Publications

Abstract

It has been suggested that external and/or internal limitations paradoxically may lead to superior learning, that is, the concepts of starting small and less is more (Elman, 1993; Newport, 1990). In this paper, we explore the type of incremental ordering during training that might help learning, and what mechanism explains this facilitation. We report four artificial grammar learning experiments with human participants. In Experiments 1a and 1b we found a beneficial effect of starting small using two types of simple recursive grammars: right‐branching and center‐embedding, with recursive embedded clauses in fixed positions and fixed length. This effect was replicated in Experiment 2 (N = 100). In Experiment 3 and 4, we used a more complex center‐embedded grammar with recursive loops in variable positions, producing strings of variable length. When participants were presented an incremental ordering of training stimuli, as in natural language, they were better able to generalize their knowledge of simple units to more complex units when the training input “grew” according to structural complexity, compared to when it “grew” according to string length. Overall, the results suggest that starting small confers an advantage for learning complex center‐embedded structures when the input is organized according to structural complexity.

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.