Differentiating Visual from Response Sequencing During Long-Term Skill Learning

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Differentiating Visual from Response Sequencing During Long-Term Skill Learning Differentiating Visual from Response Sequencing during Long-term Skill Learning Brighid Lynch1, Patrick Beukema2, and Timothy Verstynen1 Abstract ■ The dual-system model of sequence learning posits that dur- order of key presses across training days), Combined (same ing early learning there is an advantage for encoding sequences serial order of cues and responses on all training days), and a in sensory frames; however, it remains unclear whether this ad- Control group (a novel sequence each training day). Across 5 days vantage extends to long-term consolidation. Using the serial RT of training, sequence-specific measures of response speed and task, we set out to distinguish the dynamics of learning sequen- accuracy improved faster in the Visual group than any of the tial orders of visual cues from learning sequential responses. On other three groups, despite no group differences in explicit each day, most participants learned a new mapping between awareness of the sequence. The two groups that were exposed a set of symbolic cues and responses made with one of four to the same visual sequence across days showed a marginal im- fingers, after which they were exposed to trial blocks of either provement in response binding that was not found in the other randomly ordered cues or deterministic ordered cues (12-item groups. These results indicate that there is an advantage, in sequence). Participants were randomly assigned to one of four terms of rate of consolidation across multiple days of training, groups (n = 15 per group): Visual sequences (same sequence for learning sequences of actions in a sensory representational of visual cues across training days), Response sequences (same space, rather than as motoric representations. ■ INTRODUCTION found that explicit awareness afforded an advantage Many complex skills require learning to bind temporally when learning a novel sensorimotor sequence (see also distinct movements into a unified sequence of actions. Curran & Keele, 1993). More recent studies suggest that For example, when learning to play a novel arpeggio on providing explicit knowledge of a cued sequence of the piano, a student begins by playing each individual movements immediately improves motor vigor and ac- note in successive fashion. With long-term practice, she curacy (Wong, Lindquist, Haith, & Krakauer, 2015), allud- can eventually master executing whole phrases and melo- ing to the possibility that high-level concept knowledge of dies as a singular, unified action. This mastery of complex action goals improves basic motoric efficiency in action sequential skills can arise from learning at multiple levels. execution. For example, the student may learn the piece by serially Hikosaka, Nakamura, Sakai, and Nakahara (2002) for- predicting the high-level action goals of the piece, as mally proposed a dual-system model for sequence learn- would occur if she memorized the written notes on the ing. According to their model, prefrontal cortico-basal sheet of music or the sequence of spatial locations along ganglia and cortico-cerebellar networks learn sequences the keyboard. In contrast, the student may learn action- of high-level planning features, such as perceptually driven specific motoric synergies such that individual finger spatial goals. This system learns quickly, is effector inde- movements become encoded as a singular, sequential pendent, and has a relatively short time scale of retention, action. Of course, these are not mutually exclusive, and on the order of a few seconds or minutes. In contrast, learning can occur at many levels of representation. cortico-basal ganglia and cortico-cerebellar circuits, routed The idea that multiple systems are recruited during through the primary motor cortex, learn sequences of sequential skill learning dates back to some of the first motoric actions. This motor sequence system learns slowly, studies of sensorimotor sequence learning (for review, is effector specific, and has a very long time scale of reten- see Abrahamse, Jiménez, Verwey, & Clegg, 2010; Ashe, tion. According to their model, the basal ganglia circuits Lungu, Basford, & Lu, 2006). In their initial experiments evaluate reward likelihoods of individual actions, whereas with the serial RT task (SRTT), Nissen and Bullemer (1987) the cerebellar circuits monitor execution errors. Several behavioral (Albouy et al., 2013; Witt, Margraf, Bieber, Born, &Deuschl,2010;Cohen,Pascual-leone,Press,&Robertson, 1Carnegie Mellon University, Pittsburgh, PA, 2Center for 2005; Willingham, Wells, Farrell, & Stemwedel, 2000; Neuroscience, University of Pittsburgh Willingham, 1999) and neuroimaging (Albouy et al., 2015; © 2016 Massachusetts Institute of Technology Journal of Cognitive Neuroscience 29:1, pp. 125–136 doi:10.1162/jocn_a_01037 Rose, Haider, Salari, & Buchel, 2011) studies have also Although aspects of skill crystallization, such as binding validated the general dichotomy proposed by Hikosaka and chunking, require several days of training, to date it and colleagues (2002). This dual-system model is also remains unclear whether one level of representation has consistent with more recent models of general response an advantage for binding sequential representations over planning, where potential decisions compete on two sepa- other levels of representation. Diedrichsen and Kornysheva rate levels: a competition between the relative values of (2015) proposed that response chunking during move- action goals and a competition between low-level execu- ment sequencing occurs at lower, motoric levels of repre- tion response costs (Cisek, 2012). sentation. According to this hypothesis, long-term skill Along with representing sequential goals or actions, learning entrains synergies of motor primitives together another key aspect of skill learning is the ability to bind so that they are triggered by a singular upstream motor individual responses together into a unified action (Lashley, command. This hierarchical model of sequence chunking 1951), and this binding results in movements that can be is effector-dependent and predicts a specific advantage for parceled into meaningful chunks (Verwey, 1994). In the repeating sequences of individual actions together, regard- context of cued motor actions, chunking is typically stud- less of how they are cued. An alternative model is one in ied by training a participant to learn a simple (i.e., three to which sequences of independent action plans or sensory nine items) sequence. Here, the repetition structure of goals get bound with training, leaving low-level motor syn- specific elements in the sequence is manipulated so as ergies contingent on high-level action plans. This is con- to be easily detectable (e.g., “1-2-3” or “1-3-1”). With sistent with the fact that learning is facilitated by explicit practice, the first item in the concatenated set of actions awareness of sequence structure (Curran & Keele, 1993; exhibits a slower RT than the rest of the elements in the Nissen & Bullemer, 1987). It predicts that response binding set. A larger RT difference between the first and the will be effector-independent and that serial ordering of second action or between the first, second, and third cues will be advantageous for chunking actions together. key press reveals the bound responses. This slowing is Alternatively, response binding could happen between used as an index of the segmentation of the learned chunk levels of representation. For example, previous behavioral (Verwey, Abrahamse, Ruitenberg, Jiménez, & de Kleine, models have proposed that learning of event sequences in 2011; Verwey, Abrahamse, & Jiménez, 2009; Kennerley, the SRTT is mediated by learning a relationship between Sakai, & Rushworth, 2004; Verwey & Eikelboom, 2003; a current manual response and the stimulus cue that fol- Verwey, Lammens, & Van Honk, 2002; Verwey, 1996, lows it (Ziessler & Nattkemper, 2001; Ziessler, 1998). 2001). Several lines of evidence suggest that this type of Here we set out to disambiguate long-term (i.e., mul- response binding may happen upstream from motor tiday) sequence learning at sensory levels of representa- execution systems: Chunking is correlated with working tion (i.e., visual cues) from representations at motoric memory capacity, but not simple motor production abil- levels (i.e., manual responses). Specifically, we wanted ities (Bo, Jennett, & Seidler, 2011; Bo & Seidler, 2009; to evaluate which level of representation better facilitates Bapi, Doya, & Harner, 2000), chunking efficiency is learning rate across multiple days of training and tempo- context-specific (i.e., chunks of one sequence do not trans- ral correlation measures of binding. The focus on multi- fer to another sequence with similar structure; Verwey, day learning, as opposed to learning within one or two 2001), the structure of chunked responses is not affected training sessions, is necessary because stable response by manipulations of execution parameters (e.g., target binding is only observed after multiple days of training distance, effector; Sakai, Kitaguchi, & Hikosaka, 2003), (Acuna et al., 2014; Verstynen et al., 2012; Wymbs and chunking performance is impaired by disruptions et al., 2012). To do this, we used a version of the SRTT of striatal dopamine pathways, suggesting that reinforce- to train participants to learn an embedded 12-item se- ment learning mechanisms contribute to binding actions quence. By remapping the cue–key associations on each together (Tremblay et al., 2009, 2010). More nuanced sig- day
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