Human performance in a large range of visual tasks improves with practice. One process by which observers' accuracy increases is due to an improvement in their ability to use task-relevant (signal) information. However, the dynamics of the human neural algorithm mediating this learning process is mostly unknown. We hypothesize that a new experimental paradigm can help elucidate the human learning algorithm as well as identify the human sources of learning inefficiency by allowing comparisons of empirically measured human perceptual learning performance to that of an optimal Bayesian learner and other suboptimal learning models. To achieve this goal we propose four Specific Aims: (1) to develop an experimental paradigm that allows the investigator to compare human perceptual learning to the learning of an optimal Bayesian learner as well as other suboptimal learning models for five specific tasks involving learning about different visual attributes; (2) to develop a battery of diagnostic tests to compare human and model learning; (3) to measure human visual performance psychophysically for the five proposed visual tasks and compare them to model performance using the battery of diagnostic tests; and (4) to use the developed experimental and theoretical framework to evaluate the efficiency of the human use of different modes of feedback on learning. The proposed work will improve our understanding of the human neural algorithms mediating the dynamics of adult perceptual learning and identify different sources of inefficiency in human learning. Finally, the experimental protocols and theoretical framework proposed will provide a novel, powerful and flexible framework that other researchers can use to evaluate normal adult and infant perceptual learning in a variety of tasks and sensory modalities, to assess learning in humans with visual disorders and/or learning disabilities, and could potentially be used in conjunction with cell recording and/or brain imaging. ? ?

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
5R01EY015925-04
Application #
7250143
Study Section
Central Visual Processing Study Section (CVP)
Program Officer
Oberdorfer, Michael
Project Start
2004-09-01
Project End
2010-06-30
Budget Start
2007-07-01
Budget End
2010-06-30
Support Year
4
Fiscal Year
2007
Total Cost
$238,652
Indirect Cost
Name
University of California Santa Barbara
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
094878394
City
Santa Barbara
State
CA
Country
United States
Zip Code
93106
Or, Charles C-F; Peterson, Matthew F; Eckstein, Miguel P (2015) Initial eye movements during face identification are optimal and similar across cultures. J Vis 15:12
Eckstein, Miguel P; Schoonveld, Wade; Zhang, Sheng et al. (2015) Optimal and human eye movements to clustered low value cues to increase decision rewards during search. Vision Res 113:137-54
Abbey, Craig K; Eckstein, Miguel P (2014) Observer efficiency in free-localization tasks with correlated noise. Front Psychol 5:345
Peterson, Matthew F; Eckstein, Miguel P (2014) Learning optimal eye movements to unusual faces. Vision Res 99:57-68
Kurki, Ilmari; Eckstein, Miguel P (2014) Template changes with perceptual learning are driven by feature informativeness. J Vis 14:
Eckstein, Miguel P; Mack, Stephen C; Liston, Dorion B et al. (2013) Rethinking human visual attention: spatial cueing effects and optimality of decisions by honeybees, monkeys and humans. Vision Res 85:5-19
Peterson, Matthew F; Eckstein, Miguel P (2013) Individual differences in eye movements during face identification reflect observer-specific optimal points of fixation. Psychol Sci 24:1216-25
Peterson, Matthew F; Eckstein, Miguel P (2012) Looking just below the eyes is optimal across face recognition tasks. Proc Natl Acad Sci U S A 109:E3314-23
Eckstein, Miguel P (2011) Visual search: a retrospective. J Vis 11:
Trenti, Edgardo J; Barraza, José F; Eckstein, Miguel P (2010) Learning motion: human vs. optimal Bayesian learner. Vision Res 50:460-72

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