Learning and recalling routes through complex environments is a common task which is essential in maintaining independence in everyday life. Typically aged adults often report difficulty with general navigation (Burns,
) and show a reduced ability to learn and recall routes (Head & Isom,
; Moffat, Zonderman & Resnick,
), to retrace a route backwards (Wiener, Kmecova & de Condappa,
), to understand the layout of a known intersection when approaching it from a novel direction (Wiener, de Condappa, Harris & Wolbers,
), to bind landmarks to specific locations (Newman & Kazniak,
; Head & Isom,
) and to learn the sequence of turns along a route (O’Malley, Innes & Wiener,
). Older adults also shift preference away from allocentric navigation strategies and use egocentric strategies more than younger adults (Rodgers, Sindone & Moffat,
). Age-related navigation deficits are more pronounced in unfamiliar than in familiar environments (Devlin,
) and typically become apparent in adults aged between 60 and 69 years (Barrash,
). The current explanations of age-related decline in route learning ability focus on neurodegeneration of structures related to stimulus-response-based, egocentric navigation, such as the caudate (see Lester, Moffat, Wiener, Barnes & Wolbers,
). In contrast, the roles played by other cognitive domains in age-related route learning declines have received little attention (for a review see Klencklen, Després & Dufour,
). In this study, we investigated whether control of visual attention and attentional engagement also contribute to age-related declines in route learning.
Visual information is a vital input for successful navigation (see Ekstom,
), particularly in route navigation, where strategies rely heavily on visual cues (Waller & Lippa,
). At decision points, for example, gaze is directed towards the eventually chosen path and to specific geometric features such as long lines of sight or changes in geometry (Wiener, Hölscher, Büchner & Konieczny,
). In environments with landmarks that are easily identified, the selection and encoding of relevant landmarks is reflected in gaze behaviour (Hamid, Stankiewicz & Hayhoe,
; Wenczel, Hepperle & von Stülpnagel,
; de Condappa & Wiener,
). While these studies demonstrate that gaze behaviour is a measure which is sensitive to behaviour during route learning, so far no study has addressed the question of whether age-related differences in route learning abilities are reflected in differences in gaze behaviour.
Systematic differences in gaze behaviour between younger and older adults have been reported in non-route learning tasks. Dowiasch, Marx, Einhäuser and Bremmer (
) measured several gaze parameters, whilst older and younger participants walked through an environment. While participants did not solve a specific navigation task, older adults showed reduced saccade frequency, amplitude, peak and average velocity. This is in line with a driving study (Maltz & Shinar,
) in which older adults have been reported to make shorter saccades and more fixations, although fixation durations remained the same as in younger adults. This work also reports that when assessing a spatial scene, older adults focus on smaller sub-regions of the stimuli and are less exploratory than younger adults. Similarly, during locomotion, older adults focused on lower portions of the visual scene and to areas closer to themselves in an effort to reduce task error (see Uiga, Cheng, Wilson, Masters & Capio,
). These studies include tasks which are not the focus of this experiment, such as locomotion, but they provide some insight into how cognitive ageing affects gaze behaviour.
Control of visual attention is part of the executive function network (Diamond,
), which is known to undergo age-related decline, often characterised by working memory deficits (Klencklen, Lavenex, Brandner & Lavenex,
). The ageing brain, however, shows increased activation of the prefrontal cortex across both hemispheres (Cabeza,
) as a compensatory mechanism to complete executive functioning tasks (Kirova, Bays & Lagalwar,
). Dorsal frontal regions have also been implicated in the top-down control of visual attention (Kastner,
) and show similar patterns of increased activation in older adults when completing tasks such as visual search (Madden,
). The extent to which both declining executive function and neural compensation in ageing contribute to differences in control of visual attention remains unclear. Given that this is not the focus of the current study, we used age as the indicator for potential decline rather than characterising it through other measures such as working memory performance. Control of visual attention measured by gaze behaviour (see Kristjánsson,
for discussion of the relationship between eye movements and visual attention) may, at least partially, explain age-related route learning differences.
Not all locations in an environment are equally important for route navigation. The parts of a route where a decision about the direction of travel has to be made are known as decision points (e.g. intersections), while other parts which only allow for one possible direction of travel are referred to as non-decision points. Route navigation can be conceptualised as a series of paths between decision points (Schinazi & Epstein,
). Objects at such decision points, i.e. landmarks, not only yield better recognition memory and recall of associated direction than objects located at non-decision points (Janzen,
; von Stülpnagel & Steffens,
), they also selectively recruit the parahippocampal gyrus (Janzen & van Turennout,
; Janzen & Weststeijn,
). Good navigators demonstrate better memory consolidation of decision point information than poor navigators (Janzen, Jansen & van Turennout,
Given the importance of decision points for successful route learning, it is not surprising that navigators pay particular attention to these locations. Using a secondary auditory probe task, Allen and Kirasic (
) demonstrated stronger attentional engagement at areas of high navigational relevance, such as decision points. In their task, participants learned a route from a series of photographs, whilst responding to an auditory probe (a beep). Time to disengage from the primary route learning task and respond to the probe reflects the level of attentional engagement and increased at navigationally relevant locations. Hartmeyer, Grzeschik, Wolbers and Wiener (
) replicated these findings in an ageing study using videos instead of photographs. Interestingly, the effect was similar in the younger and older age groups, even though the latter group showed marked route learning performance deficits. However, the environment used in this experiment was very simplistic, featuring empty corridors and single landmarks at decision points and turns. In view of research demonstrating that older adults have difficulty ignoring task-irrelevant stimuli (e.g. Tusch, Alperin, Holcomb & Daffner,
), it is conceivable that attentional control will be more strongly affected by age in environments which are visually more complex. If older adults directed attentional resources towards task-irrelevant stimuli, fewer resources would remain available for the primary route learning task. The disadvantage of poor resource allocation may be particularly costly for older adults considering the suggestion that their overall pool of cognitive resources may already be diminished compared to younger adults (Meulenbroek et al.,
). Assigning already diminished attentional resources to non-task-relevant information in a complex environment would likely impact route learning performance.
In the present study, we used a paradigm similar to that of Hartmeyer et al. (
), but we used a more visually complex environment and we tracked participants’ gaze behaviour. Our main research questions were: (1) Will previous attentional engagement findings from an auditory probe task be replicated in a complex environment? (2) Can age-related differences in route learning ability be related to differences in gaze behaviour?
The behavioural part of this study was confirmatory. We expected impaired route learning performance in our older as compared to our younger participants (c.f. Wiener et al.,
). Moreover, we expected longer response times to the auditory probe at decision points vs. non-decision points in the younger participant group (c.f. Hartmeyer et al.,
). If older adults were impaired in their ability to modulate engagement of attention during route learning in an information-rich environment, we expected a reduced difference in response times to the auditory probe at decision points vs. non-decision points as compared to the response time difference in younger adults.
The lack of previous research investigating how cognitive ageing affects gaze behaviour during route learning warrants an exploratory approach to the eye-tracking part of this study. If control of visual attention contributes to age-related differences in route learning, we expected systematic differences in gaze behaviour between age groups.
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