Strong inference in functional neuroimaging
Article first published online: 16 DEC 2011
© 2011 The Australian Psychological Society
Australian Journal of Psychology
Special Issue: Cognitive modeling ‘versus’ cognitive neuroscience: Competing approaches or compatible levels of explanation? Guest editors: Stephan Lewandowsky and Max Coltheart
Volume 64, Issue 1, pages 19–28, March 2012
How to Cite
de Zubicaray, G. (2012), Strong inference in functional neuroimaging. Australian Jnl of Psychology, 64: 19–28. doi: 10.1111/j.1742-9536.2011.00047.x
- Issue published online: 23 FEB 2012
- Article first published online: 16 DEC 2011
- Received 30 March 2011. Accepted for publication 1 November 2011.
- cognitive models;
- speech production
A recurring question for cognitive science is whether functional neuroimaging data can provide evidence for or against psychological theories. As posed, the question reflects an adherence to a popular scientific method known as ‘strong inference’. The method entails constructing multiple hypotheses (Hs) and designing experiments so that alternative possible outcomes will refute at least one (i.e., ‘falsify’ it). In this article, after first delineating some well-documented limitations of strong inference, I provide examples of functional neuroimaging data being used to test Hs from rival modular information-processing models of spoken word production. ‘Strong inference’ for neuroimaging involves first establishing a systematic mapping of ‘processes to processors’ for a common modular architecture. Alternate Hs are then constructed from psychological theories that attribute the outcome of manipulating an experimental factor to two or more distinct processing stages within this architecture. Hs are then refutable by a finding of activity differentiated spatially and chronometrically by experimental condition. When employed in this manner, the data offered by functional neuroimaging may be more useful for adjudicating between accounts of processing loci than behavioural measures.