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Practising simple visual tasks leads to a dramatic improvement in performing them. This learning is specific to the stimuli used for training. We show here that the degree of specificity depends on the difficulty of the training conditions. We find that the pattern of specificities maps onto the pattern of receptive field selectivities along the visual pathway. With easy conditions, learning generalizes across orientation and retinal position, matching the spatial generalization of higher visual areas. As task difficulty increases, learning becomes more specific with respect to both orientation and position, matching the fine spatial retinotopy exhibited by lower areas. Consequently, we enjoy the benefits of learning generalization when possible, and of fine grain but specific training when necessary. The dynamics of learning show a corresponding feature. Improvement begins with easy cases (when the subject is allowed long processing times) and only subsequently proceeds to harder cases. This learning cascade implies that easy conditions guide the learning of hard ones. Taken together, the specificity and dynamics suggest that learning proceeds as a countercurrent along the cortical hierarchy. Improvement begins at higher generalizing levels, which, in turn, direct harder-condition learning to the subdomain of their lower-level inputs. As predicted by this reverse hierarchy model, learning can be effective using only difficult trials, but on condition that learning onset has previously been enabled. A single prolonged presentation suffices to initiate learning. We call this single-encounter enabling effect 'eureka'.

The performance of adult humans in simple vsual tasks improves dramatically with practice. This improvement is highly speciffc to basic attributes of the trained stimulus, suggesting that the underlying changes occur at low-level processing stages in the brain, where different orientations and spatial frequencies are handled by separate channels. We asked whether these practice effects are determined solely by activity in stimulus-driven mechanisms or whether high-level attentional mechanisms, which are linked to the perceptual task, might control the learning process. We found that practicing one task did not improve performance in an alternative task, even though both tasks used exactly the same visual stimuli but depended on different stimulus attributes (either orientation of local elements or global shape). Moreover, even when the experiment was designed so that the same responses were associated with the same stimuli (although subjects were instructed to attend to the attribute underlying one task), learning did not transfer from one task to the other. These results suggest that specific high-level attentional mechanisms, controlling changes at early visual processing levels, are essential in perceptual learning.Recent psychophysical studies have found that perceptual learning (in various visual tasks) is specific to stimulus attributes. Improvement in performance does not transfer to different spatial locations (1-4, 35), orientations (4-6, 35), spatial frequencies (5), or even, in some cases, across eyes (refs. 4 and 7 and somewhat in ref. 8). This has implications about the site(s) of perceptual learning. There is both anatomical and physiological evidence for hierarchical information processing in the visual system. Neurons at lower levels ofthe visual pathway are highly specialized for position, size, orientation, spatial frequency, and eye of origin. Neurons in higher anatomical visual areas (9), however, generalize over these stimulus variables (see ref. 10 and 11 for reviews) and are sensitive to increasingly more complex aspects of the stimuli. These considerations imply that stimulus-specific learning-induced changes occur at an early cortical processing stage, perhaps in primary visual cortex.However, previous studies of perceptual learning did not test the possibility that there might be both stimulus specificity as well as specificity to the behavioral context of the training. Computational considerations suggest that solving many complex image processing tasks in vision may involve an initial "bottom-up" processing stage (i.e., determined only by retinal input) followed by analysis at later stages, which are top-down modulated (i.e., controlled by extraretinal input such as prior knowledge about the task; refs. 12 and 13). This notion agrees with findings from behaving monkeys where the firing rates of neurons in higher visual areas are more strongly modulated by top-down task-dependent influences than neurons in early cortical areas (refs. 14-17; for a review, see ref. 18). According...

We propose that explicit vision advances in reverse hierarchical direction, as shown for perceptual learning. Processing along the feedforward hierarchy of areas, leading to increasingly complex representations, is automatic and implicit, while conscious perception begins at the hierarchy's top, gradually returning downward as needed. Thus, our initial conscious percept--vision at a glance--matches a high-level, generalized, categorical scene interpretation, identifying "forest before trees." For later vision with scrutiny, reverse hierarchy routines focus attention to specific, active, low-level units, incorporating into conscious perception detailed information available there. Reverse Hierarchy Theory dissociates between early explicit perception and implicit low-level vision, explaining a variety of phenomena. Feature search "pop-out" is attributed to high areas, where large receptive fields underlie spread attention detecting categorical differences. Search for conjunctions or fine discriminations depends on reentry to low-level specific receptive fields using serial focused attention, consistent with recently reported primary visual cortex effects.

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