Adaptive precision pooling of model neuron activities predicts the efficiency of human visual learning.

When performing a perceptual task, precision pooling occurs when an organism's decisions are based on the activities of a small set of highly informative neurons. The Adaptive Precision Pooling Hypothesis links perceptual learning and decision making by stating that improvements in performance occur when an organism starts to base its decisions on the responses of neurons that are more informative for a task than the responses that the organism had previously used. We trained human subjects on a visual slant discrimination task and found their performances to be suboptimal relative to an ideal probabilistic observer. Why were subjects suboptimal learners? Our computer simulation results suggest a possible explanation, namely that there are few neurons providing highly reliable information for the perceptual task, and that learning involves searching for these rare, informative neurons during the course of training. This explanation can account for several characteristics of human visual learning, including the fact that people often show large differences in their learning performances with some individuals showing no performance improvements, other individuals showing gradual improvements during the course of training, and still others showing abrupt improvements. The approach described here potentially provides a unifying framework for several theories of perceptual learning including theories stating that learning is due to adaptations of the weightings of read-out connections from early visual representations, external noise filtering or internal noise reduction, increases in the efficiency with which learners encode task-relevant information, and attentional selection of specific neural populations which should undergo adaptation.

[1]  Z L Lu,et al.  Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[2]  T. Poggio,et al.  The role of insight in perceptual learning: evidence from illusory contour perception , 2002 .

[3]  Jeff W. Lichtman,et al.  Principles of neural development , 1985 .

[4]  J. Cutting,et al.  Three gradients and the perception of flat and curved surfaces. , 1984, Journal of experimental psychology. General.

[5]  B. Dosher,et al.  Mechanisms of perceptual learning , 1999, Vision Research.

[6]  T. Poggio,et al.  Fast perceptual learning in hyperacuity , 1995, Vision Research.

[7]  Zili Liu,et al.  Computing dynamic classification images from correlation maps. , 2006, Journal of vision.

[8]  Dennis M Levi,et al.  Classification images for detection and position discrimination in the fovea and parafovea. , 2002, Journal of vision.

[9]  Miguel P Eckstein,et al.  Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments. , 2002, Journal of vision.

[10]  D. Knill Ideal observer perturbation analysis reveals human strategies for inferring surface orientation from texture , 1998, Vision Research.

[11]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[12]  R. Rescorla A theory of pavlovian conditioning: The effectiveness of reinforcement and non-reinforcement , 1972 .

[13]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[14]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[15]  Robert A Jacobs,et al.  Learning optimal integration of arbitrary features in a perceptual discrimination task. , 2008, Journal of vision.

[16]  H B BARLOW,et al.  Increment thresholds at low intensities considered as signal/noise discriminations , 1957, The Journal of physiology.

[17]  W. Geisler Sequential ideal-observer analysis of visual discriminations. , 1989 .

[18]  K. H. Britten,et al.  A relationship between behavioral choice and the visual responses of neurons in macaque MT , 1996, Visual Neuroscience.

[19]  H. Barlow The neuron doctrine in perception. , 1995 .

[20]  I. Ohzawa,et al.  Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. , 1990, Science.

[21]  D. Levi,et al.  Perceptual learning in vernier acuity: What is learned? , 1995, Vision Research.

[22]  A. Ahumada Classification image weights and internal noise level estimation. , 2002, Journal of vision.

[23]  D. Heeger Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[24]  P. Lennie The Cost of Cortical Computation , 2003, Current Biology.

[25]  M. Fahle Perceptual learning: a case for early selection. , 2004, Journal of vision.

[26]  Daphna Weinshall,et al.  Mechanisms of generalization in perceptual learning , 1998, Vision Research.

[27]  J. Movshon,et al.  A new perceptual illusion reveals mechanisms of sensory decoding , 2007, Nature.

[28]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[29]  D. Bradley,et al.  Neural population code for fine perceptual decisions in area MT , 2005, Nature Neuroscience.

[30]  J. Gibson,et al.  Perceptual learning; differentiation or enrichment? , 1955, Psychological review.

[31]  B. Dosher,et al.  The dynamics of perceptual learning: an incremental reweighting model. , 2005, Psychological review.

[32]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[33]  Michael S. Landy,et al.  Orthogonal Distribution Analysis: A New Approach to the Study of Texture Perception , 1991 .

[34]  G. Orban,et al.  Practising orientation identi ® cation improves orientation coding in V 1 neurons , 2022 .

[35]  A. Parker,et al.  Effects of different texture cues on curved surfaces viewed stereoscopically , 1993, Vision Research.

[36]  B L Beard,et al.  Detection in fixed and random noise in foveal and parafoveal vision explained by template learning. , 1999, Journal of the Optical Society of America. A, Optics, image science, and vision.

[37]  S. Hochstein,et al.  View from the Top Hierarchies and Reverse Hierarchies in the Visual System , 2002, Neuron.

[38]  Theodore G. Birdsall,et al.  Definitions of d′ and η as Psychophysical Measures , 1958 .

[39]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[40]  Peter Bonfield,et al.  THE VIEW FROM THE TOP , 1986 .

[41]  A. B. Sekuler,et al.  Signal but not noise changes with perceptual learning , 1999, Nature.

[42]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[43]  B. Dosher,et al.  Mechanisms of perceptual learning , 1999, Vision Research.

[44]  C. Chubb,et al.  Attentional control of texture orientation judgments , 2002, Vision Research.

[45]  J. Movshon,et al.  A computational analysis of the relationship between neuronal and behavioral responses to visual motion , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[46]  I. Ohzawa,et al.  Visual orientation and spatial frequency discrimination: a comparison of single neurons and behavior. , 1987, Journal of neurophysiology.

[47]  G. Orban,et al.  Practising orientation identification improves orientation coding in V1 neurons , 2001, Nature.

[48]  P. Földiák,et al.  Forming sparse representations by local anti-Hebbian learning , 1990, Biological Cybernetics.

[49]  C. Law,et al.  Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area , 2008, Nature Neuroscience.

[50]  M. Eckstein,et al.  Perceptual learning through optimization of attentional weighting: human versus optimal Bayesian learner. , 2004, Journal of vision.

[51]  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.

[52]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[53]  S. Kakade,et al.  Learning and selective attention , 2000, Nature Neuroscience.

[54]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[55]  Gregory C DeAngelis,et al.  Precision pooling predicts primate perceptual performance , 2005, Nature Neuroscience.

[56]  A. Fiorentini,et al.  Perceptual learning specific for orientation and spatial frequency , 1980, Nature.

[57]  R. Zemel,et al.  Inference and computation with population codes. , 2003, Annual review of neuroscience.

[58]  A. Parker,et al.  Sense and the single neuron: probing the physiology of perception. , 1998, Annual review of neuroscience.

[59]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[60]  D. Sheinberg,et al.  Shape from texture: ideal observers and human psychophysics , 1996 .

[61]  Jason M. Gold,et al.  Characterizing perceptual learning with external noise , 2004, Cogn. Sci..