Memory across eye-movements: 1/f Dynamic in visual search

Aks, Deborah J.; Zelinsky, Gregory J.; Sprott, J. C.

doi:10.1167/1.3.230

Cited by 39 publications

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“…62 1/f scaling and fractality were observed in a range of human activity. Standard tools used to asses the structure of variability were applied to perceptual-motor behaviors such as finger tapping, [63][64][65][66] eye movements, 67 and locomotion. [68][69][70][71] Warren 72 developed a general modeling framework for behavioral dynamics, articulated in terms of the emergence of behavioral trajectories from informational and forceful (mechanical) animalenvironment and perception-action couplings in the context of behavioral goals (modeled as attractors) and obstacles (modeled as repellers).…”

Section: Complexity and Self-organizationmentioning

confidence: 99%

Dynamics of cognition

Riley

Holden

2012

WIRES Cognitive Science

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The application of dynamical systems methods and concepts to cognitive phenomena has broadened the range of testable hypotheses and theoretical narratives available to cognitive scientists. Most research in cognitive dynamics tests the degree to which observed cognitive performance is consistent with one or another core phenomena associated with complex dynamical systems, such as tests for phase transitions, coupling among processes, or scaling laws. Early applications of dynamical systems theory to perceptual-motor performance and developmental psychology paved the way for more recent applications of dynamical systems analyses, models, and theoretical concepts in areas such as learning, memory, speech perception, decision making, problem solving, and reading, among others. Reviews of the empirical results of both foundational and contemporary cognitive dynamics are provided. © 2012 John Wiley & Sons, Ltd. How to cite this article:WIREs Cogn Sci 2012, 3:593-606. doi: 10.1002/wcs.1200 INTRODUCTION Adynamical system is simply one that changes over time (see Box 1). It is apparent that many of the processes cognitive scientists study-development, learning, the spread of activation in a semantic network, changing patterns of cortical activity, motor behavior, and so on-are intrinsically dynamical. Dynamical systems theory supplies new tools and concepts for understanding these and other cognitive phenomena. The theory offers a full complement of analytical methods (e.g., nonlinear time series analyses) and modeling strategies (e.g., differential equations). Most importantly, it motivates novel theoretical and empirical questions: One may explore the relative stability of a cognitive activity, test for meta-or multi-stability, or test for empirical patterns consistent with scaling behavior, emergence, and self-organization.These kinds of questions complement those posed by mainstream cognitive science-which traditionally has emphasized static properties of mind such as symbolic representations or structural mechanisms of information processing-by focusing explicitly on change 1-9 (see, e.g., Elman 10 for a dynamical interpretation of connectionist models). Much work in cognitive dynamics was inspired by parallel * Correspondence to: michael.riley@uc.edu Center for Cognition, Action, & Perception, Department of Psychology, University of Cincinnati, Cincinnati, OH, USA efforts to understand the dynamics of perceptionaction. [11][12][13][14][15] Early and significant progress emerged in the domain of motor coordination, where the behavioral phenomena were more obviously dynamical and high-resolution measurement technologies were more readily available. 16 The dynamical account of interlimb rhythmic coordination 12,14 is an example of an early success.Research in cognitive dynamics is often allied with some version of embodiment. [17][18][19][20][21] Dynamical systems were sometimes framed as replacements for computational-representational accounts, 8,19 as distinct but complementary, 22 or even as consistent with inform...

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Section: Complexity and Self-organizationmentioning

confidence: 99%

Dynamics of cognition

Riley

Holden

2012

WIRES Cognitive Science

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“…Finally, it may be enlightening to further compare our computational model and results with those of (1) an alternative approach to active visual search based on time series analysis and complex dynamics (Aks et al, 2002), (2) a previous set of models and simulations asserting that visual search has memory (Peterson et al, 2001), and (3) a contrasting perspective of an ideal Bayesian observer (Najemnik & Geisler, 2005). Since these comparisons require fairly detailed reviews and analyses of corresponding articles, they are reported in the three separate subsections of Supplemental Appendix A, posted online.…”

Section: Discussionmentioning

confidence: 99%

“…An adequate review and analysis of such models and issues are beyond the scope of this article. However, in a subsection of Supplemental Appendix A, we discuss, to some extent, the application of time series analysis and methods of complex dynamics in the study of active visual search (Aks, 2005(Aks, , 2009Aks, Zelinsky, & Sprott, 2002;Sprott, 2003) and related NN models (Kwok & Smith, 2005;Usher, Stemmler, AC is then defined as a steeply decreasing probability of target recognition with increasing retinal eccentricity of the target (or its distance from the foveation center): See, for example, the "sensitivity curves" shown in Figure 5A in Motter and Belky (1998b) or, equivalently, the "signalto-noise ratio" shown in Figure 2C in Najemnik and Geisler (2005). The AC also "zooms" as a function of stimulus spacing.…”

Section: Source Modelmentioning

confidence: 99%

“…The full text of Supplemental Appendix A, containing comparisons of our model with those of Aks et al (2002), Peterson et al (2001), and Najemnik and Geisler (2005) …”

Section: Supplemental Materialsmentioning

confidence: 99%

“…For such scale-free distributions, consideration of tails and outliers is essential, whereas those are curtailed by a time constraint on the search. Studies of complex dynamics in visual search tasks relatively similar to ours, but with reduced or removed time constraints, have been conducted by Aks (2005Aks ( , 2009) and Aks et al (2002). See Appendix A for a brief review.…”

Section: Notesmentioning

confidence: 99%

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Eye movements in active visual search: A computable phenomenological model

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Attention, Perception, & Psychophysics

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Perceptual evaluation of synthetic gaze jitter

Krejtz

Duchowski

Zhou

et al. 2017

Computer Animation & Virtual

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Eye movements are an essential part of non‐verbal behavior. Non‐player characters, as they occur in many games, communicate with the player through dialogue and non‐verbal behavior and can have a strong influence on player experience or even on gameplay. In this paper, we evaluate a procedural model designed to synthesize the subtleties of eye motion. More specifically, our model adds microsaccadic jitter and pupil unrest both modeled by 1/fα or pink noise to the saccadic main sequence. In a series of perceptual two‐alternative forced‐choice experiments, we explore the perceived naturalness of different parameters of pink noise by comparing synthesized motions to rendered motion of recorded eye movements at extreme close shot and close shot distances. Our results show that, on average, animations based on a procedural model with pink noise were perceived and evaluated as highly natural, whereas data‐driven motion without any jitter or with unfiltered jitter were consistently selected as the least natural in appearance.

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Memory across eye-movements: 1/f Dynamic in visual search

Cited by 39 publications

References 0 publications

Dynamics of cognition

Dynamics of cognition

Eye movements in active visual search: A computable phenomenological model

Perceptual evaluation of synthetic gaze jitter

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