In this study, we made predictions on the effects of different interventions by adaptive automation systems designed to prevent mind-wandering while driving. Although cognitive load associated with secondary tasks tends to affect driving negatively, a simple secondary task can improve driving performance when the driving scenario is mundane. Nijboer and colleagues (2016) have hypothesized that if the driving task is simple, people might start mind-wandering, which interferes with driving. Furthermore, the authors proposed that a simple secondary task, which imposes less workload than mind-wandering, could prevent this from happening and thereby improve driving performance. Automation systems that are informed about and adapt to the cognitive state of the driver could leverage this effect by inducing mild cognitive load during mundane driving scenarios. To test suitable interventions, we combined an existing driver model with an existing model of mind-wandering implemented in the cognitive architecture ACT-R as executable theories of mind-wandering and cognitive processing in driving. Using these different models we show how mind-wandering harms driving performance at the behavioral level and that interventions eliciting mild cognitive load can mitigate this behavioral effect. However, the model indicates that some interventions incur a significant cognitive processing cost that adaptive automation systems must account for.

Abstract Adaptive learning systems offer a personalised digital environment that continually adjusts to the learner and the material, with the goal of maximising learning gains. Whenever such a system encounters a new learner, or when a returning learner starts studying new material, the system first has to determine the difficulty of the material for that specific learner. Failing to address this “cold-start” problem leads to suboptimal learning and potential disengagement from the system, as the system may present problems of an inappropriate difficulty or provide unhelpful feedback. In a simulation study conducted on a large educational data set from an adaptive fact learning system (about 100 million trials from almost 140 thousand learners), we predicted individual learning parameters from response data. Using these predicted parameters as starting estimates for the adaptive learning system yielded a more accurate model of learners’ memory performance than using default values. We found that predictions based on the difficulty of the fact (“what”) generally outperformed predictions based on the ability of the learner (“who”), though both contributed to better model estimates. This work extends a previous smaller-scale laboratory-based experiment in which using fact-specific predictions in a cold-start scenario improved learning outcomes. The current findings suggest that similar cold-start alleviation may be possible in real-world educational settings. The improved predictions can be harnessed to increase the efficiency of the learning system, mitigate the negative effects of a cold start, and potentially improve learning outcomes.

Brain models typically focus either on low-level biological detail or on qualitative behavioral effects. In contrast, we present a biologically-plausible spiking-neuron model of associative learning and recognition that accounts for both human behavior and low-level brain activity across the whole task. Based on cognitive theories and insights from machine-learning analyses of M/EEG data, the model proceeds through five processing stages: stimulus encoding, familiarity judgement, associative retrieval, decision making, and motor response. The results matched human response times and source-localized MEG data in occipital, temporal, prefrontal, and precentral brain regions; as well as a classic fMRI effect in prefrontal cortex. This required two main conceptual advances: a basal-ganglia-thalamus action-selection system that relies on brief thalamic pulses to change the functional connectivity of the cortex, and a new unsupervised learning rule that causes very strong pattern separation in the hippocampus. The resulting model shows how low-level brain activity can result in goal-directed cognitive behavior in humans.

Performing a cognitive task requires going through a sequence of functionally diverse stages. Although it is typically assumed that these stages are characterized by distinct states of cortical synchrony that are triggered by sub-cortical events, little reported evidence supports this hypothesis. To test this hypothesis, we first identified cognitive stages in single-trial MEG data of an associative recognition task, showing with a novel method that each stage begins with local modulations of synchrony followed by a state of directed functional connectivity. Second, we developed the first whole-brain model that can simulate cortical synchrony throughout a task. The model suggests that the observed synchrony is caused by thalamocortical bursts at the onset of each stage, targeted at cortical synapses and interacting with the structural anatomical connectivity. These findings confirm that cognitive stages are defined by distinct states of cortical synchrony and explains the network-level mechanisms necessary for reaching stage-dependent synchrony states.

In this paper, we present a functional spiking-neuron model of human working memory (WM).This model combines neural firing for encoding of information with activity-silent maintenance.While it used to be widely assumed that information in WM is maintained through persistent recurrent activity, recent studies have shown that information can be maintained without persistent firing; instead, information can be stored in activity-silent states.A candidate mechanism underlying this type of storage is short-term synaptic plasticity (STSP), by which the strength of connections between neurons rapidly changes to encode new information.To demonstrate that STSP can lead to functional behavior, we integrated STSP by means of calcium-mediated synaptic facilitation in a large-scale spikingneuron model and added a decision mechanism.The model was used to simulate a recent study that measured behavior and EEG activity of participants in three delayed-response tasks.In these tasks, one or two visual gratings had to be maintained in WM, and compared to subsequent probes.The original study demonstrated that WM contents and its priority status could be decoded from neural activity elicited by a task-irrelevant stimulus displayed during the activity-silent maintenance period.In support of our model, we show that it can perform these tasks, and that both its behavior as well as its neural representations are in agreement with the human data.We conclude that information in WM can be effectively maintained in activity-silent states by means of calcium-mediated STSP. Author summaryMentally maintaining information for short periods of time in working memory is crucial for human adaptive behavior.It was recently shown that the human brain does not only store information through neural firing-as was widely believed-but also maintains information in activity-silent states.Here, we present a detailed neural model of how this could

Interruptions are prevalent in everyday life and can be very disruptive. An important factor that affects the level of disruptiveness is the timing of the interruption: Interruptions at low-workload moments are known to be less disruptive than interruptions at high-workload moments. In this study, we developed a task-independent interruption management system (IMS) that interrupts users at low-workload moments in order to minimize the disruptiveness of interruptions. The IMS identifies low-workload moments in real time by measuring users’ pupil dilation, which is a well-known indicator of workload. Using an experimental setup we showed that the IMS succeeded in finding the optimal moments for interruptions and marginally improved performance. Because our IMS is task-independent—it does not require a task analysis—it can be broadly applied.

In this paper we present a computational cognitive model of task interruption and resumption, focusing on the effects of the problem state bottleneck. Previous studies have shown that the disruptiveness of interruptions is for an important part determined by three factors: interruption duration, interrupting-task complexity, and moment of interruption. However, an integrated theory of these effects is still missing. Based on previous research into multitasking, we propose a first step towards such a theory in the form of a process model that attributes these effects to problem state requirements of both the interrupted and the interrupting task. Subsequently, we tested two predictions of this model in two experiments. The experiments confirmed that problem state requirements are an important predictor for the disruptiveness of interruptions. This suggests that interfaces should be designed to a) interrupt users at low-problem state moments and b) maintain the problem state for the user when interrupted.

Multitasking in user behavior can be represented along a continuum in terms of the time spent on one task before switching to another. In this paper, we present a theory of behavior along the multitasking continuum, from concurrent tasks with rapid switching to sequential tasks with longer time between switching. Our theory unifies several theoretical effects - the ACT-R cognitive architecture, the threaded cognition theory of concurrent multitasking, and the memory-for-goals theory of interruption and resumption - to better understand and predict multitasking behavior. We outline the theory and discuss how it accounts for numerous phenomena in the recent empirical literature.