Strengthening the case for stimulus-specificity in artificial grammar learning: no evidence for abstract representations with extended exposure

Stability of individual differences in implicitly guided attention

Daily activities often occur in familiar environments, affording us an opportunity to learn. Laboratory studies have shown that people readily acquire an implicit spatial preference for locations that frequently contained a search target in the past. These studies, however, have focused on group characteristics, downplaying the significance of individual differences. In a pre-registered study, we examined the stability of individual differences in two variants of an implicit location probability learning (LPL) task. We tested the possibility that individual differences were stable in variants that shared the same search process, but not in variants involving different search processes. In Experiment 1, participants performed alternating blocks of T-among-Ls and 5-among-2s search tasks. Unbeknownst to them, the search target appeared disproportionately often in one region of space; the high-probability regions differed between the two tasks. LPL transferred between the two tasks. In addition, individuals who showed greater LPL in the T-task also did so in the 5-task and vice versa. In Experiment 2, participants searched for either a camouflaged-T against background noise or a well-segmented T among well-segmented Ls. These two tasks produced task-specific learning that did not transfer between tasks. Moreover, individual differences in learning did not correlate between tasks. Thus, LPL is associated with stable individual differences across variants, but only when the variants share common search processes.

Concurrent visual sequence learning

Many researchers in the field of implicit statistical learning agree that there does not exist one general implicit learning mechanism, but rather, that implicit learning takes place in highly specialized encapsulated modules. However, the exact representational content of these modules is still under debate. While there is ample evidence for a distinction between modalities (e.g., visual, auditory perception), the representational content of the modules might even be distinguished by features within the same modalities (e.g., location, color, and shape within the visual modality). In implicit sequence learning, there is evidence for the latter hypothesis, as a stimulus-color sequence can be learned concurrently with a stimulus-location sequence. Our aim was to test whether this also holds true for non-spatial features within the visual modality. This has been shown in artificial grammar learning, but not yet in implicit sequence learning. Hence, in Experiment 1, we replicated an artificial grammar learning experiment of Conway and Christiansen (2006) in which participants were supposed to learn color and shape grammars concurrently. In Experiment 2, we investigated concurrent learning of sequences with an implicit sequence learning paradigm: the serial reaction time task. Here, we found evidence for concurrent learning of two sequences, a color and shape sequence. Overall, the findings converge to the assumption that implicit learning might be based on features.

Does auditory deprivation impairs statistical learning in the auditory modality?

Early sensory deprivation allows assessing the extent of reorganisation of cognitive functions, well beyond sensory processing. As such, it is a good model to explore the links between sensory experience and cognitive functions. One of these functions, statistical learning - the ability to extract and use regularities present in the environment - is suspected to be impaired in prelingually deaf children with a cochlear implant. However, empirical evidence supporting this claim is very scarce and studies have reported contradictory results. This might be because previous studies have tested statistical learning only in the visual modality and did not make clear distinctions between multiple types of statistical regularities. To overcome these problems, we designed a modified serial reaction time task where cochlear implanted children and normal hearing children had to react to auditory sequences that embed multiple statistical regularities, namely transition probabilities of 0^th, 1^st or 2^nd order. We compared the reaction times of the children with the output of a simple computational model that learns transition probabilities. First, 6-12 years old children were able to learn 0^th and 1^st order transition probabilities but not 2^nd order ones. Second, there were no differences between cochlear implanted children and their normal hearing peers. These results indicate that auditory statistical learning is preserved in congenitally deaf children with cochlear implants. This suggests in turn that early auditory deprivation might not be crucially detrimental for the normal development of statistical learning.

Lack of Cross-Modal Effects in Dual-Modality Implicit Statistical Learning

A current controversy in the area of implicit statistical learning (ISL) is whether this process consists of a single, central mechanism or multiple modality-specific ones. To provide insight into this question, the current study involved three ISL experiments to explore whether multimodal input sources are processed separately in each modality or are integrated together across modalities. In Experiment 1, visual and auditory ISL were measured under unimodal conditions, with the results providing a baseline level of learning for subsequent experiments. Visual and auditory sequences were presented separately, and the underlying grammar used for both modalities was the same. In Experiment 2, visual and auditory sequences were presented simultaneously with each modality using the same artificial grammar to investigate whether redundant multisensory information would result in a facilitative effect (i.e., increased learning) compared to the baseline. In Experiment 3, visual and auditory sequences were again presented simultaneously but this time with each modality employing different artificial grammars to investigate whether an interference effect (i.e., decreased learning) would be observed compared to the baseline. Results showed that there was neither a facilitative learning effect in Experiment 2 nor an interference effect in Experiment 3. These findings suggest that participants were able to track simultaneously and independently two sets of sequential regularities under dual-modality conditions. These findings are consistent with the theories that posit the existence of multiple, modality-specific ISL mechanisms rather than a single central one.

Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, 6 months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, whereas both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli.

Domain generality versus modality specificity: the paradox of statistical learning

Statistical learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. However, recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal modality and stimulus specificity. Therefore, important questions are how and why a hypothesized domain-general learning mechanism systematically produces such effects. Here, we offer a theoretical framework according to which SL is not a unitary mechanism, but a set of domain-general computational principles that operate in different modalities and, therefore, are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.