How working memory capacity constrains the learning of relational concepts

How Working Memory Capacity Constrains the Learning of Relational Concepts Adam Chuderski (adam.chuderski@uj.edu.pl) Institute of Philosophy and Institute of Psychology, Jagiellonian University Grodzka 52, 31-044 Krakow, Poland Anna Chuderska (ania.chuderska@gmail.com) Institute of Psychology, Jagiellonian University Mickiewicza 3, 31-120 Krakow, Poland A key goal of cognitive science is to understand what re- lational concepts are, how they are acquired in childhood and adulthood, and how they are used in relational thinking. Consequently, the present paper aims to deal with one speci- fic problem in this domain: it investigates in what way the constraints of human cognitive architecture, particularly its working memory capacity (WMC), influence the learning of relational concepts (from here on, the process/ability referred to as relational learning). Abstract We investigated the way in which working memory (WM) constrains the learning of relational concepts – categories de- fined by the way objects are assigned to roles in the structure of an underlying relation, and not by objects’ intrinsic featu- res. By applying to a large sample a novel test of concept le- arning as well as the battery of WM tasks, we found that WM is a strong predictor of the scores on the test, but the WM- learning correlation decreases as the relational complexity of the to-be-learned concepts increases. Such results support those theoretical models of relational learning, which assume that learning of relational concepts (and relations, in general) consumes more WM resources than just the processing of relations which have already been learned. Working memory and relational learning Introduction The issue of relational thinking – the humankind’s ability to acquire, process, and effectively use mental representations of relations – has huge importance in cognitive science (Gentner & Kurtz, 2005; Halford, Wilson, & Phillips, 1998; Hummel & Holyoak, 2003). A relation can be described as an ordered list (a structure, a predicate) of well-defined roles and objects that fulfill them. The key aspect of relations consists of the fact that understanding of them as well as inferring from them depends primarily on the way objects are assigned to roles in the relation’s structure, and not necessarily on objects’ intrinsic features. Relational repre- sentations constitute the core of human complex cognition, including abstraction, reasoning, analogy making, creativity, and language (Halford, Wilson, & Phillips, 2010). The extension of an n-ary relation (where n is a number of roles in a relation; its arity) is a subset of Cartesian product of n sets, which includes all lists of objects (n-tuples) that can fulfill roles in that relation (i.e., an object from the first set in a tuple fulfills the first role, etc.; Halford et al., 1998). So, each relation can be treated as a relational category/ concept (Gentner & Kurtz, 2005). Unlike so-called entity categories, that is, categories formed by objects due to their perceptual or/and internal (e.g., genetic) similarity (e.g., natural kinds), relational concepts in the first place organize entity categories (or lower level relational categories), and so their exemplars may drastically vary featurally. For example, the instances of the relational concept of barrier will include: a wall, a river, a person, but also an insult, and loss of support (ibidem). Relational concepts constitute the main part of culture, science, and technology. Computational models of relational thinking (e.g., Chuder- ski, Andrelczyk, & Smolen, 2013; Doumas, Hummel, & Sandhofer, 2008; Halford et al., 2010; Hummel & Holyoak, 2003) as well as psychometric studies on reasoning and analogy making (e.g., Martinez et al., 2011) suggest that processing relations is grounded in working memory (WM). WM is a neurocognitive mechanism responsible for mainte- nance of a limited, but crucial for the current task/goal, amount of information, in an active and easily available state (Cowan, 2001). It thus allows for flexible manipulation of that information (Hummel & Holyoak, 2003; Oberauer, Sus, Wilhelm, & Sander, 2007), including binding of rela- tional roles and corresponding objects, which is a necessary process for a relational representation to be constructed. People can hold in their WM up to, on average, as few as three or four chunks of information (Cowan, 2001; Luck & Vogel, 1997) and, probably, the similar number of bindings (Chuderski et al., 2013; Oberauer et al., 2007), though these values vary among individuals (approx. from 1 to 6). This clearly corresponds to the fact that accuracy of processing relations sharply decreases with increasing arity of relations, and few participants can cope with relations more complex than quaternary ones (Halford et al., 2010). An interesting research question pertains to a problem of whether similar influence of WMC, as in abovementioned case of processing relations (e.g., during analogical mapping or inference), also takes place in case of relational learning, when people have to discover an (abstract) relation between related objects and construct a mental representation of the relational concept referring to that category. A widely used paradigm of relational concept learning was proposed by Shepard, Hovland, and Jenkins (1961). They presented to participants series of eight three-feature geometric figures, each of which could take one out of two values on each featural dimension (shape, size, color), and