Neural signatures of vigilance decrements predict behavioural errors before they occur

Abstract

<jats:title>Abstract</jats:title><jats:p>There are now many environments in which humans need to monitor moving displays and only rarely act, such as train control and driving autonomous vehicles; lapses of attention in these circumstances can have tragic consequences. Problematically, we know that it is difficult to sustain attention under these monitoring or vigilance conditions and performance drops: when target events are rare, we tend to miss them, or are slower to respond. This ‘rare target’ effect becomes more marked with longer tasks, known as a vigilance decrement. Despite the importance, we still have limited understanding of how the brain processes information during monitoring, particularly with dynamic stimuli, and how this processing changes when attention lapses. Here, we designed a multiple-object monitoring (MOM) paradigm that required sustained attention to dynamic stimuli, and used multivariate analyses of magnetoencephalography (MEG) data to examine how the neural representation of the information in the display varied with target frequency and time on the task. Behavioural performance decreased over time for the rare target (monitoring) condition, but not for the frequent target (active) condition. This change was mirrored in the neural results: under monitoring conditions, there was weaker coding of the critical distance between objects during time periods when vigilance decrements in performance occurred. There was also weaker informational connectivity between peri-occipital and peri-frontal brain areas in rare versus frequent target conditions. We developed a new analysis which used the strength of information decoding to predict whether the participant was going to miss the target on a given trial. We could predict behavioural errors more than a second before they occurred. This provides a first step in developing methods to predict and pre-empt behavioural errors due to lapses in attention and provides new insight into how vigilance decrements are reflected in information coding in the brain.</jats:p>

Perceptual difficulty modulates the direction of information flow in familiar face recognition

“…The RSA-based connectivity method used in this study further develops a recent shift towards multivariate brain connectivity methods (Anzellotti and Coutanche, 2018;Basti et al, 2020;Keitzmann et al, 2019;Goddard et al, 2016;Clarke et al, 2018;Karimi-Rouzbahani, 2018;Karimi-Rouzbahani et al, 2019;Karimi-Rouzbahani et al, 2020), and introduces several advantages over previous methods of connectivity analyses.…”

“…The RSA-based connectivity method used in this study follows a recent shift towards informational brain connectivity methods (Anzellotti and Coutanche, 2018; Basti et al, 2020; Keitzmann et al, 2019; Goddard et al, 2016; Clarke et al, 2018; Karimi-Rouzbahani, 2018; Karimi-Rouzbahani et al, 2019; Karimi-Rouzbahani et al, 2020a), and introduces a few distinct features compared to previous methods of connectivity analyses. Specifically, traditional connectivity methods examine inter-area interactions through indirect measures such as gamma-band synchronization (Gregoriou et al, 2009), shifting power (Bar et al, 2006) or causality in the activity patterns (Summerfield et al, 2006; Fan et al, 2020).…”

“…Timing information is also crucial in evaluating the flows of feed-forward and feedback information as these processes often differ in the temporal dynamics. With the advent of the concept of informational connectivity analysis, we now have the potential to examine the interaction of information between feed-forward and feedback mechanisms to characterize their potential spatiotemporal contribution to familiar face recognition (Goddard et al, 2016;Goddard et al, 2019;Anzellotti and Coutanche, 2018;Basti et al, 2020;Karimi-Rouzbahani et al, 2020). However, this requires novel methods to track the flow of familiarity information from a given brain area to a destination area and link this flow to the behavioural task goals to confirm its biological relevance.…”

Temporal variabilities provide additional category-related information in object category decoding: a systematic comparison of informative EEG features

“…Karimi-Rouzbahani et al, 2017a have shown discrepancy across studies/datasets. This suggest that their results might have been driven by category-irrelevant task features such as attentional load or task demands, which can modulate the neural activity to a greater level than that evoked by the stimulus (Karimi-Rouzbahani et al, 2019;Karimi-Rouzbahani et al, 2020b). Therefore, a wider set of features and datasets should be evaluated to reach more generalizable conclusions.…”

Informative Neural Codes to Separate Object Categories

“…Third, temporal variability of brain activity should not be overlooked, as this is generally the case in multivariate decoding (Karimi-Rouzbahani et al, 2019), as the temporal codes can contain additional information. Forth, as the brain seem to process the face and object information using a common set of neural infrastructures (Dobs et al, 2019), but constructs face representations which outperform object representations (Fig. 2B), the current deep networks of object recognition, may benefit to follow the brain and move towards a unified platform for face and object recognition.…”