Abstract
We investigate reaction times for classification of visual stimuli composed of combinations of shapes, to distinguish between parallel and serial processing of stimuli. Reaction times in a visual XOR task are slower than in AND/OR tasks in which pairs of shapes are categorised. This behaviour is explained by the time needed to perceive shapes in the various tasks, using a parallel drift diffusion model. The parallel model explains reaction times in an extension of the XOR task, up to 7 shapes. Subsequently, the behaviour is explained by a combined model that assumes perceptual chunking, processing shapes within chunks in parallel, and chunks themselves in serial. The pure parallel model also explains reaction times for ALL and EXISTS tasks. An extension to the perceptual chunking model adds time taken to apply a logical rule. We are able to improve the fit to the data by including this extra parameter, but using model selection the extra parameter is not supported. We further simulate the behaviour exhibited using an echo state network, successfully recreating the behaviour seen in humans.
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References
Gregory Ashby, F.: A biased random walk model for two choice reaction times. Journal of Mathematical Psychology 27(3), 277–297 (1983)
Bogacz, R., Brown, E., Moehlis, J., Holmes, P., Cohen, J.D.: The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks. Psychological Review 113(4), 700 (2006)
Fernando, C.T., Szathmary, E., Husbands, P.: Selectionist and evolutionary approaches to brain function: A critical appraisal. Frontiers in Computational Neuroscience 6, 24 (2012)
Fific, M., Little, D.R., Nosofsky, R.M.: Logical-rule models of classification response times: A synthesis of mental-architecture, random-walk, and decision-bound approaches. Psychological Review 117(2), 309 (2010)
Jaeger, H.: The” echo state” approach to analysing and training recurrent neural networks-with an erratum note’. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report, 148 (2001)
Little, D.R., Nosofsky, R.M., Denton, S.E.: Response-time tests of logical-rule models of categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition 37(1), 1 (2011)
Nosofsky, R.M., Palmeri, T.J.: An exemplar-based random walk model of speeded classification. Psychological Review; Psychological Review 104(2), 266 (1997)
Ratcliff, R.: A theory of memory retrieval. Psychological Review 85(2), 59 (1978)
Ratcliff, R.: Methods for dealing with reaction time outliers. Psychological Bulletin 114(3), 510 (1993)
Ratcliff, R., Tuerlinckx, F.: Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin & Review 9(3), 438–481 (2002)
Schaul, T., Bayer, J., Wierstra, D., Sun, Y., Felder, M., Sehnke, F., Rückstieß, T., Schmidhuber, J.: PyBrain. Journal of Machine Learning Research (2010)
Schrauen, B.: Organic environment for reservoir computing (oger) toolbox, http://organic.elis.ugent.be/organic/engine (accessed: January 05, 2014)
Smith, P.L., Ratcliff, R.: Psychology and neurobiology of simple decisions. Trends in Neurosciences 27(3), 161–168 (2004)
Townsend, J.T., Nozawa, G.: Spatio-temporal properties of elementary perception: An investigation of parallel, serial, and coactive theories. Journal of Mathematical Psychology 39(4), 321–359 (1995)
Townsend, J.T., Wenger, M.J.: A theory of interactive parallel processing: New capacity measures and predictions for a response time inequality series. Psychological Review 111(4), 1003 (2004)
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Lewis, M., Fedor, A., Öllinger, M., Szathmáry, E., Fernando, C. (2014). Modelling Reaction Times in Non-linear Classification Tasks. In: del Pobil, A.P., Chinellato, E., Martinez-Martin, E., Hallam, J., Cervera, E., Morales, A. (eds) From Animals to Animats 13. SAB 2014. Lecture Notes in Computer Science(), vol 8575. Springer, Cham. https://doi.org/10.1007/978-3-319-08864-8_6
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DOI: https://doi.org/10.1007/978-3-319-08864-8_6
Publisher Name: Springer, Cham
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