Gambling in the Visual Periphery: A Conjoint-Measurement Analysis of Human Ability to Judge Visual Uncertainty

Recent work in motor control demonstrates that humans take their own motor uncertainty into account, adjusting the timing and goals of movement so as to maximize expected gain. Visual sensitivity varies dramatically with retinal location and target, and models of optimal visual search typically assume that the visual system takes retinal inhomogeneity into account in planning eye movements. Such models can then use the entire retina rather than just the fovea to speed search. Using a simple decision task, we evaluated human ability to compensate for retinal inhomogeneity. We first measured observers' sensitivity for targets, varying contrast and eccentricity. Observers then repeatedly chose between targets differing in eccentricity and contrast, selecting the one they would prefer to attempt: e.g., a low contrast target at 2° versus a high contrast target at 10°. Observers knew they would later attempt some of their chosen targets and receive rewards for correct classifications. We evaluated performance in three ways. Equivalence: Do observers' judgments agree with their actual performance? Do they correctly trade off eccentricity and contrast and select the more discriminable target in each pair? Transitivity: Are observers' choices self-consistent? Dominance: Do observers understand that increased contrast improves performance? Decreased eccentricity? All observers exhibited patterned failures of equivalence, and seven out of eight observers failed transitivity. There were significant but small failures of dominance. All these failures together reduced their winnings by 10%–18%.

[1]  Jiri Najemnik,et al.  Eye movement statistics in humans are consistent with an optimal search strategy. , 2008, Journal of vision.

[2]  Jens Schwarzbach,et al.  Adaptive strategies for reading with a forced retinal location. , 2008, Journal of vision.

[3]  A. Johnston,et al.  Spatial scaling of central and peripheral contrast-sensitivity functions. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[4]  Quick Rf A vector-magnitude model of contrast detection. , 1974 .

[5]  M. Ernst,et al.  Humans integrate visual and haptic information in a statistically optimal fashion , 2002, Nature.

[6]  B. Tatler,et al.  The prominence of behavioural biases in eye guidance , 2009 .

[7]  B. Cole,et al.  Imperfectly optimal animals , 1981, Behavioral Ecology and Sociobiology.

[8]  W. Weibull A Statistical Distribution Function of Wide Applicability , 1951 .

[9]  R. Luce Utility of Gains and Losses: Measurement-Theoretical and Experimental Approaches , 2000 .

[10]  N Drasdo,et al.  Non-linear projection of the retinal image in a wide-angle schematic eye. , 1974, The British journal of ophthalmology.

[11]  Preeti Verghese,et al.  Where to look next? Eye movements reduce local uncertainty. , 2007, Journal of vision.

[12]  Melchi M. Michel,et al.  Michel Gaze Contingent Displays : Analysis of Saccadic Plasticity in Visual Search , 2009 .

[13]  J. Farrell,et al.  Equating character-identification performance across the visual field. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[14]  Eileen Kowler,et al.  Eye movements during visual search: the costs of choosing the optimal path , 2001, Vision Research.

[15]  D. Whitaker,et al.  Disentangling the Role of Spatial Scale, Separation and Eccentricity in Weber's Law for Position , 1997, Vision Research.

[16]  Douglas P Looze Linear-quadratic-Gaussian control for adaptive optics systems using a hybrid model. , 2009, Journal of the Optical Society of America. A, Optics, image science, and vision.

[17]  R. Quick A vector-magnitude model of contrast detection , 2004, Kybernetik.

[18]  Michael S Landy,et al.  Statistical decision theory and the selection of rapid, goal-directed movements. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[19]  L. Maloney,et al.  Economic decision-making compared with an equivalent motor task , 2009, Proceedings of the National Academy of Sciences.

[20]  C. A. Burbeck,et al.  Two mechanisms for localization? Evidence for separation-dependent and separation-independent processing of position information , 1990, Vision Research.

[21]  A. Tversky,et al.  Foundations of Measurement, Vol. I: Additive and Polynomial Representations , 1991 .

[22]  K. Fujii,et al.  Visualization for the analysis of fluid motion , 2005, J. Vis..

[23]  Richard Gonzalez,et al.  On the Shape of the Probability Weighting Function , 1999, Cognitive Psychology.

[24]  Wilson S. Geisler,et al.  Optimal eye movement strategies in visual search , 2005, Nature.

[25]  Jaap Van Brakel,et al.  Foundations of measurement , 1983 .

[26]  D. Pelli,et al.  The uncrowded window of object recognition , 2008, Nature Neuroscience.

[27]  Gary S Rubin,et al.  Reading with simulated scotomas: attending to the right is better than attending to the left , 1999, Vision Research.

[28]  A. Tversky Intransitivity of preferences. , 1969 .

[29]  Todd S. Horowitz,et al.  Visual search has no memory , 1998, Nature.

[30]  J. Rovamo,et al.  Visual resolution, contrast sensitivity, and the cortical magnification factor , 2004, Experimental Brain Research.

[31]  Karl R Gegenfurtner,et al.  Effects of salience and reward information during saccadic decisions under risk. , 2009, Journal of the Optical Society of America. A, Optics, image science, and vision.

[32]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[33]  A. Tversky,et al.  Choices, Values, and Frames , 2000 .

[34]  G. Keren,et al.  On the ability of monitoring non-veridical perceptions and uncertain knowledge: some calibration studies. , 1988, Acta psychologica.

[35]  A. Hendrickson,et al.  Human photoreceptor topography , 1990, The Journal of comparative neurology.

[36]  Miguel P Eckstein,et al.  Saccadic and perceptual performance in visual search tasks. I. Contrast detection and discrimination. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[37]  G. Zelinsky Using Eye Saccades to Assess the Selectivity of Search Movements , 1996, Vision Research.

[38]  R. Luce,et al.  Simultaneous conjoint measurement: A new type of fundamental measurement , 1964 .

[39]  David Williams,et al.  No aliasing at edges in normal viewing , 1992, Vision Research.

[40]  W P Medendorp,et al.  Ocular kinematics and eye-hand coordination , 2003, Strabismus.

[41]  Michael S. Landy,et al.  Optimal Compensation for Temporal Uncertainty in Movement Planning , 2008, PLoS Comput. Biol..

[42]  M. Landy,et al.  Humans Rapidly Estimate Expected Gain in Movement Planning , 2006, Psychological science.

[43]  J. H. Bertera The effect of simulated scotomas on visual search in normal subjects. , 1988, Investigative ophthalmology & visual science.

[44]  Miguel P. Eckstein,et al.  Evolution and Optimality of Similar Neural Mechanisms for Perception and Action during Search , 2010, PLoS Comput. Biol..

[45]  R. Selten,et al.  Bounded rationality: The adaptive toolbox , 2000 .

[46]  G E Legge,et al.  Contrast discrimination in peripheral vision. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[47]  L. Maloney,et al.  Explicit estimation of visual uncertainty in human motion processing , 2005, Vision Research.

[48]  Jeffrey S. Perry,et al.  Visual search: the role of peripheral information measured using gaze-contingent displays. , 2006, Journal of vision.