52% Yes, a signiicant crisis 3% No, there is no crisis 7% Don't know 38% Yes, a slight crisis 38% Yes, a slight crisis 1,576 RESEARCHERS SURVEYED M ore than 70% of researchers have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments. Those are some of the telling figures that emerged from Nature's survey of 1,576 researchers who took a brief online questionnaire on reproducibility in research. The data reveal sometimes-contradictory attitudes towards reproduc-ibility. Although 52% of those surveyed agree that there is a significant 'crisis' of reproducibility, less than 31% think that failure to reproduce published results means that the result is probably wrong, and most say that they still trust the published literature. Data on how much of the scientific literature is reproducible are rare and generally bleak. The best-known analyses, from psychology 1 and cancer biology 2 , found rates of around 40% and 10%, respectively. Our survey respondents were more optimistic: 73% said that they think that at least half of the papers in their field can be trusted, with physicists and chemists generally showing the most confidence. The results capture a confusing snapshot of attitudes around these issues, says Arturo Casadevall, a microbiologist at the Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland. "At the current time there is no consensus on what reproducibility is or should be. " But just recognizing that is a step forward, he says. "The next step may be identifying what is the problem and to get a consensus. "

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...

This article provides a tutorial for analyzing pupillometric data. Pupil dilation has become increasingly popular in psychological and psycholinguistic research as a measure to trace language processing. However, there is no general consensus about procedures to analyze the data, with most studies analyzing extracted features from the pupil dilation data instead of analyzing the pupil dilation trajectories directly. Recent studies have started to apply nonlinear regression and other methods to analyze the pupil dilation trajectories directly, utilizing all available information in the continuously measured signal. This article applies a nonlinear regression analysis, generalized additive mixed modeling, and illustrates how to analyze the full-time course of the pupil dilation signal. The regression analysis is particularly suited for analyzing pupil dilation in the fields of psychological and psycholinguistic research because generalized additive mixed models can include complex nonlinear interactions for investigating the effects of properties of stimuli (e.g., formant frequency) or participants (e.g., working memory score) on the pupil dilation signal. To account for the variation due to participants and items, nonlinear random effects can be included. However, one of the challenges for analyzing time series data is dealing with the autocorrelation in the residuals, which is rather extreme for the pupillary signal. On the basis of simulations, we explain potential causes of this extreme autocorrelation, and on the basis of the experimental data, we show how to reduce their adverse effects, allowing a much more coherent interpretation of pupillary data than possible with feature-based techniques.

A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval estimation and bisection and impact of secondary tasks on attention). The authors subsequently used the module as a part of the adaptive control of thought--rational (ACT-R) architecture to model a new experiment that combines attention, learning, dual tasking, and time perception. Finally, the model predicts time estimation, learning, and attention in a new experiment. The model predictions and fits demonstrate that the proposed integrated theory of prospective time interval estimation explains detailed effects of attention and learning during time interval estimation.

The main challenge for theories of multitasking is to predict when and how tasks interfere. Here, we focus on interference related to the problem state, a directly accessible intermediate representation of the current state of a task. On the basis of Salvucci and Taatgen's (2008) threaded cognition theory, we predict interference if 2 or more tasks require a problem state but not when only one task requires one. This prediction was tested in a series of 3 experiments. In Experiment 1, a subtraction task and a text entry task had to be carried out concurrently. Both tasks were presented in 2 versions: one that required maintaining a problem state and one that did not. A significant overadditive interaction effect was observed, showing that the interference between tasks was maximal when both tasks required a problem state. The other 2 experiments tested whether the interference was indeed due to a problem state bottleneck, instead of cognitive load (Experiment 2: an alternative subtraction and text entry experiment) or a phonological loop bottleneck (Experiment 3: a triple-task experiment that added phonological processing). Both experiments supported the problem state hypothesis. To account for the observed behavior, computational cognitive models were developed using threaded cognition within the context of the cognitive architecture ACT-R (Anderson, 2007). The models confirm that a problem state bottleneck can explain the observed interference.