Publications

Abstract

A major theoretical debate in language acquisition research regards the learnability of hierarchical structures. The artificial grammar learning methodology is increasingly influential in approaching this question. Studies using an artificial centre-embedded AnBn grammar without semantics draw conflicting conclusions. This study investigates the facilitating effect of distributional biases in simple AB adjacencies in the input sample—caused in natural languages, among others, by semantic biases—on learning a centre-embedded structure. A mathematical simulation of the linguistic input and the learning, comparing various distributional biases in AB pairs, suggests that strong distributional biases might help us to grasp the complex AnBn hierarchical structure in a later stage. This theoretical investigation might contribute to our understanding of how distributional features of the input—including those caused by semantic variation—help learning complex structures in natural languages.

Abstract

The present study identified two aspects of complexity that have been manipulated in the implicit learning literature and investigated how they affect implicit and explicit learning of artificial grammars. Ten finite state grammars were used to vary complexity. The results indicated that dependency length is more relevant to the complexity of a structure than is the number of associations that have to be learned. Although implicit learning led to better performance on a grammaticality judgment test than did explicit learning, it was negatively affected by increasing complexity: Performance decreased as there was an increase in the number of previous letters that had to be taken into account to determine whether or not the next letter was a grammatical continuation. In particular, the results suggested that implicit learning of higher order dependencies is hampered by the presence of longer dependencies. Knowledge of first-order dependencies was acquired regardless of complexity and learning mode.

Abstract

Actively searching for the rules of an artificial grammar has often been shown to produce no more knowledge than memorising exemplars without knowing that they have been generated by a grammar. The present study investigated whether this ineffectiveness of intentional learning could be overcome by removing dual task demands and providing participants with more specific instructions. The results only showed a positive effect of learning intentionally for participants specifically instructed to find out which letters are allowed to follow each other. These participants were also unaffected by a salient feature. In contrast, for participants who did not know what kind of structure to expect, intentional learning was not more effective than incidental learning and knowledge acquisition was guided by salience.

Abstract

When assessing dangerousness of mentally ill persons with the objective of making a decision on civil commitment, medical and legal experts use information typically belonging to their professional frame of reference. This is investigated in two studies of the commitment decision. It is hypothesized that an ‘expertise bias’ may explain differences between the medical and the legal expert in defining the dangerousness concept (study 1), and in assessing the seriousness of the danger (study 2). Judges define dangerousness more often as harming others, whereas psychiatrists more often include harm to self in the definition. In assessing the seriousness of the danger, experts tend to be more tolerant with regard to false negatives, as the type of behavior is more familiar to them. The theoretical and practical implications of the results are discussed.

Abstract

Participants performed an artificial grammar learning task, in which the standard finite
state grammar (J. Verb. Learn. Verb. Behavior 6 (1967) 855) was extended with a recursive
rule generating self-embedded sequences. We studied the learnability of such a rule in two experiments.
The results verify the general hypothesis that recursivity can be learned in an artificial
grammar learning task. However this learning seems to be rather based on recognising
chunks than on abstract rule induction. First, performance was better for strings with more
than one level of self-embedding in the sequence, uncovering more clearly the self-embedding
pattern. Second, the infinite repeatability of the recursive rule application was not spontaneously
induced from the training, but it was when an additional cue about this possibility was
given. Finally, participants were able to verbalise their knowledge of the fragments making up
the sequences––especially in the crucial front and back positions––, whereas knowledge of the
underlying structure, to the extent it was acquired, was not articulatable. The results are discussed
in relation to previous studies on the implicit learnability of complex and abstract rules.