The size of the human pupil increases as a function of mental effort. However, this response is slow, and therefore its use is thought to be limited to measurements of slow tasks or tasks in which meaningful events are temporally well separated. Here we show that high-temporal-resolution tracking of attention and cognitive processes can be obtained from the slow pupillary response. Using automated dilation deconvolution, we isolated and tracked the dynamics of attention in a fast-paced temporal attention task, allowing us to uncover the amount of mental activity that is critical for conscious perception of relevant stimuli. We thus found evidence for specific temporal expectancy effects in attention that have eluded detection using neuroimaging methods such as EEG. Combining this approach with other neuroimaging techniques can open many research opportunities to study the temporal dynamics of the mind's inner eye in great detail.attentional blink | cognitive load T he size of the human eye pupil often is used as a measure of mental effort because it is assumed that the pupil size is related to the amount of cognitive control (1), attention (2), and cognitive processing (3) required by a given task. However, because the pupillary response is slow-pupil size increases slowly in response to a relevant event and peaks after approximately 1 smeasuring effort by assessing pupil dilation traditionally was reserved for slow tasks or tasks in which meaningful events are well separated in time.Here we show that high-temporal-resolution (∼10 Hz) tracking of attention and cognitive processes can be obtained from the slow pupillary response (∼1 Hz). Using automated dilation deconvolution, based on the quantitative analysis of the pupillary response (4), we isolated and tracked the dynamics of attention in a fast-paced temporal attention task, allowing us to uncover the amount of mental activity that is critical for conscious perception of relevant stimuli.We modeled the pupillary response as a function of a series of cognitive events, extending the approach of Hoeks and Levelt (4). In their model, each cognitive event is associated with an attentional pulse, which is assumed to trigger a dilation of the pupil as a function of that attentional pulse's strength. The number of pulses, the temporal location of pulses, and the strength of each pulse that add up to a dilation of the pupil can be set at specific values or can be free to vary. Given the additive nature of the pupillary response (4), a prediction for the pupillary response pattern evoked by a task can be derived by convolving the attentional pulses with a pupillary response function, similar to the convolution process in functional MRI (fMRI) analyses. This pupillary response function is described as an Erlang gamma function, and its constants have been determined empirically (4). Apart from predicting a pupillary response, this method also can be used to derive a pattern of pulses that underlies an observed pupillary response by means of a deconvolution process. How...

BackgroundWhen a second target (T2) is presented in close succession of a first target (T1), people often fail to identify T2, a phenomenon known as the attentional blink (AB). However, the AB can be reduced substantially when participants are distracted during the task, for instance by a concurrent task, without a cost for T1 performance. The goal of the current study was to investigate the electrophysiological correlates of this paradoxical effect.Methodology/Principal FindingsParticipants successively performed three tasks, while EEG was recorded. The first task (standard AB) consisted of identifying two target letters in a sequential stream of distractor digits. The second task (grey dots task) was similar to the first task with the addition of an irrelevant grey dot moving in the periphery, concurrent with the central stimulus stream. The third task (red dot task) was similar to the second task, except that detection of an occasional brief color change in the moving grey dot was required. AB magnitude in the latter task was significantly smaller, whereas behavioral performance in the standard and grey dots tasks did not differ. Using mixed effects models, electrophysiological activity was compared during trials in the grey dots and red dot tasks that differed in task instruction but not in perceptual input. In the red dot task, both target-related parietal brain activity associated with working memory updating (P3) as well as distractor-related occipital activity was significantly reduced.Conclusions/SignificanceThe results support the idea that the AB might (at least partly) arise from an overinvestment of attentional resources or an overexertion of attentional control, which is reduced when a distracting secondary task is carried out. The present findings bring us a step closer in understanding why and how an AB occurs, and how these temporal restrictions in selective attention can be overcome.

One of the major topics in attention literature is the attentional blink (AB), which demonstrates a limited ability to identify the second of two targets (T1 and T2) when presented in close temporal succession (200-500 msec). Given that the effect has been thought of as robust and resistant to training for over two decades, one of the most remarkable findings in recent years is that the AB can be eliminated after a 1-hr training with a color-salient T2. However, the underlying mechanism of the training effect as well as the AB itself is as of yet still poorly understood. To elucidate this training effect, we employed a refined version of our recently developed pupil dilation deconvolution method to track any training-induced changes in the amount and onset of attentional processing in response to target stimuli. Behaviorally, we replicated the original training effect with a color-salient T2. However, we showed that training without a salient target, but with a consistent short target interval, is already sufficient to attenuate the AB. Pupil deconvolution did not reveal any posttraining changes in T2-related dilation but instead an earlier onset of dilation around T1. Moreover, normalized pupil dilation was enhanced posttraining compared with pretraining. We conclude that the AB can be eliminated by training without a salient cue. Furthermore, our data point to the existence of temporal expectations at the time points of the trained targets posttraining. Therefore, we tentatively conclude that temporal expectations arise as a result of training.