The order of stimuli matters when learning second-order transitional probabilities.

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 involved when only the combination of two stimuli predicts the third. In a first experiment, TPs depended crucially on the order of presentation of a pair A - B , which led to different predictions depending on the order of the stimuli (i.e., ABC vs. BAF). Eight visuomotor sequences governed by second-order TPs were used and response times (RTs) were recorded for each transition. The task included a learning phase followed by a switch phase during which the second-order TP were reversed (e.g., the sequences ABC and BAF became respectively ABF and BAC). A decrease of RTs between the second and the third stimulus during the learning phase and an increase of RTs during the switch phase suggested that variations of orders within second-order TPs could be learned. Further analyses, however, indicated that such learning was difficult for most participants. A second experiment showed that the difficulty of learning was not solely due to the difficulty to pick up the effect of order of presentation, but that learning second-order transitional probabilities in addition to order would be the main obstacle. These experiments suggest that statistical learning is capable of learning complex associations, even if this remains a challenge for human cognition.

Learning Higher-Order Transitional Probabilities in Nonhuman Primates.

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 stimulus given the first (i.e., p(B|A)). In the present study, nonhuman primates (Guinea baboons, Papio papio) were exposed to a serial version of the XOR (i.e., exclusive-OR), in which they had to process sequences of three stimuli: A, B, and C. In this manipulation, first-order TPs (i.e., AB and BC) were uninformative due to their transitional probabilities being equal to .5 (i.e., p(B|A) = p(C|B) = .5), while second-order TPs were fully predictive of the upcoming stimulus (i.e., p(C|AB) = 1). In Experiment 1, we found that baboons were able to learn second-order TPs, while no learning occurred on first-order TPs. In Experiment 2, this pattern of results was replicated, and a final test ruled out an alternative interpretation in terms of proximity to the reward. These results indicate that a nonhuman primate species can learn a nonlinearly separable problem such as the XOR. They also provide fine-grained empirical data to test models of statistical learning on the interaction between the learning of different orders of TPs. Recent bioinspired models of associative learning are also introduced as promising alternatives to the modeling of statistical learning mechanisms.

Probability, Dependency, and Frequency Are Not All Equally Involved in Statistical Learning.

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 second-order and first-order TPs and to the frequency of the sequences. However, the relative importance of these factors and interactive effects on learning remain poorly understood. The first experiment tested the effects of TPs and dependency separately on the learning of nonlinguistic visual sequences, and the second experiment used the factors of the first experiment and added a frequency factor to test their interactive effects with verbal sequences of stimuli (pseudo-words). The results of both experiments showed higher performance during online learning for first-order TPs in adjacent pairs. Moreover, Experiment 2 indicated poorer performance during offline recall for nonadjacent dependencies and low-frequency sequences. We discuss the results that different factors are not used equally in prediction and memorization.

Benefits and pitfalls of data compression in visual working memory.

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 expect that the over-regularized features of a visual scene could produce false recognition of patterns, not because of storage capacity limits but because of an errant compression process. To prompt memory compression in our participants, we used multielement visual displays for which the underlying information varied in compressibility. The compressibility of our material could vary depending on the number of common features between the multi-dimensional objects in the displays. We measured both accuracy and response times by probing memory representations with probes that we hypothesized could modify the participants' representations. We confirm that more compressible information facilitates performance, but a more novel finding is that compression can produce both typical memory errors and lengthened response times. Our findings provide clearer evidence of the forms of compression that participants carry out.

Statistical learning of unbalanced exclusive-or temporal sequences in humans.

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 position of a red dot on a touch screen that participants were required to touch sequentially. The temporal and spatial structure of the positions corresponded to a serial version of the exclusive-or (XOR) that allowed testing of the respective effect of frequency and first- and second-order transitional probabilities. The XOR allowed the first-order transitional probability to vary while being not completely related to frequency and to vary while the second-order transitional probability was fixed (p(Z|X, Y) = 1). The findings show that first-order transitional probability prevails over frequency to predict the second stimulus from the first and that it also influences the prediction of the third item despite the presence of second-order transitional probability that could have offered a certain prediction of the third item. These results are particularly informative in light of statistical learning models.