Publications

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

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.