- Title
- Systems Factorial Technology analysis of mixtures of processing architectures
- Creator
- Little, Daniel R.; Eidels, Ami; Houpt, Jospeh W.; Garrett, Paul M.; Griffiths, David W.
- Relation
- ARC.DP160102360 http://purl.org/au-research/grants/arc/DP160102360
- Relation
- Journal of Mathematical Psychology Vol. 92, Issue October 2019, no. 102229
- Publisher Link
- http://dx.doi.org/10.1016/j.jmp.2018.10.003
- Publisher
- Academic Press
- Resource Type
- journal article
- Date
- 2019
- Description
- Human information processing is flexible in its ability to utilize mechanisms such as attention and memory along with basic perceptual processes. As a consequence, information processing is probably best thought of as not reflecting one type of standard system or architecture but as a mixture of different types of systems. We examine the predictions of mixtures of different processing modes using Systems Factorial Technology (Townsend and Nozawa, 1995). SFT offers a number of important diagnostic measures for differentiating pure processing models (e.g., serial or parallel). We show that mixtures of basic processes result in smooth, gradual changes to these measures reflecting the proportions of each process. The identifiability of these mixtures, in comparison to interactive parallel channel models, is discussed with reference to the fixed-point property of mixture models.
- Subject
- information processing; systems factorial technology; interactive parallel channel models; processing models
- Identifier
- http://hdl.handle.net/1959.13/1449339
- Identifier
- uon:43643
- Identifier
- ISSN:0022-2496
- Language
- eng
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