Modality transfer of acquired structural regularities: A preference for an acoustic route (original) (raw)

Structural selection in implicit learning of artificial grammars

Psychological Research Psychologische Forschung, 2010

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 Wnding in artiWcial 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 aVected participants' awareness of the perfectly predictive feature, selection for implicit learning was mainly based on usefulness.

Instruction effects in implicit artificial grammar learning: A preference for grammaticality

Brain Research, 2008

Human implicit learning can be investigated with implicit artificial grammar learning, a paradigm that has been proposed as a simple model for aspects of natural language acquisition. In the present study we compared the typical yes-no grammaticality classification, with yes-no preference classification. In the case of preference instruction no reference to the underlying generative mechanism (i.e., grammar) is needed and the subjects are therefore completely uninformed about an underlying structure in the acquisition material. In experiment 1, subjects engaged in a short-term memory task using only grammatical strings without performance feedback for 5 days. As a result of the 5 acquisition days, classification performance was independent of instruction type and both the preference and the grammaticality group acquired relevant knowledge of the underlying generative mechanism to a similar degree. Changing the grammatical stings to random strings in the acquisition material (experiment 2) resulted in classification being driven by local substring familiarity. Contrasting repeated vs. non-repeated preference classification (experiment 3) showed that the effect of local substring familiarity decreases with repeated classification. This was not the case for repeated grammaticality classifications. We conclude that classification performance is largely independent of instruction type and that forcedchoice preference classification is equivalent to the typical grammaticality classification. ava i l a b l e a t w w w. s c i e n c e d i r e c t . c o m w w w. e l s ev i e r. c o m / l o c a t e / b r a i n r e s

Syntactic classification of acquired structural regularities

Proceedings of the Cognitive Science …, 2005

In this paper we investigate the neural correlates of syntactic classification of an acquired grammatical sequence structure in an event-related FMRI study. During acquisition, participants were engaged in an implicit short-term memory task without performance feedback. We manipulated the statistical frequency-based and rule-based characteristics of the classification stimuli independently in order to investigate their role in artificial grammar acquisition. The participants performed reliably above chance on the classification task. We observed a partly overlapping corticostriatal processing network activated by both manipulations including inferior prefrontal, cingulate, inferior parietal regions, and the caudate nucleus. More specifically, the left inferior frontal BA 45 and the caudate nucleus were sensitive to syntactic violations and endorsement, respectively. In contrast, these structures were insensitive to the frequency-based manipulation.

Implicit AND explicit language learning

Studies in Bilingualism, 2015

Learning symbols and their arrangement in language involves learning associations across and within modalities. Research on implicit learning and chunking within modalities (e.g., N. C. Ellis, 2002) has identified how language users are sensitive to the frequency of language forms and their sequential probabilities at all levels of granularity from phoneme to phrase. This knowledge allows efficient language processing and underpins acquisition by syntactic bootstrapping. Research on explicit learning (e.g., N. C. Ellis, 2005) has shown how conscious processing promotes the acquisition of novel explicit cross-modal form-meaning associations. These breathe meaning into the processing of language form and they underpin acquisition by semantic bootstrapping. This is particularly important in establishing novel processing routines in L2 acquisition. These representations are also then available as units of implicit learning in subsequent processing.

From surface to structure: exploring implicit learning of Lindenmayer grammars

From surface to structure: exploring implicit learning of Lindenmayer grammars, 2021

Artificial grammars (systems of rules that produce sequences of symbols) are widely used in assessing implicit learning in humans. In this Artificial Grammar Learning (AGL) study, we investigate the relation between linear and hierarchical implicit learning in adults.

Constraints on Implicit Learning of Grammatical Form-Meaning Connections.

Although there is good evidence for implicit learning of associations between forms, little work has investigated implicit learning of form-meaning connections, and the findings are somewhat contradictory. Two experiments were carried out using a novel reaction time methodology to investigate implicit learning of grammatical form-meaning connections. Participants learned four novel articles but were not told about a critical semantic factor that determines agreement with the accompanying noun. Their task was to indicate as quickly as possible which of two pictures was being referred to by an article-noun combination. The measure of learning was whether response times would slow down when the agreement rule was violated (i.e., when the wrong article was used for the picture being referred to). Experiment 1 revealed such an effect when articles correlated with noun animacy, even for participants with no reported awareness of this regularity. In Experiment 2 no such effect was obtained when the regularity concerned the relative size of two objects. It is concluded that grammatical form-meaning connections may be learned implicitly, but learning is constrained by the nature of the meaning involved. It is argued that concepts are differentially available to implicit language learning processes.

Rules vs. statistics in implicit learning of biconditional grammars

Connectionist models of …, 2001

A significant part of everyday learning occurs incidentally — a process typically described as implicit learning. A central issue in this domain and others, such as language acquisition, is the extent to which performance depends on the acquisition and deployment of abstract rules. Shanks and colleagues have suggested (1) that discrimination between grammatical and ungrammatical instances of a biconditional grammar requires the acquisition and use of abstract rules, and (2) that training conditions — in particular whether instructions orient participants to identify the relevant rules or not — strongly influence the extent to which such rules will be learned. In this paper, we show (1) that a Simple Recurrent Network can in fact, under some conditions, learn a biconditional grammar, (2) that training conditions indeed influence learning in simple auto-associators networks and (3) that such networks can likewise learn about biconditional grammars, albeit to a lesser extent than human participants. These findings suggest that mastering biconditional grammars does not require the acquisition of abstract rules to the extent implied by Shanks and colleagues, and that performance on such material may in fact be based, at least in part, on simple associative learning mechanisms.

Modality effects in implicit artificial grammar learning: An EEG study

Brain research, 2018

Recently, it has been proposed that sequence learning engages a combination of modality-specific operating networks and modality-independent computational principles. In the present study, we compared the behavioural and EEG outcomes of implicit artificial grammar learning in the visual vs. auditory modality. We controlled for the influence of surface characteristics of sequences (Associative Chunk Strength), thus focusing on the strictly structural aspects of sequence learning, and we adapted the paradigms to compensate for known frailties of the visual modality compared to audition (temporal presentation, fast presentation rate). The behavioural outcomes were similar across modalities. Favouring the idea of modality-specificity, ERPs in response to grammar violations differed in topography and latency (earlier and more anterior component in the visual modality), and ERPs in response to surface features emerged only in the auditory modality. In favour of modality-independence, we o...