Offline consolidation in implicit sequence learning

The goal of this study was to investigate offline memory consolidation with regard to general motor skill learning and implicit sequence-specific learning. We trained young adults on a serial reaction time task with a retention interval of either 24 hours (Experiment 1) or 1 week (Experiment 2) between two sessions. We manipulated sequence complexity (deterministic vs. probabilistic) and motor responses (unimanual or vs. bimanual). We found no evidence of offline memory consolidation for sequencespecific learning with either interval (in the sense of no deterioration over the interval but no further improvement either). However, we did find evidence of offline enhancement of general motor skill learning with both intervals, independent of kind of sequence or kind of response. These results suggest that general motor skill learning, but not sequence-specific learning, appears to be enhanced during offline intervals in implicit sequence learning.

Beyond statistical learning? Continuous sequence repetition matters in implicit sequence learning

Typically, statistical learning is investigated by testing the acquisition of specific items or forming general rules. As implicit sequence learning also involves the extraction of regularities from the environment, it can also be considered as an instance of statistical learning. In the present study, a Serial Reaction Time Task was used to test whether the continuous versus interleaved repetition of a sequence affects implicit learning despite the equal exposure to the sequences. The results revealed a sequence learning advantage for the continuous repetition condition compared to the interleaved condition. This suggests that by repetition, additional sequence information was extracted although the exposure to the sequences was identical as in the interleaved condition. The results are discussed in terms of similarities and potential differences between typical statistical learning paradigms and sequence learning.

Multimodal sequence learning

While sequence learning research models complex phenomena, previous studies have mostly focused on unimodal sequences. The goal of the current experiment is to put implicit sequence learning into a multimodal context: to test whether it can operate across different modalities. We used the Task Sequence Learning paradigm to test whether sequence learning varies across modalities, and whether participants are able to learn multimodal sequences. Our results show that implicit sequence learning is very similar regardless of the source modality. However, the presence of correlated task and response sequences was required for learning to take place. The experiment provides new evidence for implicit sequence learning of abstract conceptual representations. In general, the results suggest that correlated sequences are necessary for implicit sequence learning to occur. Moreover, they show that elements from different modalities can be automatically integrated into one unitary multimodal sequence.

How Transcranial Direct Current Stimulation Can Modulate Implicit Motor Sequence Learning and Consolidation: A Brief Review

The purpose of this review is to investigate how transcranial direct current stimulation(tDCS)can modulate implicit motor sequence learning and consolidation. So far, most of the studies have focused on the modulating effect of tDCS for explicit motor learning. Here, we focus explicitly on implicit motor sequence learning and consolidation in order to improve our understanding about the potential of tDCS to affect this kind of unconscious learning. Specifically, we concentrate on studies with the serial reaction time task (SRTT), the classical paradigm for measuring implicit motor sequence learning. The influence of tDCS has been investigated for the primary motor cortex, the premotor cortex, the prefrontal cortex, and the cerebellum. The results indicate that tDCS above the primary motor cortex gives raise to the most consistent modulating effects for both implicit motor sequence learning and consolidation.

Implicit probabilistic sequence learning: Correlated streams and sequence length matter

We investigated whether a pure perceptual stream is sufficient for probabilistic sequence learning to occur within a single session or whether correlated streams are necessary, whether learning is affected by the transition probability between sequence elements, and how the sequence length influences learning. In each of three experiments, we used six horizontally arranged stimulus displays which consisted of randomly ordered bigrams xo and ox. The probability of the next possible target location out of two was either .50/.50 or .75/.25 and was marked by an underline. In Experiment 1, a left vs. right key response was required for the x of a marked bigram in the pure perceptual learning condition and a response key press corresponding to the marked bigram location (out of 6) was required in the correlated streams condition (i.e., the ring, middle, or index finger of the left and right hand, respectively). The same probabilistic 3-element sequence was used in both conditions. Learning occurred only in the correlated streams condition. In Experiment 2, we investigated whether sequence length affected learning correlated sequences by contrasting the 3-elements sequence with a 6-elements sequence. Significant sequence learning occurred in all conditions. In Experiment 3, we removed a potential confound, that is, the sequence of hand changes. Under these conditions, learning occurred for the 3-element sequence only and transition probability did not affect the amount of learning. Together, these results indicate that correlated streams are necessary for probabilistic sequence learning within a single session and that sequence length can reduce the chances for learning to occur.

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets.