Free viewing of dynamic stimuli by humans and monkeys.

Due to extensive homologies, monkeys provide a sophisticated animal model of human visual attention. However, for electrophysiological recording in behaving animals simplified stimuli and controlled eye position are traditionally used. To validate monkeys as a model for human attention during realistic free viewing, we contrasted human (n = 5) and monkey (n = 5) gaze behavior using 115 natural and artificial video clips. Monkeys exhibited broader ranges of saccadic endpoints and amplitudes and showed differences in fixation and intersaccadic intervals. We compared tendencies of both species to gaze toward scene elements with similar low-level visual attributes using two computational models--luminance contrast and saliency. Saliency was more predictive of both human and monkey gaze, predicting human saccades better than monkey saccades overall. Quantifying interobserver gaze consistency revealed that while humans were highly consistent, monkeys were more heterogeneous and were best predicted by the saliency model. To address these discrepancies, we further analyzed high-interest gaze targets--those locations simultaneously chosen by at least two monkeys. These were on average very similar to human gaze targets, both in terms of specific locations and saliency values. Although substantial quantitative differences were revealed, strong similarities existed between both species, especially when focusing analysis onto high-interest targets.

[1]  D. Robinson,et al.  A METHOD OF MEASURING EYE MOVEMENT USING A SCLERAL SEARCH COIL IN A MAGNETIC FIELD. , 1963, IEEE transactions on bio-medical engineering.

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

[3]  A. L. Yarbus,et al.  Eye Movements and Vision , 1967, Springer US.

[4]  A. Fuchs,et al.  Further properties of the human saccadic system: eye movements and correction saccades with and without visual fixation points. , 1969, Vision research.

[5]  F. James Rohlf,et al.  Biometry: The Principles and Practice of Statistics in Biological Research , 1969 .

[6]  L. Stark,et al.  Scanpaths in Eye Movements during Pattern Perception , 1971, Science.

[7]  B. Troost,et al.  Velocity characteristics of normal human saccades. , 1974, Investigative ophthalmology.

[8]  D. Bamber The area above the ordinal dominance graph and the area below the receiver operating characteristic graph , 1975 .

[9]  L. Stark,et al.  The main sequence, a tool for studying human eye movements , 1975 .

[10]  N. Mackworth,et al.  Cognitive determinants of fixation location during picture viewing. , 1978, Journal of experimental psychology. Human perception and performance.

[11]  B. Richmond,et al.  Implantation of magnetic search coils for measurement of eye position: An improved method , 1980, Vision Research.

[12]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[13]  Terry A. Bahill,et al.  Variability and development of a normative data base for saccadic eye movements. , 1981, Investigative ophthalmology & visual science.

[14]  S. Gielen,et al.  A quantitative analysis of generation of saccadic eye movements by burst neurons. , 1981, Journal of neurophysiology.

[15]  Lance M. Optican,et al.  Unix-based multiple-process system, for real-time data acquisition and control , 1982 .

[16]  C. Harris,et al.  Fourier analysis of saccades in monkeys and humans. , 1990, Journal of neurophysiology.

[17]  P. de Graef,et al.  Local and global contextual constraints on the identification of objects in scenes. , 1992, Canadian journal of psychology.

[18]  P. Good,et al.  Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses , 1995 .

[19]  J. Gallant,et al.  Neural activity in areas V1, V2 and V4 during free viewing of natural scenes compared to controlled viewing. , 1998, Neuroreport.

[20]  P Reinagel,et al.  Natural scene statistics at the centre of gaze. , 1999, Network.

[21]  J. Henderson,et al.  The effects of semantic consistency on eye movements during complex scene viewing , 1999 .

[22]  J L Gallant,et al.  Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.

[23]  Claudio M. Privitera,et al.  Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Christian Quaia,et al.  Extent of compensation for variations in monkey saccadic eye movements , 2000, Experimental Brain Research.

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

[26]  Pamela Reinagel How do visual neurons respond in the real world? , 2001, Current Opinion in Neurobiology.

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

[28]  S A Finney,et al.  Real-time data collection in Linux: A case study , 2001, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[29]  Douglas P. Munoz,et al.  Expression of a re-centering bias in saccade regulation by superior colliculus neurons , 2001, Experimental Brain Research.

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

[31]  Derrick J. Parkhurst,et al.  Modeling the role of salience in the allocation of overt visual attention , 2002, Vision Research.

[32]  Antonio Torralba,et al.  Top-down control of visual attention in object detection , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[33]  Derrick J. Parkhurst,et al.  Scene content selected by active vision. , 2003, Spatial vision.

[34]  Konrad Paul Kording,et al.  Processing of complex stimuli and natural scenes in the visual cortex , 2004, Current Opinion in Neurobiology.

[35]  G. Orban,et al.  Comparative mapping of higher visual areas in monkeys and humans , 2004, Trends in Cognitive Sciences.

[36]  Gidon Felsen,et al.  A natural approach to studying vision , 2005, Nature Neuroscience.

[37]  L. Itti Author address: , 1999 .

[38]  Asha Iyer,et al.  Components of bottom-up gaze allocation in natural images , 2005, Vision Research.

[39]  Iain D. Gilchrist,et al.  Visual correlates of fixation selection: effects of scale and time , 2005, Vision Research.

[40]  Jillian H. Fecteau,et al.  Salience, relevance, and firing: a priority map for target selection , 2006, Trends in Cognitive Sciences.

[41]  Mriganka Sur,et al.  Image Structure at the Center of Gaze during Free Viewing , 2006, Journal of Cognitive Neuroscience.

[42]  M. Westoby,et al.  Bivariate line‐fitting methods for allometry , 2006, Biological reviews of the Cambridge Philosophical Society.

[43]  P. König,et al.  Differences of monkey and human overt attention under natural conditions , 2006, Vision Research.

[44]  L. Itti Quantitative modelling of perceptual salience at human eye position , 2006 .

[45]  M. Goldberg,et al.  Saccades, salience and attention: the role of the lateral intraparietal area in visual behavior. , 2006, Progress in brain research.

[46]  Gregory J. Zelinsky,et al.  Scene context guides eye movements during visual search , 2006, Vision Research.

[47]  O. Meur,et al.  Predicting visual fixations on video based on low-level visual features , 2007, Vision Research.

[48]  Stefano Ramat,et al.  What clinical disorders tell us about the neural control of saccadic eye movements. , 2007, Brain : a journal of neurology.

[49]  Benjamin W Tatler,et al.  The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. , 2007, Journal of vision.

[50]  Robert A. Marino,et al.  Spatial relationships of visuomotor transformations in the superior colliculus map. , 2008, Journal of neurophysiology.

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

[52]  Pierre Baldi,et al.  Bayesian surprise attracts human attention , 2005, Vision Research.

[53]  Nicholas A. Steinmetz,et al.  Top-down control of visual attention , 2010, Current Opinion in Neurobiology.

[54]  L. Itti The iLab Neuromorphic Vision C + + Toolkit : Free tools for the next generation of vision algorithms , 2022 .