The semantics-syntax interface: Learning grammatical categories and hierarchical syntactic structure through semantics.

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.

Correction to: Effects of grammar complexity on artificial grammar learning.

In the article "Effects of Grammar Complexity on Artificial Grammar Learning" by E. Van den Bos and F. Poletiek, published in Memory & Cognition, 2008, 36(6), 1122-1131, doi:10.

Intelligent problem-solvers externalize cognitive operations.

Humans are nature's most intelligent and prolific users of external props and aids (such as written texts, slide-rules and software packages). Here we introduce a method for investigating how people make active use of their task environment during problem-solving and apply this approach to the non-verbal Raven Advanced Progressive Matrices test for fluid intelligence. We designed a click-and-drag version of the Raven test in which participants could create different external spatial configurations while solving the puzzles.

Under What Conditions Can Recursion Be Learned? Effects of Starting Small in Artificial Grammar Learning of Center-Embedded Structure.

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, ; Newport, ). 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.

What baboons can (not) tell us about natural language grammars.

Rey et al. (2012) present data from a study with baboons that they interpret in support of the idea that center-embedded structures in human language have their origin in low level memory mechanisms and associative learning. Critically, the authors claim that the baboons showed a behavioral preference that is consistent with center-embedded sequences over other types of sequences.

Learning simple and complex artificial grammars in the presence of a semantic reference field: effects on performance and awareness.

This study investigated whether the negative effect of complexity on artificial grammar learning could be compensated by adding semantics. Participants were exposed to exemplars from a simple or a complex finite state grammar presented with or without a semantic reference field. As expected, performance on a grammaticality judgment test was higher for the simple grammar than for the complex grammar.

How semantic biases in simple adjacencies affect learning a complex structure with non-adjacencies in AGL: a statistical account.

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 A(n)B(n) grammar without semantics draw conflicting conclusions.

The impact of adjacent-dependencies and staged-input on the learnability of center-embedded hierarchical structures.

A theoretical debate in artificial grammar learning (AGL) regards the learnability of hierarchical structures. Recent studies using an A(n)B(n) grammar draw conflicting conclusions (Bahlmann & Friederici, 2006; De Vries, Monaghan, Knecht, & Zwitserlood, 2008). We argue that 2 conditions crucially affect learning A(n)B(n) structures: sufficient exposure to zero-level-of-embedding (0-LoE) exemplars and a staged-input.

Stimulus set size and statistical coverage of the grammar in artificial grammar learning.

Adults and children acquire knowledge of the structure of their environment on the basis of repeated exposure to samples of structured stimuli. In the study of inductive learning, a straightforward issue is how much sample information is needed to learn the structure. The present study distinguishes between two measures for the amount of information in the sample: set size and the extent to which the set of exemplars statistically covers the underlying structure.

Structural selection in implicit learning of artificial grammars.

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.

What is learned about fragments in artificial grammar learning? A transitional probabilities approach.

Learning local regularities in sequentially structured materials is typically assumed to be based on encoding of the frequencies of these regularities. We explore the view that transitional probabilities between elements of chunks, rather than frequencies of chunks, may be the primary factor in artificial grammar learning (AGL). The transitional probability model (TPM) that we propose is argued to provide an adaptive and parsimonious strategy for encoding local regularities in order to induce sequential structure from an input set of exemplars of the grammar.

Effects of grammar complexity on artificial grammar learning.

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.

Implicit learning of a recursive rule in an artificial grammar.

Participants performed an artificial grammar learning task, in which the standard finite state grammar (J. Verb. Learn.

How psychiatrists and judges assess the dangerousness of persons with mental illness: an 'expertise bias".

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).