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• W2579744598 abstract "Grow your own representations: Computational constructivism Joseph L. Austerweil (Joseph.Austerweil@gmail.com) Robert L. Goldstone (rgoldsto@indiana.edu) Thomas L. Griffiths (Tom Griffiths@berkeley.edu) Todd Gureckis (todd.gureckis@nyu.edu) Kevin Canini (kevin@eecs.berkeley.edu) Matt Jones (mcj@colorado.edu) Keywords: representational change, Bayesian modeling, Connectionism, features, categories From a cognitivist standpoint, one main interest of psy- chology is the study of representations of the human mind as they mediate how people react to stimuli in their environment (Palmer, 1978). This can explain why two people that en- counter the same stimulus can behave in very different ways (Chomsky, 1959). For example, an art historian viewing a Jackson Pollock painting may exclaim “this is beautiful” due to her representation of his work as a rejection of painting with a brush; however, a lay person may say “this is ugly” due to his representation of the painting as a cluttered mess of col- ors. Without knowledge of the representations of each person in this example, it would be nearly impossible to explain their behavior when interacting with the Jackson Pollock painting. Over the last three decades, cognitive psychologists have demonstrated that the representations people use can change flexibly to capture changes in their environment (Hoffman & Richards, 1985; Schyns, Goldstone, & Thilbaut, 1998; Gold- stone, 2003). However, if the representations we use are de- termined by the stimuli in our environment, this threatens the explanatory utility of representations as it could be superflu- ous to use representations to explain people’s reaction to stim- uli if the representations are determined by the stimuli. Thus, cognitive psychologists need to explicitly formulate how rep- resentations change with experience. Although computational modelers, from connectionists to Bayesians, disagree on many things, one thing they do agree on is the importance of representations in their models (Mc- Clelland et al., 2010; Griffiths, Chater, Kemp, Perfors, & Tenenbaum, 2010). Recently, there has been a growing in- terest in exploring computational models that adapt their rep- resentations with experience in ways that match this human capacity. In this symposium, we explore computational mod- els that adapt their representations with experience in ways that are inspired by the human capability. Recently, there have been several proposals for computa- tional models whose representations flexibly adapt to the in- put data like people do; however, there has not been a thor- ough comparison of the different models. The goal of the symposium is the compare and contrast the different meth- ods, evaluate their ability to capture of human representation learning, and make explicit what is meant in each model by “representation change” as this can be a controversial claim (Schyns et al., 1998). Currently, it is not clear whether or not the different proposals mean the same thing by a “representa- tion” and if they are competing proposals to explain the same aspect of human cognition or different levels of explanation. Thus, the symposium will emphasize understanding what is meant by representation change and how well each model can explain human representation change. The symposium will focus on a wide variety of methods for representation learning from some of the most popular computational paradigms in computational cognitive science: nonparametric Bayesian modeling (Austerweil & Griffiths; Canini & Griffiths), connectionist modeling (Gureckis; Gold- stone), and reinforcement learning (Jones). Importantly, each presenter will focus on how their computational proposals ex- plain human experimental data and discussing what exactly is a representation in their framework and how they are inferred. Thus, the symposium should be interesting to a broad audi- ence of cognitive scientists (from computation modelers to experimentalists to philosphers). We hope it inspires a growth of new computational models and human experiments in this underdeveloped, yet incredibly important, aspect of cognitive science. Introduction and Nonparametric Bayesian Models of Fea- ture Learning Austerweil and Griffiths Cognitive psychology is concerned primarly with representations and how they mediate the re- sponse to stimuli. In this talk, we present a framework for exploring the principles underlying human feature learning using nonparametric Bayesian statistics. We show that our framework can capture how people infer features using sta- tistical information of the observed images, spatial informa- tion from the observed images, and categorization cues. Next, we extend our initial framework to infer features that are in- variant over a set of transformations and demonstrate that the model infers new invariant features like people do. Although most shapes and features can be transformed by translations and rescalings, some shapes and features lose their identity when rotated. We show how our model is easily extended to capture how people infer the allowable set of transformations of an object from their observations of the object. Finally, we conclude with the implications of our framework for refer- ence frames in shape perception and feature-based cognitive models and compare it to other approaches for inferring rep- resentations. Building flexible categorization models by grounding them in perception Goldstone One limitation of most existing models of catego- rization is that they do not start with a perceptually grounded representation of the objects that they categorize. Instead, they use dimensional or featural representations that discard information about the spatial relations among an object’s parts. This restricts the models’ ability to create psycho- logically plausible object representations that can be flexibly adapted to meet categorization demands. I will describe a" @default.
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• W2579744598 title "Grow your own representations: Computational constructivism." @default.
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