Fragment-Based Learning of Visual Object Categories in Non-Human Primates

Abstract

When we perceive a visual object, we implicitly or explicitly associate it with an object category we know. Recent research has shown that the visual system can use local, informative image fragments of a given object, rather than the whole object, to classify it into a familiar category. We have previously reported, using human psychophysical studies, that when subjects learn new object categories using whole objects, they incidentally learn informative fragments, even when not required to do so. However, the neuronal mechanisms by which we acquire and use informative fragments, as well as category knowledge itself, have remained unclear. Here we describe the methods by which we adapted the relevant human psychophysical methods to awake, behaving monkeys and replicated key previous psychophysical results. This establishes awake, behaving monkeys as a useful system for future neurophysiological studies not only of informative fragments in particular, but also of object categorization and category learning in general.

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

“…An important task in visual processing is inferring the category to which a given observed object belongs. Although the exact mechanism of this inference is unknown, there is both computational and physiological evidence 9,12,13,[30][31][32] that it involves using the information about known features of the object in the given image to infer the category of the object. Here, we will illustrate how this inferential process may work in a Bayesian framework, and how digital embryos may be useful for research in this area.…”

“…We have previously described the usefulness of VM and VP in detail l9, 10,[12][13][14] . Briefly, VM, especially the digital embryo methodology, is useful because it provides a principled and flexible method for creating novel, but naturalistic 3-D objects 14 .…”

“…Second, the existing methods have difficulty capturing the shape complexity of natural objects [11][12][13] . If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases.…”

“…Objects and object categories created by these simulations can be further manipulated by various morphing methods to generate systematic variations of shape characteristics 15,16 . The VP and morphing methods can also be applied, in principle, to novel virtual objects other than digital embryos, or to virtual versions of realworld objects 9,13 . Virtual objects created in this fashion can be rendered as visual images using a conventional graphical toolkit, with desired manipulations of surface texture, illumination, size, viewpoint and background.…”

“…Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called 'digital embryos' by simulating the biological process of embryogenesis 14 . Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection 9,12,13 . Objects and object categories created by these simulations can be further manipulated by various morphing methods to generate systematic variations of shape characteristics 15,16 .…”