What role does attention play in ensuring the temporal precision of visual perception? Behavioural studies have investigated feature selection and binding in time using fleeting sequences of stimuli in the Rapid Serial Visual Presentation (RSVP) paradigm, and found that temporal accuracy is reduced when attentional control is diminished. To reduce the efficacy of attentional deployment, these studies have employed the Attentional Blink (AB) phenomenon. In this article, we use electroencephalography (EEG) to directly investigate the temporal dynamics of conscious perception. Specifically, employing a combination of experimental analysis and neural network modelling, we test the hypothesis that the availability of attention reduces temporal jitter in the latency between a target' s visual onset and its consolidation into working memory. We perform time-frequency analysis on data from an AB study to compare the EEG trials underlying the P3 ERPs (Event-related Potential) evoked by targets seen outside vs. inside the AB time window. We find visual differences in phase-sorted ERPimages and statistical differences in the variance of the P3 phase distributions. These results argue for increased variation in the latency of conscious perception during the AB. This experimental analysis is complemented by a theoretical exploration of temporal attention and target processing. Using activation traces from the Neural-ST2 model, we generate virtual ERPs and virtual ERPimages. These are compared to their human counterparts to propose an explanation of how target consolidation in the context of the AB influences the temporal variability of selective attention. The AB provides us with a suitable phenomenon with which to investigate the interplay between attention and perception. The combination of experimental and theoretical elucidation in this article contributes to converging evidence for the notion that the AB reflects a reduction in the temporal acuity of selective attention and the timeliness of perception.
An apparent temporal limitation of visual perception is illustrated by the attentional blink (AB; [6]). The AB describes a finding that observers often fail to detect a second target stimulus (T2) presented in short succession (between 100 and 600 ms) after an identified first target stimulus (T1). If T2 is presented in immediate succession to T1, however, detection accuracy is typically excellent (‘lag 1 sparing’; [7]). Behaviourally, the AB has been replicated numerous times [8], [9]. It has also been investigated electrophysiologically [10], where researchers have compared grand average Event-related Potentials (ERPs) evoked by targets outside and inside the AB, to investigate how target processing differs during the AB. Despite extensive study of the AB, its effect on the underlying temporal mechanisms of target identification remains to be fully explored. Evidence from ERP [10], [11] and priming [12], [13] studies suggest that targets, rather than being completely lost during the AB, are processed quite extensively, but fail to enter the final stage of conscious perception. Furthermore, researchers have found that when targets in RSVP consist of multiple features, observers often report features from items neighboring the target in the RSVP stream and make binding errors referred to as illusory conjunctions [14]. Behavioural analysis of the changes in the patterns of such binding errors provides strong support for the claim that the AB reveals a reduction in the temporal precision of the deployment of transient attention and target processing [15], [16]. In this article, we use the dynamics of temporal visual processing as embodied in the (Simultaneous-Type-Serial-Token) model, a connectionist model of temporal attention and working memory [5], to propose an explanation for the observed effect of the AB on the temporal precision of transient attention. The model explains a broad set of experimental findings relating to the AB, Repetition Blindness and RSVP in general. Before elaborating on our central hypothesis, we explain the fundamental principles of how the model describes temporal attention and working memory. For a more detailed description please refer to [5]. It should be emphasised that throughout this article, we retain the model' s parameters as published in [17], and use it to generate predictions and virtual EEG traces comparable to human EEG data. The model suggests that working memory encoding involves creating a binding between the type of a stimulus (which can include its visual features and semantic attributes) and a token (an episodic representation specific to a particular occurrence of an item) [18], . In the model, Transient Attentional Enhancement (TAE) from the blaster amplifies the type representation of a salient (i. e. , task relevant) stimulus to assist in its binding to a token, in a process referred to as tokenization. This TAE can serve as an attentional gate, which can be temporarily deactivated to allow one target' s encoding to be completed before a second is begun. From the perspective of the model, the AB is an artifact of the visual system attempting to assign unique tokens to targets [22]. More specifically, the process of encoding T1 into working memory is triggered by TAE, and TAE itself is subsequently suppressed until T1 encoding has completed. The period of TAE unavailability varies from trial to trial depending on how long it takes to tokenise T1, depending on its bottom-up strength. In an RSVP stream, if a T2 is presented 100–600 ms after a perceived T1 (as is the case during the AB), its processing outcome depends on multiple factors. T2' s own strength determines its dependence on TAE, since highly salient T2s can ‘break-through’ the AB [31] and get encoded relatively early. T2s with strength values slightly lower in the range ‘outlive’ the AB (and thus the unavailability of TAE), and hence are indirectly influenced by T1 strength. Overall, the variability in the temporal dynamics of T2' s encoding process is influenced both by T1 and T2 strengths. Hence, over all possible strengths, the model proposes that there should be increased variance in processing latency for targets seen during the AB. This article investigates the hypothesis that diminished attentional control increases the temporal jitter in the latency of a target' s working memory consolidation. The AB provides us with a suitable phenomenon with which to test our hypothesis: we propose that the reduced availability of attention during the AB increases the temporal noise in visual attention. To answer this question, we compare the EEG signatures evoked by targets seen outside vs. inside the AB, and determine whether there is a comparative increase in the variability of the latency of working memory encoding of targets presented inside the AB. EEG has the advantage of excellent temporal resolution, allowing us to study short-lived cognitive events that evoke changes in ongoing EEG activity. If one averages over multiple segments of such EEG activity time-locked to the event, the resulting averaged ERP waveform contains a number of positive and negative deflections, referred to as ERP components. To test for increased temporal jitter, we analyse the P3 ERP component, commonly associated with encoding items into working memory [10], [23]. However, analysis of averaged ERP components cannot directly inform our hypothesis. This is because the averaging collapses across and hence discards information about temporal fluctuations in the individual EEG trials contributing to the ERP. Given a set of trials that are averaged together, both decreases in amplitude and increases in latency variation within that set will attenuate the mean amplitude of the ERP. Hence, examining the average does not directly provide the necessary information to decide which of the two sources of variation in the individual trials (amplitude or latency) caused the reduction in ERP amplitude. Further, measures like 50% area latency analysis [24] cannot be used to measure latencies in single trials, due to the levels of irrelevant noise activity. Consequently, we employ time-frequency analysis techniques that provide alternative measures to investigate single trial dynamics underlying grand average ERPs. These methods enable us to perform a more fine-grained analysis of EEG data, and test our hypothesis using both qualitative and quantitative means. In addition to presenting and analyzing human EEG data, we use the model' s neural network implementation to generate virtual P3 ERP components [17], which are hypothesised to correspond to the human P3 ERP component. For each of the experimental conditions, the virtual P3 is contrasted with the human P3, both at grand average and single trial level. This comparative evaluation allows us to validate the model and propose explanations for the human ERP effects.
The experiment consisted of RSVP trials presented at a rate of 105. 9 ms per item, with two letter targets, T1 and T2, embedded among digit distractors. T2 was presented at lags 1,3 and 8 following the T1. The P3 EEG data analysed in this section was recorded at the Pz electrode. Please refer to the Materials and Methods section for further information. Mean human accuracy for T1 identification was 82%. The accuracy of T2 identification (conditional on correct report of T1) was 83% at lag 1,54% at lag 3, and 74% at lag 8. There was a significant effect of lag on accuracy (F (1. 48,12. 58) = 15. 58, MSE = 0. 03, p0. 001, after applying a Greenhouse-Geisser correction on the degrees of freedom). Additionally, in pairwise comparisons, T2 accuracy was significantly lower at lag 3 compared to lag 8 (F (1,17) = 11. 66, MSE =. 03, p =. 003) and lag 1 (F (1,17) = 60. 88, MSE = 0. 01, p0. 001). Consequently, the paradigm employed in this study evoked a reliable AB effect. The ERPimages [25] in figure 2 compare the P3 evoked by targets seen outside the AB (seen T2s at lag 8 following a seen T1) with targets seen inside the AB (seen T2s at lag 3 following a seen T1). They allowed us to visualise the EEG trials underlying the grand average P3 ERPs (plotted below them) for targets seen outside and inside the AB. These ERPimages represent time with respect to target onset along the X-axis (Note that trials are time-locked to T2 onset), individual trials along the Y-axis, and the single-trial EEG amplitude using a colour scale. The trials comprising these images were sorted from bottom to top by descending order of the phase angle of the single-trial P3 at the time point indicated by the dashed line, which was set to the peak latency of the corresponding grand average P3. This phase angle was estimated at the frequency at which the power of the P3 was maximal. This sorting method effectively ordered the trials according to the approximate latency of the single-trial P3 for a target, as estimated by a wavelet-based time-frequency analysis (see the Materials and Methods section for more details). The ERPimages were then plotted for each condition, with trials having longer latency P3s being placed at the bottom, and trials with shorter latency P3s at the top. Following from our hypothesis, for targets inside the AB, we expected to observe an increased “slope” in the red “smear” representing the P3. This would indicate that these targets suffer greater temporal variance compared to targets outside the AB. A visual comparison of the ERPimages clearly suggested that the P3 for targets outside the AB (figure 2A) had relatively little variation in the phase angle across most trials. In other words, the P3 onset occurred at approximately the same time in these trials. In contrast, the P3 evoked by targets inside the AB (figure 2B) appeared to exhibit an increased temporal fluctuation, as reflected by the increased variance of the phase angle of the P3 across all trials. A natural consequence of this jitter in the temporal onset of the P3 was a ‘smearing out’ of the grand average ERP. In summary, if there was indeed a reduction in the precision of the deployment of attention in response to targets during the AB, we expected this to indirectly affect the working memory encoding of targets as reflected by the P3. The ERPimages in figure 2 provided qualitative support for our hypothesis. We then extended this investigation by analysing the distribution of phase angles corresponding to the P3, to generate numerical evidence that could be verified statistically. To back up the qualitative comparisons of the previous section, we statistically analysed the time-frequency data obtained therein. We used an approach similar to inter-trial phase coherence analysis [25], but adapted the idea to directly examine the subject-wise P3 phase distributions and quantitatively compare temporal jitter. The phase angles used to sort the individual trials comprising the P3 ERPimages in the previous section formed a circular distribution [26] of angular data values that effectively represented the temporal latency between the onset of the target and its P3. By statistically comparing the variance in the distribution of phase angles across targets outside and inside the AB, we tested whether the visual differences observed were consistent across subjects. To do so, we performed a subject-wise grouping of the P3 phase angles calculated at the peak latency of the grand average P3 for each condition (the same phase angles that were used to sort the ERPimages presented earlier). This generated multiple smaller distributions of P3 phase angles, one per condition and subject. These distributions were then modelled as von Mises distributions [26] for which the concentration parameter was calculated using maximum likelihood estimation. The parameter of a distribution is a measure of its density around its mean value, and is an analogue of the inverse of its variance. The larger the value of a circular distribution, the more concentrated it is around the mean. Importantly, is a linear parameter, and can be compared using conventional statistical tools. Hence, in order to test whether targets inside the AB suffered from increased temporal jitter, we compared values of the subject-wise P3 phase distributions evoked by targets outside and inside the AB, using a standard one-way repeated-measures ANOVA. The results of the ANOVA validated what the visual differences observed in the ERPimages clearly indicated: The of the phase distribution for the P3 for targets outside the AB was statistically greater than that for targets inside the AB: Mean for targets outside the AB was 0. 95, whereas mean for targets inside the AB was 0. 52 (F (1,17) = 15. 21, MSE = 0. 11, p = 0. 001). In order to validate the model, we used it to generate ‘artificial electrophysiological’ traces, so-called virtual ERPs [17]. In analogy to human ERP components, we generated virtual ERP components for targets outside and inside the AB. This approach, in addition to allowing us to validate the internal dynamics of the model, provided theoretical explanations for the human EEG effects observed in the previous section. Please refer to the Materials and Methods section for more details on how virtual ERPs and ERPimages were generated.
Our experimental results and theoretical explorations complement and inform previous research on temporal selection and the AB. We now discuss these findings and propose interpretations in terms of the model. In this article, we have presented human ERP evidence in favour of a reduction in the temporal precision of transient attention during the AB. The AB provides us with a suitable phenomenon with which to investigate the interplay between attention and perception. The interplay between these tightly linked cognitive processes is adversely affected during the AB, producing the reduction in precision observed in behavioural and EEG data. Using ERPimages, we have provided qualitative evidence arguing for an increase in temporal variation in the dynamics of P3s evoked by targets seen outside vs. inside the AB window. This evidence is supported quantitatively, by statistical comparison of the phase distributions corresponding to the P3. This analysis suggests that there is significantly increased temporal jitter in the ERP activity evoked by targets inside the AB. This notion of a decrease in the temporal precision of attention is inherent in the theoretical framework of the model. Specifically, we have used the model’s neural implementation to generate both virtual ERPs and ERPimages, which we have then compared to their human counterparts. We believe that correlating model and electrophysiological data in this way provides a two-fold benefit. Firstly, it has provided a sufficient explanation for the modulatory effects of the AB on the temporal precision of visual processing. Secondly, it has allowed us to instantiate and test the model at the level of single-trial dynamics, and show that the theoretical assumptions about the nature of temporal visual processing embodied by it can be validated using EEG data, in addition to traditional behavioural verification. We believe that the combination of experimental and theoretical analysis presented in this article contributes to converging evidence for the notion that the AB results in a reduction in the temporal acuity of selective attention, which is an important mechanism for ensuring the timeliness of conscious perception.
This section describes the experiment (the same as Experiment 2 from [17]) used to collect the human EEG data analysed in this article.
Our visual system keeps pace with a rapidly changing stream of information as we view the natural world. To do so, it uses a strongly regulated system of attentional filters to constrain which visual stimuli are permitted to be fully processed to the level of conscious awareness. This article explores what happens when these filters are opened and closed in response to important visual stimuli. To understand these dynamics, our neural network model provides simulations of the role played by attention. These simulations can be tested by recording neural data in the form of ‘brain waves’ (EEG) and comparing the resultant signals to the output of the model. The data discussed here confirm a prediction of the model, which suggests that after the attentional filter has opened to allow one visual stimulus in, there is increased temporal variability or ‘jitter’ in the subsequent opening of the filter within an interval of about one-half of a second. These results have implications for the way our brains process multiple important stimuli perceived in rapid succession, such as the sequence of events that might occur at a critical moment in an airline cockpit or during an automobile accident.
neuroscience/behavioral neuroscience
neuroscience/cognitive neuroscience
neuroscience/theoretical neuroscience
Attention Increases the Temporal Precision of Conscious Perception: Verifying the Neural-ST2 Model