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Electrocortical Evidence for Long-Term Incidental Spatial Learning Through Modified Navigation Instructions

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Book cover Spatial Cognition XI (Spatial Cognition 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11034))

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Abstract

The use of Navigation Assistance Systems for spatial orienting has become increasingly popular. Such automated navigation support, however, comes with a reduced processing of the surrounding environment and often with a decline of spatial orienting ability. To prevent such deskilling and to support spatial learning, the present study investigated incidental spatial learning by comparing standard navigation instructions with two modified navigation instruction conditions. The first modified instruction condition highlighted landmarks and provided additional redundant information regarding the landmark (contrast condition), while the second highlighted landmarks and included information of personal interest to the participant (personal-reference condition). Participants’ spatial knowledge of the previously unknown virtual city was tested three weeks later. Behavioral and electroencephalographic (EEG) data demonstrated enhanced spatial memory performance for participants in the modified navigation instruction conditions without further differentiating between modified instructions. Recognition performance of landmarks was better and the late positive complex of the event-related potential (ERP) revealed amplitude differences reflecting an increased amount of recollected information for modified navigation instructions. The results indicate a significant long-term spatial learning effect when landmarks are highlighted during navigation instructions.

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Notes

  1. 1.

    The test involves sending electrical impulses (n = 3600, 50 ms average recurrence) from a low-latency interface (the parallel port, controlled via MATLAB) of a PC directly to the ActiCap EEG electrodes. Impulse amplitude is limited to ~150 mV via a voltage divider (ratio: 1:20). “At the same time” this PC sends “LSL markers” (string formatted irregularly sampled time series data) over the network, the “ground truth” in this setup. Another PC runs the BrainVisionRecorder (gathering data from the USB adapters), the BrainVision LSL application (“converting” these data samples into an LSL stream), and LSL’s LabRecorder recording the “EEG data” (electrical impulses) and the markers sent from the first PC. The age of sample then is given by the time stamp difference of the markers and the corresponding impulse flanks in the BrainVision data.

  2. 2.

    https://www.optoma.de/projectorproduct/gt1080e (last access: 23.02.2018).

  3. 3.

    The “irrelevant” responses (i.e., stepping on gas pedal) were significantly more often than one third of the trials (M = 38.7%, SE = 1.8%, t(41) = 3.13, p = .003). The “relevant” responses (i.e., turning the steering wheel) were observed significantly less than in one third of the trials (M = 27.1%, SE = 2.1%, t(41) = −2.85, p = .007). The rate of “unknown” decisions (i.e., stepping on the brake pedal) demonstrated the expected response distribution (M = 34.3%, SE = 2.5%, p = .614).

  4. 4.

    Bias correction was computed via multiplication of percentage correct with 33.3% divided by mean percentage reaction kind across all conditions respectively for each landmark type.

  5. 5.

    Main effect landmark type: F(1.7,66.9) = 0.96, p = .376, \( \eta_{p}^{2} = .0.24 \); Main effect navigation instruction condition: F(2,39) = 2.24, p = .008, \( \eta_{p}^{2} = .222 \), with post hoc comparison showing a significant difference between standard (M = 51.5%, SE = 2.5%) and contrast condition (p = .013, M = 60,6%, SE = 2.4%) and personal-reference condition (p = .004, M = 62,7%, SE = 2.6%). The interaction effect of navigation instruction condition and landmark type is still not significant (F(3.4,66.9) = 2.24, p = .060, \( \eta_{p}^{2} = .113 \)).

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Acknowledgements

This work was supported by a stipend from the Stiftung der Deutschen Wirtschaft to AW. We would like to thank Matthias Rötting at TU Berlin for providing the car simulator facilities and Sabine Grieger for helping to conduct the experiment.

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Authors and Affiliations

  1. Biological Psychology and Neuroergonomics, Berlin Institute of Technology, Berlin, Germany

    Anna Wunderlich & Klaus Gramann

  2. School of Software, University of Technology Sydney, Sydney, Australia

    Klaus Gramann

  3. Center for Advanced Neurological Engineering, University of California, San Diego, CA, USA

    Klaus Gramann

Authors
  1. Anna Wunderlich
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Correspondence to Anna Wunderlich .

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Editors and Affiliations

  1. University of Utah, Salt Lake City, Utah, USA

    Sarah Creem-Regehr

  2. University of Bremen, Bremen, Germany

    Johannes Schöning

  3. Pennsylvania State University, University Park, Pennsylvania, USA

    Alexander Klippel

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Wunderlich, A., Gramann, K. (2018). Electrocortical Evidence for Long-Term Incidental Spatial Learning Through Modified Navigation Instructions. In: Creem-Regehr, S., Schöning, J., Klippel, A. (eds) Spatial Cognition XI. Spatial Cognition 2018. Lecture Notes in Computer Science(), vol 11034. Springer, Cham. https://doi.org/10.1007/978-3-319-96385-3_18

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  • DOI: https://doi.org/10.1007/978-3-319-96385-3_18

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