Ординатура / Офтальмология / Английские материалы / Eye Movements A Window on Mind and Brain_Van Gompel_2007

Figure 3. Percentage of gaze fixations (mean +/− 1 SD) on the four locations prior to onset of gait initiation: travel path, goal, elsewhere and pylon region. The letters on the bars show the results from the post-hoc analyses: same letter indicates no significant difference.

1.3.2. Gaze fixation characteristics during task performance

The frequency of gaze fixation on the four locations during the walking trial is summarized in Figure 4. Statistical analysis revealed a significant effect of gaze fixation location on the frequency of gaze fixation (F3 12 = 54 41, p < 0 001). Participants spent more time looking at the goal (64%) than at any of the other locations. The pylon region accounted for 19% of gaze fixations, which was not different from fixations on the travel path (13%). Fixations elsewhere (4%) were significantly different from fixations on the other locations. The pylons fixated on were predominantly ( 94%) pylons bordering the travel path.

Next we divided the total travel time between gait initiation and the point in time when participants reached the goal posts into four travel phases of equal duration (1–4). The relative frequencies of gaze fixations on the travel path and pylon region surrounding the travel path were combined into a single category called path/pylon. The pylons in this case were the ones bordering the path. A two-way repeated measure ANOVA with gaze location (goal and path/pylon) and travel phase (phases 1–4) was done on the relative gaze fixation frequencies. The distribution of fixation durations on the goal and path/pylon was calculated in 100 ms increment intervals. Statistical characteristics of these distributions were determined.

Location of fixation

Figure 4. Percentage of gaze fixations (mean +/− 1 SD) on the four locations after the onset of gait initiation: travel path, goal, elsewhere and pylon region. The letters on the bars show the results from the post-hoc analyses: same letter indicates no significant difference.

The frequency of gaze fixation on the goal and path/pylon region varied as a function of travel phase. Statistical analyses revealed a significant interaction between gaze fixation location and travel phase (F3 12 = 6 67, p < 0 001). While the frequency of gaze fixation on the goal increased slightly over time, the frequency of gaze fixation on the path/pylon region decreased as participants got closer to the goal (Figure 5a). The distribution of gaze fixation durations for the two locations, goal and path/pylon, are shown in Figure 5b, which shows that they are very similar. Statistical properties of the two distributions are summarized in Table 1.

1.3.3. Characteristics of gaze fixations on turn pylons

Pylons around which a turn took place were identified as turn pylons. A turn was defined as a change in travel direction and was determined from upper body yaw rotation which precedes a change in foot placement (Patla, Adkin, & Ballard, 1999). The travel area was defined by a 9 (columns) by 13 (rows) grid. The percentage of turn pylons fixated on were determined for turns occurring in rows 1–3, 4–6, 7–9, 10–12. The rows allow us to define four spatial components of the travel path. A one-way repeated measures ANOVA was carried out on the percentage of fixated turn pylons as a function of row grouping. Depending on the direction of the turn, the turn pylon was classified as being either on the inside edge or on the outside edge of the turn. The distribution of the times between the onset of gaze fixation on the turn pylon and the turn was determined for each row grouping. Statistical characteristics of these distributions were determined.

Figure 5. (a) Percentage of gaze fixations on the goal and path/pylon region as a function of travel phase (rows 1–3, 4–6, 7–9, 10–12). (b) Frequency distributions of gaze fixation durations on the goal and path/pylon region in 100 ms increments. The mean +/ − 1SD values are shown for the frequency values.

Table 1

Distribution statistics of the gaze fixation durations on the goal and the path/pylon region

The percentage of fixations on pylons along the travel path edges was 17.4% (SD: 6.6): this translates into 1–2 travel path pylons that were fixated on during each walking trial. Half of the travel path pylons were turn pylons (49%). The percentage of turn pylons fixated on was significantly influenced by the four set of rows (rows 1–3; 4–6; 7–9; 10–12) (F3 12 = 13 59, p < 0 001); post hoc analyses revealed that for turns occurring in rows 1–3, the frequency of gaze fixation was lower than in the other rows, which were not statistically different from each other (Figure 6a). For cases where the turn pylon was fixated, the fixation distributions for the time between the onset of gaze fixation and the onset of the turn are shown in Figure 6b. The statistics of these distributions are summarized in Table 2. The turn pylon that participants fixated was predominantly on the inside edge of the turn (80%, SD: 14%).

Table 2

Distribution statistics of the time lag between the gaze onset on the turn pylon and the turn initiation for the four travel phases (SD in brackets)

1.4.1. Where and when people look reveals what is important for route selection

Gaze fixations during locomotion were predominantly on the goal, travel path and pylon region ( 96%); only 4% of gaze fixations were in the elsewhere category. The three locations, goal, travel path and pylon region are all relevant for the task. Similar task specific fixation locations have been observed in a variety of other locomotor tasks (Hollands et al., 2002; Patla & Vickers, 1997, 2003) and other everyday tasks (Land & Hayhoe, 2001; Land et al., 1999); rarely do we see fixations on irrelevant objects or locations. This supports ‘top-down’ control of gaze behavior (Land & Furneaux, 1997; Shinoda, Hayhoe & Shrivastava, 2001).

Researchers have suggested two strategies for monitoring where one is heading: either monitoring optic flow (Warren, Kay, Zosh, Duchon, & Sahuc, 2001) or perceived target location (Rushton, Harris, Lloyd, & Wann, 1998). Fixations on the goal can be used to track one’s heading during goal directed locomotion. This checking of where one is heading is a common feature of purposeful locomotion (Hollands et al., 2002).

Fixations on the travel path and pylon region can be used for path planning, to determine the presence of obstacles for collision avoidance (Patla & Vickers, 1997) and the location where steering may be required (Hollands et al., 2002). What is interesting is how the frequency of gaze fixations on the goal and the path/pylon region changes with time. As participants get closer to the goal, fixations on the travel path or pylon region decreased to almost zero. This could in part be due to the simple fact that the number of pylons that are visible decreases as participants approach the goal. Earlier on in the travel path the number of fixations on the goal and path/pylon region was almost equal. These fixations in the early phase of travel allow for path planning; later on during travel fixations on the goal guide the body to the appropriate exit location. Models of how these intermittent gaze fixations on the goal and path/pylons assist path planning and selection are discussed below.

Gaze fixation duration was on average around 200 ms for both fixations on the goal and on the path/pylon region (Table 1). In a block copying task, Ballard, Hayhoe, Li, and Whitehead (1992) found that the duration of fixation varied depending on the type of information that was being acquired: identifying appropriate color blocks required longer fixation durations than identifying the location of the block in the model. In the task in the current study, the goal and path/pylon region provide spatial information to guide the

travel path. This explains why the average gaze fixation duration on the goal and the path/pylon region is similar.

1.4.2.Route planning is based on visual information acquired during locomotion, not on information acquired prior to gait initiation.

In the simpler task of single obstacle avoidance, we have shown that when individuals had approximately 1.5 s to view the environment from a standing posture and complete the task blindfolded, the success rate was about 50% (Patla, 1998; Patla & Greig, 2006). Gait initiation times in the current study clearly suggest that individuals did not spend any appreciable time scanning the environment and planning a route before they started walking: half a second with one gaze fixation on average is probably not enough to plan the whole route in a complex environment. This is consistent with our previous work (Patla et al., 2004). Therefore it must be visual information acquired during locomotion that is used for route selection. The advantage of visual information acquired during locomotion over visual sampling from a standing posture has been shown for the simple task of single obstacle avoidance (Patla, 1998; Patla & Greig, 2006). It is the retinal stimulation as one moves about the environment, termed optic flow, which provides rich information for guidance of whole body movements (Gibson, 1958).

The majority of gaze fixations prior to gait initiation were on the goal. Since there were two possible end goals, a fixation prior to gait initiation allows the individual to identify where he/she should be heading and probably helps in the planning of the direction of the first step. What is surprising is that people did not fixate the goal before gait initiation in every trial. For the two start positions S1 and S2 in Figure 1a, the goals are straight ahead, so a fixation anywhere in the travel area would allow individuals to identify the location of the goal in their upper visual field. Researchers studying mental maze solving tasks have similarly shown that people acquire information from the upper visual field through intermittent gaze fixations at different points along the route (Crowe, Averbeck, Chafee, Anderson, & Georgopoulos, 2000). In contrast, for start position S3, the end goals are in the participant’s right peripheral visual field, so the goal at E2 is out of view. While gaze fixations allow people to gather detailed visual information about visual features at specific locations, simple identification of the goal can be achieved in the peripheral visual field. This may explain the low number of fixations on the end goal prior to gait initiation.

1.4.3.Visual information is used in both feed-forward and on-line control of whole body movement

To reach the goal in the current study, modifications in gait direction were primarily required for changing direction. What visual information is needed for changing walking direction? First, participants need to know where steering has to be executed. Second, they need to know the new direction of travel in order to determine the magnitude of change in direction. In this study the location of direction change was normally around a

pylon and the difference between the current direction of travel and the goal determined the magnitude of change. Fixations on turn pylons provide the first piece of information while fixations on the goal provide the second piece of information. Surprisingly, only about 50% of the turn pylons were fixated; for turns occurring in the early phases of the trials (rows 1–3) this number was even lower ( 10%). Fixations on the goal were much more frequent, as discussed before. This suggests that visual information about the location of the turn is acquired in the peripheral visual field, consistent with previous work on gaze behavior during steering control (Hollands et al., 2002). In Hollands et al.’s study, the location and magnitude of direction change were specified by a clearly marked tape (a turn mat and light cue). The results showed that individuals primarily fixated on where they were currently heading for and where they would be going following the direction change; fixations on the turn mat and the light cue accounted for less than 15% of gaze durations. In the current study the pylons were very salient and could also be easily identified using peripheral vision, which explains why people often did not fixate them. In contrast, Land and Lee (1994) showed consistent gaze fixations on the tangent point of the road curvature during driving. This difference in results may in part be due to the consequence of error if the turn is not initiated at the correct location.

When individuals do fixate on the turn pylons, are they using it to control their steering on-line or are they using it to plan their turn in advance? We need to look at the time lag between the onset of the gaze fixation on the turn pylon and the initiation of the turn in order to address this question. But before that we need to establish what time lag would indicate that vision is used for on-line control. Unlike other gait adaptations, such as step length and width modulation, which can be successfully completed in a single step, steering control requires a minimum of two steps for successful completion (Patla et al., 1989, 1991). Therefore the minimum time lag between the onset of a gaze fixation and the initiation of a turn is approximately 1.2 s. The distribution statistics of this time lag, shown in Table 2, suggests that how vision is used for steering control depends on where the turn occurs. For turns occurring in rows 4–6, the average lag of 1.3 s indicates on-line steering control, whereas for rows 7–12, the time lags are longer, suggesting that vision is used primarily for planning turns ahead. Land and Lee (1994) showed that during driving, the time lag between a gaze fixation and turning the steering wheel was approximately 0.75 s, also suggesting that vision is used for on-line steering control. Similar to what was found by Land and Lee (1994), participants in the current task also predominantly fixated on the inside edge of the turn.

1.4.4. Evaluating models of route selection

We compared the performance of two models in their ability to predict appropriate route selection: the on-line control (Fajen & Warren, 2003) and avoid-a-crowd model (Patla et al., 2004). We also evaluated if the observed gaze patterns in the current study are compatible with these models.

The on-line control model claims that each obstacle in the travel path is avoided by minimizing the deviation from the current walking direction (Fajen & Warren, 2003;

Route selection model

Figure 7. Percentage of trials accurately predicted by the three route selection models discussed in the text. The letters on the bars show the results from the post-hoc analyses: same letter indicates no significant difference.

Shiller, 2003). This model correctly predicts the travel path in 74% of the trials in the current study (Figure 7). While its success rate is impressive, it is the lowest among the models that we compared. Gaze fixation data also suggest that participants did not use the strategy predicted by the online control model. One would have predicted more frequent gaze fixations on the pylons because they determine the changes in locomotor direction involved in the travel path. Participants fixated only on 1–2 pylons (out of a possible 6–8) bordering the travel path.

A more successful model, proposed by Patla et al. (2004), is the ‘avoid-a-crowd’ strategy. This model predicts that participants circumvent clusters of obstacles and minimize the number of turns rather than weave in and out among obstacles. While this model is better than the on-line control model in predicting travel paths (82% vs 74%; Figure 7), one would have expected more consistent gaze fixations on turn pylons because turn pylons are key features for the cluster identification routine (Patla et al., 2004). In the current study, only approximately 50% of the turn pylons were fixated.

1.4.5.A new route selection model based on gaze fixation results accurately predicts the selected travel path

It appears that avoiding clusters of obstacles and minimizing the number of turns in the travel path is not enough to predict the travel paths chosen by most participants. Gaze fixations on the travel path and the pylons surrounding the path suggest on-line monitoring of the travel area. We therefore propose a new model that involves on-line

evaluation of which spaces afford an obstacle-free passage, while minimizing changes in the current travel direction and minimizing the deviation from the end-goal. In addition to the obstacle free area, the model takes into account the width of the corridor in order to ensure that an unobstructed passage is possible without having to modify body orientation.

The first step in determining possible travel paths is to map the areas which are visible and those which are invisible, beginning with the start point on the grid. This is accomplished by dividing the visual field into rays that emanate from the observer and terminate upon falling upon the first obstacle in their path. The rays are spread in a circular pattern with a radius of 2.5 m which approximates on average three-step lengths during walking. Previous work has shown that changes in the direction of the travel path have to be planned at least two steps in advance (Patla et al., 1991). The circular pattern of the rays effectively maps the contours of the obstacles over the specified radius (Figure 8a). This is then used to construct wedges of corridors of unobstructed space between obstacles (Figure 8b). Once the corridors are established the challenge is to extract relevant geometrical information. Four measures were used to quantify the corridors and decide which corridor is best to proceed through. The first measure is the WIDTH of the corridor between the two obstacles (Figure 8c): it should be large enough to allow a person to walk through without collision, with a larger width preferred. The second factor is the AREA of each corridor as mapped by the rays. An example of four areas (A1–A4) is shown in Figure 8b. Once again the larger the obstruction-free area, the more preferred it is. While the area indirectly includes the width of the corridor, it is possible to have a large area with a narrow width and therefore area alone is not enough to choose the best corridor. The next two factors account for directional changes that are required from the current heading, termed the BACKWARD ANGLE (Figure 8d), defined as the angle between the current line of progression and the line connecting the current location to the midpoint of the selected corridor, and the FORWARD ANGLE (Figure 8c), which is the angle between the selected path (defined by the line connecting the current location with the mid-point of the selected corridor) and a straight line to the goal. Small BACKWARD ANGLES are preferred to minimize abrupt sharp turns, while small FORWARD ANGLES minimizes the deviation from the end-goal. All factors are scaled from 0 to 1 using linear functions between the maximum and the minimum distances and angles (see Figure 8e).

To select the optimum corridor the four factors are assigned different weights to arrive at a suitability index for each corridor; the weighting is modified if the exit is within the radius of the rays projecting from the current position (see Figure 8f for the specific weightings used). The weights were determined using the data from a previous study (Patla et al., 2004) and were then fixed. Therefore the model does not involve mere curve fitting of the selected travel paths; the model actually predicts travel paths. The model chooses the corridor with the highest index and the algorithm is repeated with the midpoint of that corridor (indicated by open circles in Figure 9a) becoming the new starting point for the subsequent corridor search. This algorithm thus takes into account local information about the spacing between the obstacles along with global information about the goal’s location in order to plan a path segment.

Figure 8. (a) Rays representing visual identification of obstacles and corridors from a given point on the grid.

(b) Four possible obstruction-free travel areas are identified A1 − A4 . (c) Corridor width W1 − W4 and forward angle representing the deviation from the end goal for two corridors (FA3 and FA4, indicated by arcs) influence which corridor is selected. (d) The factor backward angle representing the deviation from the current heading for two corridors (BA1 and BA2, indicated by arcs). (e) Linear weighting for the four factors area, width, forward angle and backward angle. (f) The weighting formulas for determining the suitability index for a corridor. The weightings on the left are used before the exit is in sight; the weightings on the left when the exit is in sight.