Imaging deductive reasoning and the new paradigm

Abstract

There has been a great expansion of research into human reasoning at all of Marr’s explanatory levels. There is a tendency for this work to progress within a level largely ignoring the others which can lead to slippage between levels (Chater et al., 2003). It is argued that recent brain imaging research on deductive reasoning—implementational level—has largely ignored the new paradigm in reasoning—computational level (Over, 2009). Consequently, recent imaging results are reviewed with the focus on how they relate to the new paradigm. The imaging results are drawn primarily from a recent meta-analysis by Prado et al. (2011) but further imaging results are also reviewed where relevant. Three main observations are made. First, the main function of the core brain region identified is most likely elaborative, defeasible reasoning not deductive reasoning. Second, the subtraction methodology and the meta-analytic approach may remove all traces of content specific System 1 processes thought to underpin much human reasoning. Third, interpreting the function of the brain regions activated by a task depends on theories of the function that a task engages. When there are multiple interpretations of that function, interpreting what an active brain region is doing is not clear cut. It is concluded that there is a need to more tightly connect brain activation to function, which could be achieved using formalized computational level models and a parametric variation approach.

Episodes, events, and models

“…The preponderance of evidence conflicts with such a view (Prado et al, 2011 ), which is fortunate, because the present authors know of no author or theory that defends it. And as Oaksford ( 2015 ) observes, constraints on the methodology itself may prevent diagnostic analyses. Researchers accordingly face a methodological quandary: Is it possible to marshal insights from cognitive neuroscience to inform theories of reasoning when those theories fail to make predictions of neural mechanism?…”

Mathematics Anxiety and Statistics Anxiety. Shared but Also Unshared Components and Antagonistic Contributions to Performance in Statistics

“…These assumptions are supported by cognitive theories as well as brain functional studies on probabilistic and analytical reasoning. Evans ( 2003 ) and other authors (e.g., Goel and Dolan, 2003 ; Oaksford, 2015 ) distinguish two cognitive systems underlying reasoning: an evolutionary old system and an evolutionary recent and distinctively human system. Whereas, the former system is important for probabilistic reasoning, drawing heuristics, and intuitive understanding, the latter is associated with abstract reasoning and hypothetical thinking (Evans, 2003 ).…”

Comparing Psychology Undergraduates Performance in Probabilistic Reasoning Under Verbal-Numerical and Graphical-Pictorial Problem Presentation Format: What is the Role of Individual and Contextual Dimensions?

“…In principle, this perspective could yield a useful key to explore the eventual occurrence of facilitation effects. Namely it could take into account the interactions between different aspects of problem solving and the possibility that dissimilar formats of problem presentation could support different types of data processing processes (e.g., Klaczynski, 2014;Oaksford, 2015). However, in order to achieve a higher predictive power within the specific context of facilitation effects, the Dual Process Theory must be complemented by more particular hypotheses about the mental coding processes which could be more important when searching for the solution of a probabilistic problem.…”