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Human eye movements have been the subject of research since the late 1800s(Rayner, 1998). This early research identified many of the fundamental eye movement facts, such as the size of effective vision (about 1◦to 3◦), and characteristics of saccadic eye movements (the quick movements of the eyes) such as their latency and angular distances. Humans eyes, unlike a camera, do not gaze upon a scene in a fixed steadiness, rather the eyes move and jump around to dynamically construct a mental representation of the scene.

Gaze is understood to be “both simultaneously bottom-up, stimulus-driven as well as top- down, goal-oriented”(Duchowski, 2002). This distinction demarcates what is called non-volitional from volitional gaze. Top-down goal orientated aspect of visions was already extensively stud-

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ied by Yarbus in 1967. He identified that when examining complex objects, such as a painting, gaze is fixated more on certain areas of the object over others and suggests that gaze can be read to elucidate internal cognitive mechanisms (“Eye movements reflect the human thought process”(Yarbus, 1967, p. 190)). In particular, he identified that the task at hand plays a funda- mental role: “depending on the character of the information he must obtain, the distribution of the points of fixation on an object will vary correspondingly, because different items of information are usually localized in different parts of an object”(Yarbus, 1967, p. 190).

However this is perhaps not so straight forward, as Duchowski explicates

”When examining a scanpath over a visual stimulus, we can often say that specific regions were looked at, perhaps even fixated (following analysis of eye movements), however, we can not be fully confident that these specific regions were fully per- ceived. There is (currently) no simple way of telling what the brain is doing during a particular visual scan of the scene”(Duchowski, 2002).

Although we are a long way from having a complete understanding of the biological mecha- nisms of the human vision system (see(Cavanagh, 2011) for a good overview of the field), there has been significant progress in modelling certain aspects of visual scene attention.

Itty and Coch outline a ”biologically plausible computational modelling of a saliency-based form of focal bottom-up attention”(Itti, 2001). They suggest that bottom-up visual attention to a stimulus is highly contingent on surrounding context and propose that for a visual scene, their ‘saliency map’(Koch & Ullman, 1985), that encodes the visual ‘conspicuity’ of a stimulus in a scene, is a plausible model for an aspect focal visual attention control strategy (for instance a red ball in a green field attracts gaze). ‘Inhibition of return’ where a stimuli previously attended to are prevented from being attended to again is another mechanism at play, and scene understanding and object recognition also are factors in how the gaze is directed in scene attention(Itti, 2001). Gaze is not simply directed to where things are interesting or pretty.

Numerous studies of gaze patterns during the contemplation of artworks have been carried out. Rather than being able to establish a link between gaze and aesthetic quality, these studies provide an insight into perceptual mechanisms at play in object and scene contemplation (e.g. (Buswell, 1935; Yarbus, 1967; Krupinski, Locher, Nodine, & Mello-Thoms, 2007; Wooding, 2002)). As a viewer perceives a painting, after an initial short term rapid holistic impression is made, where the viewer gets a ’gist’ of the picture, individual areas of the image are then

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attended to in greater detail(Krupinski et al., 2007). Statistical techniques can be applied to the gaze patterns to make a ‘fixation map’ demarcating the ‘regions of interest’ in the work(Wooding, 2002; Santella & DeCarlo, 2004). Where those regions are depend on many factors and vary from individual to individual, however it has been noted that there is often a link between the number and duration of fixations in regions of the artwork, and how detailed the those regions are(Wooding, 2002; Buswell, 1935).

A significant body of research suggests that there is a correlation between interest and gaze fixations, both in task orientated studies, and in passive observation. Pieters and Warlop(Pieters & Warlop, 1999) have found strong correlation between gaze fixation and saccadic movements with preference selections of branded items. Plumhoff and Schirillo(Plumhoff & Schirillo, 2009) similarly found that gaze fixation durations and saccade-distances correlated with aesthetic pref- erence of paintings. Holmes and Zanker(Holmes & Zanker, 2012) observed the gaze patterns when viewers are presented with 2 to 8 images in a circular arrangement, and found that the accumulated fixation duration correlated strongly with preference. Additional feature of the gaze data, such as the number of times the gaze returns to the object also correlated with aesthetic pref- erence. They then combined these features to create an ‘oculomotor signature’ for the prediction of stimuli preference.

Research on eye tracking in image search tasks suggests that information in gaze patterns can be used to make inferences about human interests. Haji Mirza et al.(Haji Mirza, Proulx, & Izquierdo, 2012) extracted a large number of gaze-data features in an image search task, and using statistical techniques extracted weightings for the features that provided the best prediction of interest. They found that the most significant features related to the durations of gaze visits of the stimulus (e.g total visit length, average duration of visits). Hardoon and Psupa(Hardoon & Pasupa, 2010)successfully applied similar techniques in an image search system supported by implicit gaze information.

The earlier research on gaze used complex analogue mechanisms and manual techniques to collect gaze information. This was then superseded by computer based eye-tracking technology, which has became more accurate and affordable in recent decades. Today eye-trackers are no longer just used exclusively in visual perception research, but as interface mechanisms. These are briefly surveyed in the next section.

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