The Cognitive Neuroscience Laboratory

Research

 

Computational Analyses

Computational analyses play an important role in investigating and understanding perception. Of particular interest (and challenge) is how to use computers to learn about higher cognitive functions. Computational analyses aid in deciphering the mechanisms and complexity involved in processing information and experimentally address a variety of research questions. For example, investigating what information is available to the cognitive system, and how it is represented. Such details are a key in understanding what a cognitive system does, and how. Recent computational work has provided important insights into how shape information is encoded in sonar. These insights have been used to develop artificial sonar systems that can recognize faces and the speed of a moving target, and perhaps a way of using sonar as a full perceptual modality for robotics.

Computer simulations can also examine the computational and cognitive significance of biological mechanisms, thus, bridging brain and behavior. For example, examining the role of dendritic growth as a computational compensatory mechanism in the ageing brain. However, computational modeling does not have to be biologically plausible in order to provide important insights into cognition.


Related publications:

* Dror, I. E. & Gallogly, D. (1999). Computational analyses in cognitive neuroscience: In defense of biological implausibility. Psychonomic Bulletin & Review, 6 (2), 173-182.

* Sung, M., Johnson, J.E.V. & Dror, I. E. (in press). Complexity as a guide to understanding decision bias: A contribution to the favorite-longshot bias debate. Journal of Behavioral Decision Making.

* Dror, I. E. & Peron, A.E. (2004). Computationalism new directions (book review). Pragmatics and Cognition.
* Dror, I. E., Zagaeski, M., & Moss, C. F. (1995). Three-dimensional target recognition via sonar: A neural network model. Neural Networks, 8 (1), 143-154.

* Sung, M., Johnson, J.E.V. & Dror, I. E. (in press). Complexity as a guide to understanding decision bias: A contribution to the favorite-longshot bias debate. Journal of Behavioral Decision Making.

* Makany, T., Redhead E., & Dror, I. E. (in press). Spatial exploration patterns determine navigation efficiency: Trade-off between memory demands and distance travelled. Quarterly Journal of Experimental Psychology.

* Dror, I. E., Busemeyer, J.R., & Basola, B. (1999). Decision making under time pressure: An independent test of sequential sampling models.  Memory and Cognition, 27 (4), 713-725.

* Dror, I.E., Smith, W., & Schmitz-Williams, I.C. (in press). Older adults use mental representations that reduce cognitive load: Mental rotation utilises holistic representations and processing. Experimental Aging Research, 31(4).

* Dror, I. E. & Thomas, R. D. (2004). The cognitive neuroscience laboratory: A framework for the science of the mind. In D. Johnson (Ed.), Mind as a Scientific Object: Between Brain and Culture. Oxford University Press.
* Dror, I. E., Florer, F.L., Rios, D., & Zagaeski, M. (1996). Using artificial bat sonar neural networks for complex pattern recognition: Recognizing faces and the speed of a moving target. Biological Cybernetics, 74, 331-338.
* Dror, I. E., (1997). Computational adaptations and cognitive strategy changes as compensation for age-related decline in cognitive resources. Society of Neuroscience Abstracts, 23, 1457. New Orleans.
* Dror, I. E. (1994). Neural network models as tools for understanding high-level cognition: Developing paradigms for cognitive interpretation of neural network models. In M. C. Mozer, P. Smolensky, D. S. Touretzky, J. L. Elman, & A. S. Weigend (Eds.), Proceedings of the 1993 Connectionist Models Summer School, (pp. 87-94). Hillsdale, NJ: Erlbaum.
* Dror, I. E., Girdler, B., & Schreiner, C. S. (in preparation). A computational look at cognition and aging.
* Dror, I. E. & Morgret, C. C. (1996). A computational investigation of dendritic growth as a compensatory mechanism for neuronal loss in the aging brain. Society of Neuroscience Abstracts, 22, 1891. Washington, DC.
* Dror, I. E. & Schreiner, C. S. (1998). Neural networks and perception. In J. S. Jordan (Ed.), Systems Theories and A prior Aspects of Perception, (pp. 77-85). Elsevier Press.
* Dror, I. E., Zagaeski, M., Rios, D. & Moss, C. F. (1997). Neural network sonar as a perceptual modality for robotics. In P. Smagt & O. Omidvar (Eds.), Neural Systems and Robotics, (pp. 1-15). San Diego, CA: Academic Press.



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