Frontal and Parietal Contributions to Probabilistic Association Learning

Neuroimaging studies have shown both dorsolateral prefrontal (DLPFC) and inferior parietal cortex (iPARC) activation during probabilistic association learning. Whether these cortical brain regions are necessary for probabilistic association learning is presently unknown. Participants' ability to acquire probabilistic associations was assessed during disruptive 1 Hz repetitive transcranial magnetic stimulation (rTMS) of the left DLPFC, left iPARC, and sham using a crossover single-blind design. On subsequent sessions, performance improved relative to baseline except during DLPFC rTMS that disrupted the early acquisition beneficial effect of prior exposure. A second experiment examining rTMS effects on task-naive participants showed that neither DLPFC rTMS nor sham influenced naive acquisition of probabilistic associations. A third experiment examining consecutive administration of the probabilistic association learning test revealed early trial interference from previous exposure to different probability schedules. These experiments, showing disrupted acquisition of probabilistic associations by rTMS only during subsequent sessions with an intervening night's sleep, suggest that the DLPFC may facilitate early access to learned strategies or prior task-related memories via consolidation. Although neuroimaging studies implicate DLPFC and iPARC in probabilistic association learning, the present findings suggest that early acquisition of the probabilistic cue-outcome associations in task-naive participants is not dependent on either region

Brain rhythms: enhancing memories

Summary: A new study shows that different processes are responsible for maintaining relevant and suppressing irrelevant information. By promoting the suppression of irrelevant information, memories may be enhanced

Modulating Visuomotor Sequence Learning by Repetitive Transcranial Magnetic Stimulation: What Do We Know So Far?

Predictive processes and numerous cognitive, motor, and social skills depend heavily on sequence learning. The visuomotor Serial Reaction Time Task (SRTT) can measure this fundamental cognitive process. To comprehend the neural underpinnings of the SRTT, non-invasive brain stimulation stands out as one of the most effective methodologies. Nevertheless, a systematic list of considerations for the design of such interventional studies is currently lacking. To address this gap, this review aimed to investigate whether repetitive transcranial magnetic stimulation (rTMS) is a viable method of modulating visuomotor sequence learning and to identify the factors that mediate its efficacy. We systematically analyzed the eligible records (n = 17) that attempted to modulate the performance of the SRTT with rTMS. The purpose of the analysis was to determine how the following factors affected SRTT performance: (1) stimulated brain regions, (2) rTMS protocols, (3) stimulated hemisphere, (4) timing of the stimulation, (5) SRTT sequence properties, and (6) other methodological features. The primary motor cortex (M1) and the dorsolateral prefrontal cortex (DLPFC) were found to be the most promising stimulation targets. Low-frequency protocols over M1 usually weaken performance, but the results are less consistent for the DLPFC. This review provides a comprehensive discussion about the behavioral effects of six factors that are crucial in designing future studies to modulate sequence learning with rTMS. Future studies may preferentially and synergistically combine functional neuroimaging with rTMS to adequately link the rTMS-induced network effects with behavioral findings, which are crucial to develop a unified cognitive model of visuomotor sequence learning

Noninvasive brain stimulation techniques can modulate cognitive processing

Recent methods that allow a noninvasive modulation of brain activity are able to modulate human cognitive behavior. Among these methods are transcranial electric stimulation and transcranial magnetic stimulation that both come in multiple variants. A property of both types of brain stimulation is that they modulate brain activity and in turn modulate cognitive behavior. Here, we describe the methods with their assumed neural mechanisms for readers from the economic and social sciences and little prior knowledge of these techniques. Our emphasis is on available protocols and experimental parameters to choose from when designing a study. We also review a selection of recent studies that have successfully applied them in the respective field. We provide short pointers to limitations that need to be considered and refer to the relevant papers where appropriate

Unlocking adults’ implicit statistical learning by cognitive depletion

Publisher Copyright: © 2022 National Academy of Sciences. All rights reserved.Human learning is supported by multiple neural mechanisms that maturate at different rates and interact in mostly cooperative but also sometimes competitive ways. We tested the hypothesis that mature cognitive mechanisms constrain implicit statistical learning mechanisms that contribute to early language acquisition. Specifically, we tested the prediction that depleting cognitive control mechanisms in adults enhances their implicit, auditory word-segmentation abilities. Young adults were exposed to continuous streams of syllables that repeated into hidden novel words while watching a silent film. Afterward, learning was measured in a forced-choice test that contrasted hidden words with nonwords. The participants also had to indicate whether they explicitly recalled the word or not in order to dissociate explicit versus implicit knowledge. We additionally measured electroencephalography during exposure to measure neural entrainment to the repeating words. Engagement of the cognitive mechanisms was manipulated by using two methods. In experiment 1 (n = 36), inhibitory theta-burst stimulation (TBS) was applied to the left dorsolateral prefrontal cortex or to a control region. In experiment 2 (n = 60), participants performed a dual working-memory task that induced high or low levels of cognitive fatigue. In both experiments, cognitive depletion enhanced word recognition, especially when participants reported low confidence in remembering the words (i.e., when their knowledge was implicit). TBS additionally modulated neural entrainment to the words and syllables. These findings suggest that cognitive depletion improves the acquisition of linguistic knowledge in adults by unlocking implicit statistical learning mechanisms and support the hypothesis that adult language learning is antagonized by higher cognitive mechanisms.Peer reviewe

Cognitive Contributions to Motor Learning

Transcranial direct current stimulation (tDCS) can facilitate motor learning. However, the tDCS literature scarcely addresses whether stimulation to prefrontal brain regions affects motor learning, whether chunking together of individual actions can be influenced by tDCS, and whether there are age differences in how stimulation affects sequence learning. Here we completed a series of studies that examined the application of tDCS to the prefrontal cortex (PFC), motor cortex (M1), or the presupplementary motor area (preSMA) and its impact on motor sequence learning to understand the neural bases of motor learning. First, we found both left and right PFC stimulation slowed reaction time decreases and chunking. Stimulation to the preSMA lowered reaction time but came at the expense of a higher number of chunks. and tDCS over M1 helped with reaction time decreases and chunking. Further, contrasts revealed the M1 group had overall faster reaction times and fewer chunks. In order to understand the sequence learning impairment of left PFC anodal tDCS group, we added a left PFC cathodal montage. The left PFC cathodal group demonstrated impaired learning, with longer reaction time and a greater number of chunks, results similar to the left PFC anodal montage. In experiment two, participants from the left PFC, M1, and sham tDCS groups returned for a fourth session to assess long-term effects of tDCS. Participants completed a single session of practice without tDCS on the same sequences assigned to them the year before. We found the M1 tDCS group reduced reaction time at a faster rate relative to sham and the left PFC group demonstrated less forgetting over the course of a year, but overall slower reaction times. Finally, we determined how tDCS applied to the same four brain regions as in the first study affected sequence learning and chunking in older adults. We found no age differences regarding stimulation effects on reaction time reductions; both age groups benefited from M1 stimulation, whereas stimulation to the prefrontal cortices impaired learning. However, we did find age-group differences in chunking. Stimulation to M1 helped chunking processes for both age groups and to a greater extent for older adults. Thus, our findings suggest that regardless of age, stimulation to prefrontal cortices impairs learning, likely interfering with the automatization of sequence, whereas stimulation to M1 facilitates learning, especially in chunk formation. In light of our findings, we suggest the Cognitive framework for Sequential Motor Behavior (C-SMB), a framework that accounts for motor sequence learning should be modified to account for our findings.PHDKines & Psychology PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144056/1/bgreeley_1.pd

Right hemisphere advantage in statistical learning: evidence from a probabilistic sequence learning task

Picking up statistical regularities of patterns from the environment is essential for predictive and adaptive behavior. One of the most important challenges is to understand how statistical learning occurs and how the acquired information consolidates and stabilizes in the brain. Evidence suggests that the prefrontal cortex (PFC) has a critical role in these processes; the division of labor between hemispheres, however, is less characterized. The aim of the present study was to directly investigate the causal role of the right and left PFC in statistical learning and its consolidation. Healthy, young adults were trained on a probabilistic sequence learning task. Anodal transcranial direct current stimulation (tDCS) over the right or left dorsolateral PFC (DLPFC) was applied during the training in order to modify learning-related cortical plasticity in the targeted brain regions by increasing neural excitability. Performance was tested during and immediately after the stimulation, 2-hour and 24-hour later. We found that the anodal tDCS over the right DLPFC led to enhanced learning performance both after the 2-hour and 24-hour retention periods, suggesting the causal role of this area in statistical learning. In contrast, we did not find any effect of left DLPFC stimulation on learning. These results highlight the role of the right fronto-striatal network in statistical learning and its consolidation

Dual enhancement mechanisms for overnight motor memory consolidation

Our brains are constantly processing past events<sup>1</sup>. These offline processes consolidate memories, leading in the case of motor skill memories to an enhancement in performance between training sessions. A similar magnitude of enhancement develops over a night of sleep following an implicit task, in which a sequence of movements is acquired unintentionally, or following an explicit task, in which the same sequence is acquired intentionally<sup>2</sup>. What remains poorly understood, however, is whether these similar offline improvements are supported by similar circuits, or through distinct circuits. We set out to distinguish between these possibilities by applying transcranial magnetic stimulation over the primary motor cortex (M1) or the inferior parietal lobule (IPL) immediately after learning in either the explicit or implicit task. These brain areas have both been implicated in encoding aspects of a motor sequence and subsequently supporting offline improvements over sleep<sup>3,​4,​5</sup>. Here we show that offline improvements following the explicit task are dependent on a circuit that includes M1 but not IPL. In contrast, offline improvements following the implicit task are dependent on a circuit that includes IPL but not M1. Our work establishes the critical contribution made by M1 and IPL circuits to offline memory processing, and reveals that distinct circuits support similar offline improvements

Examining the Paradox of Adult Second Language Word and Grammar Learning

Background: Adults generally demonstrate advanced cognitive skills compared to children, with second language (L2) learning being a key exception, particularly within the grammar domain. As optimal vocabulary and grammar learning are believed to engage in distinct explicit and implicit learning mechanisms, respectively, the advanced neurocognitive mechanisms underpinning adults’ higher-order cognitive skills may particularly interfere with implicit grammar learning. The objective of this dissertation was to examine select neural and cognitive factors that may contribute to word and grammar learning differences. In Study 1, I investigated the neural correlates of artificial vocabulary and morphology learning using functional Near-Infrared Spectroscopy (fNIRS). Despite adults outperforming in explicit vocabulary outcomes compared to implicit grammar generalization, cortical differences between processing the two language components were minimal. On the other hand, significant changes in neural activity were observed in all four cortical lobes over the course of the initial language learning period, demonstrating the widespread cortical engagement inherent in the process of L2 learning. In Study 2, I examined the impact of effortful learning on implicit word and grammar learning outcomes using a modified statistical language learning paradigm with an underlying grammatical pattern. Performance on speeded syllable detection tasks using familiar and unfamiliar targets revealed that effortful and passive learning conditions resulted in comparable implicit learning outcomes related to word segmentation and grammar generalization. Thus, directing effort towards learning neither facilitated nor interfered with implicit L2 attainment. In Study 3, I investigated whether individual differences in statistical learning of words and/or grammatical patterns were related to domain-general cognitive abilities. The findings indicate that performance on tasks evaluating short-term memory, attention, strategic thinking, reasoning, and planning skills were not related to implicit word or grammar learning outcomes. Conclusion: Together, this dissertation presents empirical evidence that adults learn vocabulary more easily than grammatical patterns, but learning success is not related to domain-general cognitive mechanisms, at least concerning implicit representations of language. These findings are discussed in relation to existing literature and emerging theories of L2 learning. This research has important methodological implications and provides valuable insights to inform pedagogical practices for foreign language instruction