BibTex format
@inproceedings{Mehraban:2012,
author = {Mehraban, Pour Behbahani F and Faisal, AA},
title = {Visual Object Classification is Consistent with Bayesian Generative Representations},
year = {2012}
}
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TY - CPAPER
AB - The ability to learn and distinguish categories is essential for human behavior, and the underlying neural computations are actively investigated (Freedman, 2011). Taking a normative view, we can relate categorisation to the distinction between generative and discriminative classification in machine learning. Generative approaches solve the categorization problem by building a probabilistic model of how each category was formed and infer then category labels. In contrast, the discriminative approach learns a direct mapping between input and label. Recent work (Hsu and Griffiths, 2010) shows human classification is consistent with discriminative and generative classification depending on conditions. We hypothesize that humans employ generative mechanisms for classification, when not encouraged otherwise. To test this we exploit a counterintuitive prediction for generative classification, namely how the discrimination boundary between two classes shifts if one category’s distribution is revealed to be broader during learning. We trained N=17 subjects to distinguish two classes, A and B in two tasks (two Persian-characters, armadillo-horse stick-drawings). The classes in each task were parameterized by two scalars; objects for each class are drawn from Gaussian parameter distributions, with equal variance and different means (class “prototypes”). Next, subjects classify unlabelled examples drawn between the classes, so we can infer their discrimination boundary. This process is then repeated but includes training data for class A, which lie far away from B. Counter-intuitively, generative classification predicts a shift of the discrimination boundary closer to B. Conversely, discriminative classifiers will show either no shift of the boundary or a shift of the boundary away from class B. Our results show that categorization in both tasks is consistent with generative and not discriminative classifiers, as classification boundaries shifted towards B fo
AU - Mehraban,Pour Behbahani F
AU - Faisal,AA
PY - 2012///
TI - Visual Object Classification is Consistent with Bayesian Generative Representations
ER -