LAZARTIGUES , Laura

Sequential processing is a fundamental cognitive mechanism allowing extracting regularities to facilitate learning. The literature in cognitive psychology shows that the extraction of these regularities can operate through statistical learning. Most of the previous work has focused on first-order transitional probability (TP), which corresponds...

The ability to learn transitional probabilities (TPs), adjacent and non-adjacent dependencies and frequency is central to language processing. Current evidence indicates that both frequency and transitional probability impact the memorization of sequences, but it is not sure yet which type of information prevails. Also, adjacent and...

The ability to learn transitional probabilities (TPs) and frequency is central to language processing. Current evidence indicates that both frequency and transitional probability are involved in the memorization of sequences, but the questions of which prevails and why it would prevail in statistical learning remain unclear. The present study...

A pervasive issue in statistical learning has been to determine the parameters of regularity extraction. Our hypothesis was that the extraction of transitional probabilities can prevail over frequency if the task involves prediction. Participants were exposed to four repeated sequences of three stimuli (XYZ) with each stimulus corresponding to...

The order of stimuli within sequences and the transitional probabilities (TPs) these orders generate are central information in language acquisition, but less is known about how this type of information is extracted by general learning mechanisms. The present study focused on the statistical learning of second-order TPs (i.e., only the...

The ability to learn sequences depends on different factors governing sequence structure, such as transitional probability (TP, probability of a stimulus given a previous stimulus), adjacent or nonadjacent dependency, and frequency. Current evidence indicates that adjacent and nonadjacent pairs are not equally learnable; the same applies to...

A typical cognitive mechanism involved in language comprehension relates to the processing of sequences of stimuli. To learn sequences, statistical learning enables the computation of transitional probabilities (TP). A TP corresponds to the probability to encounter successive events in a sequence. While forward TP (FTP: probability to encounter...

Working memory (WM) is known to be limited in capacity, but mechanisms based on compression of information could contribute to the storage process. It has been shown that chunking governed by compression could be one of these mechanisms. The present study investigated how chunks can be formed using patterns to be discovered on the spot, that...

The order of stimuli within sequences and the transitional probabilities (TPs) it generates are central information in sequence processing. However, less is known about what type of information and how it is extracted by general learning mechanisms. The present study focused on statistical learning of second-order TPs. Second-order TPs are...

The present study manipulates first and second order transitional probabilities during the statistical learning of short sequences. Participants were exposed to four sequences of three stimuli (ABC) repeated during the task, with each stimulus corresponding to the position of a red dot on a touchscreen. Participants were required to touch the...

The ability to learn adjacent and non-adjacent pairs is central in language processing. However, current evidence indicates that adjacent and non-adjacent pairs are not equally learnable. The present study investigated the role of transitional probabilities during the learning of adjacent and non-adjacent pairs appearing in non-linguistic short...

Data compression in memory is a cognitive process allowing participants to cope with complexity to reduce information load. However, previous studies have not yet considered the hypothesis that this process could also lead to over-simplifying information due to haphazard amplification of the compression process itself. For instance, we could...

The extraction of cooccurrences between two events, A and B, is a central learning mechanism shared by all species capable of associative learning. Formally, the cooccurrence of events A and B appearing in a sequence is measured by the transitional probability (TP) between these events, and it corresponds to the probability of the second...