No abstract

Hazard perception tests are used in several developed countries as part of the driver licensing curriculum, however little research has been done in developing countries where road safety is a primary concern. We conducted a cross-cultural hazard perception study to examine the transferability of hazard perception skills between Malaysia and the UK, using hazard clips filmed in both countries. The results showed that familiarity with both the driving environment and type of hazard facilitated drivers' ability to discriminate hazards in a timely manner, although overall drivers viewed and responded to hazards largely similarly regardless of origin. Visual strategies also appeared to be moderated mainly by the immediate driving environment rather than driver origin. Finally, Malaysian drivers required a higher threshold of danger than UK drivers before they would identify a situation as hazardous, possibly reflecting the more hazardous road environment in Malaysia. We suggest that hazard perception testing in developing countries requires a test where performance cannot be confounded with differing thresholds for hazardousness.

Hazard perception (HP) tests are used in several developed countries as part of the driver licensing process, where they are believed to have improved road safety; however, relatively little HP research has been conducted in developing countries, which account for 80% of the world's road fatalities. Previous research suggests that drivers in these countries may be desensitized to hazardous road situations and thus have increased response latencies to hazards, creating validity issues with the typical HP reaction time paradigm. The present study compared Malaysian and UK drivers' HP skills when watching video clips filmed in both countries, using a predictive paradigm where hazard criterion could not affect performance. Clips filmed in the UK successfully differentiated experience in participants from both countries, however there was no such differentiation in the Malaysian set of videos. Malaysian drivers also predicted hazards less accurately overall, indicating that exposure to a greater number of hazards on Malaysian roads did not have a positive effect on participants' predictive hazard perception skill.Nonetheless the experiential discrimination noted in this predictive paradigm may provide a practical alternative for hazard perception testing in developing countries.

Working memory (WM) is critical to many aspects of cognition, but it frequently fails. Much WM research has focused on capacity limits, but even for single, simple features, the fidelity of individual representations is limited. Why is this? One possibility is that, because of neural noise and interference, neural representations do not remain stable across a WM delay, nor do they simply decay, but instead, they may “drift” over time to a new, less accurate state. We tested this hypothesis in a functional magnetic resonance imaging study of a match/nonmatch WM recognition task for a single item with a single critical feature: orientation. We developed a novel pattern-based index of “representational drift” to characterize ongoing changes in brain activity patterns throughout the WM maintenance period, and we were successfully able to predict performance on the match/nonmatch recognition task using this representational drift index. Specifically, in trials where the target and probe stimuli matched, participants incorrectly reported more nonmatches when their activity patterns drifted away from the target. In trials where the target and probe did not match, participants incorrectly reported more matches when their activity patterns drifted toward the probe. On the basis of these results, we contend that neural noise does not cause WM errors merely by degrading representations and increasing random guessing; instead, one means by which noise introduces errors is by pushing WM representations away from the target and toward other meaningful (yet incorrect) configurations. Thus, we demonstrate that behaviorally meaningful drift within representation space can be indexed by neuroimaging.

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere.