Executive control in analogical reasoning: Beyond interference resolution

Executive Control in Analogical Reasoning: Beyond Interference Resolution Anna Chuderska (anna.chuderska@uj.edu.pl) Adam Chuderski (achuders@apple.phils.uj.edu.pl) Institute of Psychology, Jagiellonian University al. Mickiewicza 3, 31-120 Krakow, Poland Abstract In recent studies, analogy-making has been shown to depend on the ability to resist interference. In our large-sample corre- lational study we found that efficiency of analogical reasoning is strongly related to measures of diverse executive control functions, far beyond interference resolution. These functions included interference resolution itself, but also: goal activa- tion and application, inhibition of response tendency, and controlled visual search. The results implicate that executive control is an important factor for efficiency of analogical (and probably for most of types of relational) reasoning and that accounting for inter-individual differences in control and reasoning may be important for assessing the psychological plausibility of computational models of analogy-making. Introduction Analogy-making comprise high-level cognitive processes of structuring two phenomena (analogs, e.g. objects, events, ideas) by similar relations, even if they totally differ seman- tically or perceptually, and of inferring new goal-relevant information about one phenomenon (target) from elements of the second phenomenon (source) by means of finding out the systematic relational correspondences between them. Ability to reason by analogy is essential in face of any novelty one needs to understand (e.g., in discovery) or to produce (e.g., in problem solving). Since seminal structure- mapping theory (Gentner, 1983) analogical reasoning proce- sses have received much attention in cognitive science and contributed to more general theories of relational reasoning. The major postulate of these theories is that processing relational representations is highly dependent on limited working memory (WM) capacity. Relational complexity theory quantifies this limit with the complexity of relations – the number of interacting variables that must be processed in parallel. The more variables have to be processed simul- taneously, the higher task’s difficulty is and the less people can solve it (Halford, Baker, McCredden, & Bain, 2005). However, the more variables are active in WM, the bigger challenge arises to integrate them according to a goal, especially if they conflict across structural and semantic constraints (e.g. Markman & Gentner, 1993). So, it seems probable that even very capacious WM may be not enough to successfully analogize. For example, LISA – artificial neural network model (Hummel & Holyoak, 2003), which dynamically binds roles (i.e., arguments) and fillers (i.e., arguments’ content) into relations by synchrony of firing their distributed representations, contains an intrinsic capa- city limit (phase set size), since only a finite number of such role-filler bindings can oscillate asynchronously in one processing cycle. Thus, the model must control which units enter a current phase set and monitor its goal-progress. This is realized by lateral inhibition between competing units and by overall setup of relational representations, influencing the way activation spreads. If these discriminatory functions are weak, the model would be less efficient than its counter- part with the same phase-set size, but strong inhibition. Thus, indispensible need to chose relational input and to manage distraction make some researchers (e.g., Viskontas, Morisson, Holyoak, Hummel, & Knowlton, 2004) believe that the fundamental source of constraints on human rela- tional reasoning comes from the effectiveness of executive control over cognitive processing in WM during reasoning, instead of a sole structural limit in storage capacity. However, the role of executive functions in relational reasoning only begins to be explored in detail. Moreover, despite the fact that efficiency of representing and manipulating relations is repeatedly differentiating participants (into those who are and who are not able to make the right analogy), there has been relatively little curiosity for exploiting this fact for identifying cognitive mechanisms underlying relational reasoning. Following others, we believe that, instead of averaging individual or group data, which leads to loosing important information, careful examination of inter-individual variability in reaso- ning by linking it to other tangible measures of lower-level cognitive abilities is a promising path for discerning cogniti- ve mechanisms of reasoning (Lewandowsky & Heit, 2006). Executive Control Executive control (EC), also referred to as cognitive control, may be defined as a set of cognitive processes that instead of representing mental states directly, influence and organi- ze such states in the context of some internal goal. Execu- tive processes are especially involved in novel situations, when arbitrary sequences of responses are to be emitted, when great amount of planning is required, when errors are likely and must be quickly corrected, and when dominant but not relevant response tendencies need to be overcome. Recent theories assume that EC is an emergent process arising from dynamical interaction of many independent, elementary control mechanisms (e.g., Braver, Gray, & Burgess, 2007). A popular approach to EC is to identify executive functions. A taxonomy proposed by Miyake, Friedman, Emerson, Witzki, and Howerter (2000) includes three such functions: updating of WM, inhibition of domi- nant responses, and shifting. Updating consists in conti- nuous “refreshment” of WM contents by inserting some stimuli to active memory while deleting others. Inhibition deals with volitional stopping of dominant but task- irrelevant thought or response tendency. Mental set shifting involves frequent alternating task-sets in WM. Other often