Our results provide several important implications for researchers who work on optic flow and are interested in the visually guided walking as well as researchers who work in the field of spatial learning and navigation.
As pointed out at the beginning of the thesis, a large body of work on visually guided walking has rested on two assumptions: (1) that the richness of optic flow predicts walking trajectories; and (2) that heading judgements tap the same processes as the online control of walking (and the latter could be inferred from the performance on the former). Our results challenged both assumptions. It is, therefore, necessary to reconsider these assumptions.
Our results pointed to a previously unrecognised role of allocentric location cues in the visual guidance of walking. As stressed throughout the thesis, the human walking literature has focused on optic flow and egocentric direction. So far, very few studies have considered other visual cues in the online control of walking towards a visible target (see Herlihey 2010).
Further, we uncovered a role for prior knowledge of the environment in the guidance of walking. This finding goes considerably beyond the earlier work on “blind walking” (Thomson, 1980, 1983) that showed an observer is able to walk open-loop to an object in the previously seen environment. Our finding suggests that, like animals, humans might develop some forms of internal representation of the spatial structure of the environment, which may inspire a new line of research in the field of visually guided walking.
7.2.1 Cue combination in the guidance of walking
The extensive literature on perceptual cue combination has demonstrated that humans are capable of integrating information from multiple sources of cues to estimate
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(MLE) rule. According to the MLE rule, the final estimate of the property is the weighted average of estimates from each individual cue (Ernst & Banks, 2002; Hillis, Ernst, Banks, & Landy, 2002; Landy, Maloney, Johnston, & Young, 1995). More recently, cue integration consistent with the MLE rule has also been suggested in the domain of spatial navigation (e.g., Chen, McNamara, Kelly, & Wolbers, 2017) and reorientation (e.g., Xu, Regier, & Newcombe, 2017).
How does the brain assign weight to each individual cue? It has been suggested that the weight of the individual estimates is determined by variance as well as the discrepancy between them (Ernst & Banks, 2002; Landy et al., 1995). When the discrepancy between cues is small, the weight of the estimate is inversely proportional to its variance. In other words, the less noisy and more reliable the stimulus is, the more contribution it makes to the final estimate. When the discrepancy is large, however, the brain may discount the discrepant cues and rely on other stronger cues (Landy et al. 1995). Hillis and colleagues (2002) found that when the cues were from the same modality, e.g. vision, information from single cues was lost and the observer relies on the combination of the cues, whereas when the cues were from different modalities, single-cue information was used in case of large discrepancies between cues.
For the guidance of walking, there are several visual cues that an observer could use, e.g., egocentric direction, optic flow and allocentric location cues. As these cues are subject to noise, it would be beneficial for an observer to use multiple cues in combination in guiding their walking, and in cases of large discrepancies, to discount cues.
Warren et al. (2001) have proposed a simple model that combines the egocentric direction and optic flow cues in a linear way (see the beginning of Chapter 3). According to this model, the egocentric direction of the target is used when the environment is visually impoverished and optic flow becomes increasingly important as the visual richness of the environment increases. In this thesis, the evidence is provided for the role of allocentric location cues and mental representations in the guidance of walking. Therefore, the
contribution of these cues should be taken into consideration. Moreover, the straightness of trajectories varied as a function of walking distance. For example, in the Line condition of Experiment 3.1, the trajectory straightened up when the observer got closer to the target. As the target drift cue becomes more salient as the distance from the target decreases, the straightening suggests that target drift might come into play at a later point of the trial. Therefore, the weighting of cues in the guidance of walking may be a dynamic process (Landy et al., 1995).
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One thing should be noted is that prisms (or ‘virtual prisms’) were used in this thesis. The use of prisms introduced a discrepancy between egocentric direction and other cues (i.e., optic flow and allocentric location cues). This is what we need in order to distinguish the contribution of optic flow and allocentric location cues. However, the use of prisms may also bring some confounds according to cue combination models. For example, the brain could discount egocentric direction and rely on other cues, or probably the other way around. If so, one would expect either very much straight trajectories or fully curved trajectories in the conditions with optic flow and allocentric location cues. However, the trajectories had an intermediate curvature, suggesting that the cues were combined in our experiments.
In addition to visual cues, body-based cues such as proprioceptive and vestibular cues could also provide important information for the guidance. Again, wearing prisms displaces the visual direction from the physical movement direction. Therefore, there was also a discrepancy between the shifted visual cues (i.e., egocentric direction cues) and the body- based cues. According to Ernst and Banks (2002), when visual stimuli are noisy, cues from other sensory modalities may become dominant. Would this be the case in those visually impoverished conditions (e.g., the Line condition in Chapter 3 and the Dark conditions in Chapter 5) that the participants relied on body-based cues rather than visual cues? If so, one would expect a straighter trajectory in these conditions; however, the observed trajectories were as curved as the sole use of egocentric direction predicts. The reason may be that, although the environment was poor in visual richness, the target was clearly visible. Therefore, as compared to body-based cues, the egocentric direction cues may have less variance and hence could be assigned more weight in combination.