Perceptual learning depends on perceptual constancy

Abstract

Perceptual learning refers to experience-induced improvements in the pick-up of information. Perceptual constancy describes the fact that, despite variable sensory input, perceptual representations typically correspond to stable properties of objects. Here, we show evidence of a strong link between perceptual learning and perceptual constancy: Perceptual learning depends on constancybased perceptual representations. Perceptual learning may involve changes in early sensory analyzers, but such changes may in general be constrained by categorical distinctions among the high-level perceptual representations to which they contribute. Using established relations of perceptual constancy and sensory inputs, we tested the ability to discover regularities in tasks that dissociated perceptual and sensory invariants. We found that human subjects could learn to classify based on a perceptual invariant that depended on an underlying sensory invariant but could not learn the identical sensory invariant when it did not correlate with a perceptual invariant. These results suggest that constancy-based representations, known to be important for thought and action, also guide learning and plasticity.abstract ͉ representation C lassical theories and contemporary computational accounts of sensation and perception distinguish between variables encoded in early sensory analysis and higher level representations of objects, scenes, and events. Whereas early analyzers involve relatively local responses to energy, perceptual representations most often correspond to stable properties of material objects. Object properties persist across changes in the energy reaching the senses, so that comprehending the world requires perceptual constancy-attainment of relatively constant perceptual descriptions despite variation in the sensory inputs used to compute them. A common example is constancy of size: Under a variety of conditions, an object's perceived size does not vary as the observer's viewing distance changes, even though such changes alter the projected (retinal) size. Similarly, an object's surface lightness (shade of gray) does not appear to change when an object is viewed outside in sunshine or indoors, despite changes of more than three orders of magnitude in

Perceptual Learning, Cognition, and Expertise

“…Emphasis on the discovery of invariance amidst robust variation is crucial in realistic learning tasks, but it differs from most contemporary laboratory studies of PL, which typically target simple sensory discriminations and involve large numbers of trials with a small set of fixed stimuli (e.g. Fahle & Poggio, 2002; for a discussion, see Garrigan & Kellman, 2008). Finally, PL interventions tend to have m inimal emphasis on explicit instruction.…”

“…As we have seen, selective computation of abstract properties, from simple ones such as edge orientation in space, to more complex ones such as shape or sets of motion relationships that specify objects, surfaces or events, is a fundamental characteristic of perception ( J. Gibson, 1979) and appears to be presumed by learning mechanisms as well (Garrigan & Kellman, 2008). …”

“…The relative neglect and occasional focus on PL in the history of learning research and its recent emergence have been described elsewhere, as have issues of modeling PL and understanding its neural bases (for a review, see Kellman & Garrigan, 2009). Another important question has been the relation between simple laboratory tasks involving PL and more complex, real-world tasks typically involving the extraction of invariance amidst variation; recent work suggests that all of these tasks partake of a unified learning process in which the discovery of relevant information and its selective extraction are key notions (Ahissar, Laiwand, Kozminsky, & Hochstein, 1998;Garrigan & Kellman, 2008;Li, Levi, & Klein, 2004;Mollon & Danilova, 1996;Petrov, Dosher, & Lu, 2005;Zhang et al, 2010). In the present discussion, we build on this recent work but do not revisit it.…”

“…In fact, there are reasons to believe that early activations of "feature detectors" in early visual processing are not accessible to even momentary conscious awareness (e.g. Crick & Koch, 1995;He & MacLeod, 2001) and cannot be accessed by learning mechanisms (Garrigan & Kellman, 2008). In short, the perception of a simple environmental property, such as the edge of a car, is a complex abstraction, based on relational information; the relations of this abstraction to the outputs of populations of detectors, such as the orientations signaled by neuron in early visual areas, are highly variable; and the latter are not preserved in any accessible outputs of the process.…”

Reverse hierarchies and sensory learning

“…Thus, Garrigan & Kellman (2008) have found that high-level perceptual constancy rather than low-level sensory constancy is crucial for learning. Zhang et al (2008) have found that top-down segregating cues ('tagging') allowed the perceptual system to dissociate between similar, sequentially presented inputs and hence to improve contrast discrimination.…”