Laura Matzen

Cognitive Neuroscience

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Laura Matzen is a Principal Member of the Technical Staff in the Cognitive Science and Systems department (1463) at Sandia National Laboratories.  Her primary research interests lie in using cognitive neuroscience methods to understand how humans process and remember information while performing complex reasoning tasks. Laura manages Sandia’s Human Performance Laboratory, which houses EEG, eye tracking, transcranial direct current stimulation (tDCS), and behavioral research capabilities. She received a Ph.D. in cognitive psychology with a concentration in cognitive neuroscience from the University of Illinois at Urbana-Champaign in 2008 and a BA in linguistics and cognitive science from Rice University in 2003. Her work at Sandia has focused on visual search performance in experts and novices and methods for improving human memory performance. Both lines of research touch on training (e.g. how do we maximize retention of information during training? How can we use data about expert performance to train novices?) and human-system interactions (e.g. how can we display information in a way that maximizes human performance?).

Research Areas

Cognitive Neuroscience

  • Using novel eye tracking methods to study information foraging
  • Testing the effects of tDCS on cognitive performance
  • Predicting future memory performance using event-related potentials (ERPs)

Human-System Interactions

  • Studying expert and novice performance in real-world visual search tasks
  • Designing training for imagery analysts
  • Using cognitive load as a metric for software evaluation

Publications

  1. Matzen, L. E., Trumbo, M. C., Leach, R. C., & Leshikar, E. D. (2015). Effects of non-invasive brain stimulation on associative memory. Brain Research, 1624, 286-296.
  2. Silva, A., Emmanuel, G., McClain, J. T., Matzen, L. E., & Forsythe, C. (2015). Measuring expert and novice performance within computer security incident response teams. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Foundations of Augmented Cognition. Lecture Notes in Artificial Intelligence, 9183, 144-152.
  3. Matzen, L. E., Haass, M. J., McNamara, L. A., Stevens-Adams, S. M., & McMichael, S. N. (2015). Effects of professional visual search experience on domain-general and domain-specific visual cognition. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Foundations of Augmented Cognition. Lecture Notes in Artificial Intelligence, 9183, 481-491.
  4. Haass, M. J., Matzen, L. E., Stevens-Adams, S. M. & Roach, A. R. (2015). Methodology for knowledge elicitation in visual abductive reasoning tasks. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Foundations of Augmented Cognition. Lecture Notes in Artificial Intelligence, 9183, 401-409.
  5. McNamara, L. A.,  Cole, K. S. , Haass, M. J., Matzen, L. E., Morrow, J. D., Stevens-Adams, S. M., & McMichael, S. N. (2015). Ethnographic Methods for Experimental Design: Case Studies in Visual Search. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Foundations of Augmented Cognition. Lecture Notes in Artificial Intelligence, 9183, 492-503.
  6. Trumbo, M. C., Matzen, L. E., Silva, A., Haass, M. J., Divis, K. & Speed, A. (2015). Through a scanner quickly: Elicitation of P3 in Transportation Security Officers following rapid imager presentation and categorization. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Foundations of Augmented Cognition. Lecture Notes in Artificial Intelligence, 9183, 348-360.
  7. Matzen, L. E., Benjamin, A. S. (2013). Older and wiser: Episodic word memory in older adults benefits from sentence study contexts. Psychology of Aging, 28, 754-764.
  8. Santarnecchi, E., Godone, M., Polizzotto, N. R., Giovannelli, F., Feurra, M., Matzen, L. E., Rossi, A., & Rossi, S. (2013). Frequency-dependent enhancement of fluid intelligence induced by transcranial oscillatory potentials. Current Biology, 23, 1-5.
  9. Benjamin, A. S., Diaz, M., Matzen, L. E., & Johnson, B. (2012). Tests of the DRYAD theory of the age-related deficit in memory for context: Not about context, and not about aging. Psychology and Aging, 27, 418-428.
  10. Matzen, L.E. (2011). Cultural neuroscience and individual differences: Implications for augmented cognition. Foundations of Augmented Cognition: Directing the Future of Adaptive Systems. Lecture Notes in Artificial Intelligence, 6780/2011, 194-198.
  11. Haass, M. J. & Matzen, L. E. (2011). Using computational modeling to assess use of cognitive strategies. Foundations of Augmented Cognition: Directing the Future of Adaptive Systems. Lecture Notes in Artificial Intelligence, 6780/2011, 77-86.
  12. Anderson, E., Potter, K., Matzen, L., Shepherd, J., Preston, G., & Silva, C. (2011). A user study of visualization effectiveness using EEG and cognitive load. Computer Graphics Forum, Proceedings of EuroVis 2011.
  13. Matzen, L. E., Taylor, E. G., & Benjamin, A. S. (2011). Contributions of familiarity and recollection rejection to recognition: Evidence from the time course of false recognition for semantic and conjunction lures. Memory, 19 (1), 1-16.
  14. Matzen, L. E., Benz, Z. O., Dixon, K. R., Posey, J., Kroger, J. K., & Speed, A. E. (2010). Recreating Raven’s: Software for systematically generating large numbers of Raven-like matrix problems with normed properties. Behavior Research Methods, 42 (2), 525-541.
  15. Matzen, L. E., & Benjamin, A. S. (2009). Remembering words not presented in sentences: How study context changes patterns of false memories. Memory & Cognition, 37, 52-64.