Retrieval of a well-established skill is resistant to distraction: Evidence from an implicit probabilistic sequence learning task

Do they know or just do it? Investigating implicit and explicit sequence learning by capuchin monkeys, human adults and children

In humans, it is now established that sequential regularities can be learned implicitly (i.e. without acquiring conscious knowledge) or explicitly (with acquisition of conscious knowledge). Is this dual-processing capability also the case for non-human primates? In this study, we designed a non-verbal task to probe implicit and explicit sequence learning in capuchin monkeys (Sapajus sp., n = 12), human adults (n = 12), and children from 5 to 10 years old (n = 64). After learning spatial sequences on a touchscreen, participants' conscious access to the sequences was probed with a forced choice sequence completion test. All performed above chance level in this test, without being instructed or trained to do so. However, only human adults who reported the presence of regularities performed at ceiling level. We suggest future directions that could build on our findings to disentangle implicit and explicit learning in monkeys and children.

Co-actors represent each other's task regularity through social statistical learning

Numerous joint action studies have demonstrated that certain low-level aspects (e.g., stimuli and responses) of a co-actor's task can be automatically and implicitly represented by us as actors, biasing our own task performance in a joint action setup. However, it remains unclear whether individuals also represent more abstract, high-level aspects of a co-actor's task, such as regularity. In the first five experiments, participants participated alongside their co-actors and responded to a mixed shape sequence generated by randomly interleaving two fixed order sequences of shapes in both the pre- and post-test sessions. But different intermediate practice sessions were undergone by participants across experiments. When practicing their own fixed order sequences in a mixed shape sequence, either together with another person (Experiment 1) or alone but informed that their partner was performing the same practice task in a different room (Experiment 4), participants exhibited a learning effect on their co-actors' practiced sequences. This indirect learning effect was absent when one of the co-actors did not participate due to either being removed from the practice (Experiment 2) or sitting still without offering responses (Experiment 3), as well as when the two co-actors practiced together but responded to two distinct properties of stimuli (e.g., colour and shape, respectively), with one having regularity and the other not. Finally, participants exhibited comparable direct learning effects on their own practiced sequences for Experiments 1-5 as when performing the pre-test, practice, and post-test sessions alone for Experiment 6. These results demonstrate that, when practicing together, or even when believing that they are acting together with a partner, co-actors do represent the task regularity of one another through social statistical learning and transfer this learned regularity to subsequent task performances. The present study extends our understanding of co-representation in the joint action context in terms of the more abstract and high-level task features people co-represent, such as a co-actor's task regularity.

Tracking human skill learning with a hierarchical Bayesian sequence model

Humans can implicitly learn complex perceptuo-motor skills over the course of large numbers of trials. This likely depends on our becoming better able to take advantage of ever richer and temporally deeper predictive relationships in the environment. Here, we offer a novel characterization of this process, fitting a non-parametric, hierarchical Bayesian sequence model to the reaction times of human participants' responses over ten sessions, each comprising thousands of trials, in a serial reaction time task involving higher-order dependencies. The model, adapted from the domain of language, forgetfully updates trial-by-trial, and seamlessly combines predictive information from shorter and longer windows onto past events, weighing the windows proportionally to their predictive power. As the model implies a posterior over window depths, we were able to determine how, and how many, previous sequence elements influenced individual participants' internal predictions, and how this changed with practice. Already in the first session, the model showed that participants had begun to rely on two previous elements (i.e., trigrams), thereby successfully adapting to the most prominent higher-order structure in the task. The extent to which local statistical fluctuations in trigram frequency influenced participants' responses waned over subsequent sessions, as participants forgot the trigrams less and evidenced skilled performance. By the eighth session, a subset of participants shifted their prior further to consider a context deeper than two previous elements. Finally, participants showed resistance to interference and slow forgetting of the old sequence when it was changed in the final sessions. Model parameters for individual participants covaried appropriately with independent measures of working memory and error characteristics. In sum, the model offers the first principled account of the adaptive complexity and nuanced dynamics of humans' internal sequence representations during long-term implicit skill learning.

The Effects of Cooperative and Competitive Situations on Statistical Learning

Devising cooperative or competitive situations is an important teaching strategy in educational practices. Nevertheless, there is still controversy regarding which situation is better for learning. This study was conducted to explore the effects of cooperative and competitive situations on statistical learning, through the alternating serial reaction time (ASRT) task. Individual cooperative and competitive situations were devised in this study, in which individual situation served as the control condition. Ninety recruited participants were randomly assigned to a cooperative, competitive, or individual group to perform the ASRT task. For general learning, cooperative and competitive situations could indeed make learners respond faster, and there was no significant difference in the RT between the cooperative and competitive groups. Moreover, statistical learning was observed in all three groups. An additional analysis of the early stage of the experiment showed that the learning effect of the competitive group was greater than those of the cooperative and individual groups, in terms of statistical learning. However, the final learning effect was not significantly different among the three groups. Overall, the cooperative and competitive situations had a positive impact on learning and enabled the students to acquire approximately the same learning effect in a shorter time period, compared with the individual situation. Specifically, the competitive situation accelerated the statistical learning process but not the general learning process.

Inhibitory control hinders habit change

Our habits constantly influence the environment, often in negative ways that amplify global environmental and health risks. Hence, change is urgent. To facilitate habit change, inhibiting unwanted behaviors appears to be a natural human reaction. Here, we use a novel experimental design to test how inhibitory control affects two key components of changing (rewiring) habit-like behaviors in healthy humans: the acquisition of new habit-like behavior and the simultaneous unlearning of an old one. We found that, while the new behavior was acquired, the old behavior persisted and coexisted with the new. Critically, inhibition hindered both overcoming the old behavior and establishing the new one. Our findings highlight that suppressing unwanted behaviors is not only ineffective but may even further strengthen them. Meanwhile, actively engaging in a preferred behavior appears indispensable for its successful acquisition. Our design could be used to uncover how new approaches affect the cognitive basis of changing habit-like behaviors.

Cautious or causal? Key implicit sequence learning paradigms should not be overlooked when assessing the role of DLPFC (Commentary on Prutean et al.)

The role of the dorsolateral prefrontal cortex (DLPFC) in implicit sequence/statistical learning has received considerable attention in recent cognitive neuroscience research. Studies have used non-invasive brain stimulation methods to test whether the DLPFC plays a role in the incidental acquisition and expression of implicit sequence learning. In a recent study, Prutean et al. has concluded that stimulating the left or the right DLPFC might not affect the expression of implicit sequence learning measured by the Serial Reaction Time (SRT) task. The authors speculated that the previous results revealing improved implicit sequence learning following DLPFC stimulation might have been found because explicit awareness accumulated with the use of Alternating Serial Reaction Time (ASRT) task. Our response presents solid evidence that the ASRT task measures implicit sequence learning that remains unconscious both at the judgment and structural level. Therefore, contrary to the conclusion of Prutean et al., we argue that the DLPFC could have a crucial effect on implicit sequence learning that may be task-dependent. We suggest that future research should focus on the specific cognitive processes that may be differentially involved in the SRT versus ASRT tasks, and test what the role of the DLPFC is in those specific cognitive processes.

Prompting teaching modulates children's encoding of novel information by facilitating higher-level structure learning and hindering lower-level statistical learning

Young children are not only prepared to learn from teaching, but they also start to spontaneously teach others, indicating that teaching is a natural instinct of the humankind. During childhood, teaching seems to precede the emergence of several cognitive abilities, so the question arises: how does teaching affect the development of later emerging cognitive skills? Since teaching requires explicit, accessible representations of the knowledge of the teacher, we hypothesized that the motivation to teach might impact the way children encode novel information, by biasing them towards a model-based encoding, which can help them to structure the incoming information in a more abstract and explicitly accessible way. In our study, 7-10-year-old children were presented with a well-established probabilistic sequence learning task on two consecutive days, after receiving an instruction that on the second day, they would have to teach a peer about the task. During the task, we could simultaneously measure two different types of learning: model-free learning of local (lower-level) statistical correlations and model-based learning of the global (higher-level) statistical structures of the sequences. We predicted that in case the motivation to teach facilitates model-based encoding, children who received the instruction to teach would perform better in learning the higher-level statistical structures than children in the control group, who did not receive an instruction to teach. Furthermore, since previous studies showed competition between the two types of encoding processes during development, we also predicted that facilitating children's model-based learning will impair their model-free learning of the lower-level statistical correlations. Our results confirmed both predictions, showing an improved model-based higher-level structure learning and an impaired model-free lower-level statistical correlation learning in the Teaching Group, compared to the controls. Thus, prompting teaching affects children's encoding of the novel information, by biasing them to learn in a model-based way, which can help to build more abstract and explicitly accessible representations that could be shared with others.

Divided attention does not affect the acquisition and consolidation of transitional probabilities

Statistical learning facilitates the efficient processing and prediction of environmental events and contributes to the acquisition of automatic behaviors. Whereas a minimal level of attention seems to be required for learning to occur, it is still unclear how acquisition and consolidation of statistical knowledge are affected when attention is divided during learning. To test the effect of divided attention on statistical learning and consolidation, ninety-six healthy young adults performed the Alternating Serial Reaction Time task in which they incidentally acquired second-order transitional probabilities. Half of the participants completed the task with a concurrent secondary intentional sequence learning task that was applied to the same stimulus stream. The other half of the participants performed the task without any attention manipulation. Performance was retested after a 12-h post-learning offline period. Half of each group slept during the delay, while the other half had normal daily activity, enabling us to test the effect of delay activity (sleep vs. wake) on the consolidation of statistical knowledge. Divided attention had no effect on statistical learning: The acquisition of second-order transitional probabilities was comparable with and without the secondary task. Consolidation was neither affected by divided attention: Statistical knowledge was similarly retained over the 12-h delay, irrespective of the delay activity. Our findings can contribute to a better understanding of the role of attentional processes in and the robustness of visuomotor statistical learning and consolidation.

Speed or Accuracy Instructions During Skill Learning do not Affect the Acquired Knowledge

A crucial question in skill learning research is how instruction affects the performance or the underlying representations. Little is known about the effects of instructions on one critical aspect of skill learning, namely, picking-up statistical regularities. More specifically, the present study tests how prelearning speed or accuracy instructions affect the acquisition of non-adjacent second-order dependencies. We trained 2 groups of participants on an implicit probabilistic sequence learning task: one group focused on being fast and the other on being accurate. As expected, we detected a strong instruction effect: accuracy instruction resulted in a nearly errorless performance, and speed instruction caused short reaction times (RTs). Despite the differences in the average RTs and accuracy scores, we found a similar level of statistical learning performance in the training phase. After the training phase, we tested the 2 groups under the same instruction (focusing on both speed and accuracy), and they showed comparable performance, suggesting a similar level of underlying statistical representations. Our findings support that skill learning can result in robust representations, and they highlight that this form of knowledge may appear with almost errorless performance. Moreover, multiple sessions with different instructions enabled the separation of competence from performance.