By Birte U. Forstmann, Eric-Jan Wagenmakers
Two fresh strategies, the emergence of formal cognitive versions and the addition of cognitive neuroscience facts to the normal behavioral facts, have led to the beginning of a brand new, interdisciplinary box of research: model-based cognitive neuroscience. regardless of the expanding medical curiosity in model-based cognitive neuroscience, few lively researchers or even fewer scholars have an outstanding wisdom of the 2 constituent disciplines. the most objective of this edited assortment is to advertise the mixing of cognitive modeling and cognitive neuroscience. specialists within the box will offer tutorial-style chapters that designate specific options and spotlight their usefulness via concrete examples and various case reports. The publication also will contain a radical record of references pointing the reader in the direction of extra literature and on-line resources.
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Extra info for An Introduction to Model-Based Cognitive Neuroscience
Farrell and S. Lewandowsky 7. Norris, D. (2005). How do computational models help us build better theories? In A. ), Twenty-first century psycholinguistics: Four cornerstones (pp. 331–346). Mahwah, NJ: Lawrence Erlbaum. References 1. Muter P (1980) Very rapid forgetting. Mem Cognit 8:174–179 2. Latané B (1996) Dynamic social impact: the creation of culture by communication. J Commun 46:13–25 3. Kenrick DT, Li NP, Butner J (2003) Dynamical evolutionary psychology: individual decision rules and emergent social norms.
An alternative way to establish model validity is via tests of selective influence. Rather than using external data as the benchmark of validity, this method uses experimental manipulations. Selective influence testing is based on the idea that a valid model can titrate complex effects in raw data into separate and simpler accounts in terms of latent variables. From this perspective, a model is valid to the extent that it make sense of otherwise confusing data. , sensitivity and bias). Similarly, models of speeded decision-making can convert complex changes in the mean, variance, and accuracy of response time data into a single effect of just one latent variable.
Psychon Bull Rev 14:256–260 48. Brown GDA, Lewandowsky S. (2010) Forgetting in memory models: arguments against trace decay and consolidation failure. In: Della Sala S (ed) Forgetting. Psychology Press, Hove, pp 49–75 49. Lewandowsky S, Ecker UKH, Farrell S, Brown GDA (2011) Models of cognition and constraints from neuroscience: a case study involving consolidation. Aust J Psychol 64:37–45 50. Kane MJ, Bleckley MK, Conway ARA Engle RW(2001) A controlled-attention view of working-memory capacity.