A neuroplausible computational model of vision also exhibits asymmetry in developmental category learning

A neuroplausible computational model of vision also exhibits asymmetry in developmental category learning Ankit Gupta (ankit0370@gmail.com) Department of Electrical Engineering, Indian Institute of Technology Kanpur, India Devesh Kumar Singh (deveshks@iitk.ac.in) Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, India Amitabha Mukerjee (amit@cse.iitk.ac.in) Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, India Abstract Computational models are increasingly used to explore possi- ble mechanisms underlying infant capability in various tasks. Often, such models do not work directly on perceptual data, but on hand-computed features of images; such models are open to the criticism that these high-level features may not be what is actually computed in the neural computation. Here we explore the feasibility of the Serre-Poggio (S-P) model which emulates the early ventral stream of the primate visual cortex, and constructs a probabilistic model of the tuned cells of the V4-IT cortex. In experiment 1, we use this system to model asymmetry in visual category learning in early infancy (e.g. cats vs dogs), and show that surprisal for the novel category is higher when habituated on CAT than on DOG. In experiment 2, we show that face habituation can be used to discriminate on full bodies. Experiment 3 demonstrates that superordinate cat- egory discriminations are easier than for the basic level. These experiments agree with earlier psychological data and partially validate the S-P model for such tasks. Introduction Infant perceptual ability has been demonstrated for a wide range of visual tasks, and computational models are increas- ingly used to analyze possible mechanisms underlying such behaviour. However, using computational models in visual development is limited owing to the high dimensionality of visual data and the complexity of extracting meaningful struc- tures from images. Thus, a review of computational simula- tion in development cites only two (out of thirty) papers for perceptual categorization (Schlesinger & Parisi, 2001). Yet, the infant’s strongest cue to abstracting from the world is perception, since its motor functions are under-developed. Between two and six months, infants demonstrate increasing ability to discriminate a number of complex concepts, primar- ily based on perception. Computational studies investigating infant visual perception can throw light on the the internal mechanisms for such learning, and also throw light on de- bates such as degree of innateness, learnability, etc. One of the areas that has attracted considerable attention in visual learning by infants is that of asymmetry in infant vi- sual categorization (e.g. DOG vs. CAT ) (Mareschal, French, & Quinn, 2000 ; Quinn, Eimas, & Rosenkrantz, 1993). This intriguing phenomenon has attracted considerable atten- tion, and has also been addressed by a connectionist model (Mareschal et al., 2000). Here 3-6 month infants are shown a series of images from a certain category, and their prefer- ential looking (or looking time) is analyzed for objects of the same or different categories. Based on the novelty prefer- ence paradigm, it is expected that infants would look longer at objects from the novel category. The asymmetry result, demonstrated by Quinn and co-workers nearly two decades ago, is that infants who are habituated to cats demonstrate a preference for dogs as novel stimuli, whereas the preference is weaker when habituated on dogs and exhibited a cat. Such an asymmetry is especially surprising because it is assumed that such young infants may not have had much exposure to the classes of cats and dogs per se, and that any priors they form are learned only from this immediate experience. Quinn and co-workers conjectured that the asymmetry may have arisen due to greater variation among dogs, so that some novel cats may also be accepted on the DOG schema, whereas the CAT schema is tighter and rejects most of the novel dogs (Quinn et al., 1993). This hypothesis was vali- dated using a perceptron network by Mareschal, French and Quinn (Mareschal et al., 2000), who found that certain fea- tures had lower spread among cats than among dogs. For the simulation, they hand-computed ten traits from the images: head length, head width, eye separation, ear separation, ear length, nose length, nose width, leg length, vertical extent, horizontal extent. These features were then given as input to a three-layer perceptron, which was trained to discriminate on cats and dogs over 250 epochs. The network error rates (used internally in the backpropagation algorithm) were used as an analog for looking time, and results were shown to correlate with the original experiments on infants. Also, the gaussian distribution of these features was computed and the features for the DOG were found to have a wider variation than that for the CAT . This analysis thus corroborated the the early sugges- tion that the asymmetry arose because the CAT class had less variability than the class DOG . However, such an approach is open to criticism since the computation of high-level features requires that one is able to decompose the image into ear, nose, eye etc. While there is considerable evidence that the infant is sensitive to parts of the face (Perrett & Benson, 1992), nonetheless the task of mapping from a raw image to such parallel data on faces from different species remains a formidable challenge. The images, if we consider them to be the equivalent of 300 × 200 pixels, have about 60,000 dimensions. The manually exe- cuted task of obtaining ten features from the image space in-