How are natural symbol systems best understood? Traditional “symbolic” approaches seek to understand cognition by analogy to highly structured, prescriptive computer programs. Here, we describe some problems the traditional computational metaphor inevitably leads to, and a very different approach to computation (Ramscar, Yarlett, Dye, Denny, & Thorpe, 2010; Turing, 1950) that allows these problems to be avoided. The way we conceive of natural symbol systems depends to a large degree on the computational metaphors we use to understand them, and machine (...) learning suggests an understanding of symbolic thought that is very different to traditional views (Hummel, 2010). The empirical question then is: Which metaphor is best? (shrink)

Theories of relational concept acquisition based on structured intersection discovery predict that relational concepts with a probabilistic structure ought to be extremely difficult to learn. We report four experiments testing this prediction by investigating conditions hypothesized to facilitate the learning of such categories. Experiment 1 showed that changing the task from a category-learning task to choosing the “winning” object in each stimulus greatly facilitated participants' ability to learn probabilistic relational categories. Experiments 2 and 3 further investigated the mechanisms underlying this (...) “who's winning” effect. Experiment 4 replicated and generalized the “who's winning” effect with more natural stimuli. Together, our findings suggest that people learn relational concepts by a process of intersection discovery akin to schema induction, and that any task that encourages people to discover a higher order relation that remains invariant over members of a category will facilitate the learning of putatively probabilistic relational concepts. (shrink)

Theories of embodied cognition assume that concepts are grounded in non-linguistic, sensorimotor experience. In support of this assumption, previous studies have shown that upwards response movements are faster than downwards movements after participants have been presented with words whose referents are typically located in the upper vertical space. This is taken as evidence that processing these words reactivates sensorimotor experiential traces. This congruency effect was also found for novel words, after participants learned these words as labels for novel objects that (...) they encountered either in their upper or lower visual field. While this indicates that direct experience with a word's referent is sufficient to evoke said congruency effects, the present study investigates whether this direct experience is also a necessary condition. To this end, we conducted five experiments in which participants learned novel words from purely linguistic input: Novel words were presented in pairs with real up- or down-words ; they were presented in natural sentences where they replaced these real words ; they were presented as new labels for these real words ; and they were presented as labels for novel combined concepts based on these real words. In all five experiments, we did not find any congruency effects elicited by the novel words; however, participants were always able to make correct explicit judgements about the vertical dimension associated to the novel words. These results suggest that direct experience is necessary for reactivating experiential traces, but this reactivation is not a necessary condition for understanding the corresponding aspects of word meaning. (shrink)