Concurrent multitasking:From neural activity to human cognition

Multitasking has become an important part of our daily lives. This delicate juggling act between several activities occurs when people drive, when they are working, and even when they should be paying attention in the classroom. While multitasking is typically considered as something to avoid, there are instances where we are perfectly capable at performing multiple activities concurrently. It is therefore important that we understand how multitasking works, so that we can predict when engaging in multitasking is a good or bad idea. In this dissertation we examine how our brains are able to multitask, and how multitasking affects task performance. Through a series of behavioral and neuroimaging experiments we investigate what the cognitive mechanisms of concurrent multitasking are, when multitasking negatively or positively influences performance, and whether people choose to avoid multitasking situations that compromise their task performance

Driving and Multitasking:The Good, the Bad, and the Dangerous

Previous research has shown that multitasking can have a positive or a negative influence on driving performance. The aim of this study was to determine how the interaction between driving circumstances and cognitive requirements of secondary tasks affect a driver’s ability to control a car. We created a driving simulator paradigm where participants had to perform one of two scenarios: one with no traffic in the driver’s lane, and one with substantial traffic in both lanes, some of which had to be overtaken. Four different secondary task conditions were combined with these driving scenarios. In both driving scenarios, using a tablet resulted in the worst, most dangerous, performance, while passively listening to the radio or answering questions for a radio quiz led to the best driving performance. Interestingly, driving as a single task did not produce better performance than driving in combination with one of the radio tasks, and even tended to be slightly worse. These results suggest that drivers switch to internally focused secondary tasks when nothing else is available during monotonous or repetitive driving environments. This mind wandering potentially has a stronger interference effect with driving than non-visual secondary tasks

Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery

Background: Mobility impairment is common in older adults and negatively influences the quality of life. Mobility level may change rapidly following surgery or hospitalization in the elderly. The timed up and go (TUG) is a simple, frequently used clinical test for functional mobility; however, TUG requires supervision from a trained clinician, resulting in infrequent assessments. Additionally, assessment by TUG in clinic settings may not be completely representative of the individual's mobility in their home environment. Objective: In this paper, we introduce a method to estimate TUG from activities detected in free-living, enabling continuous remote mobility monitoring without expert supervision. The method is used to monitor changes in mobility following total hip arthroplasty (THA). Methods: Community-living elderly (n = 239, 65-91 years) performed a standardized TUG in a laboratory and wore a wearable pendant device that recorded accelerometer and barometric sensor data for at least three days. Activities of daily living (ADLs), including walks and sit-to-stand transitions, and their related mobility features were extracted and used to develop a regularized linear model for remote TUG test estimation. Changes in the remote TUG were evaluated in orthopaedic patients (n = 15, 55-75 years), during 12-weeks period following THA. Main results: In leave-one-out-cross-validation (LOOCV), a strong correlation (p = 0.70) was observed between the new remote TUG and standardized TUG times. Test-retest reliability of 3-days estimates was high (ICC = 0.94). Compared to week 2 post-THA, remote TUG was significantly improved at week 6 (11.7 +/- 3.9 s versus 8.0 +/- 1.8 s,p &lt;0.001), with no further change at 12-weeks (8.1 +/- 3.9s, p = 0.37). Significance: Remote TUG can be estimated in older adults using 3-days of ADLs data recorded using a wearable pendant. Remote TUG has discriminatory potential for identifying frail elderly and may provide a convenient way to monitor changes in mobility in unsupervised settings.</p

Using data-driven model-brain mappings to constrain formal models of cognition

In this paper we propose a method to create data-driven mappings from components of cognitive models to brain regions. Cognitive models are notoriously hard to evaluate, especially based on behavioral measures alone. Neuroimaging data can provide additional constraints, but this requires a mapping from model components to brain regions. Although such mappings can be based on the experience of the modeler or on a reading of the literature, a formal method is preferred to prevent researcher-based biases. In this paper we used model-based fMRI analysis to create a data-driven model-brain mapping for five modules of the ACT-R cognitive architecture. We then validated this mapping by applying it to two new datasets with associated models. The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved. We conclude that data-driven model-brain mappings can provide strong constraints on cognitive models, and that model-based fMRI is a suitable way to create such mappings