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An intriguing and unsolved problem in cognitive science concerns the nature of and the relationship between unsupervised and supervised categorization behavior. The former refers to assessing how observers naturally sort multidimensional objects into groups and investigating whether they can learn more complicated groupings without external feedback from the environment. Conversely, the latter refers to experimental investigations aiming to predict and explain how observers inductively learn a predetermined grouping of stimuli upon receiving “correct” or “incorrect” feedback after each classification response.Although these approaches are very different, a few attempts have been put forth with the goal of connecting behavioral outcomes between the two tasks. In general, these investigations implement both types of tasks and seek to explain the results under a common theoretical or formal framework. Although the results are promising, there is a lack of consensus regarding which theoretical or formal approach best accounts for the data. Following this tradition of integration, we present a novel attempt at connecting unsupervised and supervised categorization behavior. We employ generalized invariance structure theory (GIST; Vigo 2013, 2014), generalized representational information theory (GRIT; Vigo 2011, 2012, 2014), and their associated formal models to predict and explain results from two separate experiments.For the first set of experiments, we assessed unsupervised categorization and associated learning behavior by employing a “construction” task previously implemented by the authors (Doan & Vigo, 2016). Importantly, we modified the procedure in accord with similar techniques as those found in prior investigations to facilitate establishing the connection between unsupervised and supervised learning behavior. We replicated Doan and Vigo (2016) and also observed a decrease in response times for each of the three sub experiments, suggesting participants may have learned how to efficiently apply their selection strategy through the course of each task.For the second set of experiments, we assessed supervised categorization and learning behavior by employing a “parainformative” task with the same categorical stimuli as those tested in the first experiment. We observed higher proportions of classification errors and higher mean response times across the tested categorical stimuli in a way predicted with the non-parametric classification model derived from generalized invariance structure theory. In connecting the behavioral outcomes between both tasks, we demonstrate how the formal model from generalized representational information theory (Vigo, 2013a; GRIT-NPE) naturally accounts for the observed choice frequencies of experiment 1 and how the categorization model underlying GRIT (Vigo, 2013b; GISTM-NPE) naturally accounts for the observed proportion of classification errors observed across the three structure types of experiment 2. |
