Anexo: Listado de MF reportados en el Catalogo GCMT

In scanpath tradition, scanpaths are compared between individuals to uncover top– down guidance from the internal cognitive model. The more comparable the

experiences—and therefore the internal representation—of the scenario, the more similar the between-observer scanpaths will be. By comparing scanpaths within and across a priori groupings, factors contributing to the top–down guidance.

Scanpath comparisons involve calculation of string edit distances (SED) between the relevant scanpaths (Brandt & Stark, 1997). The SED is an extension of the

Levenshtein distance to gaze data (Levenshtein, 1966), which involves the quantification of insertions, deletions and substitions to derive an overall editing cost before two

sequences become identical. The SED is a similarity score that ranges from 0 to 1, with 1 being ‘most identical’ (i.e., identical). To derive this similarity score, each individual’s scanpaths are translated into single letters for each gaze behaviour (or target) to form gaze strings. These strings are then truncated to one consistent length across all participants. A minimum (or maximum) string length of ten letters, for example, can be decided: all strings failing this minimum (or maximum) requirement are dispensed of. The remaining strings for each individual are compared with those of other individuals through an

algorithm, eventually yielding a mean similarity score (i.e., SED). This similarity score is derived by the number of edits—both deletions and additions—on one of the two strings that are required before the two strings are identical. This number of edits is the distance between the two strings, which is normalised by dividing the distance by the string length. The similarity score (i.e., SED) is finally obtained by subtracting this normalised distance score from 1 (Figure 6.2). The expectation is that, the more experiences shared by

individuals, the more similar their cognitive model (i.e., top–down guidance of eye movements), and the greater the similarity score will be when their scanpaths when compared.

Accordingly, the value of scanpath comparison lies in its capability to go beyond aggregated quantities of gaze behaviours to compare the structure of gaze patterns across participants by examining the whole sequences of gaze. Additionally, scanpath

comparisons address sequences of semantic gaze behaviours, which is appropriate when geometric gaze coordinates are not available Indeed, an array of scanpath metrics have emerged since Brandt and Stark’s (1997) time (for direct comparisons, see Anderson, Anderson, Kingstone & Bischof, 2014).

Most notably, alternatives to the SED take into account temporal and spatial properties of gaze behaviour, which SED does not. For example, in addition to factoring in sequential aspects of gaze, ScanMatch (Cristino, Mâthot, Theeuwes & Gilchrist, 2010) takes gaze durations and spatial (i.e., geometric) locations into account by taking the fixation location and duration data at the same time as the order of each fixation. Like the SED, ScanMatch compares strings of letters; unlike SED, it factors in gaze duration by an additional decision to represent a ‘bin’ of time (e.g., 0.50 second per letter). In the

‘substitution matrix’, ScanMatch addresses either spatial or semantic (non-spatial) similarities. Rather than the Levenshtein (edit-driven) distance, the Euclidean distance is computed instead, which is a geometric measure of distance between two points (or spatial gaze positions). Segments that are the same as each other receive the highest, positive score; dissimilarities between segments receive lower, negative scores. The researcher determines degree of similarity based on geometric distance, or based on a theory-driven view of distance (or similarity) between concepts (or gaze targets). ScanMatch’s

equivalent to the SED similarity score is the ‘gap penalty’, which represents increasing difference with increasing values. An extension of ScanMatch is MultiMatch (e.g.,

all, MultiMatch takes into account five dimensions: namely, gaze direction, shape, length, position and duration.

However, the added computations of ScanMatch and MultiMatch are both improvements and limitations when compared with the SED. First, by taking temporal information (i.e., gaze duration) into account alongside spatial details, the decision on which channel to prioritise—temporal or spatial—is in fact problematic. Second, spatial details are not necessarily the central information of interest for every research question or possible with all eye-tracking data. That is, SED alternatives rely on consistency of the visual stimuli, such that they must be two-dimensional and by-and-large static. Yet, real- world eye-tracking data is three-dimensional, dynamic and unpredictable, making

geometric coding is redundant. I therefore concluded that the SED is has both the flexibility (i.e., no requirement of spatial data) and detail (i.e., sequential analysis) that I needed as the logical next step in analysis.

Figure 6.2. String editing procedure. The string edit distance is the number of changes that are needed by one scanpath out of two before both are identical. (Image from Choi et al., 1995, p. 445.)

Whereas bottom–up guidance for gaze is highlighted through between-observer consistency of scanpaths, top–down gaze is shown through between-observer difference— and within-observer similarity. Mannan leads this approach to identifying when top– down perception is taking place (Mannan, Kennard & Husain, 2009). Mannan showed the same image under several visual manipulations, through frequency filtering (Mannan, Ruddock & Wooding, 1995). Whereas the initial period of viewing a stimulus triggers significant between-observer similarity, image-viewing from the third second onwards yield significant between-observer differences and within-observer consistency (Mannan et al., 1995, 1997). It seems between-observer similarity occurs in scanpaths towards unfamiliar images, during which bottom–up guidance occurs, but top–down vision takes over once the viewer is accustomed to the stimulus. Top–down vision is further supported by the way hemianopic patients—whose unilateral lesions result in blindness to half of their visual field—use significantly different scanpaths (i.e., between-observer difference) to controls who have no deficits in their visual field (Pambakian, Wooding, Patel,

Morland, Kennard & Mannan, 2000). Meanwhile, neither the hemianopic patients of their non-clinical counterparts required more gaze correction, as indicated by comparable refixations and gaze amplitudes (Pambakian et al., 2000). Thus, between-observer differences—and within-observer similarity—is a consistent indication of top–down visual guidance. Corresponding comparisons have been found through posterior cortical atrophy patients, who suffer from visual degeneration. In initial gaze, such patients yielded between-observer similarity when compared with controls, but subsequently differed notably as viewing continued (Benson, Davis & Snyder, 1988). Together, greater within-observer similarity was expected from the present sample, in anticipation of the top–down guidance of expertise and culture in teacher scanpaths.

So far, this thesis has demonstrated both the importance of expertise in shaping teacher gaze. Different measures demonstrate expertise variations in teacher gaze in different cultures. Some aspects are universal; others culture-specific. The present and final ‘part’ of this thesis aims to analyse teacher gaze in one further level of detail: that is, sequentially. As the literature above has demonstrated, scanpaths are repetitive within individuals because they are guided top–down (e.g., Stark & Privitera, 1997). This top– down guidance is referred to as a cognitive model (Noton, 1970), which is constructed during learning phases—or early-stage viewing—and accessed in face of the same scenarios as part of a recognition, or compare-and-contrast, process. The present thesis aims to apply the scanpath theory and comparison derived to teacher attentional gaze. I expected the role of teacher expertise to accord expertise findings so far in the scanpath literature (e.g., Humphrey & Underwood, 2009). I also extended the principle of cognitive model construction (e.g., DeAngelus & Pelz, 2009) by anticipating classroom culture to result in culturally-divergent expert teacher scanpaths. I additionally extended scanpath theory to the present dual-cognition approach to teacher gaze. That is, whereas traditional scanpath research has only applied to attentional gaze, I apply scanpath comparisons to communicative as well as attentional gaze.