Chunking or not chunking? How do we find words in artificial language learning?

What is the nature of the representations acquired in implicit statistical learning? Recent results in the field of language learning have shown that adults and infants are able to find the words of an artificial language when exposed to a continuous auditory sequence consisting in a random ordering of these words. Such performance can only be based on processing the transitional probabilities between sequence elements. Two different kinds of mechanisms may account for these data: Participants may either parse the sequence into smaller chunks corresponding to the words of the artificial language, or they may become progressively sensitive to the actual values of the transitional probabilities between syllables. The two accounts are difficult to differentiate because they make similar predictions in comparable experimental settings. In this study, we present two experiments that aimed at contrasting these two theories. In these experiments, participants had to learn 2 sets of pseudo-linguistic regularities: Language 1 (L1) and Language 2 (L2) presented in the context of a serial reaction time task. L1 and L2 were either unrelated (none of the syllabic transitions of L1 were present in L2), or partly related (some of the intra-words transitions of L1 were used as inter-words transitions of L2). The two accounts make opposite predictions in these two settings. Our results indicate that the nature of the representations depends on the learning condition. When cues were presented to facilitate parsing of the sequence, participants learned the words of the artificial language. However, when no cues were provided, performance was strongly influenced by the employed transitional probabilities

Statistical Learning of Two Artificial Languages Presented Successively: How Conscious?

Statistical learning is assumed to occur automatically and implicitly, but little is known about the extent to which the representations acquired over training are available to conscious awareness. In this study, we focus on whether the knowledge acquired in a statistical learning situation is available to conscious control. Participants were first exposed to an artificial language presented auditorily. Immediately thereafter, they were exposed to a second artificial language. Both languages were composed of the same corpus of syllables and differed only in the transitional probabilities. We first determined that both languages were equally learnable (Experiment 1) and that participants could learn the two languages and differentiate between them (Experiment 2). Then, in Experiment 3, we used an adaptation of the Process-Dissociation Procedure (Jacoby, 1991) to explore whether participants could consciously manipulate the acquired knowledge. Results suggest that statistical information can be used to parse and differentiate between two different artificial languages, and that the resulting representations are available to conscious control

Cognitive control of sequential knowledge in 2-year-olds: evidence from an incidental sequence-learning and generation-task

Thirty-eight two-year-olds were trained under incidental instructions on a six element deterministic sequence of spatial locations. Following training, participants were informed of the presence of a sequence and asked to either reproduce or suppress the learned material. Children's production of the trained sequence was modulated by these instructions. When asked to suppress the trained sequence they were able to increase generation of paths that were not from the training sequence. Their performance was thus dependent on active suppresion of knowledge rather than a random generation strategy. This degree of control in two-year-olds stands in stark contrast to 3-year-olds' failure to control explicitly instructed rule-based knowledge (as measured the Dimensional Change Card Sort Task). We suggest that this is because the incidental nature of the learning enables the acquisition of a more procedural form of knowledge with which this age-group have more experience prior to the onset of fluent language

Implicit learning in a prediction task: Neither abstract nor based on exemplars

L’hypothèse selon laquelle l’apprentissage de règles peut avoir lieu implicitement a été remise en question car, dans plusieurs études, des connaissances abstraites se sont révélées inutiles pour accomplir les tâches utilisées pour mesurer l’apprentissage. Dans cette étude, nous étudions cette question à l’aide d’une tâche séquentielle de prédiction décrite initialement par Kushner, Reber, et Cleeremans (1991). La tâche consiste à prédire la position du cinquième élément d’une séquence parmi trois positions possibles. La réponse dépend, à l’insu des sujets, de la relation existant entre deux des quatre éléments de la séquence. Après l’apprentissage, la performance de transfert est comparée dans deux conditions. Dans la condition “Rule Deletion”, les séquences de transfert incluent de nouvelles combinaisons d’éléments pertinents et, dans la condition “Context Deletion”, de nouvelles combinaisons d’éléments non-pertinents. Sur base des résultats comportementaux et de simulations connexionnistes, nous confirmons l’influence importante de la similarité dans le transfert mais nous montrons également que les sujets ont appris implicitement à différencier les éléments pertinents et non-pertinents — un processus d’apprentissage qui ne peut être assimilé à de la simple mémorisation.The notion that rule-based learning can occur implicitly has been previously challenged based on the observation that abstract information was not always necessary to perform the tasks used to assess the acquired knowledge. Some authors suggest instead that implicit learning is based on memorization of training material. In this study, we address this issue in the context of a sequential prediction task initially described in Kushner, Cleeremans & Reber (1991). The task consists in predicting the location of the fifth element of a sequence amongst three possible locations. Unknown to the participants, the correct location can be predicted based on the relationship between two of the four preceding sequence elements. After training, we compared transfer performance in two conditions. In the “rule deletion” condition, transfer sequences contained new combinations of relevant elements and in the “context deletion” condition, new combinations of irrelevant elements. Based on behavioral and modeling results, we confirm the strong influence of similarity in transfer performance but, crucially, we also conclude that participants progressively learned implicitly to differentiate between relevant and irrelevant elements for the prediction task — a learning process that is not equivalent to rule abstraction but that is clearly a step away from rote memorization

Rapid detection of snakes modulates spatial orienting in infancy

Recent evidence for an evolved fear module in the brain comes from studies showing that adults, children and infants detect evolutionarily threatening stimuli such as snakes faster than non-threatening ones. A decisive argument for a threat detection system efficient early in life would come from data showing, in young infants, a functional threat-detection mechanism in terms of “what” and “where” visual pathways. The present study used a variant of Posner’s cuing paradigm, adapted to 7–11-month-olds. On each trial, a threat-irrelevant or a threat-relevant cue was presented (a flower or a snake, i.e., “what”). We measured how fast infants detected these cues and the extent to which they further influenced the spatial allocation of attention (“where”). In line with previous findings, we observed that infants oriented faster towards snake than flower cues. Importantly, a facilitation effect was found at the cued location for flowers but not for snakes, suggesting that these latter cues elicit a broadening of attention and arguing in favour of sophisticated “what–where” connections. These results strongly support the claim that humans have an early propensity to detect evolutionarily threat-relevant stimuli

The relationship between strategic control and conscious structural knowledge in artificial grammar learning

We address Jacoby’s (1991) proposal that strategic control over knowledge requires conscious awareness of that knowledge. In a two-grammar artificial grammar learning experiment all participants were trained on two grammars, consisting of a regularity in letter sequences, while two other dimensions (colours and fonts) varied randomly. Strategic control was measured as the ability to selectively apply the grammars during classification. For each classification, participants also made a combined judgement of (a) decision strategy and (b) relevant stimulus dimension. Strategic control was found for all types of decision strategy, including trials where participants claimed to lack conscious structural knowledge. However, strong evidence of strategic control only occurred when participants knew or guessed that the letter dimension was relevant, suggesting that strategic control might be associated with – or even causally requires – global awareness of the nature of the rules even though it does not require detailed knowledge of their content

Cerebral correlates of explicit sequence learning

peer reviewedUsing positron emission tomography (PET) and regional cerebral blood flow (rCBF) measurements, we investigated the cerebral correlates of consciousness in a sequence learning task through a novel application of the Process Dissociation Procedure, a behavioral paradigm that makes it possible to separately assess conscious and unconscious contributions to performance. Results show that the metabolic response in the anterior cingulate/mesial prefrontal cortex (ACC/MPFC) is exclusively and specifically correlated with the explicit component of performance during recollection of a learned sequence. This suggests a significant role for the ACC/MPFC in the explicit processing of sequential material. © 2003 Elsevier Science B.V. All rights reserved

Statistical learning leads to persistent memory: evidence for one-year consolidation

Statistical learning is a robust mechanism of the brain that enables the extraction of environmental patterns, which is crucial in perceptual and cognitive domains. However, the dynamical change of processes underlying long-term statistical memory formation has not been tested in an appropriately controlled design. Here we show that a memory trace acquired by statistical learning is resistant to inference as well as to forgetting after one year. Participants performed a statistical learning task and were retested one year later without further practice. The acquired statistical knowledge was resistant to interference, since after one year, participants showed similar memory performance on the previously practiced statistical structure after being tested with a new statistical structure. These results could be key to understand the stability of long-term statistical knowledge