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A large body of work has shown that a perceived gaze shift produces a shift in a viewer’s spatial attention in the direction of the seen gaze. A controversial issue surrounds the extent to which this gaze-cued orienting effect is stimulus-driven, or is under a degree of top-down control. In two experiments we show that the gaze-cued orienting effect is disrupted by a concurrent task that has been shown to place high demands on executive resources: random number generation (RNG). In Experiment 1 participants were faster to locate targets that appeared in gaze-cued locations relative to targets that appeared in locations opposite to those indicated by the gaze shifts, while simultaneously and continuously reciting aloud the digits 1–9 in order; however, this gaze-cueing effect was eliminated when participants continuously recited the same digits in a random order. RNG was also found to interfere with gaze-cued orienting in Experiment 2 where participants performed a speeded letter identification response. Together, these data suggest that gaze-cued orienting is actually under top-down control. We argue that top-down signals sustain a goal to shift attention in response to gazes, such that orienting ordinarily occurs when they are perceived; however, the goal cannot always be maintained when concurrent, multiple, competing goals are simultaneously active in working memory.
In various social contexts, people tend to take notice of others’ gaze direction. The past two decades have seen a large number of studies investigating this social orienting phenomenon utilizing a modified version of
Researchers have drawn a broad distinction between, on the one hand, exogenous, bottom-up, reflexive, or stimulus-driven attention, and on the other, endogenous, top-down, or wilful attention (e.g.,
Despite this compelling evidence for the stimulus-driven character of social orienting, some authors suggest that a top-down component is involved in the process (e.g.,
Ostensibly, these neuropsychological data do seem to suggest that gaze-cued orienting is rather less like a stimulus-driven reflex and more akin to endogenous, wilful orienting of attention. However, as pointed out by
If gaze cued attention is modulated by top-down processes, WM is the likely mechanism responsible for the modulation. Indeed, numerous studies have shown that WM is linked to attentional control in the antisaccade task (
In summary, although the work of
In this paper we revisit the finding that gaze-cued orienting is unaffected by a concurrent cognitive load. One of the problems with the digit load concurrent task used by both
Our argument is therefore that, regardless of whether or not the digit load task places excessively high demands on participants’ executive resources, the demands are not necessarily imposed during the period when participants are shifting attention in response to the seen gazes. Clearly what is needed is a secondary task that must genuinely be carried out simultaneously and continuously with the gaze cueing procedure.
In the experiments reported in this paper we employed an executively demanding secondary task that must genuinely be completed concurrently with the gaze cueing procedure: random number generation (RNG). Generating random sequences from a well known and well defined set of items, such as the numbers one to nine, or letters of the alphabet, requires participants to generate and run a plan for the retrieval of an item from the appropriate set. They must keep track of the frequency with which they have generated each item, and compare sequences to some conception of randomness. If recent sequences are judged to be insufficiently random, a new strategy must be devised and initiated. In addition, well-learned or stereotypical sequences (e.g., 1-2-3-4, or A-B-C-D) must be inhibited. Random sequence generation therefore seems to draw on a range of executive processes, a claim supported by the work of
The evidence that RNG taps executive processes, particularly those involved in spatial WM tasks, and the fact that it can be performed continuously, make it a good candidate for a secondary task with which to investigate the impact of WM on the gaze-cueing effect. In each of the experiments reported here, participants performed blocks of standard gaze-cueing trials with target localization (Experiment 1) and target identification (Experiment 2) responses. In easy secondary task conditions, participants repeatedly recited aloud the digits 1 to 9 in sequence at the rate of one digit per second while performing the gaze cueing trials. In the hard secondary task conditions, participants generated random numbers, again at the rate of one per second, from the same set of digits. Counting numbers aloud, in order, is a stereotyped response, which should not be demanding of executive resources. Gaze cued orienting, whether stimulus-driven or involving a volitional component, ought to be observed under these conditions. However, if attention shifts in response to seen gazes share executive processes with RNG, we would expect the effect to be reduced, or absent when participants are engaged in the hard secondary task.
University of Stirling students and visitors (17 women, 7 men, with a mean age of 23.71 years, and range of 18–40 years) were recruited through the online sign-up system and online advertising. Psychology students were awarded experimental credits for their participation and the remaining volunteers participated on an entirely voluntary basis. All participants had self-reported normal or corrected-to-normal vision. All experimental procedures have been approved by the University of Stirling Research Ethics Committee and adhere to the principles of the 1964 Helsinki Declaration. Written informed consent was obtained from all participants.
A color photograph of a male face with neutral facial expression cropped of all external features subtending 5.7 × 3.7° of visual angle was used in the experiment. The face model was selected from the Radboud Faces Database (
In the secondary tasks participants were required to produce random sequences of numbers from 1 to 9 in the hard condition, or, in the easy condition, recite out loud the digits from 1 to 9 in sequence at the rate of one digit per second. The pace was indicated by a JOYO JM-65 metronome. Sequences were recorded using Olympus VN-5500 Digital Voice Recorder to ensure that participants were, indeed, performing the relevant secondary task.
All stimuli were presented against a black background on a 17-inch monitor set to 1152 × 864 pixels and refreshing at the rate of 75 MHz using E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA). Reaction times and responses to targets were registered using a Serial Response Box (Psychology Software Tools, Pittsburgh, PA, USA).
The experiment employed a within-subjects design with three independent variables: cue validity (cued, uncued), secondary task (hard, easy), and (SOA, 300 ms, 1000 ms). The dependent variable was RT in response to targets.
All participants were seated 70 cm away from the computer screen in a dimly lit room. Participants performed the secondary tasks concurrently with the gaze trials. In the hard secondary task condition, participants were asked to imagine an infinite number of numbers from one to nine in a hat and pulling them out one at a time, replacing each after it has been read. They were asked to generate the numbers out loud at a rate of one per second indicated by the sound of a metronome and informed that their voice was to be recorded for the purpose of further analysis. In the easy secondary task participants were instructed to recite the digit sequence from 1 to 9 repeatedly at a rate of one digit per second. Again, participants were asked to keep pace with the metronome, and informed about the active recording of their voice.
An example of a gaze cueing trial is illustrated in Figure
Participants completed a set of four blocks of 32 trials under each of the secondary task conditions. These comprised 16 repetitions of the factorial combinations of cue validity (cued, uncued), SOA (300 ms, 1000 ms), and gaze direction (left, right). Whether participants began with a set of four blocks of trials under easy or hard secondary task conditions was counterbalanced between participants. Prior to starting each set of four blocks, participants completed a block of 16 practice trials. Blocks in each set of four consisted of trials drawn randomly, without replacement from the pool of 128 trials. Participants were given five seconds before the first trial in each block to begin reciting the appropriate digit sequence (i.e., random or sequential).
Volunteers were informed that the gaze direction of the displayed face did not reliably predict the future localization of the target stimulus and advised that both tasks were of equal importance and that they should aim to maximize performance on each of the tasks.
Gaze cueing trials with errors were removed from analysis, resulting in the loss of 1.47% of the data. From the remaining data, median RTs were computed for each participant in each condition of the experiment. The interparticipant means of these RTs are recorded in the top row of Table
Original data | 384 (57) | 400 (49) | 540 (146) | 527 (111) | 371 (54) | 373 (51) | 514 (110) | 500 (99) |
Transformed data | 0.002665 |
0.002538 |
0.001973 |
0.001985 |
0.002755 |
0.002735 |
0.002036 |
0.002083 |
Transformed data (original scale) | 375 | 394 | 507 | 504 | 363 | 366 | 491 | 480 |
% correct | 99.5 | 99.6 | 97.3 | 97.8 | 99.8 | 99.8 | 97.5 | 97.4 |
The transformed data were subjected to an analysis of variance (ANOVA) with cue validity, secondary task and SOA as repeated measures factors. There was a significant main effect of secondary task
Finally, the ANOVA revealed a marginally significant interaction between cue validity and SOA,
The percentages of correct responses are also shown in Table
The overall pattern of the data indicated a cueing effect under easy dual task conditions, which disappeared when participants were engaged in an executively demanding secondary task. Participants were also slower and somewhat less accurate at target localization under hard relative to easy secondary task conditions, which suggests that generating random number sequences is indeed a more demanding task than reciting ordered sequences of digits. However, although participants’ accuracy was slightly lower under hard secondary task conditions, it was still very high indeed, suggesting that participants did not simply abandon the target localization task, or avert their gazes from the screen when performing the demanding secondary task. One possibility, however, is that participants may have maintained relatively high accuracy at target localization under difficult secondary task conditions by compromising their performance in generating random numbers. For example, they might have waivered from the requirement to generate numbers at the rate of one per second, or they may not have maintained an acceptable level of randomness. As we did not analyze these data we cannot address this possibility directly. The available data do suggest, however, that the RNG task had a detrimental effect on gazecued orienting. So, whether or not participants strayed from the maximum demands of the RNG task, it was still sufficient to disrupt gaze-cued orienting relative to performance in the easy secondary task condition.
The results of Experiment 1 imply that those mechanisms that are involved in the generation of random number sequences are also involved in the generation of an attention shift in response to a seen gaze. A key assumption underlying this interpretation of the data is that the difference in RTs for the localization of uncued versus cued targets is caused by the allocation of visual attention in response to the gaze cue. However, an alternative interpretation is that the RT difference between uncued and cued conditions could actually reflect a difference in the degree of stimulus-response compatibility between these cases. The argument is as follows. First, there is evidence that gazes and other social cues automatically trigger the generation of spatial codes (
In order to eliminate a response selection account for the cueing effect observed in Experiment 1, in Experiment 2 we used a target identification, rather than a target localization task. Additionally, we also included a condition that ought to be immune from a demanding secondary task—one where the identity of a target is assessed as a function of whether or not its location has been indicated by a peripheral luminance change.
Undergraduates from the University of Stirling (
These were identical to those used in Experiment 1 in all but the following respects. The target stimuli for both the gaze cueing and peripheral cueing tasks comprised the letters T and F in 18 point Arial font. In the peripheral cueing task, two grey boxes appeared centered 4.1° to the left and right of the central fixation cross. The lines of these boxes were 1 pixel thick and the boxes measured 1.6° in height and 1.4° in width. The spatial cue in this condition was rendered by replacing one of the grey placeholder boxes with an identically sized white box, the lines of which were six pixels thick.
The experiment had a 2 × 2 × 2 design with cue type (gaze cue, peripheral cue) as a between-subjects independent variable and cue validity (cued, uncued), and task type (hard, easy) as within-subjects variables. SOA was not manipulated in this experiment and was instead fixed at 300 ms for both cue types. This SOA produced the largest magnitude of gaze-cueing in Experiment 1, and is also short enough to elicit a cueing effect from peripheral onsets (
The easy and hard secondary tasks were identical to those used in Experiment 1. The procedure for gaze-cueing trials was identical to that of Experiment 1, save for the facts that the SOA was fixed at 300 ms for all trials, targets comprised the letters T and F, and participants were asked to identify the target letter on each trial by pressing the topmost button on the response box for the letter T and the bottom button for the letter F.
Trials in the peripheral cue condition began with a 2000 ms presentation of the display comprising the fixation cross and placeholders. One of the placeholder boxes was then replaced by the white cue box. The target letter (T or F) appeared centered in either the cued box, or the uncued box 300 ms after the onset of the cue, and remained on the screen until the participant had responded.
Participants completed 64 trials under each secondary task condition, divided into two blocks of 32 trials. A block of 16 practice trials preceded each pair of experimental blocks. The order in which participants completed each pair of easy and hard secondary task blocks was counterbalanced across participants, and participants were randomly allocated to either the gazecueing or peripheral cueing task, with the constraint that an equal number took part in each task.
Participants made errors on 4% of all gaze-cueing trials in Experiment 2 and these responses were removed from subsequent analyses of the RT data. Median RTs were then computed as in Experiment 1, and the interparticipant means and standard deviations of these data are presented in Table
Original data | 467 (74) | 489 (78) | 619 (181) | 634 (224) | 438 (52) | 533 (63) | 566 (131) | 648 (159) |
Transformed data | 0.002182 |
0.002086 |
0.001737 |
0.001718 |
0.002311 |
0.001901 |
0.001847 |
0.001631 |
Transformed data (original scale) | 458 | 479 | 576 | 582 | 433 | 526 | 541 | 613 |
% correct | 96.1 | 96.4 | 94.9 | 95.6 | 97.9 | 95.1 | 96.3 | 94.2 |
An ANOVA was conducted on the reciprocally transformed RT data, with secondary task (easy vs. hard), and cue validity (cued vs. uncued) as repeated measures factors, and cue-type (gaze vs. peripheral) as a between-subjects factor. This analysis yielded a main effect of secondary task,
In order to explore the significant 3-way interaction, separate repeated measures ANOVAs were conducted on the RT data from the group who performed the gaze-cueing primary task and those who performed the peripheral cueing task, each with cue validity and secondary task as factors.
For the group performing the gaze cueing trials, the ANOVA yielded significant main effects of secondary task,
The equivalent analysis conducted on the data from participants who performed the peripheral cueing trials yielded main effects of secondary task,
The percentage of correct responses are also shown in Table
In Experiment 2 all participants performed a target identification task instead of the target localization task used in Experiment 1. For half of the participants, spatial cues were provided by a gaze shift, as in Experiment 1, whereas peripheral luminance transients formed the cues for the remaining participants. Once again, participants carried out the gaze-cueing task, or peripheral orienting task while simultaneously performing an easy secondary task in some blocks of trials, and a hard secondary task RNG in others. Results indicated significant cueing effects under the easy secondary task conditions for both types of cue; however, the gaze cueing effect, but not the peripheral cueing effect, was eliminated when participants simultaneously performed the executively demanding RNG task. This finding supports the conclusion from Experiment 1 that gaze-cued orienting of attention and RNG involve at least some of the same cognitive mechanisms.
One curious aspect of the data is the observation that the peripheral cueing effect was actually reduced, though not eliminated, under hard secondary task conditions. Peripheral luminance changes are thought to capture attention in a purely stimulus-driven fashion (e.g.,
A similar argument might be made for gaze-cued orienting. At an SOA of 300 ms the advantage for target identification at cued versus uncued locations could involve both an exogenous and an endogenous deployment of attention, with RNG disrupting only the latter. However, as we have observed, there is no residual cueing effect under difficult dual task conditions that could be attributed to exogenous factors. Therefore, the gaze-cueing effect observed under easy secondary task conditions is likely to be driven by some of the same endogenous mechanisms that are involved in RNG.
The two experiments reported here investigated the extent to which gaze-cued orienting of attention is under top-down control. In each experiment, we assessed RT to targets whose location was cued by a gaze shift, relative to targets that appeared in a location opposite to that indicated by the direction of gaze. In order to assess the involvement of voluntary control in gaze cueing, performance was assessed while participants simultaneously completed an easy secondary task, and compared with performance while executing a demanding secondary task. With both a target localization (Experiment 1) and a target identification (Experiment 2) decision, a gaze cueing effect was observed when participants were simultaneously executing the undemanding secondary task—repeatedly reciting the digits 1–9 in sequence; however, gaze cueing was disrupted when participants were simultaneously generating random numbers. RNG is argued to place high demands on WM resources (e.g.,
The results obtained in these experiments contradict those of
Should we therefore understand gaze-cued orienting to be simply another manifestation of volitional, endogenous orienting of attention—in other words, the deliberate allocation of attentional resources in response to current goals? The answer seems to be no. While our data suggest that gaze-cued orienting shares resources with whatever control processes are used in RNG, plenty of other data point to it being much more like a stimulus-driven effect—the allocation of resources based on factors external to the observer; for example, it is observed even when gazes are known to be uninformative or even counter-informative of the likely location of an upcoming target (see
So, gaze-cued attention should not be thought of as another example of a purely volitional process (i.e., endogenous orienting), but then neither can it be described as a stimulus-driven reflex (i.e., exogenous orienting). Stimulus-driven processes occur whenever their triggering stimuli are present, and are resistant to concurrent load manipulations. The data reported here suggest that, in contrast, gaze-cued orienting
The key idea is that “look where others look” is a goal state that is almost permanently maintained by top-down signals that activate mechanisms involved in detecting and responding to the appropriate environmental trigger (a gaze shift, for example). This top-down activation is what gives gaze-cued orienting its resemblance to endogenous attentional control. However, because of this top-down activation, any stimulus that meets the relevant criteria (e.g., moving eyes or eye-like stimuli) will trigger the associated behavior (an attention shift). This attention shift occurs as long as the default goal state remains undisrupted by other, highly demanding attentional goals that engage WM concomitantly.
Notably, the gaze-cued orienting effect will persist even in the face of concurrent task demands, as long as the concurrent task does not recruit the same top-down mechanisms that are involved in maintaining the “look where others look” goal state. Repeatedly counting from 1 to 9 is a well practiced routine, which does not require the generation and maintenance of complex stimulus-response mappings, establishment of novel module-to-module couplings, iterative monitoring and modification of performance and so on. Maintaining a digit load in WM may be similarly untaxing, as it relies on a dedicated component of WM (e.g., the phonological loop in the WM model, see
This theory suggests that it is the number of simultaneously active sub-goals required of RNG that disrupts the orienting of attention to seen gazes; however, it is of course possible that the source of interference is one or more of the component processes themselves. Further research will be required to explore this possibility. The theory also presents a solution to another puzzle: if gaze-cued orienting were truly a stimulus-driven process, it ought to occur every time a gaze shift is viewed, and would likely be accompanied by an overt shift in gaze as covert and overt orienting usually, but not inevitably, occur in tandem (see
An alternative explanation for our data is that rather than imposing high general cognitive demands, RNG exerts its effects on gaze cued orienting specifically through disrupting the spatial processing involved in extracting gaze direction from the eyes and executing an attention shift in the computed direction. In support of this suggestion, it is well known that the mental representations of numbers are associated with spatial codes (e.g.,
The proposal is, then, that the same spatial processing resources may be involved in gaze-cued orienting and RNG. This is an intriguing suggestion as it could account for why RNG disrupts gaze cued orienting, whereas other high load tasks do not. It is not immediately obvious, however, why the generation of numbers in an ordered sequence in our easy secondary tasks would not also involve the same spatial resources as does generating the same digits in a random order. Indeed, one might argue that spatial coding is actually stronger in the case of ordered number generation as one can readily imagine the ordered sequence in a number line from left to right. On this view it seems likely that any spatial coding induced by the generation of numbers is controlled across the secondary tasks used in our experiments. In support of a spatial account, it could be argued that RNG draws more heavily on spatial resources than does ordered number generation, for the latter simply involves reading off a stereotyped verbal sequence, which might not involve the activation of individual spatial representations to the same extent as RNG. Indeed, numbers are likely associated with different kinds of representations—verbal as well as visuo-spatial—with different representations deployed according to the nature of the number-involving task (e.g.,
Our data do not allow us to tease apart these possibilities directly, although the fact that the spatial location of the target interacted with neither secondary task nor cue validity hints that spatial coding may not be a crucial factor1,2. Nevertheless, the suggestion that RNG exerts its effects on gazed-cued orienting through a spatial mechanism is clearly one that warrants further research.
In summary, in two experiments, we assessed the effects of a concurrent WM demand on social orienting. Our main finding was that social attention was disrupted by the RNG task. Data from this study stands in contrast to previous laboratory-based findings in suggesting that attention cued by gazes is, indeed, dependent on top-down control.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This research was supported by the Economic and Social Research Council (grant number ES/1034803/1). The authors thank Graeme Lavery and Amy Walker who collected the data for Experiment 2, and two reviewers for their helpful comments on an earlier draft of the manuscript.
In order to examine whether the source of the interference effect of RNG on gaze cued orienting might be an incompatibility between the spatial code generated by the appearance of the target and one that might be associated with the generation of random numbers (e.g., producing number sequences from left to right in visual imagery), we also performed an ANOVA with target location (left vs. right) as an additional repeated measures factor. However, target location was found to interact with neither of the other two factors, and nor did the predicted interaction between target location, secondary task and cue validity reach statistical significance (
As with Experiment 1, we also performed an ANOVA including target location (left vs. right) as an additional repeated measures factor, but again this analysis failed to yield any significant effects involving this factor (