Formal cognitive models: A tool for the knowledge integration of cognitive psychology and cognitive neuroscience

Authors

  • Odir Rodríguez Villagra Centro de Investigación en Neurociencias e Instituto de Investigaciones Psicológicas, Universidad de Costa Rica Author

DOI:

https://doi.org/10.15517/ap.v28i117.14481

Keywords:

Cognitive psychology, Cognitive neuroscience, formal cognitive models, reasoning

Abstract

The aim of this review was to propose some pieces of advice to cognitive psychologists interested in incorporate the study of the nervous system on their researches. First, this work presents the scholars’ resistances to take into account theories and findings from neurosciences or cognitive psychology in their respective research area; then, some risks related to the allure of neurosciences are offered. Second, formal cognitive models are proposed as an important tool to incorporate in the cognitive neuroscience research. Third, some arguments and examples showing how formal cognitive models aid reasoning and research in cognitive neuroscience are given. Finally, some proposals are presented in order to promote a suitable start in the cognitive neuroscience research.

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References

Baddeley, A. D., Thomson, N. & Buchanan, M. (1975). Word length and the structure of shortterm memory. Journal of Verbal Learning and Verbal Behavior, 14, 575-589. DOI: https://doi.org/10.1016/S0022-5371(75)80045-4

Barrouillet, P., Bernardin, S. & Camos, V. (2004). Time constraints and resource sharing in adults’ working memory spans. Journal of Experimental Psychology: General, 133, 83-100. DOI: https://doi.org/10.1037/0096-3445.133.1.83

Barrouillet, P., Bernarding, S., Portrat, S., Vergauwe, E. & Camos, V. (2007). Time and cognitive load in working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 570-585. DOI: https://doi.org/10.1037/0278-7393.33.3.570

Bennett, M. R. & Hacker, P. M. (2006). Language and cortical function: Conceptual developments. Progress in Neurobiology, 80, 20-52. DOI: https://doi.org/10.1016/j.pneurobio.2006.07.002

Brown, G. D. A. & Lewandowsky, S. (2010). Forgetting in memory models: Arguments againts trace decay and consolidation failure. In S. Della Sala (Ed.), Forgetting (pp. 49-75). Hove: Psychology Press.

Brown, S. D. (2012). Common ground for behavioural and neuroimaging research. Australian Journal of Psychology, 64, 4-10. DOI: https://doi.org/10.1111/j.1742-9536.2011.00046.x

Camos, V., Lagner, P. & Barrouillet, P. (2009). Two maintenance mechanisms of verbal information in working memory. Journal of Memory and Language, 61, 457-469. DOI: https://doi.org/10.1016/j.jml.2009.06.002

Colthear, M. (2012). The cognitive level of explanation. Australian Journal of Psychology, 64, 11-18. DOI: https://doi.org/10.1111/j.1742-9536.2011.00045.x

Corrado, G. & Doya, K. (2007). Understanding neural coding through the model-based analysis of decision making. Journal of Neuroscience, 27, 8178-8180. DOI: https://doi.org/10.1523/JNEUROSCI.1590-07.2007

Dolan, R. J. (2008). Neuroimaging of cognition: past, present, and future. Neuro, 60, 496-502. DOI: https://doi.org/10.1016/j.neuron.2008.10.038

Farrell, S. & Lewandowsky, S. (2010). Computational models as aids to better reasoning in psychology. Current Directions in Psychological Science, 19, 329-335. DOI: https://doi.org/10.1177/0963721410386677

Forstmann, B. U., Wagenmakers, E.-J., Eichele, T., Brown, S. & Serences, J. T. (2011). Reciprocal relations between cognitive neuroscience and formal cognitive models: Opposites attract? Trends in Cognitive Sciences, 15, 272-279. DOI: https://doi.org/10.1016/j.tics.2011.04.002

Gallistel, C. R. (1999). Themes of thought and thinking. In R. J. Sternberg (Ed.), The Nature of Cognition. Science, 285, 842–843. DOI: https://doi.org/10.1126/science.285.5429.842b

Kieras, D. E., Meyer, D. E., Mueller, S. & Seymour, T. (1999). Insights into working memory from the perspective of the EPIC architecture for modelling skilled perceptualmotor and cognitive human performance. In A. Miyake and P. Shah (Eds.), Models of working memory: Mechanisms DOI: https://doi.org/10.1017/CBO9781139174909.009

of active maintenance and control (pp. 183-223). New York, NY: Cambridge University Press.

Kihlstrom, J. F. (2010). Social neuroscience: The footprints of Phineas Gage. Social Cognition, 28, 757–783. DOI: https://doi.org/10.1521/soco.2010.28.6.757

Kim, J-J., Kim, M. S., Lee, J. S., Lee, D. S., Lee, M. C., & Kwon, J. S. (2002). Dissociation of working memory processing associated with native and second languages: PET investigation. NeuroImage, DOI: https://doi.org/10.1006/nimg.2001.1025

15, 879-891.

Lewandowsky, S. (1993). The rewards and hazards of computer simulations. Psychological Science, 4, 236-243. DOI: https://doi.org/10.1111/j.1467-9280.1993.tb00267.x

Lewandowsky, S. & Farrell, S. (2011). Computational Modeling in Cognition: Principles and Practice. Thousand Oaks, CA: Sage. DOI: https://doi.org/10.4135/9781483349428

Lewandowsky, S., Ecker, U. K. H., Farrell, S. & Brown, G. D. A. (2012). Models of cognition and constraints from neuroscience: A case study involving consolidation. Australian Journal of Psychology, 64, 37-45. DOI: https://doi.org/10.1111/j.1742-9536.2011.00042.x

Mercier, H. & Sperber, D. (2011). Why do humans reason? Arguments for an argumentative theory. Behavioral and Brain Sciences, 34, 57-111. DOI: https://doi.org/10.1017/S0140525X10000968

Mulder, M. J., Wagenmakers, E.-J., Ratcliff, R., Boekel, W., & Forstmann, B. U. (2012). Bias in the brain: A diffusion model analysis of prior probability and potential payoff. The Journal of Neuroscience, 32, 2335-2343. DOI: https://doi.org/10.1523/JNEUROSCI.4156-11.2012

Naughtin, C. K., Mattingley, J. B. & Dux, P. E. (2014). Distributed and overlapping neural substrates for object individuation and identification in visual short-term memory. Cerebral Cortex. Advance online

publication. doi:10.1093/cercor/bhu212 DOI: https://doi.org/10.1093/cercor/bhu212

O’Doherty, J. P., Hampton, A. & Kim, H. (2007) Model-based fMRI and its application to reward learing and decision making. Annals of the New York Academy of Sciences, 1104, 35-53. DOI: https://doi.org/10.1196/annals.1390.022

Oberauer, K. & Kliegl, R. (2006). A formal model of capacity limits in working memory. Journal of Memory and Language, 55, 601-626. DOI: https://doi.org/10.1016/j.jml.2006.08.009

Oberauer, K. & Lewandowsky, S. (2011). Modeling working memory: A computational implementation of the Time-Based Resource-Sharing theory. Psychonomic Bulletin & Review, 18, 10-45. DOI: https://doi.org/10.3758/s13423-010-0020-6

Oberauer, K. Lewandowsky, S., Farrell, S., Jarrod, C. & Greaves, M. (2012). Modeling working memory: An interference model of complex span. Psychonomic Bulletin & Review, 19, 779-819. DOI: https://doi.org/10.3758/s13423-012-0272-4

Pages, M. P. A. & Norris, D. (1998). The primacy model: A new model of immediate serial recall. Psychological Review, 105, 761-781. DOI: https://doi.org/10.1037/0033-295X.105.4.761-781

Peigneux P., Schmitz R., & Urbain, C. (2010). Sleep and Forgetting. In S. Della Sala (Ed.), Forgetting (pp. 165- 184). Hove: Psychology Press.

Ratcliff, R. (2002). A diffusion model account of reaction time and accuracy in a two choice brightness discrimination task: Fitting real data and failing to fit fake but plausible data. Psychonomic Bulletin and Review, 9, 278–291. DOI: https://doi.org/10.3758/BF03196283

Ratcliff, R. & McKoon, G. (2007). The diffusion decision model: Theory and data for two-choice decision task. Neural Computation, 20, 873-922. DOI: https://doi.org/10.1162/neco.2008.12-06-420

Sauseng, P., Klimesch, W., Heise, K. F., Gruber, W. R., Holz, E., Karim, A. A.,… Hummel, F. C. (2009). Brain oscillatory substrates of visual short-term memory capacity. Current Biology, 19, 1846-1852. DOI: https://doi.org/10.1016/j.cub.2009.08.062

Sternberg, R. J. & Sternberg, K. (2011). Cognitive Psychology. Cengage Learning: Belmont California. Vogel, E., McCollough, A. W. & Machizawa, M. G. (2005). Neural measures reveal individual diferences in controlling access to working memory. Nature, 438, 500-503. DOI: https://doi.org/10.1038/nature04171

Voss, A., Nagler, M. & Lerche, V. (2013). Diffusion models in experimental psychology: a practical introducction. Experimental Psychology, 60, 385-402. DOI: https://doi.org/10.1027/1618-3169/a000218

Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E. & Gray, J. R. (2008). The seductive allure of neuroscience explanations. Journal of Cognitive Neuroscience, 20, 470-477. DOI: https://doi.org/10.1162/jocn.2008.20040

Wixted, J. T. (2004). The psychology and neuroscience of forgetting. Annual Review of Psychology, 55, 235-269. DOI: https://doi.org/10.1146/annurev.psych.55.090902.141555

Wixted, J. T. (2010). The role of retroactive interference and consolidation in everyday forgetting. In S. Della Sala (Ed.), Forgetting (pp. 285-312). Hove: Psychology Press.

Yechiam, E., Busemeyer, J. R., Stout, J. C. & Bechara, A. (2005). Using cognitive models to map relations between neuropsychological disorders and human decision-making déficits. Psychological Science, 16, 973-978. DOI: https://doi.org/10.1111/j.1467-9280.2005.01646.x

Additional Files

Published

2014-11-20

How to Cite

Rodríguez Villagra, O. (2014). Formal cognitive models: A tool for the knowledge integration of cognitive psychology and cognitive neuroscience. Actualidades En Psicología, 28(117), 79-91. https://doi.org/10.15517/ap.v28i117.14481