Browsing by Subject "Category learning"

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Building BRIDGES : combining analogy and category learning to learn relation-based categories
(2010-05) Tomlinson, Marc Thomas; Love, Bradley C.; Echols, Catharine H.; Loewenstein, Jeffrey; Markman, Arthur B.; Porter, Bruce W.
The field of category learning is replete with theories that detail how similarity and comparison based processes are used to learn categories, but these theories are limited to cases in which item and category representations consist of feature vectors. This precludes these methods from learning relational categories, where membership is determined by the structured relations binding the features of a stimulus together. Fortuitously, researchers within the analogy literature have developed theories of comparison that account for this structure. This thesis bridges the two approaches, describing a theory of category learning that utilizes the representational frameworks provided by the analogy literature to learn categories that may only be described through the appreciation of the structured relations within their members. This theory is formalized in a model, Building Relations through Instance Driven Gradient Error Shifting (BRIDGES), that shows how relational categories can be learned through attention-driven analogies between concrete exemplars. This approach is demonstrated through several simulations that compare similarity-based learning and alternatives, such as rule-based abstractions and re-representation. We then present a series of experiments that explore the reciprocal impact of relational comparison on category structure and category structure on relational comparison. This work provides a theoretical framework and formal model suggesting that feature-based and relation-based categories are a continuum that are learned through selective attention and similarity-based comparison.
Declarative category learning system
(2010-05) Davis, Tyler Harrison; Love, Bradley C.; Maddox, W T.; Neubauer, Raymond; Preston, Alison R.; Schnyer, David M.
Categorization is a fundamental process that underlies much of cognition. People form categories that allow them to generalize to and make inferences about novel objects and events. Current accounts of category learning suggest that there are two systems for learning categories, an explicit rule-based system that depends on frontal-striatal loops and working memory, and a procedural system that learns implicitly and depends on the tail of the caudate nucleus and occipital regions. In the present thesis, I propose that an additional declarative category learning system exists that is recruited to learn categories that are associated with multiple conjunctive and explicit, but not strictly rule-based, representations. The basis of the declarative category learning system is then tested in several behavioral and physiological recording experiments. The first issue that is examined in relation to the declarative category learning system is how subjects’ ability to encode stimuli affects their ability to form new flexible conjunctive representations. I provide evidence consistent with the idea that there are two ways to encode stimuli in category learning, either as a conjunction of individual parts or as holistic images. Forming part-based representations is found to be especially critical for forming new conjunctive representations for exceptions in brief single session experiments. A second question is how emotional processes interact with the declarative category learning system. Numerous lines of evidence suggest that emotional processes strongly affect learning and behavior. In a study using skin conductance, I find that anticipatory emotions (i.e., emotions present before a behavioral response) show a pattern consistent with orienting attention to behaviorally significant or potentially novel events. A final fMRI project ties together hypotheses about anticipatory emotions and encoding to their neural basis and provides a test of the predicted mapping of the declarative category learning system to the brain. By relating quantitative predictions from SUSTAIN, a model that shares relationships to the medial temporal lobes (MTL) and declarative category learning system, to fMRI data, I find clusters in an MTL-midbrain-PFC network that show patterns of activation consistent with recognizing exception items and updating these representations in response to error or surprise.
The role of corticostriatal loops in auditory category learning
(2017-05-15) Yi, Han-Gyol; Chandrasekaran, Bharath; Booth, James R; Henry, Maya L; Smiljanic, Rajka
Sounds can signal danger (e.g., roar of a lion), pleasure, (e.g., music), or carry linguistic relevance (e.g. speech). For sounds to guide behavior, the complex soundscape must first be appropriately categorized (Bizley & Cohen, 2013; Nelken, Bizley, Shamma, & Wang, 2014). Currently, our understanding of the neural correlates of auditory categorization and learning is largely constrained to the cerebral cortex (Leech, Holt, Devlin, & Dick, 2009; Lim, Fiez, & Holt, 2014; F. Ohl, Scheich, & Freeman, 2001; F. W. Ohl & Scheich, 2005). Here, I focus on the striatum and its extensive connectivity between the cerebral cortex, referred to as corticostriatal loops (Parent & Hazrati, 1995). In vision, these loops have been purported to be involved in sensory, executive, motivational, and motor processing during acquisition of novel categories (Seger & Miller, 2010). An influential theory in visual category learning posits that the executive loop is critical in developing, testing, and using reflective rules to categorize percepts, whereas the motor loop is critical in reflexively learning categories (Ashby & Maddox, 2005, 2011). In this dissertation, I use a combination of structural and functional neuroimaging methods and behavioral training approach to examine the role of corticostriatal loops in auditory category learning. Structurally, I show that the connectivity between the auditory cortex and the caudate nucleus (sensory loop) relates to individual variability in speech category learning. Functionally, I show that successful categorization of speech sounds is associated with greater recruitment of the motor loop during stimulus, and a combination of executive, motivational, and motor loops during feedback processing. Finally, I present evidence that reflective learning of the auditory categories involves recruitment of the prefrontal cortex, whereas reflexive learning primarily involves the motor loop (Ashby & Maddox, 2005, 2011). Altogether, these results suggest that (1) multiple corticostriatal loops are engaged during auditory category learning; (2) successful categorization of a stimulus is contingent on recruitment of the prefrontal or motor cortex; and (3) feedback is integrated throughout training via executive and motivational loops.