Event Abstract

Demonstrating brain-level interactions between working memory load and driving demand level using fNIRS

  • 1 University of Oldenburg, Department of Psychology, Germany
  • 2 Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institute of Transportation Systems, Germany

Introduction and Goal The concept of mental workload is of particular importance in human factors and neuroergonomics because mental workload levels that saturate processing capacities will likely decrease performance and cognitive flexibility. Driving, like many other dynamic control tasks, concurrently imposes demands on multiple cognitive resources. In order to assess driver’s cognitive capacity utilization, we need to distinguish loads imposed on different cognitive resources and to characterize their interactions. It is currently not clearly understood how different cognitive task demands interact at the brain level and whether the neuronal markers that have been attributed to certain cognitive resources remain discriminative and predictive of cognitive states when humans operate in complex natural task environments such as driving. Hence, our goal is to investigate how changes in cognitive task demands related to increased lateral control in driving, a visuospatial control task, interacts with working memory load (WML) at the brain level and to predict the driving demand level from multichannel functional near infrared spectroscopy (fNIRS) deoxyhemoglobin (HbR) brain activation measurements. Methods We collected data from participants driving on a highway with moderate concurrent traffic and low or high levels of lateral control demands (driving outside and within construction sites with a narrower lane width) in the realistic Virtual-Reality lab at DLR, Braunschweig (Fischer et al., 2014). In order to manipulate WML levels, participants (n=15) performed an integrated speed regulation task in which they had to remember sequences of speed of varying length. The task resembles a number span task and required the adjustment of the speed when a new speed sign occurred but according to the sign shown n-signs back and to remember the new sign. We introduced five different WML levels (i.e. 0-back to 4-back) and new speed signs appeared every 20 seconds (for details see Unni et al. 2017). We recorded almost whole head fNIRS measurements at a rate close to 2Hz using two 16-channel NIRScout systems (NIRx Medizintechnik GmbH, Germany) during the 60 minutes of the experiment. We then used all fNIRS channels in a multivariate logistic ridge regression classifier with a 5-fold cross-validation for temporally resolved prediction of driving demand level (i.e. whether they drove within or outside the construction site). To gain insights into specific brain areas associated with driving demand level, we performed a univariate group-level analysis where we calculated the prediction model weights from a 5-fold cross-validated univariate logistic ridge regression of channel-wise fNIRS HbR separately for each WML level. Results On average over participants, we correctly predicted the driving demand (lateral control) level in 74.88% of the timepoints in an out-of-sample cross-validation of classifiers trained on the fNIRS data separately for each WML level. Mean accuracy scores significantly varied across WML levels (range 62% to 85% correct; F(4,70) = 7.32, p < .001) with better predictions for intermediate WML levels (1-back to 3-back). Training one classifier on fNIRS data across all WML levels severely degraded prediction performance (mean accuracy of 46%). These results indicate that cognitive resources recruited by lateral lane control, a visuospatial control task, interact with resources recruited by working memory at the brain level. In order to better understand the nature of these interactions, we analyzed how predictivity of brain regions for driving demands develops over WML levels. Figure 1B depicts group-level brain maps of classification separability from the univariate logistic ridge regression model. Activation changes in the parietal and lateral dorsal frontal areas allow for prediction of increased lateral control demand. The frontal areas (putative BA9/46/10) are known to be involved in tactical planning and execution of behavior and the parietal in visuomotor control. The maps show increasing predictivity of these areas with increasing WML levels up to WML-level 2. At WML level 3 and in particular level 4, the discriminative patterns diminish, indicating an interaction between WML and driving demands. We compared these brain maps to the results from Unni et al. 2017, who showed that it was possible to predict WML independent of lateral controls demands from the fNIRS data (average correlation between predicted and induced WML r = 0.61). Figure 1A shows brain areas that predict WML over different lateral control demand levels. In particular, the inferior frontal gyrus (IFG) and the temporo-occipital areas are predictive to the current WML level. The comparison of the anatomical locations of predictive maxima in Figures 1A (marked by white shapes) and 1B suggests only partial overlap between the brain resources predictive of the different task demands. Discussion We demonstrated fNIRS brain level indicators for interaction between WML and driving complexity. The prediction of driving demands from fNIRS data was possible for each WML level separately but dropped to guessing level when we tried to predict driving demands across WML levels. Conversely, we demonstrated the continuous time-resolved predictions of WML over different levels of driving demands in an earlier analysis performed on the same data (Unni et al. 2017). These results indicate an asymmetric pattern of interaction between tasks at the brain level. Moreover, despite the functional interaction, the predictive brain activation patterns show little overlap between the resources predictive for the different tasks. Interpreted in the framework of a multi-resource theory (Wickens, 2002), our brain recordings indicate that working memory and visuomotor coordination in vehicle control recruit different resources and more so with increasing task demand. Moreover, the two tasks appear to interact at a common, task unspecific cognitive resource at the brain level. The changing pattern of driving demand related brain areas across WML levels could indicate potential changes in the multitasking strategy with high levels of WML demand. Figure Caption Figure 1. (A) Weighted averaged group-level univariate correlation HbR brain maps showing brain areas sensitive to changes in WML independent of driving complexity. White shapes mark WML prediction maxima in all maps. (B) Weighted mean of channel-wise predictivity (Tjur R-squared coefficients) for driving demands at the different WML levels. Data for the two analyses were recorded in the same session with concurrent manipulation of WML and lateral control demands.

Figure 1


This work was funded by a grant of the Volkswagen Foundations to the Center for Critical Systems Engineering, a DFG-grant RI1511/2-1 to JWR, and KO 1990/5-1 to FK.


Fischer, M., Richter, A., Schindler, J., Plättner, J., Temme, G., Kelsch, J., & et al. (2014). Modular and Scalable Driving Simulator Hardware and Software for the Development of Future Driver Assistence and Automation Systems. In New Developments in Driving Simulation Design and Experiments: Driving Simulation Conference 2014. Paris, France. Retrieved from http://elib.dlr.de/90638/

Unni, A., Ihme, K., Jipp, M., & Rieger, J. W. (2017). Assessing the driver’s current level of working memory load with high density functional near-infrared spectroscopy: A realistic driving simulator study. Frontiers in Human Neuroscience, 11. https://doi.org/10.3389/fnhum.2017.00167

Wickens, C. D. (2002). Multiple resources and performance prediction, 3(2), 159–177. https://doi.org/10.1080/14639220210123806

Keywords: fNIRS, driving simulator, driving demands, lateral control demands, working memory, visuospatial attention, visuomotor control, Working memory load, Task interaction, task interaction at brain level, parietal cortex, inferior frontal cortex, occitpito temporal cortex

Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018.

Presentation Type: Oral Presentation

Topic: Neuroergonomics

Citation: Rieger JW, Scheunemann J, Ihme K, Köster F, Jipp M and Unni A (2019). Demonstrating brain-level interactions between working memory load and driving demand level using fNIRS. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00141

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Received: 27 Mar 2018; Published Online: 27 Sep 2019.

* Correspondence: Prof. Jochem W Rieger, University of Oldenburg, Department of Psychology, Oldenburg, Lower Saxony, 26111, Germany, jochem.rieger@uni-oldenburg.de