Introduction

It is widely known that the size of the pupil changes as a function of brightness (e.g., [1]; for a review see [2]), success in memory retrieval (e.g., [3]), and effort (e.g., [4]). Mathôt et al. [5] even used the link between pupil light response and covert attention to letters of alternating brightness, whereas Binda and Gamlin [6] suggested a connection of attentional processes and pupil light responses.

Recently, Brocher et al. [7] used pupil size to investigate covert shifts of attention. However, unlike Mathôt et al. [5], who showed that pupil-light response is associated with covert attention, Brocher et al. [7] explored the well-established link between pupil dilation, on the one hand, and task effort on the other without making use of foveal vision to two spatially separated stimuli. They used a modified version of the attention-window task [8, 9] in the form of an attention-demanding conjunction task which requires attentive processing of two stimuli presented in the peripheral vision. Brocher et al. [7] found that pupil size correlates with the size of the visual angle at which the two stimuli are presented relative to the fixation point: The further away from the fixation point the two to-be-attended-to objects appeared, the stronger the pupils dilated. Furthermore, reacting to low contrast stimuli led to larger pupils than reacting to high contrast stimuli, and the inspection of such stimuli in the peripheral vision resulted in overall larger pupils than the mere detection of these stimuli. These findings complement previous studies showing that pupil sizes are modulated by attentional load when using attention-demanding visuo-spatial tasks [10].

In visual search tasks (e.g., [11]) as well as in visuomotor tasks (e.g., [12]), researchers have demonstrated that pupil size can provide a general metric to assess attentional load. With regard to their own results, Brocher and colleagues [7] also concluded that the attention-window task, in combination with measurement of the pupil size, is a promising tool for testing specific features of the various processes that underlie perception and attentional shifts to peripheral vision. The great advantage of this reported paradigm is that the assessment of a participant’s breadth of attention does not hinge on the output of a specific task, such as the accuracy with which objects in peripheral vision are evaluated. The paradigm could therefore, in principle, involve a variety of tasks, and the participants would not necessarily need to be directed to attend to one or the other stimulus in the periphery (i.e., as long as task demands do not afford different amounts of effort). Indeed, it seems that the only precondition for a successful use of the paradigm is that the attentional shifts of interest correlate with differences in effort: More effort is required for the evaluation of stimuli that appear further away from eye fixation compared to those closer to the eye fixation (for a review, see [13]).

In this paper, we test a potential extension of the paradigm reported by Brocher et al. [7]. We investigate if it can be used to track whether a person attends to static, non-moving objects, or a dynamic, moving objects. As the visual world contains both static and dynamic components, the human visual system is required to respond to a wide variety of stimuli. The ability to identify moving objects (particularly in the horizontal dimension of the attentional focus) is considered essential for many daily activities, such as participating in sports activities and driving a car (e.g., [14, 15]).

Work by Ludvigh and Miller [16] has already described the ability of the eyes to resolve stimuli that move relative to an observer as “dynamic visual acuity”. It is interesting to note that most methods that have been used to investigate specific perceptual and attentional capabilities or processes of the cognitive system, have focused on interactions with static stimuli; and that the various studies that have compared static and dynamic stimuli suggest that dynamic events show an overall higher sensitivity over the visual field than static events (see e.g., [17]). However, it has also been claimed that attending to dynamic stimuli comes at a higher cost than static stimuli [18].

Specific differences between attentional shifts to static vs. dynamic objects have not yet been explored in detail, and it is important to point out that the purpose of the present study was not to shed light on the processes that might underlie these differences. Rather, our goal was to test an online experimental paradigm that could help researchers focus on specific properties of the attentional system, particularly with respect to the difference between static and dynamic visual acuity. As previous research has found that dynamic stimuli determine stronger brightness effects compared to static ones (e.g., [19]), we predict relatively large changes in the pupil size when dynamic stimuli are presented than the corresponding static ones.

Method

Procedure

The participants received written information that the study was about peripheral vision and the question of how well objects can be perceived in the periphery. Afterwards calibration of the eye tracker took place. In each trial, the participants were asked to fixate on the center of the screen and inspect the various objects only peripherally, with no fixation on these objects. Each trial started with a screen indicating to the participants which mode was about to follow. In the static mode, the words No Movement appeared on the display. In the dynamic mode, the word Movement appeared. All the displays in the experiment were dark grey, with RGB settings of 153, 153, 153, and the illuminance, measured at the participants’ right eye, was maintained at an average of 26 lx. An example of a trial is provided in Fig 1.

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Fig 1. Example of a trial in the experiment.

The size ratio of the stimuli has been adjusted to better illustrate the procedure.

https://doi.org/10.1371/journal.pone.0250027.g001

After the participants pressed the button on the mouse, seven hash marks appeared at the center of the screen. After 250ms, the two pre-cues appeared, either at one of the five angles (static mode) or 7.5° closer to the fixation point than the respective angle (dynamic mode). The pre-cues lasted for 250ms and were then replaced by a blank screen. After 200ms, the two target stimuli appeared and, in the static mode, remained at their location for 300ms or, in the dynamic mode, moved towards their landing positions within 300ms. Next, the seven hash marks re-appeared and stayed on screen for 2000ms. During these 2000ms, we measured the size of the right pupil in response to the target stimuli inspection.

After the final fixation screen, a black arrow emerged at the center of the screen. When it pointed to the left, the participants were asked to provide their response for the target stimulus that appeared at the left of the center; when it pointed to the right, the participants were asked to provide their response for the target stimulus that appeared at the right of the center. Next, five numbers appeared on the screen and the participants clicked on the number that represented their response. For example, when the participants counted three white triangles in the left target stimulus and the first arrow pointed to the left, participants were supposed to click on the number three. After the first response, another arrow appeared which pointed in the opposite direction, so that participants could provide their response for the second stimulus. The order of appearance of the left and right arrows was randomized between trials. After the second response and a delay of 2000ms the subsequent trial started. The experiment started with 15 random practice trials.

Results

We will first discuss the response data results and then turn to the pupil size data results. As can be seen in Fig 2, participants’ accuracy decreased with increasing angle; this is true for both the static and the dynamic mode. However, for all the five angles, the accuracy was slightly higher for the static than the dynamic mode. This is also reflected by results of a 2 (presentation mode: static, dynamic) x 5 (angle: 12.5°, 20°, 27.5°, 35°, and 42.5°) repeated measures ANOVA on the accuracy data which showed a significant main effects of presentation mode, F(1, 19) = 6.346, p = .021, and angle, F(4, 76) = 5.928, p < .001.

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Fig 2. Mean response accuracy per angle.

Bars indicate the proportion of correct responses. Error bars provide 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0250027.g002

Model comparison revealed that the presentation mode model (AIC = 3291.7) fit the data better than the intercept-only model, Χ²(1) = 60.88, p < .001, (AIC = 3350.6). When we also included angle as the dependent variable, model fit improved further, Χ²(4) = 118.95, p < .001, AIC = 3180.8 (R² = 0.151, b₀ = 0.418, b _{presentation mode} = 0.545, b _angle = 2.23, ps < .001). When we also included the Presentation mode x Angle interaction in the model, no further improvement occurred, Χ²(4) = 4.88, p = .30, (AIC = 3183.9).

The timeline of the baseline-corrected pupil dilations are plotted in Fig 3. As the plot shows, pupil dilation increased with increasing angle, just like it was observed by Brocher et al. [7] with their static stimuli. In addition, pupil dilation was much stronger in the static than the dynamic mode for all five angles. This was, however, not supported by results of an additional 2 (presentation mode: static, dynamic) x 5 (angle: 12.5°, 20°, 27.5°, 35°, and 42.5°) repeated measures ANOVA on the pupil size data. There was no effect of presentation mode, F(1, 19) = 2.203, p = .154, or angle, F(4, 76) = 0.049, p = .154, and also no interaction of both factors, F(4, 76) = 2.408, p = .057.

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Fig 3. Timeline of the mean pupil size deviations (in arbitrary units) from the baseline per angle, in number of pixels starting at the beginning of a trial (cf. Fig 1).

Each angle is represented as a separate line. The dashed vertical lines represent the onsets of the pre-cues and the measurement phase, respectively.

https://doi.org/10.1371/journal.pone.0250027.g003

Nevertheless, statistical analyses revealed that the model including presentation mode as predictor (AIC = 40206), yielded a better fit to the data than the intercept-only model (AIC = 40214), Χ²(1) = 9.68, p = .002, meaning that static vs. dynamic stimuli presentation differently affected the size of the pupil. Also, including the angle further improved model fit (AIC = 40163), Χ²(4) = 51.93, p < .001, (R² = 0.02, b₀ = 247.82, b _{presentation mode} = -54.165, b _angle = 11.22, ps > .09). This suggests that pupil size was additionally affected by the distance between the stimuli and eye fixation. The inclusion of the interaction term did not improve the model fit any further, Χ²(4) = 1.17, p = .883, (AIC = 40169).

Before we turn to the discussion, we provide two additional pieces of information. First, one might argue that the starting points of the moving stimuli in the dynamic mode were always closer to eye fixation than the starting (and end) points of the static stimuli. This could have led to smaller pupils in the dynamic mode in comparison to the static mode, as inspection might be easier when the first visual contact occurs closer to the fixation point than when it occurs farther away from eye fixation. Although, the timeline in Fig 3 already reveal that the smallest angle in the static mode yielded a mean peak that is comparable to the largest angle in the dynamic mode, Fig 4 shows even more clearly that when we compare the starting points of the dynamic mode with the respective angles of the static mode, pupils still dilated much more strongly for each angle in the static than in the dynamic mode.

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Fig 4. Mean pupil size deviations from the baseline per angle when comparing the starting points of the moving stimuli in the dynamic mode with the respective angle of the non-moving stimuli in the static mode.

Error bars provide 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0250027.g004

Second, because stimuli closer to the central fixation point possibly have stronger effects of luminance on the pupil dilation than stimuli farther away from the fixation point, it is plausible that the correlation of angle and pupil dilation was (also) due to differences in eccentricity (see [7] for a longer discussion). If this were the case, we would expect much larger differences between particularly bright and dark stimuli for the smaller angles compared to the larger angles, as differences in luminance should then decrease as the respective stimuli move away from eye fixation. To test this, we conducted another 2 (number of white objects: zero vs. eight) x 5 (angle: 12.5°, 20°, 27.5°, 35°, and 42.5°) repeated measures ANOVA on the pupil size data of the dynamic mode condition. There was no significant effect of the number of white objects, F(1, 19) = 1.8, p = .196, and also no interaction effect involving the number of white objects, F(4, 76) = 2.118, p = .087. That is, there seemed to be no systematic relation between the number of white objects and pupil dilation for the five angles, although we do note that, for the 20° and 27.5° angles, stimuli with eight white objects yielded the weakest dilations.

Discussion

In this study, we used an attention-window task to investigate if the pupil size can be used as an indication of whether participants attend to stimuli that move vs. do not move in their peripheral vision. Our rationale was that if this was the case and the pupil size was found to be a valid indicator, the attention-window task might be a valuable tool for research on specific properties of the visual attentional system. As we pointed out earlier, the use of the task and the measurement employed in the current study has one great advantage. Both can be adopted in experiments in which the participants can self-determinately choose which object(s) to attend to, rather than being explicitly instructed on which object(s) to attend to if future studies in which participants are less restricted in their allocation of attention confirm the current results. In other words, if attentional shifts to objects in the peripheral vision are measured through response accuracy alone, we typically need to make sure that we know in advance which object(s) the participants will attend to, as response accuracy is the only measure of attentional shifting.

On a more general level, if we assume that the observed differences in the pupil size during the inspection of static and dynamic objects are due to differences in the exerted effort on the task (cf. [7]), the results of the present study might be taken as an indication that tasks involving attentional shifts to dynamic stimuli require less effort than tasks involving shifts to static stimuli (contra [18]). However, again, our experiment was not designed to address this question, but was instead conducted to provide empirical evidence that we can use pupil dilation to track attentional shifts to dynamic vs. static objects. Taken together with the results reported in Brocher et al.’s [7] study, we summarize that pupil dilation can reflect differences in attention to close vs. distant stimuli, stimuli of high vs. low contrast, and, as confirmed by the present results, dynamic vs. static stimuli. Lastly, pupil dilation also seems to indicate whether an observer directs attention to an object or whether she or he merely detects stimuli in their peripheral vision.

Another interesting outcome is that the pupil dilation decreased when dynamic stimuli, instead of static stimuli, were presented, (as if attending to the moving stimuli required less mental effort) although, the accuracy decreased as well. This finding is not in line with previous findings which state that invariably, not only manipulations increased difficulty (as indexed by more frequent errors), but also yielded larger pupil dilations when moving objects were presented [10, 22].

There are some limitations of the present study and considerations for future research that should be addressed. In the current study, the eccentricity of target stimuli was not matched between the two modes: the moving stimuli appeared 7.5 degrees closer to fixation and ended at the same eccentricities as the static stimuli, and were therefore, on average, closer to the fixation point. It can be argued that the difference in eccentricity by itself is insufficient to account for the difference between pupil dilations in static vs. dynamic modes. However, we did not consider that the effect of eccentricity on pupil dilation (and accuracy) does not necessarily need to be a linear function of the eccentricity angle: moving the target closer to the fixation point by 7.5 degrees of visual angle might have a larger effect when it is done in relation to a position at 12.5 degrees than, say, a position at 35 degrees of visual angle. However, as shown in Fig 4, at least for some of the values, the results seem to roughly point to a linear relationship of eccentricity and pupil dilation: The pupil response to moving targets, which started at 20 degrees and terminated at 27.5 degrees, was roughly the same as the response to the static target at 20 degrees. Future studies are needed to exclude that the difference in pupil dilation across static and moving stimuli is not due to an eccentricity error, but rather due to the fact that pupil dilation does discriminate between moving and static stimuli.

In addition, pre-cues in the static condition might have been more valid than pre-cues in the dynamic condition (especially during the first trials of every participant). That is, the circle cues in the static condition appeared in the center of where subsequent target stimuli would appear. That is not the case in the dynamic condition, in which the circle cues appeared at the end position of stimulus movement, thus not where stimuli would initially appear. The former may be associated with less effort than the latter. To effectively rule this out, future should change the way pre-cues are used in the dynamic presentation mode.

Furthermore, future studies should differentiate between the movement direction of the dynamic stimuli so that they do not always move away from the center but also towards the center (i.e., from inside to outside, but also from outside to inside). Also, the target stimuli should not always move from one angle to the next, as was in the current study, but should also move across multiple angles and at different velocities. Another recommendation for future research would be to use some kind of a moving object, like in phase-shifting Gabor (cf. [23]), or a rotating pattern, that does not change position at all, in order to circumvent the moving distances and potential eccentricity biases.

Of course, any experiment that implements pupil size as a measure of attentional shifting needs to include thorough initial calibration and balancing of trials across the conditions under investigation. In particular, when participants are free to pay attention to their chosen object(s) from the available objects onscreen, one needs some initial measure that links the various conditions to the size of the pupil. For example, if in future studies we were to present four objects in peripheral vision at the same time and aim to investigate which object(s) did the participants attend to, we would need a sufficient number of initial trials, in which we control object(s) which the participants attend to (as was done in the current study). Only then are we able to compare the pupil size in free-choice trials with the pupil size in forced-choice trials, and determine which object(s) the participants attended to in the free-choice trials from that comparison. Such a design, we propose, should be the next step in the implementation of pupil size as a measure in investigations on attentional shifts to peripheral vision.