Executive Control in Analogical Mapping: Two Facets

Executive Control in Analogical Mapping: Two Facets Anna Chuderska (ania.chuderska@gmail.com) 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 within working memory (WM). Less evidence refers to the other facets of executive control (EC), especially to the goal-directed selection of relational information. In this study, the load on two above mentioned EC functions and on WM capacity was manipulated in a single picture mapping task. Next to replicating the previous findings on the importance of dealing with distracter interference within WM, the current results demonstrate that the efficiency of relational mapping also depends on the goal-directed search for WM input, and that these two EC functions may be dissociable in mapping. Moreover, it was found that the impact of distraction can be linked to whether relations, in which distracters occur, have not exceeded WM capacity. Introduction Analogical reasoning is a flagship example of the human ability to flexibly form and manipulate explicit representations of structure (Hummel & Holyoak, 2003). Making an analogy requires identifying systematic relational correspondences between two analogs (e.g., situations), irrespective of superficial similarities (if they conflict with relational ones) or differences (especially, if they are huge) between them (e.g., Gentner, 1983). This structure-mapping process allows one to infer new goal- relevant information about one analog (target) from the second analog (source). Thus, analogy is an important tool for dealing with novelty and one of the major vehicles of human intelligence (e.g., Holyoak, 2005). The inherent computational challenges of processing relational representations (Doumas & Hummel, 2005), and the fact that variance in the efficiency of analogical reasoning is only partially explicable by knowledge accretion (e.g., Doumas, Morrison & Richland, 2009), has made many researchers and theorists postulate that the emergence of analogy is underlain by the efficiency of some constitutional cognitive capacities (or parameters). Of these, working memory (WM) was considered to be the most important (see Morrison, 2005 for review). Yet, explaining analogy-making by WM constraints seems to be quite intricate. More than WM Capacity WM is a capacity-limited system responsible for active maintenance, rapid access and easy updating of goal- relevant information (Cowan, 2005). If WM is overloaded by a parallel task (Waltz, Lau, Grewal & Holyoak, 2000), impaired by brain damage (Waltz et al., 1999), or if the number of variables interacting in relational represent- tation grows (Halford, Baker, McCredden, & Bain, 2005), relational reasoning becomes less efficient. According to relational complexity theory, the load of relational repre- sentation on WM increases exponentially with the number of interacting variables that must be concurrently manipu- lated (i.e., relationally integrated). Human WM is probab- ly typically limited to the parallel processing of up to one quaternary relation, that is a relational representation with four variables (Halford, Wilson & Phillips, 1998). However, when reflecting on the limited nature of WM capacity, it is important to understand internal cognitive constraints on the proper selection of WM input, by means of which humans single-handedly, but with various degrees of success (e.g., Chuderska & Chuderski, 2009), abstract structural similarity between analogs from other structural and (sometimes very compelling) semantic information. It seems plausible that relational integration might be influenced by the efficiency of earlier goal- driven attentional selection (see Awh, Vogel & Oh, 2006 for a discussion of attention as the “gatekeeper” for WM), or by the efficiency of managing subsequent reasoning steps (e.g., Carpenter, Just & Shell, 1990), which are necessarily isolated out for reduction of complexity (Halford et al., 1998). On the other hand, the content of variables integrated in WM may perceptually or semantically conflict with the structural information they convey (e.g., Markman and Gentner, 1993). Since processing many distracters leads to no success, the need to deal with distraction within WM, while analogizing, seems indispensible (e.g., Viskontas, Morisson, Holyoak, Hummel, & Knowlton, 2004). The potential causes for processing irrelevant informa- tion by WM, or doing it inefficiently, are delineated in LISA – an artificial neural network model of relational reasoning (Hummel & Holyoak, 2003). LISA dynamically binds roles (i.e., variables) and fillers (i.e., their content) into relations by the synchrony of firing their distributed semantic (featural) and localist (structural) represent- ations. The model contains an intrinsic capacity limit, since only a confined number of such role-filler bindings can oscillate cleanly asynchronously in one processing cycle. The weaker the inhibitory competition between active units, the less role-filler bindings are cleanly discri- minated. The strength of inhibitory competition between propositions in problem representation also determines which of them will enter WM and in what order; this is critical for mapping performance (Kubose, Holyoak & Hummel, 2002). The weaker the inhibition, the less relian- ce LISA has on the importance assigned to propositions, and the less accurate will be its eventual mapping. Thus, an important source of cognitive constraints in relational reasoning might come from the effectiveness of executive control. Executive control (EC) can be defined as a set of cognitive processes that, instead of representing mental states directly, influence and organize such states in the context of some internal goal. Recent theories assume that EC is an emergent process arising from the dynamic interaction of several independent, elementary control mechanisms (Braver, Gray, & Burgess, 2007; Engle & Kane, 2004). There is some evidence that these functions significantly correlate with abstract reasoning (see Chuderski & Necka, 2010, for a review).