A Neural Model of Human Image Categorization

A Neural Model of Human Image Categorization Eric Hunsberger (ehunsberger@uwaterloo.ca) Peter Blouw (pblouw@uwaterloo.ca) James Bergstra (jbergstra@uwaterloo.ca) Chris Eliasmith (celiasmith@uwaterloo.ca) Centre for Theoretical Neuroscience University of Waterloo, Waterloo, ON, Canada N2L 3G1 Abstract does not use raw percepts as input). Many researchers now recognize that object perception and conceptual cognition are not distinct (Palmeri & Gauthier, 2004), making it important that models integrate both perception and cognition. In this paper, we argue that advances in our understanding of the visual system and new principles for the design of neu- ral architectures can be used to overcome many of the difficul- ties in providing a viable, neurally grounded, computational model of image categorization. We use the techniques of the Neural Engineering Framework (NEF) (Eliasmith & Ander- son, 2003) to develop a model of category representation that connects retinal activity to high level cognitive judgments us- ing a class of vector-symbolic representations called semantic pointers (Eliasmith et al., 2012). The model receives natural images as input, produces category judgments as output, and carries out intermediate processing in simulated neurons. The proposed model replicates human performance on two inde- pendent studies of human judgment in prototype-based and exemplar-based image categorization, with no changes to the model. Semantic pointer architectures have been shown to support several important cognitive capacities (e.g. Stewart & Eliasmith, 2011; Eliasmith et al., 2012). Our study ex- tends this line of research, showing that semantic pointers computed by a plausible visual system model can be used to replicate human category judgments. Although studies of categorization have been a staple of psy- chological research for decades, there continues to be substan- tial disagreement about how unique classes of objects are rep- resented in the brain. We present a neural architecture for categorizing visual stimuli based on the Neural Engineering Framework and the manipulation of semantic pointers. The model accounts for how the visual system computes semantic representations from raw images, and how those representa- tions are then manipulated to produce category judgments. All computations of the model are carried out in simulated spiking neurons. We demonstrate that the model matches human per- formance on two seminal behavioural studies of image-based concept acquisition: Posner and Keele (1968) and Regehr and Brooks (1993). Keywords: category representation; image categorization; neural engineering framework; vector symbolic architecture Introduction Although studies of categorization have been a staple of psy- chological research for decades, there continues to be sub- stantial disagreement about how the mind represents informa- tion about unique classes of objects. Theories involving pro- totypes, exemplars, and explanatory schemas have all been shown to account for only a subset of known categorization phenomena, and progress toward a unified theory of cate- gory representation has been limited (for reviews, see Mur- phy, 2002; Machery, 2009; Smith & Medin, 1981). Histori- cally, the difficulty in modelling category representation has been to balance generality and accuracy. On one hand, many of the models developed from these theories have a fairly narrow scope of application. Con- sider, for instance, similarity-based accounts of concept ref- erence; these models produce impressive results at matching human behaviour in tasks that involve feature comparisons (see Smith & Medin, 1981), but they do not generalize well to other tasks that require the use of deeper category knowl- edge or explanatory inferences (see Murphy, 2002). On the other hand, approaches with greater scope tend to pay a price in terms of predictive accuracy or viability. For example, Barsalou’s (1999) theory of perceptual symbol sys- tems is a more or less unified account of category repre- sentation, but it lacks a corresponding computational model (Dennett & Viger, 1999). Rogers and McClelland’s (2004) account of semantic cognition provides a powerful model that performs well across a range of categorization tasks, but em- ploys both an idealized neural architecture and an idealized set of inputs (i.e. it is an abstract connectionist network that Model Description We developed a model of human image categorization that consists of a feed-forward visual perception model (similar to Hinton & Salakhutdinov, 2006) driving a vector-symbolic as- sociative memory (see Gayler, 2003; Plate, 2003). The model was first constructed using a rate approximation of the spik- ing leaky integrate-and-fire (LIF) neuron model for the visual system and explicit vector computations for the associative memory. The model was then implemented fully in spiking neurons using the principles of the NEF. The visual system component of the model is a sequence of feed-forward neural populations that compresses high di- mensional input images into comparatively low dimensional vectors, which we refer to as semantic pointers. The first pop- ulation, analogous to the retina and lateral geniculate nucleus (LGN), is a rasterized image, as would be captured by a con- ventional digital camera. Like the retina, a camera adapts to global lighting conditions and provides an image with stan- dard intensity levels. Our (small) LGN population corre-