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. 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.     

Hypothesis testing as risk behaviour with regard to beliefs

In this paper hypothesis‐testing behaviour is compared to risk‐taking behaviour. It is proposed that choosing a suitable test for a given hypothesis requires making a preposterior analysis of two aspects of such a test: the probability of obtaining supporting evidence and the evidential value of this evidence. This consideration resembles the one a gambler makes when choosing among bets, each having a probability of winning and an amount to be won. A confirmatory testing strategy can be defined within this framework as a strategy directed at maximizing either the probability or the value of a confirming outcome. Previous theories on testing behaviour have focused on the human tendency to maximize the probability of a confirming outcome. In this paper, two experiments are presented in which participants tend to maximize the confirming value of the test outcome. Motivational factors enhance this tendency dependent on the context of the testing situation. Both this result and the framework are discussed in relation to other studies in the field of testing behaviour. Copyright © 2000 John Wiley & Sons, Ltd.     

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). 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.     

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. 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.     

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. 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.     

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. In an artificial grammar learning experiment, learning was affected by the sample’s statistical coverage of the grammar, but not by its mere size. Our result suggests an alternative explanation of the set size effects on learning found in previous studies (McAndrews & Moscovitch, 1985; Meulemans & Van der Linden, 1997), because, as we argue, set size was confounded with statistical coverage in these studies. nt]mis|This research was supported by a grant from the Netherlands Organization for Scientific Research. We thank Jarry Porsius for his help with the data analyses.     

Intentional artificial grammar learning: When does it work?

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.     

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 AⁿBⁿ grammar draw conflicting conclusions (Bahlmann and Friederici, 2006 and [De Vries et al., 2008] ). We argue that 2 conditions crucially affect learning AⁿBⁿ structures: sufficient exposure to zero-level-of-embedding (0-LoE) exemplars and a staged-input. In 2 AGL experiments, learning was observed only when the training set was staged and contained 0-LoE exemplars. Our results might help understanding how natural complex structures are learned from exemplars.     

Paradoxes of Falsification

This paper deals with the concept of falsification in hypothesis testing research. A theoretical analysis of assumptions about falsifying behaviour and hypothesis-falsifying observations is presented, with two experimental studies. Both the theoretical analysis and the experimental results point to a number of paradoxes underlying the normative principle of falsification in cognitive psychology. First, subjects experience the falsificatory testing strategy as an impossible strategy to conduct. Obtaining falsifying results is a consequence of the quality of the hypothesis rather than of specific testing behaviour (Experiment 1 and Experiment 2). Second, under some conditions falsifying results impede rather than facilitate discovery (Experiment 2). Confirmatory testing and falsificatory testing, which have been the crucial concepts in the study of hypothesis-testing behaviour, may actually be questionable approaches to testing behaviour. The theoretical analysis is related to the standard analyses of Popper (1963) and Klayman and Ha (1987). The empirical results are discussed in relation to previous studies on falsificatory testing behaviour.