Task difficulty and the specificity of perceptual learning

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'.

[1]  O Braddick,et al.  Orientation-Specific Learning in Stereopsis , 1973, Perception.

[2]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[3]  B. Julesz Textons, the elements of texture perception, and their interactions , 1981, Nature.

[4]  A. Fiorentini,et al.  Learning in grating waveform discrimination: Specificity for orientation and spatial frequency , 1981, Vision Research.

[5]  R. Sekuler,et al.  A specific and enduring improvement in visual motion discrimination. , 1982, Science.

[6]  G. Orban,et al.  The effect of practice on the oblique effect in line orientation judgments , 1985, Vision Research.

[7]  A Fiorentini,et al.  Interhemispheric transfer of visual information in humans: spatial characteristics. , 1987, The Journal of physiology.

[8]  J. O'Regan,et al.  Some results on translation invariance in the human visual system. , 1990, Spatial vision.

[9]  John K. Tsotsos Analyzing vision at the complexity level , 1990, Behavioral and Brain Sciences.

[10]  C. Blakemore,et al.  Vision: The iconic bottleneck and the tenuous link between early visual processing and perception , 1990 .

[11]  Colin Blakemore,et al.  Vision: Coding and Efficiency , 1991 .

[12]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[13]  D Sagi,et al.  Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[14]  A. Treisman,et al.  Automaticity and preattentive processing. , 1992, The American journal of psychology.

[15]  T Poggio,et al.  Fast perceptual learning in visual hyperacuity. , 1991, Science.

[16]  S. Edelman,et al.  Long-term learning in vernier acuity: Effects of stimulus orientation, range and of feedback , 1993, Vision Research.

[17]  M Ahissar,et al.  The role of attention in early perceptual learning , 1993 .

[18]  S. Hochstein,et al.  Attentional control of early perceptual learning. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[19]  A. Karni,et al.  The time course of learning a visual skill , 1993, Nature.

[20]  G. Orban,et al.  Human perceptual learning in identifying the oblique orientation: retinotopy, orientation specificity and monocularity. , 1995, The Journal of physiology.

[21]  S Ullman,et al.  Sequence seeking and counter streams: a computational model for bidirectional information flow in the visual cortex. , 1995, Cerebral cortex.

[22]  D. Levi,et al.  Perceptual learning in parafoveal vision , 1995, Vision Research.

[23]  A. Treisman The binding problem , 1996, Current Opinion in Neurobiology.

[24]  Abrupt learning in illusory contour perception , 1996 .

[25]  S. Hochstein,et al.  Learning Pop-out Detection: Specificities to Stimulus Characteristics , 1996, Vision Research.

[26]  Thomas V. Papathomas,et al.  Attending to attributes in double-conjunction texture segregation: The role of color, luminance, and orientation , 1996 .

[27]  G A Orban,et al.  Interocular transfer in perceptual learning of a pop-out discrimination task. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[28]  S. Hochstein,et al.  Eureka: one shot viewing enables perceptual learning , 1996 .

[29]  M. Fahle,et al.  Limited translation invariance of human visual pattern recognition , 1998, Perception & psychophysics.