The neural basis of visual object learning

Abstract

Object vision in human and nonhuman primates is often cited as a primary example of adult plasticity in neural information processing. It has been hypothesized that visual experience leads to single neurons in the monkey brain with strong selectivity for complex objects, and to regions in the human brain with a preference for particular categories of highly familiar objects. This view suggests that adult visual experience causes dramatic local changes in the response properties of high-level visual cortex. Here, we review the current neurophysiological and neuroimaging evidence and find that the available data support a different conclusion: adult visual experience introduces moderate, relatively distributed effects that modulate a pre-existing, rich and flexible set of neural object representations. The pervasive role of experience in visual processingSensory information processing in adult mammalian brains is highly malleable [1] with neural processing at all levels adapting to both the short-and long-term properties of the incoming information. In vision, prominent examples include short-term adaptation to input statistics in the retina [2], primary visual cortex (V1) [3] and subsequent cortical stages [4], and long-term reorganization due to changed visual input [5].Nevertheless, cortical neural plasticity has often been viewed as more likely in visual regions selective for complex objects than in the input stage of processing, V1 [6]. It is unlikely that cortical representations could be constructed, a priori, to represent all possible objects that might be encountered throughout life. Indeed, human beings can recognize an almost infinite number of objects, most of us sharing the ability to individuate thousands of faces despite their similarity (two eyes, nose, and mouth in a standard configuration). Further, we often develop expertise in the recognition of particular types of objects such as cars, birds or wild mushrooms [7].In apparent support of extensive learning processes, the past two decades have seen numerous electrophysiological and brain imaging studies reporting effects of learning on high-level visual representations. By learning we mean the effects of any form of experience, whether that be through passive exposure or some sort of explicit training, such as learning to categorize [8] Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access Author ManuscriptTrends Cogn Sci. Author manuscript; available in PMC 2011 January 1. [9] between objects. Most of these studies have focused on the lateral occipital, occipitotemporal and inferior ...

The Role of Top-Down Task Context in Learning to Perceive Objects

“…Finally, here we only directly manipulated the top-down task context (association versus discrimination learning). It is also plausible that the distribution of learning effects may be influenced by how informative the selectivity of regions is for to-be-trained stimuli as well (Op de Beeck and Baker, 2010). Consistent with this hypothesis, we found that the learning effect was restricted to the left hemisphere when high spatial-frequency stimuli were used, whereas previous studies using stimuli with smoothed surfaces reported a right-hemisphere advantage in object learning (e.g., Grill-Spector et al, 2000;Op de Beeck et al, 2006).…”

“…Rather, the learning-induced changes are distributed throughout the visual system (Op de Beeck and Baker, 2010). However, regarding the question of where and how object representations are constructed through learning, hypotheses of stimulus-driven learning and task-guided learning have different predictions (Kanwisher, 2001).…”

High-resolution imaging of expertise reveals reliable object selectivity in the fusiform face area related to perceptual performance

“…We used SR-fMRI (2.2 × 2.2 × 2.5 mm) to localize several bilateral regions of interest (ROIs) ( Table S1). In addition to defining the FFA as a single area, as has been the standard for the last 15 y, we also defined posterior (FFA1) and anterior (FFA2) portions of the FFA, as recently proposed (21,29). Of the subjects, 11 of 20 and 14 of 21 had two clear foci corresponding to these regions in the right and left hemispheres, respectively, and for another four (right) and three (left), a single FFA region was large enough to bisect into two FFA parcels (30).…”

“…Neurophysiology in the monkey reveals face patches consisting almost solely of face-selective neurons (13). Based on such near-absolute selectivity for faces, authors have concluded against the role of expertise in understanding FFA function (20)(21)(22)(23). Thus, FFA responses to objects obtained with SR-fMRI in novices or experts are sometimes attributed to spatial blurring.…”

A Neurocognitive Approach to Expertise in Visual Object Recognition

“…For example, extensive training on orientation discrimination tasks results in stronger responses and narrower orientation tuning curves in early visual areas [13]. Similarly, long-term training with artificial objects is associated with specific changes in the response of high-level visual cortex [14].…”