Microsaccades during high speed continuous visual search

Here, we provide an analysis of the microsaccades that occurred during continuous visual search and targeting of small faces that we pasted either into cluttered background photos or into a simple gray background. Subjects continuously used their eyes to target singular 3-degree upright or inverted faces in changing scenes. As soon as the participant’s gaze reached the target face, a new face was displayed in a different and random location. Regardless of the experimental context (e.g. background scene, no background scene), or target eccentricity (from 4 to 20 degrees of visual angle), we found that the microsaccade rate dropped to near zero levels within only 12 milliseconds after trial onset. There were almost never any microsaccades before the saccade to the face. One subject completed 118 consecutive trials without a single microsaccade. However, in about 20% of the trials, there was a single corrective microsaccade that occurred almost immediately after the preceding saccade’s offset. These corrective microsaccades were task oriented because their facial landmark targeting distributions matched those of saccades within both the upright and inverted face conditions. Our findings show that a single feedforward pass through the visual hierarchy for each stimulus is likely all that is needed to effectuate prolonged continuous visual search. In addition, we provide evidence that microsaccades can serve perceptual functions like correcting saccades or effectuating task-oriented goals during continuous visual search.

[1]  R. C. Langford How People Look at Pictures, A Study of the Psychology of Perception in Art. , 1936 .

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

[3]  James W Tanaka,et al.  Does face inversion qualitatively change face processing: an eye movement study using a face change detection task. , 2013, Journal of vision.

[4]  Charles E. Davis,et al.  High Resolution Human Eye Tracking During Continuous Visual Search , 2018, Front. Hum. Neurosci..

[5]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[6]  Arnaud Delorme,et al.  Face identification using one spike per neuron: resistance to image degradations , 2001, Neural Networks.

[7]  Patrick M. Pilarski,et al.  First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning , 2014 .

[8]  M. Land Eye movements and the control of actions in everyday life , 2006, Progress in Retinal and Eye Research.

[9]  Marcus Nyström,et al.  An adaptive algorithm for fixation, saccade, and glissade detection in eyetracking data , 2010, Behavior research methods.

[10]  Xoana G. Troncoso,et al.  Saccades and microsaccades during visual fixation, exploration, and search: foundations for a common saccadic generator. , 2008, Journal of vision.

[11]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[12]  M. B. Stegmann,et al.  A Brief Introduction to Statistical Shape Analysis , 2002 .

[13]  Hong-Jin Sun,et al.  Modulation of microsaccade rate by task difficulty revealed through between- and within-trial comparisons. , 2015, Journal of vision.

[14]  S. Martinez-Conde,et al.  The impact of microsaccades on vision: towards a unified theory of saccadic function , 2013, Nature Reviews Neuroscience.

[15]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[16]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Xun He,et al.  A consumer-grade LCD monitor for precise visual stimulation , 2018, Behavior research methods.

[18]  Jeremy M. Wolfe,et al.  The Rules of Guidance in Visual Search , 2012, PerMIn.

[19]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[20]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

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

[22]  Ralf Engbert,et al.  Microsaccades are different from saccades in scene perception , 2010, Experimental Brain Research.

[23]  John Wang,et al.  Finite Element Analysis and Test Correlation of a 10-Meter Inflation-Deployed Solar Sail , 2005 .

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

[25]  Ralf Engbert,et al.  Microsaccades are triggered by low retinal image slip. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

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

[27]  Christopher S Kallie,et al.  On the relative detectability of configural properties. , 2014, Journal of vision.

[28]  Sang Wook Hong,et al.  Radial bias for orientation and direction of motion modulates access to visual awareness during continuous flash suppression. , 2015, Journal of vision.

[29]  Stephen L Macknik,et al.  Unsupervised clustering method to detect microsaccades. , 2014, Journal of vision.

[30]  Jui-Kai Wang,et al.  Retinal and Choroidal Folds in Papilledema. , 2015, Investigative ophthalmology & visual science.

[31]  J. Wolfe Moving towards solutions to some enduring controversies in visual search , 2003, Trends in Cognitive Sciences.

[32]  Ralf Engbert,et al.  Microsaccades uncover the orientation of covert attention , 2003, Vision Research.

[33]  M. Rolfs Microsaccades: Small steps on a long way , 2009, Vision Research.

[34]  Simon Barthelmé,et al.  Spatial statistics and attentional dynamics in scene viewing. , 2014, Journal of vision.

[35]  S. Thorpe,et al.  Surfing a spike wave down the ventral stream , 2002, Vision Research.

[36]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[37]  Charles E. Davis,et al.  Zapping 500 faces in less than 100 seconds: Evidence for extremely fast and sustained continuous visual search , 2018, Scientific Reports.

[38]  Christopher W Tyler,et al.  A simpler structure for local spatial channels revealed by sustained perifoveal stimuli. , 2013, Journal of vision.