The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions.

By B. Tatler. Journal of Vision, 2007

1,055 citations recorded by the source corpus.

Observers show a marked tendency to fixate the center of the screen when viewing scenes on computer monitors. This is often assumed to arise because image features tend to be biased toward the center of natural images and fixations are correlated with image features. A common alternative explanation is that experiments typically use a central pre-trial fixation marker, and observers tend to make small amplitude saccades. In the present study, the central bias was explored by dividing images post hoc according to biases in their image feature distributions. Central biases could not be explained by motor biases for making small saccades and were found irrespective of the distribution of image features. When the scene appeared, the initial response was to orient to the center of the screen. Following this, fixation distributions did not vary with image feature distributions when freely viewing scenes. When searching the scenes, fixation distributions shifted slightly toward the distribution of features in the image, primarily during the first few fixations after the initial orienting response. The endurance of the central fixation bias irrespective of the distribution of image features, or the observer's task, implies one of three possible explanations: First, the center of the screen may be an optimal location for early information processing of the scene. Second, it may simply be that the center of the screen is a convenient location from which to start oculomotor exploration of the scene. Third, it may be that the central bias reflects a tendency to re-center the eye in its orbit.

Subfields: Automotive Engineering, Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/caed9b6de0142c9a4e07c96043e85e38ec22dd1e

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Citations in the corpus

Visual Fixation Durations and Saccade Amplitudes: Shifting Relationship in a Variety of Conditions (2008, background): “…combination of both approaches can account for the guidance of saccades (Treisman & Sato, 1990; van Zoest & Donk, 2004) some evidence is provided that task-demands have a higher priority than saliency features (Einhäuser, Rutishauser, & Koch, 2008; Foulsham & Underwood, 2008; Tatler, 2007).”
Hierarchical Image Saliency Detection on Extended CSSD (2016, positive): “Human attention favors central regions [39].”
You look where I look! Effect of gaze cues on overt and covert attention in misdirection (2009, background): “Other behavioural biases such as the general tendency to look near the centre of monitor displays (Tatler, 2007) and the tendency to make particular eye movements more frequently than others (Tatler & Vincent, 2009, this issue) have also been shown to influence where observers fixate.”
Predicting human gaze beyond pixels. (2014, result): “Consistent with previous findings (Tatler, 2007; Cerf et al., 2009; Judd et al., 2009; Zhao & Koch, 2013), our data display a center bias.”
Modeling Search for People in 900 Scenes: A combined source model of eye guidance (2009, neutral): “The first block consisted of the same 48 images for all participants, and was used as a practice block to verify that the eye could be tracked accurately.”
Facilitation of return during scene viewing (2009, background): “Other systematic tendencies in eye movement behaviour during scene viewing such as a bias of fixations towards the screen centre and a prevalence of horizontal and vertical saccades have recently been reported by Tatler and colleagues (Tatler, 2007; Tatler & Vincent, 2008).”
Cluster-based Co-saliency Detection (2013, neutral): “image spatial coordinates, and the color denotes the saliency distribution of center bias map, which is similar to [33], [34].”
Saliency Detection via Graph-Based Manifold Ranking (2013, background): “The boundary priors are proposed inspired on the recent works of human fixations on images [31], which shows that humans tend to gaze at the center of images.”
Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes (2021, background): “This behavior is known as ”center bias” and is well documented [76],[77], [78].”
Visual saliency estimation by nonlinearly integrating features using region covariances. (2013, background): “…(b) the viewing strategy (tendency of participants to focus on the center to obtain more information), or (c) the motor bias (center being the optimal location to initiate a visual search) (Judd et al., 2009; Tatler, 2007; Tseng, Carmi, Cameron, Munoz, & Itti, 2009; Zhang et al., 2008).”
SALICON: Saliency in Context (2015, background): “In almost all eye-tracking datasets, there exists a spatial prior that pixels near the image center attract more fixations, known as the center bias [30].”
Predicting Human Eye Fixations via an LSTM-Based Saliency Attentive Model (2016, background): “Psychological studies have shown that when observers look at images, their gazes are biased toward the center [21], [22].”
Gaze allocation in natural stimuli: Comparing free exploration to head-fixed viewing conditions (2009, background): “This avoids any confound from shared spatial biases of stimuli and observer (e.g., Tatler, 2007; Tatler et al., 2005) as the baseline and the actual data share condition, setup, and observer biases.”
Eye guidance in natural vision: Reinterpreting salience (2011, neutral): “Recent special issues ofCognitive Computation (Taylor & Cutsuridis, 2011) and of Visual Cognition (Tatler, 2009) reflect the continuing prominence of image salience and similar conspicuity-based factors in current research.”
Get real! Resolving the debate about equivalent social stimuli (2009, methodology): “This control value was chosen to account for the known bias to fixate the lower central regions of scenes (see Tatler, 2007, for more on the central fixation bias).”