Publications

Abstract

Language is infinitely productive because syntax defines dependencies between grammatical categories of words and constituents, so there is interchangeability of these words and constituents within syntactic structures. Previous laboratory-based studies of language learning have shown that complex language structures like hierarchical center embeddings (HCE) are very hard to learn, but these studies tend to simplify the language learning task, omitting semantics and focusing either on learning dependencies between individual words or on acquiring the category membership of those words. We tested whether categories of words and dependencies between these categories and between constituents, could be learned simultaneously in an artificial language with HCE’s, when accompanied by scenes illustrating the sentence’s intended meaning. Across four experiments, we showed that participants were able to learn the HCE language varying words across categories and category-dependencies, and constituents across constituents-dependencies. They also were able to generalize the learned structure to novel sentences and novel scenes that they had not previously experienced. This simultaneous learning resulting in a productive complex language system, may be a consequence of grounding complex syntax acquisition in semantics.

Abstract

In the contextual cueing paradigm, Endo and Takeda (in Percept Psychophys 66:293–302, 2004) provided evidence that implicit learning involves selection of the aspect of a structure that is most useful to one’s task. The present study attempted to replicate this finding in artificial grammar learning to investigate whether or not implicit learning commonly involves such a selection. Participants in Experiment 1 were presented with an induction task that could be facilitated by several characteristics of the exemplars. For some participants, those characteristics included a perfectly predictive feature. The results suggested that the aspect of the structure that was most useful to the induction task was selected and learned implicitly. Experiment 2 provided evidence that, although salience affected participants’ awareness of the perfectly predictive feature, selection for implicit learning was mainly based on usefulness.

Abstract

In this paper hypothesis‐testing behaviour is compared to risk‐taking behaviour. It is proposed that choosing a suitable test for a given hypothesis requires making a preposterior analysis of two aspects of such a test: the probability of obtaining supporting evidence and the evidential value of this evidence. This consideration resembles the one a gambler makes when choosing among bets, each having a probability of winning and an amount to be won. A confirmatory testing strategy can be defined within this framework as a strategy directed at maximizing either the probability or the value of a confirming outcome. Previous theories on testing behaviour have focused on the human tendency to maximize the probability of a confirming outcome. In this paper, two experiments are presented in which participants tend to maximize the confirming value of the test outcome. Motivational factors enhance this tendency dependent on the context of the testing situation. Both this result and the framework are discussed in relation to other studies in the field of testing behaviour.