Explaining representational shifts by selective attention, selective memorization, and random chance

Explaining representational shifts by selective attention, selective memorization, and random chance Toshihiko Matsuka (matsukat@muscat.L.chiba-u.ac.jp) Hidehito Honda (hito@muscat.L.chiba-u.ac.jp) Sou Matsuura (matsuura@muscat.L.chiba-u.ac.jp) Department of Cognitive and Information Science Chiba University Chiba, JAPAN this model was built on the basis of a stochastic optimiza- tion method, and it assumes that less-accurate simpler strate- gies (i.e., rule) are more likely to be realized in earlier stages of learning, while more complex strategies that bear higher classification accuracy (i.e., exemplar-like representation) are more likely to be maintained and applied in latter stages of learning. Abstract Recent studies in category learning have shown that there are shifts in category representation. In the present study, we de- velop three models categorization that consisted of different learning objectives to examine cognitive mechanism underly- ing the representational shifts. The results of simulation indi- cated that the representational shift observed in Johansen & Palmeri (2002) can be explained by selective attention, se- lective exemplar memorization, or mere random chance. Al- though these models could not be differentiated based on clas- sification generalization patterns, a detail examination of ac- quired model coefficients were conducted in order to design future studies. Computational Models Overview Introduction Recent studies in category learning and usage have shown that there are shifts in category representation. For exam- ple, Johansen and Palmeri (2002) showed that participants’ category generalization patterns in early learning stages were more consistent with rule-like representations, but as learn- ing progressed they exhibited generalization pattern that were more consistent with an exemplar-based representation. Jo- hansen and Palmeri (2002) hypothesized that the selective at- tention process was the key cognitive mechanism that pro- duced the representational shifts. They developed and suc- cessfully tested a model that initially paid attention to a single feature dimension and then gradually distributed its attention to achieve accurate classification. In contrast, Bourne and his colleague (Bourne, Healy, Kole & Graham, 2006; Bourne, Healy, Parker & Richard, 1999) suggested that representa- tional shifts can occur both rule-to-exemplar and exemplar- to-rule fashion, depending on cognitive demands of tasks be- ing performed. In particular, Bourne et al. (1999, 2006) claimed that memorabilities of strategies plays an important role in representational or strategy shifts. In the present study, we developed three models of cate- gorization that differed in their learning objectives. The first model was built on the basis of Johansen and Palmeri’s (2002) idea that selective attention causes representational shifts. In particular, the model tries to acquire accurate categorization strategies while paying attention to a small number of dimen- sions. The second model was built on the basis of Bourne et al. (1999,2006) claim that the strategy memorability is the key process in representational shifts. This model tries to acquire accurate strategies while memorizing and using a smaller number of exemplars. The last model was somewhat different from previous two models in that it assumes that representational shifts occur by random chance. Given that In the present paper, we used ALCOVE (Kruschke, 1992) as the model of categorization, and CLEAR framework (Mat- suka, Sakamoto, Chouchourelou & Nickerson, 2008) as the model of learning. CLEAR framework is a straightforward application of stochastic optimization method, namely Evolu- tional Strategy to human learning models. We refer CLEAR- augmented ALCOVE to as CaALCOVE. Three variants of CaAlCOVE were tested in the present study, namely standard CaALCOVE, attention-penalizing CaALCOVE, and (exem- plar) memorization-penalizing CaALCOVE. The difference among those models is their learning objectives and we test how the learning objectives affect acquisition of different types of internal representations. Categorization Algorithm - ALCOVE In ALCOVE (Kruschke, 1992), categorization decision is based on the activations of stored exemplars. As shown in Eq. 1, each exemplar’s activation in ALCOVE, scaled by specificity, β (which determines generalization gradient), is based on the inverse distance between an input, x, and a stored exemplar, ψ j , in multi-dimensional representational space where each dimension (i) is scaled by non-negative se- lective attention weights, a i . The exemplar activations are then fed forward to the k-th output node (e.g., output for cate- gory k), O k , weighted by w kj , which determines the strength of association between exemplar j and output node k: J I (n) (n) (n) O k (x) = w kj exp −β · a i |ψ ji − x i | j=1 i=1 where superscript n indicates n-th categorization strategy be- ing utilized. Given that CaALCOVE’s learning algorithm are based on stochastic optimization, dimensional attention weights takes the following form to obtain reasonable stabil- ity: (n) (n) a i = 1 + exp −D i