Analogical Reasoning and Working Memory

Abstract

Analogical Reasoning (AR) is the ability to find a relationship between two objects that is not based on featural (attribute-based) similarities. As such, reasoning by analogy is thought to be crucial in learning and scientific discovery.

Analogies have played an important role in the conceptualisation of both IQ (Spearman, 1927) and cognitive development (Piaget, Montangero & Billeter, 1977). Yet very little is understood regarding the component processes which underlie analogical thought. Recently, there has been a resurgent interest in the field: one brought about by modern computational methodologies which purport to model the cognitive architecture of analogical thinking. A prominent feature has been the introduction of capacity based processing constraints claimed to arise in the reasoning processes from limited Working Memory Capacity (WMC) resources (Halford, 1992, 1993, 1998; Hummel & Holyoak, 1997; Morrison, Doumas, & Richland, 2011; Richland, Morrison & Holyoak, 2004, 2006, 2010).

Adopting a Working Memory (WM) perspective (Baddeley & Hitch, 1974; Baddeley 2000) the aim of this research is to investigate whether individual differences in WM mediate AR, as well as critically assessing the current theories of AR in relation to this.

In chapter 1 the research behind AR-WM is reviewed with reference to modern interpretations of what analogy is and how it might be measured. In chapter 2 (Experiment 1), a flexible new scene-based measure of analogical ability, the Richland Picture Analogies (RPA; Richland, et al, 2004, 2006) is introduced, the data confirming effects of complexity and distraction hypothesized by Richland and her colleagues. Experiment 2 related performance on the RPA with quantitative measures of WM, concluding that IQ was related to relational responding in the RPA over and above that of WMC. Experiment 3 further explored the role of WM, observing an effect of processing/storage (WMC) but not storage (STS).

In chapter 3, the role of WMC was further examined. Experiment 4 using a reaction time (RT) paradigm demonstrated that featural responding was unlikely to be a prepotent response, and instead related to conflict resolution. Experiment 5 adopted a dual-task methodology and attempted to explore the involvement of WMC under load in conditions of complexity and distraction. Unfortunately, the low level of variance proved an insurmountable problem. Experiment 6 examined Executive Functions (EFs) as a potential explanation for both IQ and WMC effects in the RPA. Overall, it is concluded that WM does indeed mediate analogical performance within the RPA, but that effects of relational-complexity, as suggested by Halford (1992, 1993, 1998) are not as evident as might have been supposed.

Instead the data from Experiments 2-6 suggests that individual differences in processing efficiency as well as the ability to divide and control attention in novel circumstances may explain the variance in relation responding reported by Richland et al. (2004, 2006) and found in Experiment 1. It is hypothesized that one of the core aspects of AR is task relevance, the research concluding that other interpretations of how WM affects AR should be considered beyond the traditional theories.