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Poletiek, F. H., & Lai, J. (2012). How semantic biases in simple adjacencies affect learning a complex structure with non-adjacencies in AGL: A statistical account. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 367, 2046 -2054. doi:10.1098/rstb.2012.0100.
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. -
Poletiek, F. H. (2002). [Review of the book Adaptive thinking: Rationality in the real world by G. Gigerenzer]. Acta Psychologica, 111(3), 351-354. doi:10.1016/S0001-6918(02)00046-X.
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Poletiek, F. H. (2002). How psychiatrists and judges assess the dangerousness of persons with mental illness: An 'expertise bias'. Behavioral Sciences & the Law, 20(1-2), 19-29. doi:10.1002/bsl.468.
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. -
Poletiek, F. H. (2002). Implicit learning of a recursive rule in an artificial grammar. Acta Psychologica, 111(3), 323-335. doi:10.1016/S0001-6918(02)00057-4.
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.
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