Deconstructing Procedural Memory: Different Learning Trajectories and Consolidation of Sequence and Statistical Learning

Abstract

Procedural learning is a fundamental cognitive function that facilitates efficient processing of and automatic responses to complex environmental stimuli. Here, we examined training-dependent and off-line changes of two sub-processes of procedural learning: namely, sequence learning and statistical learning. Whereas sequence learning requires the acquisition of order-based relationships between the elements of a sequence, statistical learning is based on the acquisition of probabilistic associations between elements. Seventy-eight healthy young adults (58 females and 20 males) completed the modified version of the Alternating Serial Reaction Time task that was designed to measure Sequence and Statistical Learning simultaneously. After training, participants were randomly assigned to one of three conditions: active wakefulness, quiet rest, or daytime sleep. We examined off-line changes in Sequence and Statistical Learning as well as further improvements after extended practice. Performance in Sequence Learning increased during training, while Statistical Learning plateaued relatively rapidly. After the off-line period, both the acquired sequence and statistical knowledge was preserved, irrespective of the vigilance state (awake, quiet rest or sleep). Sequence Learning further improved during extended practice, while Statistical Learning did not. Moreover, within the sleep group, cortical oscillations and sleep spindle parameters showed differential associations with Sequence and Statistical Learning. Our findings can contribute to a deeper understanding of the dynamic changes of multiple parallel learning and consolidation processes that occur during procedural memory formation.

A positive influence of basal ganglia iron concentration on implicit sequence learning

“…Statistical learning is the ability to extract regularities, or frequencies, from environmental input and may play an important role in language learning (Orbán et al 2008;Turk-Browne et al 2010), whereas sequence learning is defined as the acquisition of temporal order information in a series of stimuli (Nemeth et al 2011). These may rely on different brain substrates (Rose et al 2011;Kóbor et al 2018;Simor et al 2019), follow different learning curves (Simor et al 2019), and are differentially affected by sleep (Doyon et al 2009b).…”

<p>Comparing the Effects of Sleep and Rest on Memory Consolidation</p>

“…34 Another study reported that both sleep and resting wakefulness (with alerting tones administered to maintain wakefulness) similarly benefitted performance on an auditory sequencelearning task, in comparison to active wake. 33 Yet other recent studies have compared sleep, active wake, and quiet wake and found no difference between conditions (including, perhaps surprisingly, no difference between sleep and active wakefulness) for an alternating serial reaction time task 35 and a paired associates task. 36 While the above findings suggest that wakefulness and sleep might show equivalent memory benefits under some conditions, a direct comparison of the memory effects of sleep to those of completely task-and stimulus-free eyes closed rest has not yet been conducted.…”

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

“…Thus, greater serial-order learning was determined as faster responses to pattern vs. random high trials. Note that in previous ASRT studies, serial-order learning was often referred to as sequence learning (Nemeth et al, 2013;Simor et al, 2019). Statistical learning was measured by the difference in RTs between random low and random high trials (that is, the average RTs for random low trials minus the average RTs for random high trials).…”

“…For the analysis of the cued ASRT, we followed procedures outlined in previous studies (Howard and Howard, 1997;Song et al, 2007;Nemeth et al, 2013;Simor et al, 2019). First, the blocks of the task were collapsed into epochs of five blocks to facilitate data processing and to increase statistical power.…”