Individual differences in human eye movements: An oculomotor signature?

Bargary, Gary; Bosten, Jenny; Goodbourn, Patrick T.; Lawrance-Owen, Adam J.; Hogg, Ruth; Mollon, J. D.

doi:10.1016/j.visres.2017.03.001

Cited by 153 publications

(147 citation statements)

References 70 publications

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“…However, even the earliest studies of fixation behaviour noted considerable individual differences 19,20 , and basic occulomotor traits vary reliably between observers [21][22][23][24][25][26] . Recent twinstudies revealed that social attention and gaze traces across complex scenes are highly heritable 27,28 .…”

Section: Introductionmentioning

confidence: 99%

Individual differences in visual salience vary along semantic dimensions

Haas

Iakovidis

Schwarzkopf

et al. 2018

Preprint

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150 words; Main text: 2615 words (excl. Methods, fig. and table captions); Methods: 17 1526 words; 5 figures:1 table; 1 supplementary figure 18 19 20 21 22 23 features and task demands govern fixation behaviour, while differences between observers 24 are 'noise'. Here, we investigated the fixations of > 100 human adults freely viewing a large 25 set of complex scenes. We found systematic individual differences in fixation frequencies 26 along six semantic stimulus dimensions. These differences were large (> twofold) and 27 highly stable across images and time. Surprisingly, they also held for first fixations directed 28 towards each image, commonly interpreted as 'bottom-up' visual salience. Their 29 perceptual relevance was documented by a correlation between individual face salience and 30 recognition skills. The dimensions of individual salience and their covariance pattern 31 replicated across samples from three different countries, suggesting they reflect 32 fundamental biological mechanisms of attention. Our findings show stable individual 33 salience differences along semantic dimensions, with meaningful perceptual implications. 34 Salience reflects features of the observer as well as the image. 35 36 image discontinuities of low level attributes, such as luminance, colour and orientation 13 . These 45 low-level models are inspired by 'early' visual neurons and their output correlates with neural 46 responses in subcortical 14 and cortical 15 areas thought to represent neural 'salience maps'. 47However, while these models work relatively well for impoverished stimuli, human gaze 48 behaviour towards richer scenes can be predicted at least as well by the locations of objects 16 and 49 perceived meaning 9 . When sematic object properties are taken into account, their weight for gaze 50 prediction far exceeds that of low-level attributes 8,17 . A common thread of low-and high-level 51 salience models is that they interpret salience as a property of the image and treat inter-individual 52 differences as unpredictable 7,18 , often using them as a 'noise ceiling' for model evaluations 18 . 53 However, even the earliest studies of fixation behaviour noted considerable individual 54 differences 19,20 and basic occulomotor traits, like mean saccadic amplitude and velocity, reliably 55 vary between observers 21-27 . Moreover, recent twin-studies revealed that social attention and 56 gaze traces across complex scenes are highly heritable 28,29 . This suggests individual differences 57 in fixation behaviour are not random, but systematic. However, it is largely unclear, how 58 individuals differ in their fixation behaviour and what may explain these differences. Can 59 individual fixation behaviour be captured along a limited set of dimensions? 60Here, we tested the hypothesis that individual gaze reflects individual salience differences 61 along a limited number of semantic dimensions. We investigated the fixation behaviour of > 100 62 human adults freely viewing 700 complex scenes, containing thousands of semantica...

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Section: Introductionmentioning

confidence: 99%

Individual differences in visual salience vary along semantic dimensions

Haas

Iakovidis

Schwarzkopf

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…saccade errors occurred in all groups more often during the presentation of food stimuli. In addition, the BED1 sample committed significantly more errors than the NWC sample, suggesting inhibitory control difficulties in the BED1 sample, which might be food-specific (see Schag et al, 2013a;Svaldi et al, 2014 (Bargary et al, 2017;Kissler & Keil, 2008;van Steenbergen, Band, & Hommel, 2011).…”

mentioning

confidence: 99%

Food specific inhibitory control under negative mood in binge‐eating disorder: Evidence from a multimethod approach*

Leehr

Schag

Dresler

et al. 2018

Intl J Eating Disorders

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Participants with BED reported higher impulsivity and lower emotion regulation capacities. The combined investigation of electrocortical processes and behavior contributes to an advanced understanding of behavioral and electrocortical processes underlying inhibitory control in BED. Inhibitory control and negative mood, probably amplified by emotion regulation deficits, should be addressed further in the investigation and treatment of BED.

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“…In healthy persons, personal differences in oculomotor behavior are observed when looking at a monitor . Differences in eye movements in healthy individuals are referred as their “oculomotor signature.” Variations in gaze patterns during face‐to‐face communication have also recently been described …”

Section: Discussionmentioning

confidence: 87%

“…29,30 Differences in eye movements in healthy individuals are referred as their "oculomotor signature." 31 Variations in gaze patterns during face-to-face communication have also recently been described. 32 The arrow indicates the direction of gaze when a person looks at an object.…”

Section: Recent Developments In Eye Trackersmentioning

confidence: 99%

Application of eye trackers for understanding mental disorders: Cases for schizophrenia and autism spectrum disorder

Shishido

Ogawa

Miyata

et al. 2019

Neuropsychopharm Rep

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Studies of eye movement have become an essential tool of basic neuroscience research. Measures of eye movement have been applied to higher brain functions such as cognition, social behavior, and higher‐level decision‐making. With the development of eye trackers, a growing body of research has described eye movements in relation to mental disorders, reporting that the basic oculomotor properties of patients with mental disorders differ from those of healthy controls. Using discrimination analysis, several independent research groups have used eye movements to differentiate patients with schizophrenia from a mixed population of patients and controls. Recently, in addition to traditional oculomotor measures, several new techniques have been applied to measure and analyze eye movement data. One research group investigated eye movements in relation to the risk of autism spectrum disorder several years prior to the emergence of verbal‐behavioral abnormalities. Research on eye movement in humans in social communication is therefore considered important, but has not been well explored. Since eye movement patterns vary between patients with mental disorders and healthy controls, it is necessary to collect a large amount of eye movement data from various populations and age groups. The application of eye trackers in the clinical setting could contribute to the early treatment of mental disorders.

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Individual differences in human eye movements: An oculomotor signature?

Cited by 153 publications

References 70 publications

Individual differences in visual salience vary along semantic dimensions

Individual differences in visual salience vary along semantic dimensions

Food specific inhibitory control under negative mood in binge‐eating disorder: Evidence from a multimethod approach*

Application of eye trackers for understanding mental disorders: Cases for schizophrenia and autism spectrum disorder

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