceptual Category Learning Darrell A. Worthy (worthyda@mail.utexas.edu) Department of Psychology, 1 University Station A8000 Austin, TX 78712-0187 USA W. Todd Maddox (maddox@psy.utexas.edu) Department of Psychology, 1 University Station A8000 Austin, TX 78712-0187 USA Arthur B. Markman (markman@psy.utexas.edu) Department of Psychology, 1 University Station A8000 Austin, TX 78712-0187 USA Figure 1 shows stimuli from rule-based and information- integration category structures. The stimuli are sine-wave gratings (Gabor patches) that vary in their spatial frequency and spatial orientation. Figure 1a shows a rule-based category structure. Here the optimal rule is to classify stimuli with relatively low spatial frequency into one category and stimuli with relatively high spatial frequency into the other category. In contrast, there is no verbalizable rule that can be employed to optimally distinguish the categories in the information-integration structure shown in Figure 1b. Here the optimal rule may be verbalized as: ‘if the spatial orientation is greater than the spatial frequency, classify the stimuli into one category, but if the spatial orientation is less than the spatial frequency, classify the stimuli into the other category. ’ However, this type of rule is difficult or impossible to implement to solve the task because the relative magnitude of each spatial dimension cannot be easily compared. Abstract This study examined the effects of stimulus-feedback co- occurrence on rule-based and information-integration category learning. Rule-based categories are those for which a verbalizable rule is optimal. Information-integration categories are those for which the optimal rule is non- verbalizable. Participants performed a rule-based or an information-integration task where the stimulus co-occurred with the feedback (Stimulus Present) or was removed prior to feedback presentation (Stimulus Absent). Previous research examining the neural substrates of rule-based and information-integration category learning suggests that stimulus-feedback co-occurrence should support rule-based learning, but should harm information-integration learning because it will increase the prevalence of rule use. This prediction was confirmed in the current study. Implications for theories of category learning are discussed. Keywords: Category learning; Feedback; Procedural Learning Introduction There is nearly unanimous agreement that category learning is mediated by multiple, distinct neural systems (e.g. Ashby, Alfonso-Reese, Turken, & Waldron, 1998). Even so, much is still unknown about the processing characteristics of each system. Two distinct neural systems have been the focus of much research and are thought to mediate different types of category-learning. An explicit hypothesis-testing system is thought to mediate rule-based category learning, and relies on frontal brain regions and the head of the caudate nucleus. An implicit procedural-learning system is thought to mediate information-integration category learning, and relies on the body and tail of the caudate nucleus. Rule- based learning involves testing verbalizable rules in order find the rule that optimally separates stimuli into the categories. Information-integration learning involves associating regions of perceptual space with actions that lead to reward (e.g. Maddox, Ing, & Bohil, 2004; Ashby, Ell, & Waldron,, 2003; Spiering and Ashby, 2008) The hypothesis testing system uses executive resources to construct and test verbalizable rules, whereas the procedural system uses dopamine-mediated reward learning to associate regions of the stimulus space with a response. a. b. Figure 1: (a) Plot of stimuli from the rule-based category structure. (b) Plot of stimuli from the information- integration category structure. Previous research suggests that the hypothesis testing system relies on executive attention and is not vulnerable to manipulations of feedback delay (e.g. Zeithamova &