Neural signatures of vigilance decrements predict behavioural errors before they occur

When people monitor displays for rare targets, they are slower to respond and more likely to miss those targets relative to frequent target conditions (Wolfe et al., 2005; Warm et al., 2008; Rich et al., 2008; Reason, 1990; Reason, 2000). This effect is more pronounced as the time doing the task increases, which is often called a ‘vigilance decrement’. Theoretical accounts of vigilance decrements fall into two main categories. ‘Cognitive depletion’ theories suggest performance drops as cognitive resources are ‘used up’ by the difficulty of sustaining attention under vigilance conditions (Helton and Warm, 2008; Helton and Russell, 2011; Warm et al., 2008). In contrast, ‘mind wandering’ theories suggest that the boredom of the task tends to result in insufficient involvement of cognitive resources, which in turn leads to performance decrements (Manly et al., 1999; Smallwood and Schooler, 2006; Young and Stanton, 2002). Either way, there are many real-life situations where such a decrease in performance over time can lead to tragic consequences, such as the Paddington railway disaster (UK, 1999), in which a slow response time to a stop signal resulted in a train moving another 600 m past the signal into the path of an oncoming train. With the move towards automated and semi-automated systems in many high-risk domains (e.g., power-generation and trains), humans now commonly need to monitor systems for infrequent computer failures or errors. These modern environments challenge our attentional systems and make it urgent to understand the way in which monitoring conditions change the way important information about the task is encoded in the human brain.

To date, most vigilance and rare target studies have used simple displays with static stimuli. Traditional vigilance tasks, inspired by radar operators in WWII, require participants to respond to infrequent visual events on otherwise blank screens, and show more targets are missed as time on task increases (Mackworth, 1948). More recent vigilance tasks have participants detect infrequent target stimuli among non-targets, and typically show an increase in misses as time on task increases. In Temple et al., 2000, for example, with only 20% targets, after 10 min target detection rates declined from 97% to 93% for high contrast (easy) and from 95% to 83% for low (hard) contrast targets. Other approaches have been to test for vigilance effects using frequent responses to non-targets, which have the advantage of more data points for analysis. The Sustained Attention to Response Task (SART), for example, requires participants to respond to each non-target item in a rapid stream of stimuli and occasionally withhold a response to a target item (Beck et al., 1956; Rosenberg et al., 2013). These approaches usually show effects on reaction times (RTs), which increase and become more variable with time on task (Rosenberg et al., 2013; Möckel et al., 2015; Singleton, 1953), although others have found RTs decrease (Rubinstein, 2020). Faster RTs also occur for ‘target absent’ responses in rare target visual search (Wolfe et al., 2005; Rich et al., 2008). Overall, vigilance decrements in terms of poorer performance can be seen in both accuracy and in RTs, depending on the task.

Despite these efforts, modern environments (e.g., rail and air traffic control) have additional challenges not encapsulated by these measures. This includes multiple moving objects, potentially appearing at different times, and moving simultaneously in different directions. When an object moves in the space, its neural representation has to be continuously updated so we can perceive the object as having the same identity. Tracking moving objects also requires considerable neural computation: in addition to spatial remapping, for example, we need to predict direction, speed, and the distance of the object to a particular destination. These features cannot be studied using static stimuli; they require objects that shift across space over time. In addition, operators have complex displays requiring selection of some items while ignoring others. We therefore need new approaches to study vigilance decrements in situations that more closely resemble the real-life environments in which humans are now operating. Developing these methods will provide a new perspective on fundamental questions of how the brain implements sustained attention in moving displays, and the way in which monitoring changes the encoding of information compared with active task involvement. These new methods may also provide avenues to optimise performance in high-risk monitoring environments.

The brain regions involved in maintaining attention over time has been studied using functional magnetic resonance imaging (fMRI), which measures changes in cerebral blood flow (Adler et al., 2001; Benedict et al., 2002; Coull et al., 1996; Gilbert et al., 2006; Johannsen et al., 1997; Ortuño et al., 2002; Périn et al., 2010; Schnell et al., 2007; Sturm et al., 1999; Tana et al., 2010; Thakral and Slotnick, 2009; Wingen et al., 2008). These studies compared brain activation in task vs. resting baseline or sensorimotor control (which involved no action) conditions and used univariate analyses to identify regions with higher activation under task conditions. This has the limitation that there are many features that differ between the contrasted (subtracted) conditions, not just the matter of sustained attention. Specifically, this comparison cannot distinguish whether the activation during sustained attention is caused by the differences in the task, stimuli, responses, or a combination of these factors. As it is challenging to get sufficient data from monitoring (vigilance) tasks in the scanner, many previous studies used tasks with relatively frequent targets, in which vigilance decrements usually do not occur. However, despite these challenges, Langner and Eickhoff, 2013 reviewed vigilance neuroimaging studies and identified a network of right-lateralised brain regions including dorsomedial, mid- and ventrolateral prefrontal cortex, anterior insula, parietal and a few subcortical areas that they argue form the core network subserving vigilant attention in humans. The areas identified by Langner and Eickhoff, 2013 show considerable overlap with a network previously identified as being recruited by many cognitively challenging tasks, the ‘multiple demand’ (MD) regions, which include the right inferior frontal gyrus, anterior insula, and intra-parietal sulcus (Duncan and Owen, 2000; Duncan, 2010; Fedorenko et al., 2013; Woolgar et al., 2011; Woolgar et al., 2015a; Woolgar et al., 2015b).

Other fMRI studies of vigilance have focused on the default mode network, composed of discrete areas in the lateral and medial parietal, medial prefrontal, and medial and lateral temporal cortices such as posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), which is thought to be active during ‘resting state’ and less active during tasks (Greicius et al., 2003; Greicius et al., 2009; Raichle, 2015). Eichele et al., 2008 suggested that lapses in attention can be predicted by decrease of deactivation of this default mode network. In contrast, Weissman et al., 2006 identified deactivation in the anterior cingulate and right prefrontal regions in pre-stimulus time windows when targets were missed. Ekman et al., 2012 also observed decreased connectivity between sensory visual areas and frontal brain areas on the pre-stimulus time span of incorrect trials in colour/motion judgement tasks. More recently, Sadaghiani et al., 2015 showed that the functional connectivity between sensory and ‘vigilance-related’ (cingulo-opercular) brain areas decreased prior to behavioural misses in an auditory task while between the same sensory area and the default-mode network the connectivity increased. These findings suggest that modulation of interactions between sensory and vigilance-related brain areas might be related to behavioural misses in monitoring tasks.

Detecting changes in brain activation that correlate with lapses of attention can be particularly challenging with fMRI, given that it has poor temporal resolution. Electroencephalography (EEG), which records electrical activity at the scalp, has much better temporal resolution, and has been the other major approach for examining changes in brain activity during sustained attention tasks. Frequency band analyses have shown that low-frequency alpha (8–10.9 Hz) oscillations predict task workload and performance during monitoring of simulated air traffic (static) displays with rare targets, while frontal theta band (4–7.9 Hz) activity predicts task workload only in later stages of the experiment (Kamzanova et al., 2014). Other studies find that increases in occipital alpha oscillations can predict upcoming error responses (Mazaheri et al., 2009) and misses (O'Connell et al., 2009) in go/no-go visual tasks with target frequencies of 11% and 9%, respectively. These changes in signal power that correlate with the task workload or behavioural outcome of trials are useful, but provide relatively coarse-level information about what changes in the brain during vigilance decrements.

Understanding the neural basis of decreases in performance over time under vigilance conditions is not just theoretically important, it also has potential real-world applications. In particular, if we could identify a reliable neural signature of attentional lapses, then we could potentially intervene prior to any overt error. For example, with the development of autonomous vehicles, being able to detect when a driver is not engaged, combined with information about a potential threat, could allow emergency braking procedures to be initiated. Previous studies have used physiological measures such as pupil size (Yoss et al., 1970), body temperature (Molina et al., 2019), skin conductance, and blood pressure (Lohani et al., 2019) to indicate the level of human arousal or alertness, but these lack the fine-grained information necessary to distinguish transient dips from problematic levels of inattention in which task-related information is lost. In particular, we lack detail on how information processing changes in the brain during vigilance decrements. This knowledge is crucial to develop a greater understanding of how humans sustain vigilance.

In this study, we developed a new task, multiple-object monitoring (MOM), which includes key features of real-life situations confronting human operators in high-risk environments. These features include moving objects, varying levels of target frequency, and a requirement to detect and avoid collisions. A key feature of our MOM task is that it allows measurement of the specific decrements in performance during vigilance (sustaining attention in a situation where only infrequent responses are needed) separate from more general decreases in performance simply due to doing a task for an extended period. Surprisingly, this is not typically the case in vigilance tasks. We recorded neural data using the highly sensitive method of magnetoencephalography (Baillet, 2017) and used multivariate pattern analyses (MVPA) to determine how behavioural vigilance decrements correlate with changes in the neural representation of information. We used these new approaches to better understand the way in which changes between active and monitoring tasks affect neural representation, including functional connectivity. We then examined the potential for using these neural measures to predict forthcoming behavioural misses based on brain activity.

Participants completed the MOM task during which they monitored several dots moving on visible trajectories towards a centrally presented fixed object (Figure 1A). The trajectories spanned from corners of the screen towards the central object and deflected at 90° before contacting the central object. The participants’ task was to keep fixation on the central object and press the button to deflect the moving dot if it violated its trajectory and continued towards the central object after reaching the deflection point. They were tasked to do so before the object ‘collided’ with the central object. In each block, only dots of one colour (either green or red; called Attended vs. Unattended) was relevant and should be responded to by the participants (~110 s). Either 50% or 6% of the attended dots (cued colour) were targets (i.e., violated their trajectory requiring a response; see Materials and methods) generating Active and Monitoring conditions, respectively.

Figure 1

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Behavioural data: The MOM task evokes a reliable vigilance decrement

In the first block of trials (i.e., the first 110 s, excluding the two practice blocks), participants missed 29% of targets in the Active condition and 40% of targets in the Monitoring condition. However, note the number of targets in any single block is necessarily very low for the Monitoring (for a single block, there are 16 targets for Active but only two targets for Monitoring). The pattern becomes more robust over blocks, and Figure 2A shows the miss rates changed over time in different directions for the Active vs. Monitoring conditions. For Active blocks, miss rates decreased over the first five blocks and then plateaued at ~17%. For Monitoring, however, miss rates increased throughout the experiment: by the final block, these miss rates were up to 76% (but again, the low number of targets in Monitoring mean that we should use caution in interpreting the results of any single block alone). There was evidence that miss rates were higher in the Monitoring than Active conditions from the fourth block onwards (BF >3; Figure 2A). Participants’ RTs on correct trials also showed evidence of specific vigilance decrements, increasing over time under Monitoring but decreasing under Active task conditions (Figure 2B). There was evidence that RTs were slower for Monitoring compared with Active from the sixth block onwards (BF >3, except for Block #11). The characteristic pattern of increasing miss rates and slower RTs over time in the Monitoring relative to the Active condition validates the MOM task as effectively evoking vigilance decrements.

Figure 2

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Effects of target frequency on direction of approach information

Direction of approach information is a very clear visual signal (‘from the left’ vs. ‘from the right’) and therefore is unlikely to be strongly modulated by other factors, except perhaps whether the dot was in the cued colour (Attended) or the distractor colour (could be ignored: Unattended). There was moderate or strong evidence for a main effect of Attention (Figure 3A; BF >3, Bayes factor ANOVA, cyan dots) starting from 265 ms and lasting until dots faded. This is consistent with maintenance of information about the attended dots and attenuation of the information about unattended dots (Figure 3—figure supplement 1A). The large difference in coding attributable to attention remained for as long as the dots were visible.

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In contrast, there was no sustained main effect of Target Frequency on the same direction of approach coding. For the majority of the epoch there was moderate or strong evidence for the null hypothesis (BF <1/3; Bayes factor ANOVA, Figure 3A, purple dots). The sporadic time point with a main effect of Target Frequency, observed before the deflection (BF >3), likely reflects noise in the data as there is no clustering. Recall that we only focus on time points prior to deflection, as after this point there are visual differences between Active and Monitoring, with more dots deflecting in the Monitoring condition.

There was also no sustained main effect of the Time on Task on information about the direction of approach (BF <3; Bayes factor ANOVA, green dots; Figure 3A). There were no sustained two-way or three-way interactions between Attention, Target Frequency, and Time on Task (BF <3; Bayes factor ANOVA). Note that the number of trials used in the training and testing of the classifiers were equalised across the eight conditions and equalled the minimum available number of trials across those conditions shown in Figure 3. Therefore, the observed effects cannot be attributed to a difference in the number of trials across conditions.

Is informational brain connectivity modulated by Attention, Target Frequency, and Time on Task?

Using graph-theory-based univariate connectivity analysis, it has been shown that the connectivity between relevant sensory areas and ‘vigilance-related’ cognitive areas changes prior to lapses in attention (behavioural errors; Ekman et al., 2012; Sadaghiani et al., 2015). Therefore, we asked whether vigilance decrements across the time course of our task corresponded to changes in multivariate informational connectivity, which evaluates the similarity of information encoding, between frontal attentional networks and sensory visual areas. In line with attentional effects on sensory perception, we predicted that connectivity between the frontal attentional and sensory networks should be lower when not attending (vs. attending; Goddard et al., 2019). Behavioural errors were also previously predicted by reduced connection between sensory and ‘vigilance-related’ frontal brain areas (Ekman et al., 2012; Sadaghiani et al., 2015). Therefore, we predicted a decline in connectivity when targets were lower in frequency, and with increased time on task, as these led to increased errors in behaviour, specifically under vigilance conditions in our task (i.e., late blocks in Monitoring vs. late blocks in Active; Figure 2). We used a simplified version of our method of RSA-based informational connectivity to evaluate the (Spearman’s rank) correlation between distance information RDMs across the peri-frontal and peri-occipital electrodes (Goddard et al., 2016; Figure 4A).

Figure 4

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Results showed strong evidence (Bayes factor ANOVA, BF = 6.5e³) for higher informational connectivity for trials with Attended compared to Unattended dots, and moderate evidence for no effect of Target Frequency (Bayes factor ANOVA, BF = 0.11; Figure 4B). There was insufficient evidence to determine whether there was a main effect of Time on Task (Bayes factor ANOVA, BF = 0.72). There was evidence in the direction of the null for the two-way interactions between the factors (Bayes factor ANOVA, two-way Time on Task-Target Frequency: BF = 0.36; Time on Task-Attention: BF = 0.39; Target Frequency-Attention: BF = 0.15) and insufficient evidence regarding their three-way interaction (BF = 0.95). These results suggest that [--deleted text--] trials in which the dots are in the distractor (Unattended) colour, in which the attentional load is low, result in less informational connectivity between occipital and frontal brain areas compared to [--deleted text--] Attended trials. This is consistent with a previous study (Alnæs et al., 2015), which suggested that large-scale functional brain connectivity depends on the attentional load, and might underpin or accompany the decrease in information decoding across the brain in the unattended condition., which suggested that large-scale functional brain connectivity depends on the attentional load, and might underpin or accompany the decrease in information decoding across the brain in these conditions.

We also compared the connectivity for the correct vs. miss trials (Figure 4C). This analysis was performed only for Attended condition as there are no miss trials for Unattended condition, by definition. There was moderate evidence for no difference in connectivity on miss compared to correct trials (Bayes factor ANOVA, BF = 0.11). In addition, there was moderate evidence for no effect of Time on Task and Target Frequency (BF = 0.11 and BF = 0.10, respectively), as well as for two-way and three-way interactions between the three factors (Bayes factor ANOVA, Behaviour-Target Frequency: BF = 0.14; Behaviour-Time on Task: BF = 0.14; Target Frequency-Time on Task: BF = 0.15; their 3-way interaction BF = 0.14). Therefore, in contrast to an auditory monitoring task which showed decline in univariate graph-theoretic connectivity before behavioural errors (Sadaghiani et al., 2015), we observed no change in informational connectivity on error. Note that, the number of trials is equalized across the 8 conditions in each of our analyses separately.

Is neural representation different on miss trials?

The results presented in Figure 3, which used only correct trials, showed changes due to target frequency to the representation of task-relevant information when the task was performed successfully. We next move on to our second question, which is whether these neural representations change when overt behaviour is affected, and therefore, whether we can use the neural activity as measured by MEG to predict behavioural errors before they occur. We used our method of error data analysis (Woolgar et al., 2019) to examine whether the patterns of information coding on miss trials differed from correct trials. For these analyses we used only attended dots, as unattended dots do not have behavioural responses, and we matched the total number of trials in our implementation of correct and miss classification.

First, we evaluated the representation of the less relevant information – the direction of approach measure (Figure 5A). The results for correct trials provided information dynamics very similar to the Attended condition in Figure 3A, except for higher overall decoding, which is explained by the inclusion of the data from the whole experiment (15 blocks) rather than just the five early and late blocks (note the number of trials is still matched to miss trials).

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For the direction of approach information, there was moderate or strong evidence (i.e., BF >3) in both Active and Monitoring conditions after ~100 ms for above-chance decoding. However, when the classifiers were tested on miss trials, from onset to deflection, the pattern of information dynamics were different, even though we had matched the number of trials. Specifically, while the level of information was comparable to correct trials with spurious instances (but no sustained time windows) of difference (BF >3 as indicated by black dots) before 500 ms, decoding traces were much noisier for miss trials with more variation across trials and between nearby time points (Figure 5A). Note that after the deflection, the visual signal is different for correct and miss trials, so the difference between their decoding curves (BF >3) is not meaningful. These results suggest a noisier representation of direction of approach information for the missed dots compared to correctly deflected dots.

We then repeated the same procedure on the representation of the most task-relevant distance to object information on correct vs. miss trials (Figure 5B). On correct trials, the distance information for both Active and Monitoring conditions was above chance (Figure 5B left panels; BF > 10⁴). For miss trials, the corresponding distance information was still above chance (Figure 5B right panels; BF > 10³) but the direct comparison revealed that distance information dropped on miss trials compared to correct trials (Figure 5B; Black dots; BF >3 across all distances; Active and Monitoring results were averaged for correct and miss trials separately before Bayes analyses).

In principle, the average decoding levels could be composed of ‘all or none’ misses or graded drops in information, and it is possible that on some miss trials there is a good representation but the target is missed for other reasons (e.g., a response-level error). As neural data are noisy and multivariate decoding needs cross-validation across subsamples of the data, and because each trial, at each distance, can only be classified correctly or incorrectly by a two-way classifier, we tend not to compare the decoding accuracies in a trial-by-trial manner, but rather on average (Grootswagers et al., 2017). However, if we look at an individual data set and examine all the miss trials (averaged over the 15 distances and cross-validation runs) in our distance-to-object decoding, we can get some insights into the underlying distributions (Figure 5—figure supplement 1). Results showed that, for all participants, the distribution of classifier accuracies for both correct and miss trials followed approximate normal distributions. However, while the distribution of decoding accuracies for correct trials was centred around 60%, the decoding accuracies for individual miss trials were centred around 56%. We evaluated the difference in the distribution of classification accuracies between the two types of trials using Cohen’s d. Cohen’s d ranged from 0 to 2.5 across participants and conditions. 14 out of 21 subjects showed moderate (d > 0.5) to large (d > 0.8; Cohen, 1969) differences between the distribution of correct and miss trials in either Active or Monitoring condition or both. Therefore, although the miss trials vary somewhat in levels of information, only a minority of (< 24%) miss trials are as informative as the least informative correct trials. These results are consistent with the interpretation that there was less effective representation of the crucial information about the distance from the object preceding a behavioural miss.

Please note that the results presented so far were from correct and miss trials and we excluded early, late, and wrong-colour false alarms to be more specific about the error type. However, the false alarm results (collapsed across all three types of false alarms) were very similar (Figure 5—figure supplement 2) to those of the missed trials (Figure 5): noisy information about the direction of approach and at-chance information about the distance to object. This may suggest that both miss and false alarm trials are caused by impaired processing of information, or at least, are captured similarly by our decoding methods. The average number of miss trials was 58.17 (±21.63 SD) and false alarm trials was 65.94 (±21.13 SD; out of 1920 trials).

Can we predict behavioural errors using neuroimaging?

Finally, we asked whether we could use this information to predict the behavioural outcome of each trial. To do so, we developed a new method that classified trials based on their behavioural outcomes (correct vs. miss) by asking how well a set of classifiers, pre-trained on correct trials, would classify the distance of the dot from the target (Figure 6A). To achieve this, we used a second-level classifier which labelled a trial as correct or miss based on the average accumulated accuracies obtained for that dot at every distance from the first-level decoding classifiers which were trained on correct trials (Figure 6A,B). If the accumulated accuracy for the given dot at the given distance was less than the average accuracy obtained from testing on the validation set minus a specific threshold (based on standard deviation), the testing dot (trial) was labelled as correct, otherwise miss. In this analysis, the goal was to maximise the accuracy of predicting behaviour. For that purpose, we accumulated classification accuracies along the distances. Moreover, as each classifier performs a binary classification for each testing dot at each distance, the accumulation of classification accuracies also avoided the spurious classification accuracies to drive the decision, providing smooth ‘accumulated’ accuracies for predicting the behaviour. As Figure 6B shows, there was strong evidence (BF >10) that decoding accuracy of distances was higher for correct than miss trials with the inclusion of more classifier accuracies as the dot approached from the corner of the screen towards the centre. This clear separation of accumulated accuracies for correct vs. miss trials allowed us to predict with above-chance accuracy the behavioural outcome of the ongoing trial (Figure 6D). To find the optimal threshold for each participant, we evaluated the thresholds used for all other participants except for a single testing participant for whom we used the average of the best thresholds that led to highest prediction accuracy for other participants. This was ~0.4 standard deviation below the average accuracy on the other participants’ validation (correct trial) sets (Figure 6C).

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The prediction accuracy of behavioural outcome was above chance level (59% vs. 50%; BF > 10) even when the dot had only been on the screen for 80 ms, which corresponds to our furthest distance #15 (1160ms prior to deflection point; Figure 6D). The accuracy increased to 65.4% as the dot approached the centre of the screen, with ~64% accuracy with still 800 ms to go before required response. Importantly, the prediction algorithm showed generalisable results across participants; the threshold for decision obtained from the other participants could predict the accuracy of an independent participant’s behaviour using only their neural data.

The prediction of behavioural outcome (Figure 6) was performed using the data from the whole data set. To test if prediction accuracy depended on the stage of the experiment, we performed the behavioural prediction procedure on data sets obtained from the first 5 (early) and the last 5 (late) stages of the experiment separately (Figure 6—figure supplement 1). There was no evidence for a change in the prediction power in the late vs. early blocks of trials.

This study developed new methods to gain insights into how attention, the frequency of target events, and the time doing a task affect the representation of information in the brain. Our new MOM task evoked reliable specific vigilance decrements in both accuracy and RT in a situation that more closely resembles real-life modern tasks than classic vigilance tasks. Using the sensitive analysis method of MVPA, we showed that neural coding was stronger for attended compared to distractor information. There was also one time-window where the interaction between the time on the task and target frequency affected decoding, with a larger decline in coding under monitoring conditions, which may reflect a neural correlate of the behavioural vigilance decrements. We also developed a novel informational brain connectivity analysis, which showed that the correlation between information coding across peri-occipital and peri-frontal areas varied with different levels of attention but did not change with errors. Finally, we utilised our recent error data analysis to predict forthcoming behavioural misses based on the neural data. In the following sections, we explain each of these findings in detail and compare them with relevant literature.

First, the MOM task includes key features of real-world monitoring situations that are not usually part of other vigilance tasks (e.g., Mackworth, 1948; Temple et al., 2000; Beck et al., 1956; Rosenberg et al., 2013), and the results show clear evidence of vigilance decrements. Behavioural performance, measured with both RT and accuracy, deteriorated over time in Monitoring (infrequent targets) relative to Active (frequent targets) conditions. One important additional advantage of the MOM task over conventional vigilance tasks is that it allows us to be specific about the vigilance decrements (by comparing Active and Monitoring conditions) separate from general time on task effects which affect both Active and Monitoring conditions (c.f. Figure 3). These vigilance decrements demonstrate that the MOM task can be used to explore vigilance in situations more closely resembling modern environments, namely those involving moving stimuli and selection of relevant from irrelevant information, giving a useful tool for future research.

Second, the high sensitivity of MVPA to extract information from neural signals allowed us to investigate the temporal dynamics of information processing along the time course of each trial. The manipulation of attention showed a strong overall effect with enhanced representation of both the less important direction of approach and the most task-relevant distance to object information for cued dots, regardless of how frequent the targets were (Figure 3). The improved representation of information under attention extends previous findings from us and others (Woolgar et al., 2015b; Goddard et al., 2019; Nastase et al., 2017) to moving displays, in which the participants monitor multiple objects simultaneously. When targets were infrequent, modelling real-life monitoring situations, there was a strong behavioural drop in performance (i.e., vigilance effects in both accuracy and RT; Figure 2) and a hint in the brain activity data of a change in neural coding (namely one time-window showing evidence of an interaction between Target Frequency and Time on Task). We need more data to fully test this effect, however, our main finding is that of being able to use the difference in decoding between correct and miss trials to predict behaviour. Although the results replicated after standard eye-artefact removal, as algorithms of artefact removal are not perfect, there is still the possibility that our MEG data could be affected by some residual patterns of eye movements across conditions. In a real-world setting, it may be possible to combine information from the brain and eye-movements to further improve the prediction accuracy.

When people miss targets, they might process or encode the relevant sensory information less effectively than when they correctly respond to targets. This is consistent with our finding that on the majority of miss trials, there was less effective representation about the task-relevant information in the neural signal, in contrast to the consistently more effective representation on correct trials. Note that our vigilance decrement effects are defined as the difference between Active and Monitoring conditions, which allows us to be sure that we are not interpreting general task (e.g., participant fatigue) or hardware-related effects as vigilance decrements.

It is important to note that previous studies have tried other physiological/behavioural measures to determine participants’ vigilance or alertness, such as pupil size (Yoss et al., 1970), response time variability (Rosenberg et al., 2013), blood pressure and thermal energy (Lohani et al., 2019) or even body temperature (Molina et al., 2019). We used highly sensitive analysis of neuroimaging data so that we could address two questions that could not be answered using these more general vigilance measures. We tested for changes in the way information is processed in the brain, particularly testing for differences in the impact of monitoring on the relevance of the information, rather than whether the participants were vigilant and alert in general. Moreover, we could also investigate how the most relevant and less relevant information was affected by the target frequency and time on the task, to find neural correlates for the behavioural vigilance decrements observed in many previous studies (e.g., Dehais et al., 2019; Wolfe et al., 2005; Wolfe et al., 2007; Kamzanova et al., 2014; Ishibashi et al., 2012). The less relevant information about direction of approach was modulated by attention, but its representation was not detectably affected by target frequency and time on task, and was noisier, but not noticeably attenuated, on error trials. The relative stability of these representations might reflect the large visual difference between stimuli approaching from the top left vs bottom right of the screen. In contrast, the task-relevant information of distance to object was affected by attention and was attenuated on errors. The difference might reflect the fact that only the distance information is relevant to deciding whether an item is a target, and/or the classifier having to rely on much more subtle differences to distinguish the distance categories, which collapsed over stimuli appearing on the left and right sides of the display, removing the major visual signal.

Our information-based brain connectivity method showed moderate evidence for no change in connectivity between correct and error trials. Informational connectivity is unaffected by differences in absolute levels of information encoding (e.g., lower coding on miss vs. correct trials). It could be sensitive to different levels of noise between conditions, but there was no evidence for that in this case. Apart from sensory information coding and sensory-based informational connectivity, which were evaluated here, there may be other correlates we have not addressed. Effects on response-level selection, for example, independently or in conjunction with sensory information coding, could also affect performance under vigilance conditions, and need further research.

Our connectivity method follows the recent major recent shift in literature from univariate to multivariate informational connectivity analyses (Goddard et al., 2016; Karimi-Rouzbahani et al., 2017; Anzellotti and Coutanche, 2018; Goddard et al., 2019; Kietzmann et al., 2019; Karimi-Rouzbahani et al., 2019; Basti et al., 2020; Karimi-Rouzbahani et al., 2021). This is in contrast with the majority of neuroimaging studies using univariate connectivity analyses which can miss existing connectivity across areas when encountering low-amplitude activity on individual sensors (Anzellotti and Coutanche, 2018; Basti et al., 2020). Informational connectivity, on the other hand, is measured either through calculating the correlation between temporally resolved patterns of decoding accuracies across a pair of areas (Coutanche and Thompson-Schill, 2013) or the correlation between representational dissimilarity matrices (RDMs) obtained from a pair of areas (Kietzmann et al., 2019; Goddard et al., 2016; Goddard et al., 2019; Karimi-Rouzbahani et al., 2019; Karimi-Rouzbahani et al., 2021). Either one measures how much similarity in information coding there is between two brain areas across conditions, which is interpreted as reflecting their potential informational connectivity, and is less affected by absolute activity values compared to conventional univariate connectivity measures (Anzellotti and Coutanche, 2018). The method we used here evaluated the correlation between RDMs, which has provided high-dimensional information about distance to object, obtained from multiple sensors across the brain areas. This makes our analysis sensitive to different aspects of connectivity compared to conventional univariate analyses.

Fourth, building upon our recently developed method of error analysis (Woolgar et al., 2019), we were able to predict forthcoming behavioural misses based on the decoding data, before the response was given. Our method is different from the conventional method of error prediction, in which people directly discriminate correct and miss trials by feeding both types of trials to classifiers in the training phase and testing the classifiers on the left-out correct and miss trials (e.g., Bode and Stahl, 2014). Our method only uses correct trials for training, which makes its implementation plausible for real-world situations since we usually have plenty of correct trials and only few miss trials (i.e., cases when the railway controller diverts the trains correctly vs. misses and a collision happens). Moreover, it allows us to directly test whether the neural representations of correct trials contain information which is (on average) less observable in miss trials. We statistically compared the two types of trials and showed a reliable advantage in the level of information contained at individual-trial-level in correct vs. miss trials.

Our error prediction results showed a reliable decline in the crucial task-relevant (i.e., distance to object) information decoding on miss vs. correct trials but less decline in the less task-relevant information (i.e., direction of approach). A complementary analysis allowed the prediction of behaviourally missed trials as soon as the stimulus appeared on the screen (after ~80 ms), which was ~1160 ms before the time of response. This method was generalisable across participants, with the decision threshold for trial classification based on other participants’ data successful in predicting errors for a left-out participant. A number of previous studies have shown that behavioural performance can be correlated with aspects of brain activity even before the stimulus onset (Bode and Stahl, 2014; Eichele et al., 2008; Eichele et al., 2010; Weissman et al., 2006; Ekman et al., 2012; Sadaghiani et al., 2015). Those studies have explained the behavioural errors by implicit measures such as less deactivation of the default-mode network, reduced stimulus-evoked sensory activity (Weissman et al., 2006; Eichele et al., 2008), and even the connectivity between sensory and vigilance-related/default-mode brain areas (Sadaghiani et al., 2015). It would be informative, however, if they could show how (if at all) the processing of task-relevant information is disrupted in the brain and how this might lead to behavioural errors. To serve an applied purpose, it would be ideal if there was a procedure to use those neural signatures to predict behavioural outcomes. Only three previous studies have approached this goal. Bode and Stahl, 2014, Sadaghiani et al., 2015, and Dehais et al., 2019 reported maximum prediction accuracies of 62%, 63%, and 72% (with adjusted chance levels of 50%, 55%, and 59%, respectively). Here, we obtained up to 65% prediction (with a chance level of 50%), suggesting our method accesses relevant neural signatures of attention lapses, and may be sensitive in discriminating these. The successful prediction of an error from neural data more than a second in advance of the impending response provides a promising avenue for detecting lapses of attention before any consequences occur.

The overall goal of this study was to understand how neural representation of dynamic displays was affected by attention and target frequency, and whether reliable changes in behaviour over time could be predicted on the basis of neural patterns. We observed that the neural representation of critically relevant information in the brain was particularly poor on trials where participants missed the target. We used this observation to predict behavioural outcome of individual trials and showed that we could predict behavioural outcome more than a second before action was needed. These results provide new insights about how momentary lapses in attention impact information coding in the brain and propose an avenue for predicting behavioural errors using novel neuroimaging analysis techniques.

We have shared the Magnetoencephalography data (i.e. time series) as well as behavioral data in Matlab '.mat' format on the Open Science Framework website at https://osf.io/5aw8v/ with the DOI: 10.17605/OSF.IO/5AW8V. We have also uploaded a video of the "Multiple-Object-Monitoring" paradigm, developed for this study, for easier understanding of the task at the same address. The mentioned address is dedicated to this project and we will regularly update the contents to make them easier to follow for other researchers.

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Author details

1. Hamid Karimi-Rouzbahani

   1. Perception in Action Research Centre, Faculty of Human Sciences, Macquarie University, Sydney, Australia
   2. Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
   3. Department of Cognitive Science, Faculty of Human Sciences, Macquarie University, Sydney, Australia

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   hamid.karimi-rouzbahani@mrc-cbu.cam.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2694-3595

2. Alexandra Woolgar

   1. Perception in Action Research Centre, Faculty of Human Sciences, Macquarie University, Sydney, Australia
   2. Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
   3. Department of Cognitive Science, Faculty of Human Sciences, Macquarie University, Sydney, Australia

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

   Contributed equally with

   Anina N Rich

   Competing interests

   No competing interests declared

3. Anina N Rich

   1. Perception in Action Research Centre, Faculty of Human Sciences, Macquarie University, Sydney, Australia
   2. Department of Cognitive Science, Faculty of Human Sciences, Macquarie University, Sydney, Australia

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

   Contributed equally with

   Alexandra Woolgar

   Competing interests

   No competing interests declared

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