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Training cognition : optimizing efficiency, durability, and generalizability
\"This book describes research on training using cognitive psychology to build a complete empirical and theoretical picture of the training process. It includes a review of relevant cognitive psychological literature, a summary of recent laboratory experiments, a presentation of original theoretical ideas, and a discussion of possible applications to real-world training settings\"--Provided by publisher.
Book
Explainable AI improves task performance in human–AI collaboration
Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human–AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human–AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed
assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed
assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%,
) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%,
) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance.
Journal Article
An evaluation of mental workload with frontal EEG
Using a wireless single channel EEG device, we investigated the feasibility of using short-term frontal EEG as a means to evaluate the dynamic changes of mental workload. Frontal EEG signals were recorded from twenty healthy subjects performing four cognitive and motor tasks, including arithmetic operation, finger tapping, mental rotation and lexical decision task. Our findings revealed that theta activity is the common EEG feature that increases with difficulty across four tasks. Meanwhile, with a short-time analysis window, the level of mental workload could be classified from EEG features with 65%-75% accuracy across subjects using a SVM model. These findings suggest that frontal EEG could be used for evaluating the dynamic changes of mental workload.
Journal Article
Effects of motor–cognitive training on dual-task performance in people with Parkinson’s disease: a systematic review and meta-analysis
Motor–cognitive training in Parkinson’s disease (PD) can positively affect gait and balance, but whether motor–cognitive (dual-task) performance improves is unknown. This meta-analysis, therefore, aimed to establish the current evidence on the effects of motor–cognitive training on dual-task performance in PD. Systematic searches were conducted in five databases and 11 studies with a total of 597 people (mean age: 68.9 years; mean PD duration: 6.8 years) were included. We found a mean difference in dual-task gait speed (0.12 m/s (95% CI 0.08, 0.17)), dual-task cadence (2.91 steps/min (95% CI 0.08, 5.73)), dual-task stride length (10.12 cm (95% CI 4.86, 15.38)) and dual-task cost on gait speed (− 8.75% (95% CI − 14.57, − 2.92)) in favor of motor–cognitive training compared to controls. The GRADE analysis revealed that the findings were based on high certainty evidence. Thus, we can for the first time systematically show that people with PD can improve their dual-task ability through motor–cognitive training.
Journal Article
Changes in network connectivity during motor imagery and execution
Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data.
Twenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions.
Motor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC.
These findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.
Journal Article
Mental rotation of hands and objects in ageing and Parkinson’s disease: differentiating motor imagery and visuospatial ability
Motor imagery supports motor learning and performance and has the potential to be a useful strategy for neurorehabilitation. However, motor imagery ability may be impacted by ageing and neurodegeneration, which could limit its therapeutic effectiveness. Motor imagery can be assessed implicitly using a hand laterality task (HLT), whereby laterality judgements are slower for stimuli corresponding to physically more difficult postures, as indicated by a “biomechanical constraint” effect. Performance is also found to differ between back and palm views of the hand, which may differentially recruit visual and sensorimotor processes. Older adults and individuals with Parkinson’s disease (PD) have shown altered performance on the HLT; however, the effects of both ageing and PD on laterality judgements for the different hand views (back and palm) have not been directly examined. The present study compared healthy younger, healthy older, and PD groups on the HLT, an object-based mental rotation task, and an explicit motor imagery measure. The older and PD groups were slower than the younger group on the HLT, particularly when judging laterality from the back view, and exhibited increased biomechanical constraint effects for the palm. While response times were generally similar between older and PD groups, the PD group showed reduced accuracy for the back view. Letter rotation was slower and less accurate only in the PD group, while explicit motor imagery ratings did not differ significantly between groups. These results suggest that motor imagery may be slowed but relatively preserved in both typical ageing and neurodegeneration, while a PD-specific impairment in visuospatial processing may influence task performance. The findings have implications for the use of motor imagery in rehabilitation protocols.
Journal Article
Social interaction in augmented reality
There have been decades of research on the usability and educational value of augmented reality. However, less is known about how augmented reality affects social interactions. The current paper presents three studies that test the social psychological effects of augmented reality. Study 1 examined participants' task performance in the presence of embodied agents and replicated the typical pattern of social facilitation and inhibition. Participants performed a simple task better, but a hard task worse, in the presence of an agent compared to when participants complete the tasks alone. Study 2 examined nonverbal behavior. Participants met an agent sitting in one of two chairs and were asked to choose one of the chairs to sit on. Participants wearing the headset never sat directly on the agent when given the choice of two seats, and while approaching, most of the participants chose the rotation direction to avoid turning their heads away from the agent. A separate group of participants chose a seat after removing the augmented reality headset, and the majority still avoided the seat previously occupied by the agent. Study 3 examined the social costs of using an augmented reality headset with others who are not using a headset. Participants talked in dyads, and augmented reality users reported less social connection to their partner compared to those not using augmented reality. Overall, these studies provide evidence suggesting that task performance, nonverbal behavior, and social connectedness are significantly affected by the presence or absence of virtual content.
Journal Article
Rewarding cognitive effort increases the intrinsic value of mental labor
Current models of mental effort in psychology, behavioral economics, and cognitive neuroscience typically suggest that exerting cognitive effort is aversive, and people avoid it whenever possible. The aim of this research was to challenge this view and show that people can learn to value and seek effort intrinsically. Our experiments tested the hypothesis that effort-contingent reward in a working-memory task will induce a preference for more demanding math tasks in a transfer phase, even though participants were aware that they would no longer receive any reward for task performance. In laboratory Experiment 1 (n = 121), we made reward directly contingent on mobilized cognitive effort as assessed via cardiovascular measures (β-adrenergic sympathetic activity) during the training task. Experiments 2a to 2e (n = 1,457) were conducted online to examine whether the effects of effort-contingent reward on subsequent demand seeking replicate and generalize to community samples. Taken together, the studies yielded reliable evidence that effort-contingent reward increased participants’ demand seeking and preference for the exertion of cognitive effort on the transfer task. Our findings provide evidence that people can learn to assign positive value to mental effort. The results challenge currently dominant theories of mental effort and provide evidence and an explanation for the positive effects of environments appreciating effort and individual growth on people’s evaluation of effort and their willingness to mobilize effort and approach challenging tasks.
Journal Article
Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies
Dynamic functional connectivity (DFC) aims to maximize resolvable information from functional brain scans by considering temporal changes in network structure. Recent work has demonstrated that static, i.e. time-invariant resting-state and task-based FC predicts individual differences in behavior, including attention. Here, we show that DFC predicts attention performance across individuals. Sliding-window FC matrices were generated from fMRI data collected during rest and attention task performance by calculating Pearson's r between every pair of nodes of a whole-brain atlas within overlapping 10–60s time segments. Next, variance in r values across windows was taken to quantify temporal variability in the strength of each connection, resulting in a DFC connectome for each individual. In a leave-one-subject-out-cross-validation approach, partial-least-square-regression (PLSR) models were then trained to predict attention task performance from DFC matrices. Predicted and observed attention scores were significantly correlated, indicating successful out-of-sample predictions across rest and task conditions. Combining DFC and static FC features numerically improves predictions over either model alone, but the improvement was not statistically significant. Moreover, dynamic and combined models generalized to two independent data sets (participants performing the Attention Network Task and the stop-signal task). Edges with significant PLSR coefficients concentrated in visual, motor, and executive-control brain networks; moreover, most of these coefficients were negative. Thus, better attention may rely on more stable, i.e. less variable, information flow between brain regions.
•Temporal variability in functional connectivity predicts attention task performance.•Dynamic functional connectivity can be measured during task performance or rest.•Models generalized across 3 completely independent studies.•Higher functional connectivity variability generally predicts worse attention.
Journal Article
Why motor imagery is not really motoric: towards a re-conceptualization in terms of effect-based action control
Overt and imagined action seem inextricably linked. Both have similar timing, activate shared brain circuits, and motor imagery influences overt action and vice versa. Motor imagery is, therefore, often assumed to recruit the same motor processes that govern action execution, and which allow one to play through or simulate actions offline. Here, we advance a very different conceptualization. Accordingly, the links between imagery and overt action do not arise because action imagery is intrinsically motoric, but because action planning is intrinsically imaginistic and occurs in terms of the perceptual effects one want to achieve. Seen like this, the term ‘motor imagery’ is a misnomer of what is more appropriately portrayed as ‘effect imagery’. In this article, we review the long-standing arguments for effect-based accounts of action, which are often ignored in motor imagery research. We show that such views provide a straightforward account of motor imagery. We review the evidence for imagery-execution overlaps through this new lens and argue that they indeed emerge because every action we execute is planned, initiated and controlled through an imagery-like process. We highlight findings that this new view can now explain and point out open questions.
Journal Article