Endogenous strategy in exploration.

We examined the characteristics of endogenous exploratory behaviors in a generalized search task in which guidance signals (e.g., landmarks, semantics, visual saliency, layout) were limited or precluded. Individuals looked for the highest valued cell in an array and were scored on the quality of the best value they could find. Exploration was guided only by the cells that had been previously examined, and the value of this guidance was manipulated by adjusting spatial autocorrelation to produce relatively smooth and rough landscapes-that is, arrays in which nearby cells had unrelated values (low correlation = rough) or similar values (high correlation = smooth). For search in increasingly rough as compared with smooth arrays, we found reduced performance despite increased sampling and increased time spent searching after revelation of a searcher's best cell. Spatially, sampling strategies tended toward more excursive, branching, and space-filling patterns as correlation decreased. Using a novel generalized-recurrence analysis, we report that these patterns reflect an increase in systematic search paths, characterized by regularized sweeps with localized infilling. These tendencies were likewise enhanced for high-performance as compared with low-performance participants. The results suggest a trade-off between guidance (in smooth arrays) and systematicity (in rough arrays), and they provide insight into the particular strategic approaches adopted by searchers when exogenous guiding information is minimized.

[1]  Tai Sing Lee,et al.  An Information-Theoretic Framework for Understanding Saccadic Eye Movements , 1999, NIPS.

[2]  T. Foulsham,et al.  What can saliency models predict about eye movements? Spatial and sequential aspects of fixations during encoding and recognition. , 2008, Journal of vision.

[3]  Antonio Torralba,et al.  Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. , 2006, Psychological review.

[4]  Christoph Hölscher,et al.  Taxonomy of Human Wayfinding Tasks: A Knowledge-Based Approach , 2009, Spatial Cogn. Comput..

[5]  Donald B. Percival,et al.  Estimators of Fractal Dimension: Assessing the Roughness of Time Series and Spatial Data , 2011, 1101.1444.

[6]  Walter F. Bischof,et al.  Why we Should Not Forget About the Non-social World: Subjective Preferences, Exploratory Eye-movements, and Individual Differences , 2013, CogSci.

[7]  J. D. Gould,et al.  Eye-movement parameters and pattern discrimination , 1969 .

[8]  D. Ballard,et al.  Eye guidance in natural vision: reinterpreting salience. , 2011, Journal of vision.

[9]  Krista A. Ehinger,et al.  Modelling search for people in 900 scenes: A combined source model of eye guidance , 2009 .

[10]  J. Henderson Human gaze control during real-world scene perception , 2003, Trends in Cognitive Sciences.

[11]  C. Lawton STRATEGIES FOR INDOOR WAYFINDING: THE ROLE OF ORIENTATION , 1996 .

[12]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

[13]  Stephan Winter,et al.  Enriching Wayfinding Instructions with Local Landmarks , 2002, GIScience.

[14]  Alan Kingstone,et al.  Recurrence quantification analysis of eye movements , 2013, Behavior Research Methods.

[15]  John M Henderson,et al.  Stable individual differences across images in human saccadic eye movements. , 2008, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.

[16]  Alan Kingstone,et al.  Temporal dynamics of eye movements are related to differences in scene complexity and clutter. , 2014, Journal of vision.

[17]  A. L. I︠A︡rbus Eye Movements and Vision , 1967 .

[18]  T. Foulsham,et al.  How Does the Purpose of Inspection Influence the Potency of Visual Salience in Scene Perception? , 2007, Perception.

[19]  L. Stark,et al.  Scanpaths in saccadic eye movements while viewing and recognizing patterns. , 1971, Vision research.

[20]  Andrew P. Duchon,et al.  Do Humans Integrate Routes Into a Cognitive Map? Map- Versus Landmark-Based Navigation of Novel Shortcuts , 2005 .

[21]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[22]  T. Foulsham,et al.  Comparing scanpaths during scene encoding and recognition : A multi-dimensional approach , 2012 .

[23]  C L Webber,et al.  Dynamical assessment of physiological systems and states using recurrence plot strategies. , 1994, Journal of applied physiology.

[24]  E. Charnov Optimal foraging, the marginal value theorem. , 1976, Theoretical population biology.

[25]  Stuart A. Kauffman,et al.  The origins of order , 1993 .

[26]  G. T. Buswell How People Look At Pictures: A Study Of The Psychology Of Perception In Art , 2012 .

[27]  H. F. Brandt Ocular patterns and their psychological implications. , 1940 .

[28]  J. Zbilut,et al.  Embeddings and delays as derived from quantification of recurrence plots , 1992 .

[29]  Nicola C. Anderson,et al.  Curious eyes: Individual differences in personality predict eye movement behavior in scene-viewing , 2012, Cognition.

[30]  C. J. Erkelens,et al.  Control of fixation duration in a simple search task , 1996, Perception & psychophysics.

[31]  Florence Gaunet,et al.  Early-Blind Subjects' Spatial Representation of Manipulatory Space: Exploratory Strategies and Reaction to Change , 1997, Perception.

[32]  Jeremy M Wolfe,et al.  When is it time to move to the next raspberry bush? Foraging rules in human visual search. , 2013, Journal of vision.

[33]  I. Gilchrist,et al.  Evidence for a systematic component within scan paths in visual search , 2006 .

[34]  J. Barton,et al.  Fixation and saliency during search of natural scenes: The case of visual agnosia , 2009, Neuropsychologia.

[35]  Laurent Itti,et al.  A Goal Oriented Attention Guidance Model , 2002, Biologically Motivated Computer Vision.

[36]  Eyal M. Reingold,et al.  Area activation: a computational model of saccadic selectivity in visual search , 2003, Cogn. Sci..

[37]  Jang-Kyoo Shin,et al.  Biologically Inspired Saliency Map Model for Bottom-up Visual Attention , 2002, Biologically Motivated Computer Vision.

[38]  Jerry Weisman,et al.  Evaluating Architectural Legibility , 1981 .

[39]  J. D. Gould,et al.  Eye-Movement Patterns in Scanning Numeric Displays , 1965, Perceptual and motor skills.

[40]  T. Foulsham,et al.  Saliency and scan patterns in the inspection of real-world scenes: Eye movements during encoding and recognition , 2009 .

[41]  J. D. Gould Pattern recognition and eye-movement parameters , 1967 .

[42]  Jeremy M. Wolfe,et al.  Guided Search 4.0: Current Progress With a Model of Visual Search , 2007, Integrated Models of Cognitive Systems.

[43]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[44]  J. Kurths,et al.  Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[45]  A. Kingstone,et al.  Saliency does not account for fixations to eyes within social scenes , 2009, Vision Research.

[46]  Minho Lee,et al.  Saliency map model with adaptive masking based on independent component analysis , 2002, Neurocomputing.

[47]  Carrick C. Williams,et al.  Eye movements during information processing tasks: Individual differences and cultural effects , 2007, Vision Research.

[48]  D. Coppola,et al.  Idiosyncratic characteristics of saccadic eye movements when viewing different visual environments , 1999, Vision Research.

[49]  Michael L. Mack,et al.  Viewing task influences eye movement control during active scene perception. , 2009, Journal of vision.

[50]  Marina Bloj,et al.  Real and predicted influence of image manipulations on eye movements during scene recognition. , 2010, Journal of vision.

[51]  Norman G. Vinson,et al.  Design guidelines for landmarks to support navigation in virtual environments , 1999, CHI '99.

[52]  Nicola C. Anderson,et al.  The influence of personality on social attention , 2014 .

[53]  L. Itti,et al.  Modeling the influence of task on attention , 2005, Vision Research.