An ART Neural Network Model of Discrimination Shift Learning

An ART Neural Network Model of Discrimination Shift Learning Maartje E. J. Raijmakers (m.e.j.raijmakers@uva.nl) Department of Psychology, University of Amsterdam Emily Coffey (emily@flighteducation.net) CSCA, University of Amsterdam Claire Stevenson (CStevenson@fsw.leidenuniv.nl) CSCA, University of Amsterdam Jasper Winkel (jasperwinkel@gmail.com) CSCA, University of Amsterdam Arjan Berkeljon (Arjan@Berkeljon.com) Brigham Young University (BYU) in Provo, UT, USA Abstract (non)reversal-shift learning and reviews by [4-7]). In the current paper we focus on the developmental findings related to the difference between reversal shifts (RS) and nonreversal shifts (NRS). After a RS, all contingency relations are reversed leaving the same stimulus dimension relevant for discrimination (e.g., black is correct instead of white). A NRS makes a formerly irrelevant dimension relevant, shifting only two out of four contingency relations (e.g., circle is correct instead of white). Developmental differences in discrimination learning, the first phase of the task, are mainly manifested as an increase in learning efficiency from childhood to young adulthood [6]. In the Levels-of-function theory two distinct learning modes are posited to account for the observed difference: (1) an hypothesis-testing, rational learning mode; and (2) a slow and incremental learning mode. Subsequent analysis of these hypothesized learning modes has led to a refining of their proposed nature. A finite mixture model analysis on the error distribution of simple discrimination learning data gathered from subjects aged 6 to 10 was conducted in [8]. The error distribution was found to be bimodal, i.e. the distribution was best described as composed of two components. One component best describes a model of learning through hypothesis testing. The other component included slow learners who did not show any increase in performance during the first 16 trials (although criterion was reached in 48 trials). These findings are extended by a more detailed investigation of the aforementioned learning modes [1]. Data from a simple discrimination-learning task (230 subjects, ages 4--20) was analyzed by fitting several mathematical learning models to the sequences of responses produced by the participants, i.e. the trial-by-trial learning process was modeled. Learning models were specified as latent Markov models (or mixtures thereof). The best fit on the data was obtained by a mixture model with a component of discontinuous rational learning through hypothesis testing and a component of slow, yet discontinuous learning (as opposed to incremental learning). The relative proportions of the components depend on age and relevant dimension (with respect to reinforcement contingencies) in the following two ways: (1) the proportion of rational learners We present an ART-based neural network model (adapted from [2]) of the development of discrimination-shift learning that models the trial-by-trial learning process in great detail. In agreement with the results of human participants (4–20 years of age) in [1] the model revealed two distinct learning modes in the learning process: (1) a discontinuous rational learning process by means of hypothesis testing; and (2) a slow, yet discontinuous learning process. Categorical differences in behavior are the result of uniformly distributed dimensional preferences. In addition, it models the developmental differences between reversal and nonreversal-shift learning. The network implements attention- guided learning by selective sensory processing based on dimensional preferences mediated through reinforcement. The developmental differences consist of separate adjustment of the valuation of negative reinforcement, which is proposed in the empirical neuroscience literature [3]. Keywords: discrimination-shift development, neural network. learning, cognitive Introduction Discrimination-shift learning is a long-standing paradigm in the investigation of human category learning and concept formation. A typical discrimination-shift learning task consists of two phases: discrimination learning and shift learning. Discrimination learning requires participants to choose the correct stimulus from a pair of stimuli. The stimuli presented differ on two or more dimensions (e.g. color and shape). Each of the dimensions has two possible values (e.g. white and black; circle and triangle), resulting in four possible stimuli. Reinforcement contingencies are such that only the choice of one value on a particular dimension, the so-called relevant dimension, is reinforced positively. Choosing the opposing value on the relevant dimension leads to negative reinforcement. The other dimension is irrelevant with respect to reinforcement and thus choices based on either value on this dimension lead to positive reinforcement in 50% of the choices made. During the second, shift phase of the task, which starts once a specified learning criterion has been reached in the first phase, reinforcement contingencies are shifted. Numerous developmental differences have been found in comparing performance of children and adults on discrimination-shift tasks (see the literature on