Exp Brain Res DOI 10.1007/s00221-017-4884-9
RESEARCH ARTICLE
Electrophysiological evidence for enhanced attentional deployment in spatial learners Brandi Lee Drisdelle1 · Kyoko Konishi2,3 · Moussa Diarra1 · Veronique D. Bohbot2,3 · Pierre Jolicoeur1 · Greg L. West1
Received: 31 August 2016 / Accepted: 13 January 2017 © Springer-Verlag Berlin Heidelberg 2017
Abstract Visual spatial attention is important during navigation processes that rely on a cognitive map, because spatial relationships between environmental landmarks need to be selected, encoded, and learned. People who navigate using this strategy are spatial learners, and this process relies on the hippocampus. Conversely, response learners memorize a series of actions to navigate, which relies on the caudate nucleus. Response learning, which is more efficient, is thought to involve less demanding cognitive operations, and is related to reduced grey matter in the hippocampus. To test if navigational strategy can impact visual attention performance, we investigated if spatial and response learners showed differences in attentional engagement used during a visual spatial task. We tested 40 response learners and 39 spatial learners, as determined by the 4-on-8 Virtual Maze (4/8 VM), on a target detection task designed to elicit an N2pc component (an index visual spatial attention). Spatial learners produced a larger N2pc amplitude during target detection compared to response learners. This relationship might represent an increase in goal-directed attention towards target stimuli or a more global increase in cognitive function that has been previously observed in spatial learners.
* Greg L. West
[email protected] 1
Department of Psychology, University of Montreal, Pavillon Marie-Victorin, 90, avenue Vincent d’Indy, Montreal, QC H2V 2S9, Canada
2
Douglas Hospital Research Centre, Montreal, Canada
3
Department of Psychiatry, McGill University, Montreal, Canada
Keywords Attention · N2pc · Egocentric/allocentric · Navigation · Spatial memory
Introduction Visual spatial attention underlies improvement in performance when stimuli are presented at attended locations (Posner 1980) and is necessary to locate and identify a target in the visual environment during difficult visual search tasks (Treisman and Gelade 1980). Many electrophysiological studies (Mangun 1995) suggest that the relatively early sensory and/or perceptual processing of visual information is facilitated by attention. These attentional processes are thought to guide our everyday behaviour because limited neural processing capacity limits our ability to locate and encode target objects to accomplish goal-driven tasks (e.g. searching for one’s keys amongst an array of many objects on a desk). This process is thought to be particularly important during navigation, as attention to one’s surroundings, including landmarks, is required not to get lost. In this context, when humans move through an environment, they spontaneously adopt different navigational strategies, which rely on different parts of the brain (Iaria et al. 2003). To reach a target location, people can use a spatial strategy that involves building relationships between landmarks in the environment. This strategy generally results in the formation of a cognitive map that encodes relative directions and distances among multiple locations. Considerable evidence points to a special role of the hippocampus in this type of spatial learning (O’Keefe and Nadel 1978; McDonald and White 1993; Alvarez et al. 1995; Markus et al. 1995; Spiers et al. 2001; Maguire et al. 2000; Bohbot et al. 2007, 2013; Etchamendy et al. 2012; Konishi and Bohbot 2013; Konishi et al. 2013). The response
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strategy, in contrast, entails learning a series of local stimulus–response associations without encoding more global spatial relations among multiple locations. This navigation strategy relies on the caudate nucleus of the striatum (Alvarez et al. 1995; McDonald and White 1993; O’Keefe and Nadel 1978; Packard and McGaugh 1992, 1996; White and McDonald 2002). Although a substantial amount of research has established that both spatial and response navigation strategies rely on distinct memory systems, little is currently known about the involvement of visual spatial attention during spatial or response learning. It is hypothesized that when learning a new environment, spatial learning requires more visual attention compared to response learning. Lindberg and Gärling (1982) found evidence for this using a divided attention task during navigation, which showed that dividing attention disrupted people’s encoding of the relationships between landmarks (i.e. spatial learning), but did not disrupt the overall memorization of the general route taken (i.e. response learning). In other words, spatial learning was shown to require more visual attentional resources relative to response learning, of which the latter is thought to be more automatic and efficient. More recently, research using eye tracking found that people who used the spatial strategy made a greater number of fixations on landmarks during navigation compared to response learners (Andersen et al. 2012). These results further suggest that individual differences in navigation strategy may affect the deployment of visual attention to spatial locations, where spatial learners were more likely to attend to aspects of the environment that facilitated the construction of a cognitive map. Based on these previous findings, the current electrophysiological study aimed to test whether spatial learners display more enhanced neural activity that underlies the moment-to-moment deployment of visual spatial attention. The visual attention profiles of spatial and response learners, as determined by an independent virtual reality test [the 4-on-8 virtual maze (4/8 VM)], were examined using event-related brain potential recordings (ERPs) during a visual search task. More specifically, we used the N2pc component as an electrophysiological marker of visual spatial attention to observe possible differences in the time course of target selection between spatial and response learners. It is suggested that the N2pc component reflects neural activity involved in the spatial selection of taskrelevant stimuli in a display containing multiple stimuli, and perhaps distractor suppression (Eimer 1996; Luck and Hillyard 1994; Woodman and Luck 2003). This component is elicited 180–300 ms post-stimulus presentation at posterior electrode sites contralateral to the side of the attended peripheral target presented among non-target stimuli (Luck and Hillyard 1994). Given that most research in wayfinding and human navigation has focused primarily on memory
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systems, research using electrophysiological measures to examine visual attention is sparse. This measure will therefore give a more direct and temporally precise measure of possible differences in neural activity between response and spatial learners with respect to visual spatial attention on a cortical level compared to previous research (e.g. Andersen et al. 2012; Lindberg and Gärling 1982). Further, the N2pc has been previously used to measure subtle individual differences in the deployment of visual spatial attention. For example, the N2pc (elicited during a visual attention task) has been used to examine differences in visual spatial attention between younger and older adults (Lorenzo-Lopez et al. 2008, 2011), young adults diagnosed with neuropsychiatric disorders and healthy controls (Verleger et al. 2013), and older adults with mild cognitive impairment and healthy controls (Cespon et al. 2013). We hypothesized that spatial learners would direct more attention to items in the visual domain, which could amplify signal for better processing of environmental objects such as landmarks. We chose the N2pc as our measure to compare spatial and response learners because of its sensitivity to modulations in visual spatial attention. We used a visual attention paradigm that contained four items: three distractors, which were all the same colour, and a target, distinguished from the distractors by its unique colour. This task was chosen for its sensitivity to the deployment of visual spatial attention. Previous work using this paradigm (or a very similar paradigm) found a robust N2pc ERP component (Bolduc-Teasdale et al. 2012; Brisson and Jolicoeur 2007; De Beaumont et al. 2007; West et al. 2015). Moreover, this visual search task was also designed to produce high behavioural accuracy, minimizing the amount of trials lost due to incorrect responses. Moreover, this characteristic of the task also allows the measurement of individual differences in neural activity associated with visuospatial attention independently of accuracy (Brisson and Jolicoeur 2007; De Beaumont et al. 2007; West et al. 2015). In other words, we compared neural responses between groups, but within comparable conditions of behavioural consequence (i.e. correct performance). The resulting difference in the neural response therefore explains how the two groups reached the same behavioural endpoint. The task had two main manipulations: The frequency of target features and the distance of the target from fixation. Target feature frequency was used in previous research to assess another component, the P3 (see De Beaumont et al. 2007). Generally considered to be involved in item processing (For a review, see Verleger 1997; Polich 1997), activity associated with infrequent target processing (novel target stimuli elicit a larger P3 component) can be isolated by subtracting the frequent target condition activity from the infrequent target condition activity. However, to our knowledge, there are no a priori assumptions to be made, based
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on existing literature, with respect to a possible difference in item processing and categorization, between spatial and response learners. For this reason, we did not interpret P3 results in the present experiment and this condition is not considered in our N2pc analyses. The distance from fixation manipulation was originally incorporated into the design to give participants four possible target locations. Previous research has shown that targets near fixation will elicit a larger N2pc component (e.g. West et al. 2015; Brisson and Jolicoeur 2007), however we did not expect an interaction between navigation strategy and target distance. Further, using this same paradigm, West et al. (2015) also recently demonstrated that non-action video gamers employ a spatial navigation strategy more frequently than video game players and show an increase in N2pc. To ensure that we are not confounding our data by reproducing the video game player effect (larger N2pc for non-video game players and not spatial responders), we controlled for this factor and only included non-video game players in our sample. Our paradigm also allows for us to test if the distance of the target from fixation interacts with navigation strategy or rather if spatial learners produce an overall larger N2pc component regardless of distance. We predicted that an increase in N2pc amplitude would be due to the individual difference of navigation strategy and therefore spatial learners would show an increase in neural activity associated with the deployment of visual spatial attention, as indexed by the N2pc amplitude, when compared to response learners.
Methods Participants. Seventy-nine healthy right-handed participants (23 male) who were an average of 22.70 (±3.58) years of age were screened into the study. An extensive phone questionnaire was administered to screen for history of psychiatric or neurological disorders. The questionnaire asked about the presence or history of motion sickness, cardiovascular diseases, neurological disorders, medical conditions, psychiatric disorders, and substance abuse. Participants were screened for high levels of alcohol (>14 alcoholic beverages per week) and cigarette use (>10 cigarettes per day). Importantly, participants were screened for habitual action video game playing. Previous evidence from our laboratory suggests that habitual action video game players are biased towards using response strategies and have unique visual attentional profiles that differ from the normal population (West et al. 2015). We therefore only included non-action video game players in our sample to control for this potential confound. Testing occurred at the University of Montreal. Participants were recruited through word of mouth or through campus advertisements.
Informed consent was obtained in conformity with the local ethics committee requirements. Tasks 4 on 8 Virtual Maze. The 4/8VM is a virtual reality task that was created using programming software from a commercially available computer game (Unreal Tournament; Epic Games, Raleigh, NC) (Fig. 1). The virtual reality task consists of an eight-armed radial maze situated in an enriched environment. The environment contains both distal and proximal landmarks: two trees, a rock, and mountains. At the end of each arm there are stairs that lead to a small pit where, and in some cases, a participant can pick up an object. The participant is unable to see the objects from the centre of the maze. The number of trials is up to 10 with a minimum of 4. After 4 trials, additional trials are administered until the criteria is reached, which is no errors when retrieving the objects at the end of the radial arms. In the healthy young adults, these errors are not sensitive to detecting group differences and there is no observed effect between spatial and response learners. This is because the 4 on 8 has a dual task solution. People can either use the relationship between landmarks (spatial learners) or a rigid pattern (response learners) to solve the task with a similar level of accuracy. Each trial has two parts. In Part 1, a set of barriers block four of the eight arms. The participant is instructed to pick up objects located at the end of the four open arms. Additionally, the participant is told to remember which pathways they visited because in Part 2, all pathways are accessible and the objects that they must retrieve are situated in the pathways that were previously inaccessible. Participants always begin the task facing the same direction. All landmarks are visible during Part 1 and Part 2 of a trial. Participants are administered a minimum of three trials. If participants do not reach criterion within the first three trials, a maximum of five extra trials are given until participants reach criteria. At least 4 trials are administered. After this, trials are administered until a performance criterion is met. The criterion on the 4/8VM involves making no errors on part 2 for a single trial. This criterion ensures that all participants have learned the task before the single probe trial is administered. Once this criterion is reached, a single probe trial is administered. During Part 1 of the probe trial the participants still collect the objects from the open arms and all landmarks are present, however, in Part 2, when all the arms are accessible, a wall is erected around the maze so that the participants cannot see the environment and all landmarks are removed. Performance on the probe trial is therefore an objective measure of strategies. Participants using the spatial strategy involving learning the locations
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Fig. 1 A view of the virtual environment used in the 4 on 8 virtual maze. Note the tree and mountains that form the part of the landscape. A rock and meadow were also present in the virtual environment. In Part 1, participants retrieve 4 objects at the end of 4 available paths out of 8 that extend from a central platform. In part 2,
participants remember which pathways they have already visited and avoid these in order to find the remaining objects. Probe: after acquisition, in part 2, a wall is erected around the radial maze after learning, blocking the participants’ view of landmarks in the environment
of target objects in relation to landmarks will show an increase in errors when landmarks are removed. For example, a participant using the spatial strategy would remember the position of an object relative to the trees and the mountain, which are no longer present. On the other hand, participants using the response strategy would use a sequence of open and closed pathways from a single starting position, and therefore would have a perfect score on the probe trial even when landmarks are removed. This probe score is used to confirm the spontaneous navigation strategy that is reported by the participant. The spontaneous navigation strategy is obtained at the end of the task. Participants were asked to report how they knew which pathways contained objects and which were empty in the Part 2 trials. Using a specific objective questioning procedure, we asked about their initial method of navigation during the very first trial. This has previously been shown to be a reliable measure of initial spontaneous navigation strategy. Based on their description, participants were categorized as using either a spatial strategy or a response strategy (Bohbot et al. 2004, 2007, 2012, 2013;
Bohbot et al. 2011; Iaria et al. 2003; West et al. 2015). On the first trial, if participants reported using two or more landmarks to remember the location of the objects, and avoided reporting using a sequence from a single starting point, they were categorized as using a spatial strategy. If the participant reported using a sequence or pattern on the first trial, counting from a single starting point to remember the locations of the objects, they were categorized as using a response strategy. The reported strategy was evaluated by two experimenters who were blind to each other’s evaluations. If there was a discrepancy between these two ratings, a third independent experimenter rating was administered. This task took 1 h to complete. The single probe trial at the end of the task is used to confirm the spontaneous navigation strategy. People who report using a spatial strategy should display more probe errors when the landmarks are removed (e.g. Iaria et al. 2003; Bohbot et al. 2007; Konishi et al. 2013; West et al. 2015). N2pc Visual Attention Task. Once participants completed the 4/8VM, which distinguishes between those who rely on landmarks and those that do not during navigation,
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we administered a visual spatial attention task while EEG was recorded. This allowed us to investigate possible differences in the deployment of visuospatial attention between spatial and response learners. An illustration of the task is shown in Fig. 2. The experiment was composed of 768 experimental trials (640 target-present trials, and 128 no-go trials) and 20 practice trials. Participants were asked to fixate on a point at the centre of the screen, which remained on screen for the remainder of the trial. After 600 ms (±200 ms), a 150 ms bilateral visual display appeared consisting of four coloured squares (two on each side of fixation in the lower quadrants), each with a gap in one of their four sides. The squares were either far (centre of the squares was 3.75° below and 5.25° to the left or right) or near (centre of the squares was 2.25° below and 3° to the left or the right) fixation. All four squares in the visual display subtended a visual angle of 1.5° × 1.5°, and the gaps were 0.5°, centred on the side of the square. When present, the target stimulus was orange (frequent colour, p = 0.8) or green (rare colour, p = 0 .2) amongst blue distractors. Target colour squares were presented with equal probability to all four possible positions (p = 0.25 all locations). Participants had to indicate whether the gap on the square was on the top or not (up, left, and right), creating a frequent and infrequent target response condition. When the target square had a gap on its left, right, or bottom side (frequent condition, p = 0.75), participants responded by pressing the ‘V’ key on a keyboard, whereas they pressed the ‘N’ key only when the target square had a gap on the top side (infrequent condition, p = 0.25). In other words, participants were asked to direct attention to the square with the unique colour (e.g. the one orange or green square amongst the 3 blue squares) and then identify the position of the gap on that uniquely coloured square. All colours were adjusted to be equiluminant using a chromametre (Minolta CS100) to control for low-level sensory responses. In addition to the target-present trials, 128 no-go trials were included where no target
appeared (i.e. all boxes were the same colour) and participants had to inhibit their response. Because there was no target to discriminate, these trials were not included in final analysis. This task took 1 h to complete. Electrophysiological recordings and data analysis. The electroencephalogram (EEG) was recorded from 64 active Ag/AgCl electrodes (Biosemi Active Two system) with positions corresponding to the International 10–10 System (Sharbrough et al. 1991) mounted on an elastic cap as well as five external electrodes. The signal was re-referenced offline to the average of the left and right mastoids. The horizontal electrooculogram (HEOG), recorded as the voltage difference between two electrodes placed lateral to the external canthi, was used to measure horizontal eye movements. The average lateralised amplitude at HEOG sites was approximately −2.2 uV, corresponding to an average eye movement of less than 0.14° (Lins et al. 1993).The vertical electrooculogram (VEOG), recorded as the voltage difference between two electrodes placed below the left eye and Fp1 (above the left eye), was used to detect eye blinks. The EEG and EOG were digitized at 512 Hz. EEG data were high-pass filtered at 0.01 Hz, low-pass filtered at 30 Hz, and averaged offline. EOG data were high-pass filtered at 0.1 Hz, low-pass filtered at 10 Hz, and averaged offline. Trials with artefacts (±100 µV from baseline) and horizontal eye movements (saccades; HEOG >35 µV over 300 ms) were excluded from final analysis. Eye blinks (VEOG >50 mV over 150 ms) were removed from the data of all participants using the independent component analysis technique. Following artefact rejection, averages were created from stimulus-locked epochs (800 ms; including 200 ms prestimulus onset) separately for trials with a left visual field target and trials with a right visual field target and baseline corrected based on the 200 ms pre-stimulus onset period. To isolate the N2pc component from non-lateralized perceptual processes, averaged ipsilateral waveforms
Fig. 2 The trial sequence of the visual search paradigm used during ERPs recordings. Participants were presented with four squares. They were asked to identify a target square (coloured either orange or
green) amongst three distractor squares (coloured blue). Participants had to identify if the target square had a gap on either the top or at one of the other three possible sides (up, left, and right)
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(activity over the left hemisphere when the target stimulus was presented in the left visual field and activity over the right hemisphere when the target was presented in the right visual field) are subtracted from the averaged contralateral waveforms (activity over left hemisphere to a right visual field target and activity over right hemisphere to a left visual field target). The remaining activity is then subsequently averaged (i.e. contralateral activity—ipsilateral activity/2). N2pc measurements (mean amplitude recorded 210–260 ms post-stimulus onset) were then made on the contralateral minus ipsilateral difference waveforms. Activity from the PO7/PO8 electrode sites was chosen for analysis a priori based on previous evidence that the measurement of the N2pc is most sensitive at these regions of the scalp (e.g. Luck 2012; Jolicoeur et al. 2008; West et al. 2015).
Results 4 on 8 virtual maze The initial spontaneous navigational strategy was first assessed for each participant according to verbal reports. Two independent raters evaluated the strategy used by each participant and classified them as initially using either a response or spatial strategy when completing the 4/8 VM. There was a 92% inter-rater concordance. When there was discrepancy between both raters’ evaluation, a third rater’s evaluation was employed. This resulted in 40 participants being classified as spontaneously using a response strategy and 39 participants using a spatial strategy on the 4/8 VM. We next examined errors made during the probe trial administered after participants learned the 4/8 VM. As observed in previous studies (e.g. Bohbot et al. 2011; Iaria et al. 2003; West et al. 2015), spatial learners made significantly more probe errors (mean = 1.74) compared to response learners (mean = 1.24) (t = 1.82, p < 0.05, onetailed) thereby confirming that spatial learners relied on landmarks when navigating the virtual maze. N2pc visual attention task Behavioural and electrophysiological results Reaction times and accuracies were submitted to a 2 (Strategy: spatial; response) × 2 (Target distance: near target; far target) mixed factorial ANOVA. Both spatial and response learners had higher accuracies in the near condition (Spatial: 96.3% ± 2.9%; Response: 96.0% ± 3.2%) than in the far condition (Spatial: 95.2% ± 3.2%; Response: 94.6% ± 3.4%, F(1,77) = 37.29, p < 0.001). Moreover, both spatial and response learners were also faster in the near condition (Spatial: 672.9 ± 115.6 ms;
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Response: 649.7 ± 87.8 ms) than in the far condition (Spatial: 715.1 ± 130.8 ms; Response: 691.3 ± 99.5 ms, F(1,77) = 192.10, p < 0.001). No group difference was observed between spatial and response learners with respect to accuracy (Spatial: 95.8% ± 2.9%; Response: 95.3% ± 3.1%, F(1,77) = 0.39, p = 0.54) and reaction time (Spatial: 694.0 ± 122.7 ms; Response: 670.5 ± 92.9 ms, F(1,77) = 0.92, p = 0.34), nor did navigational strategy interact with distance from fixation for both accuracy [F(1, 77) = 0.31, p = 0.58] and RT [F(1,77) = 0.01, p = 0.93]. When examining the EEG data, we measured if the N2pc amplitude significantly differed between spatial and response learners separately for both near and far from fixation conditions. Mean N2pc amplitudes, recorded at PO7/PO8 electrode sites, were submitted to a 2 (Strategy: spatial; response) × 2 (Target Distance: near target; far target) mixed factorial ANOVA. Importantly, the ANOVA revealed a significant main effect of Strategy [F (1,77) = 4.12; p < 0.05; η2 = 0.05]. Overall spatial learners produced a larger N2pc amplitude compared to response learners (Spatial: M = −1.69 μV, SD = 1.78 μV; Response: M = −1.13 μV, SD = 1.49 μV; Fig. 3). This ANOVA also revealed a main effect of target distance [F (1,77) = 97.60; p < 0.001; η2 = 0.56]. Both spatial and response learners produced a larger N2pc amplitude in the near target condition (Spatial: M = −2.59 μV, SD = 1.48 μV; Response: M = −1.85 μV, SD = 1.49 μV) compared to the far target condition (Spatial: M = −0.80 μV, SD = 1.61 μV; Response: M = −0.41 μV, SD = 1.11 μV). No significant interaction was observed between the factors of target distance and strategy [F(1, 77) = 1.14, p = .29, η2 = 0.02]. To confirm the relationship between N2pc amplitude and navigation strategy, we conducted a Chi-square analysis. We averaged the N2pc amplitude across near and far target conditions and performed a median split, which revealed a median amplitude of −1.07 μV. The N2pc is one of the most useful event-related responses specifically related to the deployment of visual spatial attention (Brisson and Jolicoeur 2007; De Beaumont et al. 2007; Luck and Hillyard 1994; West et al. 2015; Woodman and Luck 2003). Because we wanted to examine the relationship between individual differences in spatial attention and navigation strategy, we were well justified to suppose that navigation strategy would correlate with the N2pc. The navigation strategy of people who produced either a larger or smaller N2pc amplitude than the median were therefore compared. We found that 62.25% of people with an N2pc amplitude larger than the median were spatial learners and only 35.89% of people with an N2pc amplitude smaller than the median were spatial learners, χ2(78) = 5.59, p < 0.05.
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Fig. 3 a Grand average N2pc component recorded at PO7/PO8 displays the lateralized (contralateral–ipsilateral) activity collapsed across all conditions for both spatial and response learners. Spatial learners produced a larger N2pc negativity (~210–260 ms post-stimulus) compared to response learners. b Scalp topographies at peak N2pc amplitude for Spatial Learners and Response Learners. Both
Spatial and Response Learners show negativity near electrode sites PO7 and PO8 that represents the N2pc activity. As can be observed in the Spatial Learners distribution, there is a relative increase in negativity in the scalp distribution that underlies the N2pc compared to the Response Learners
Discussion
also observed when conducting a chi-square analysis where subjects are separated into two groups using a median split: N2pc amplitude larger than the median and N2pc amplitude smaller than the median. We found that people with an N2pc component with an amplitude larger than the median were significantly more likely to be a spatial learner when compared to those who had an N2pc component with an amplitude smaller than the median. Previous reports using the 4-on-8 virtual maze have shown that young adults who solved the task using spatial strategies have increased hippocampal grey matter and fMRI activity. On the other hand, people who used response strategies have increased grey matter and fMRI activity in the caudate nucleus and lower grey matter in the hippocampus (Bohbot et al. 2007; Iaria et al. 2003). Our current results add to the known differences between these two groups to include enhanced visual attention to lateral targets during a discrimination task in spatial learners. What mechanisms are employed to support increased spatial attentional deployment during visual search in spatial learners? One possibility is that people who rely on landmarks to navigate show an increased level of goaldirected, or target-oriented, spatial attention to visual items
The purpose of this study was to examine the relationship between navigational strategy use and visual spatial attention. We hypothesized that spatial learners, who are characterized by their use of the relationship between visual landmarks (Alorda et al. 2007; Bohbot et al. 2007, 2013; Bohbot et al. 2011; Iaria et al. 2003; West et al. 2015) would show increased neural activity associated with the deployment of spatial attention during a visual search task (Andersen et al. 2012; Lindberg and Garling 1982). Our results confirmed this prediction: an enhanced N2pc component (an electrophysiological index of visual spatial attention) was observed during target discrimination for spatial learners compared with response learners. Our results suggest that spatial learners deploy an increased amount of attentional resources to the lateral target. In other words, spatial learners had an enhanced cortical brain response (as reflected by the N2pc component) related to the deployment of visual spatial attention as measured by the N2pc in previous research (Bolduc-Teasdale et al. 2012; Brisson and Jolicoeur 2007; De Beaumont et al. 2007; Luck and Hillyard 1994; Woodman and Luck 2003). This relationship was
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relevant to the task at hand. This is supported by research using eye tracking, where it was found that spatial learners display a greater number of fixations on landmarks when learning a new environment compared to response learners (Andersen et al. 2012). As foveation is a measure of the focus of attention, these data suggest that spatial learners may have directed their attention to a larger number of items (or locations containing possible targets) in the visual environment, compared to response learners. In the case of spatial learners, increased attention to items, or locations, in the visual environment might result in a more accurate encoding of spatial relations, resulting in a more accurate cognitive map and better spatial memory performance. Some indirect evidence supporting the link between attention and cognitive map development has been reported using a rodent model (Muzzio et al. 2009). In this study, cell activity in the hippocampus of rodents was recorded during navigation. It was found that when rats needed to attend to the spatial environment to locate a target item, increased neural synchronization in visual areas was observed. This increased synchronization is thought to underlie the encoding of task-relevant information in the visual domain (Muzzio et al. 2009), such as visual information necessary to use during navigation. An increase in the deployment of visual spatial attention and the resulting encoding of the visual environment may therefore aid in the creation of more accurate cognitive maps, encouraging the use of a spatial memory strategy. However, this relationship needs further study in humans. We note here some limitations of the present study. First, participants fixated the centre of the screen during the target detection task and therefore our study focuses specifically on the deployment of covert attention, as does most N2pc research. We therefore did not measure possible differences across spatial and response learners in the deployment of overt attention. Second, we do not know the direction of causality in the relationship between learning style and N2pc amplitude. It is unclear if the more robust deployment of attention towards targets as demonstrated by an enhanced N2pc resulted from the habitual use of spatial navigation strategies or if a pre-existing superior cortical attentional mechanism reflected by the N2pc made it easier for spatial learners to adopt a spatial navigation strategy. More research directly examining the genesis of this relationship is needed. Another limitation is the fact that both tasks we used are indeed quite different from each other and likely index different aspects of the treatment of visual space. In the present article, we do verify our hypothesis about a relationship between these two different aspects of the processing of information within visual space. However, more research will be needed to elucidate the exact mechanism that drives this difference in the deployment of visual attention in spatial and response learners.
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In summary, our current results demonstrate that spatial learners show an enhanced N2pc component, which is associated with the deployment of covert spatial attention to targets. Conversely, response learners, who rely more on sequential learning of stimulus–response pairings during navigation show a smaller N2pc, suggesting a less robust neural spatial attentional response. Future research also needs to explore how enhanced deployment of visual attention directly impacts navigation behaviour. Acknowledgements The funding was provided by Natural Sciences and Engineering Research Council of Canada (Grant No. 436140-2013).
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