User:Aysha Keisler/Proposed/Motor sequence learning

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Learning sequences of movements or facts is an integral part of human behavior. Sequencing our actions is important for everyday activities, from getting dressed in the morning, to playing sports or musical instruments. Equally, our cognitive lives depend upon sequencing for organizing our thoughts into clear and intelligible language constrained by grammatical rules (Ashe, Lungu, Basford, & Lu, 2006). A failure to organize actions into the correct sequence is a key characteristic of the movement disorder apraxia, and a failure to organize thoughts into a logical sequence is a hallmark trait of schizophrenia (Walker, Walker, & Sunderland, 2003). Many experimental approaches have been developed to better understand sequence learning because sequence learning holds the promise of providing insight into disease, and into fundamental aspects of human cognition.

A widely used motor sequence learning paradigm is the Serial Response Time (SRT) task (shown in Figure 1
Figure 1: A schematic of the SRTT: a visual cue appears, a participant responds by selecting the appropriate response button, the visual cue disappears, ending the trial, and following a fixed delay, another visual cue appears marking the beginning of a new trial. The position of the visual cue can either play out a repeating sequence or the position of the visual cue across the trials can be random (Robertson, 2007).
), so named because a participant must make quick reactions to a series of stimuli that, often unbeknownst to the participant, appear according to a repeating pattern. With practice participants’ reaction times (RTs) to both the sequenced trials (i.e., trials that follow the repeating pattern) and random trials (i.e., trials on which the stimulus appears randomly) decrease as they familiarize with the task and learn to map each cue with the desired response. The critical measure of sequence-specific learning is the RT difference between sequenced stimuli and randomly-appearing stimuli, and this difference increases as participants learn the sequence. A common paradigm consists of a long series of sequenced trials followed by a series of random trials (shown in Figure 2
Figure 2: Response time data, from a single participant, showing a steady decrease in response time over the initial random and sequential trials until the re-introduction of the random trials when there is a dramatic increase in response time. The magnitude of this increase is often used as a measure of sequence learning in the SRTT (Robertson, 2007).

Many variants of the task have been developed in order to better focus on particular process(es). The explicit finger-tapping task (see below for the implicit vs. explicit distinction; Karni et al., 1995) removes the visual cues, so that subjects must memorize the sequence then execute the sequence of movements without visual guidance or feedback. This task is simpler than the SRT and focuses on improvements in movement performance once the sequence itself has already been learnt declaratively. Temporal sequences (in which the timing between elements follows a repeating pattern; Penhune & Doyon, 2002; Shin & Ivry, 2002) allow one to examine the learning of when elements of a sequence appear, in addition to the order of the elements. In the Alternating Serial Response Time task (Howard & Howard, 1997), sequenced trials are interleaved with random trials (i.e., 2-R-4-R-3-R-1, where “R” represents any of the four elements, chosen at random). The ASRT allows for gradual learning and reduces or eliminates explicit awareness.

Other tasks allow researchers to assess learning of statistical properties of the sequence. For instance, in one probabilistic variant of the SRT, the order of the stimuli is determined by a “finite-state grammar”, or a set of rules that describe allowable transitions between stimuli (Cleeremans & McClelland, 1991; Peigneux et al., 2000). For example, stimulus 2 may only be followed by stimulus 1 or stimulus 5. RTs to trials that follow the rules (“grammatical” trials) may be compared to trials that violate the rules (“nongrammatical” trials). In another probabilistic variant, the stimuli follow a fixed sequence with a given probability throughout the task, but occasionally one or more element violates the sequence (Reed & Johnson, 1994; Rowland & Shanks, 2006). Probabilistic sequences may be contrasted with “deterministic” sequences, in which the sequence does not vary. Thus, a number of paradigm variations exist to adapt the task the research question at hand, allowing for a highly flexible and adaptable model of motor learning.



Sequence learning, particularly that acquired via the SRT, offers particular insight into explicit and implicit learning (Willingham et al., 1989). In the explicit case individuals are consciously aware of learning, whereas in the implicit case individuals show evidence of learning (as measured by RTs) but report little or no conscious awareness of the sequence. A challenging problem has been to define whether, and to what extent, individuals are aware of learning. There are at least two aspects of awareness: awareness that learning is taking place (awareness that a repeating sequence exists) and awareness of what is learned (declarative memory of the sequence structure).

Experimenters may query participants to determine whether sequence learning has taken place (“A repeating pattern appeared for one group of participants and not for another. What group do you think you were in?”). A variety of measures have been used to measure the content of what is learned. In free recall tests, experimenters ask participants to report (via verbal response, button presses to recreate the sequence, or other means) as much as possible of the sequence, with no cues. In recognition tests, participants are shown all or portions of the sequence and report whether the stimuli are part of the sequence or not. Some argue that performance on these tasks may be susceptible to implicit knowledge. Subjects may feel like they are guessing but nevertheless perform better than expected by chance, and therefore the tests may reflect implicit as well as explicit knowledge. The Process Dissociation Procedure addresses the issue of whether sequence knowledge is under conscious control (Destrebecqz & Cleeremans, 2001). Here, participants are told to (a) press keys to recreate the sequence, and then (b) press keys in any order so long as they avoid patterns that are part of the sequence. The difference in the production of the sequence in the two conditions reflects the degree to which sequence knowledge is under conscious control, or the degree to which participants can control expression of the sequence. Finally, an ingenious and promising awareness measure was introduced by (Persuad et al., 2007). These researchers asked participants to wager on their performance after putatively implicit learning. Even though exploiting the learned material would result in higher monetary payout participants were unable to do so, indicating that the material was indeed outside of conscious control. The authors argue that such paradigms offer a more objective measure of awareness.

A participant’s explicit awareness of the sequence may arise either intentionally or unintentionally. Experiment instructions may directly cue participants to the presence of a sequence (Robertson, Pascual-Leone, & Press, 2004; Willingham & Goedert-Eschmann, 1999; Willingham, Salidis, & Gabrieli, 2002). However, it is common for some participants to acquire some explicit awareness and declarative memory of the sequence under ostensibly implicit conditions (Honda et al., 1998; Wilkinson & Shanks, 2004). Thus, it is vitally important for sequencing researchers to obtain a reliable measure of awareness to understand the nature of learning.

Using the above measures of awareness, researchers addressed the central question of whether sequence learning could take place without awareness or, in other words, whether learning on this task could truly be implicit. Learning is often a mix of implicit and explicit learning, and so few, if any, tasks can be thought of as being “process pure” (Jacoby, 1991), making it difficult to assess whether there are distinct implicit/explicit processes. Further, because learners often report at least some awareness of the sequence, some argue that the observed RT differences on the task are exclusively supported by explicit learning. Others point out that it is not necessary to show that learning is entirely without awareness to conclude that implicit learning takes place, but simply that the degree of explicit awareness is insufficient to account for the observed performance (Reber, Allen, & Reber, 1999). Those skeptical of the implicit account counter that failures to measure awareness are due to limitations of the measures- the probes were not sufficiently sensitive to measure small but existent awareness. The preponderance of evidence, though, affirms that motor sequence learning can occur independently of explicit awareness, and that implicit and explicit learning are in fact separable processes. In addition to behavioral data using the aforementioned awareness measures (Curran, 2001; Destrebecqz & Cleeremans, 2001), anatomical and clinical studies suggest that there are separate biological systems supporting implicit and explicit sequence learning. Amnesic patients, who can neither recall nor recognize a learned sequence, nevertheless exhibit a reaction time advantage of sequenced trials over random trials (Nissen & Bullemer, 1987; Reber & Squire, 1994). Thus, structures supporting implicit learning must be intact though structures supporting explicit learning are impaired. In healthy adults, a network involving the striatum and parietal and prefrontal cortices distinguishes between learning under implicit and explicit conditions (Willingham et al., 2002), with the anterior cingulate/mesial prefrontal and primary motor cortices playing distinct roles in the support of explicit awareness (Destrebecqz et al., 2005; Rosenthal, Roche-Kelly, Husain, & Kennard, 2009).

Motoric versus abstract representation

Of central importance is the question of what type of cognitive and neural representation supports sequence learning; in other words, what is learned? Consider that, in the example of the piano player, the musician could know a sequence of finger movements, a sequence of tones, an arrangement of notes on paper, or a sequence of piano keys to press. In general, memory for sequences is supported by multiple, parallel representations. Researchers use transfer tasks (paradigms in which some element(s) of the task is changed between training and test) to determine which of these elements are critical to the sequence representation.

One possible form of representation is perceptual, i.e., dependent upon the visual cues that guide responses. Perceptual cues can be important to initially drive behaviors (Willingham, 1998) but the representation is not entirely dependent on perception, either, as the subjects are able to express sequence knowledge when the perceptual cues in the task are changed (for instance, from spatial to color cues; Hazeltine, Grafton, & Ivry, 1997; Mayr, 1996; Robertson, Tormos, Maeda, & Pascual-Leone, 2001; Willingham, 1999). Explicitly learned sequences are more dependent on perceptual stimuli than those learned implicitly (Rüsseler & Rösler, 2000; Willingham, Wells, Farrell, & Stemwedel, 2000). For instance, Willingham et al. (2000) gave participants instructions that yield explicit learning and saw that performance suffered when the perceptual stimuli change; in contrast, performance under implicit conditions was unaffected by changes to perceptual stimuli.

Another possible representation is motoric, or dependent upon the particular set of muscles used during learning. Both effector-independent (i.e., that transfers between different effectors, such as from the left to the right hand) and effector-dependent (i.e., that does not transfer between effectors) representations develop during sequence learning (Grafton, Hazeltine, & Ivry, 1998). Participants can transfer sequence knowledge between hands (Willingham et al., 2000) as well as between proximal (i.e., shoulder) and distal (i.e., finger) musculature and still express a learned sequence of movements (Cohen, Ivry, & Keele, 1990). Not surprisingly, effector-independent and effector-dependent representations are anatomically distinct. Activation of inferior parietal cortex is unchanged as participants switch effectors so long as the underlying pattern of responses is retained, implicating this area as a locus of the effector-independent representation (Bapi, Miyapuram, Graydon, & Doya, 2006; Bo, Peltier, Noll, & Seidler, 2011; Grafton et al., 1998). The effector-dependent representation, on the other hand, is supported by premotor and supplementary motor cortices and by the relevant homunculus area of the somatosensory cortex (Bapi et al., 2006; Grafton et al., 1998; Hikosaka, Nakamura, Sakai, & Nakahara, 2002). Effector-dependence is not a static property of the skill, however; rather, sequence learning becomes more effector-dependent with practice. In other words, practice increases the degree to which the skill transfers to other effectors (Bapi, Doya, & Harner, 2000; Bo et al., 2011; Japikse, Negash, Howard, & Howard, 2003; Park & Shea, 2005). In fact, some speculate that the anatomic shift in activation from parietal (early in learning) to premotor, supplementary and motor cortices supports the behavioral shift from an effector-independent to -dependent representation (Bapi et al., 2006).

Effector independent and dependent representations occupy distinct reference frames. Reference frames refer to the coordinate system in which learning takes place, such as body-centric coordinates (egocentric) or environment-centric coordinates (allocentric). In other words, does the participant learn a particular response location as, “four inches left of my body’s midline” (egocentric) or, “one foot from the edge of the table” (allocentric)? Evidence suggests that effector-independent abstract representations (specifically, response locations) are coded in allocentric coordinates and effector-dependent representations are coded in egocentric coordinates. Different coordinate frames also exhibit different patterns of consolidation (Cohen, Pascual-Leone, Press, & Robertson, 2005; Cohen & Robertson, 2007). One theory is that sequences are initially coded in egocentric space, and environment-centric coordinates emerge with practice (Willingham, 1998; Willingham et al., 2000), though the particular reference frame involved may depend on the learning stage and/or awareness (c.f. Cohen et al., 2005; Liu, Lungu, Waechter, Willingham, & Ashe, 2007; Witt, Ashe, & Willingham, 2008).

Learning sequence structure

Sequence structure refers to the statistical properties of the sequence regularities learned by participants. The simplest structure involves trial-to-trial associations between successive elements (i.e., that item 2 follows item 4). In addition, one may learn the frequency with which each item appears, facilitating responses to items that appear more often (Boyer, Destrebecqz, & Cleeremans, 2005; Cleeremans & McClelland, 1991; Stadler, 1992). Related to item frequency is the concept of “repetitive distance”, or the number of items that have passed since the last appearance of an item (e.g., the repetitive distance between instances of 1 is seven for the sequence 13243241). Boyer, Destrebecqz, and Cleeremans (2005), for instance, demonstrate RTs shorten as the repetitive distance increases. Thus, the expectation that a stimulus will appear on a given trial strengthens with repetitive distance (Boyer et al., 2005; Cleeremans & McClelland, 1991; Koch & Hoffmann, 2000; Ziessler, Hanel, & Sachse, 1990). Interestingly, this principle is apparent at the onset of training and therefore reflects an a priori expectation that stimuli appear equally often. Finally, we are also capable of learning probabilities of item occurrences. For instance, one may learn that given the appearance of items 132, the probability of a 4 or 1 may be 74% and 25%, respectively; (Cleeremans & McClelland, 1991; Remillard, 2008; Stefaniak, Willems, Adam, & Meulemans, 2008).

Participants can not only learn associations between adjacent items, but associations between non-adjacent items as well (Reed & Johnson, 1994; Remillard, 2008). For instance, the location of the stimulus on trial n might be predictable from trial n-2 (trial n-1 may or may not predict trial n as well). Sequences such as this are referred to as ‘x order’ sequences, where x refers to the lag between predictive trials (i.e., first order, second order, etc). Participants are able to learn at least fourth order sequences (trial n-4 predicts trial n). Participants can learn more abstract regularities as well. Koch and Hoffman (2000) demonstrate that participants are sensitive to systematic features such as runs of ascending or descending response positions (i.e., 23456 or 65432). In the case of artificial grammar learning, a close relative of sequencing learning, strings of letters appear according to a set of rules, or grammar (Knowlton, Ramus, & Squire, 1992; Knowlton & Squire, 1996; Reber, 1967). After training on the strings, subjects are able to judge whether new letter strings are grammatical or nongrammatical, indicating that abstract relations may be learned as well as the order of specific elements. Finally, in addition to relational structures among elements, participants can learn ordinal, or positional, information of individual elements; for example, that item x is the 3rd item to appear (O'Reilly, McCarthy, Capizzi, & Nobre, 2008; Schuck, Gaschler, Keisler, & Frensch, 2012). Thus, it is clear that there are multiple possible structural regularities which may be learned within a given sequence. Learning of one or more of these structures gives rise to the reaction time difference between sequenced and random trials that is characteristic of sequence learning.

As such regularities in the sequence are learned, participants may create further structures of their own. According to chunking theory, sequences are broken into smaller units of elements that are learned as packages. Participants may create chunks spontaneously, without instruction from experimenters. In addition, the structure of the sequence elements (such as those in the preceding paragraph) and pauses inserted between items can shape the formation of chunks (Frensch, Buchner, & Lin, 1994; Jimenez, 2008; Koch & Hoffmann, 2000; Sakai, Kitaguchi, & Hikosaka, 2003; Stadler, 1993; [[#Stadler1995|Stadler, 1995]; Verwey, 2003; Verwey & Eikelboom, 2003). Interestingly, individual chunks do not appear to be modular, in that a chunk from a practiced sequence will not impart an identical reaction time advantage when inserted into a new sequence (Perlman, Pothos, Edwards, & Tzelgov, 2010). Thus, there are multiple types of structural learning that likely occur in parallel when learning a given sequence. Humans are apparently adept at learning not only simple trial-to-trial associations, but also associations between non-adjacent items and high-order dependencies. These structures can then be arranged into chunks to facilitate learning. As discussed in the next section, attention may further modulate and interaction with learning.


Attentional requirements of sequence learning are primarily explored via dual task paradigms. Here, researchers compare performance when completing only the sequencing task to performance when completing the sequencing task simultaneously with an unrelated secondary task. For example, a participant may perform the SRT while keeping track of the number of audio tones played during the experiment. The difference between dual task and single task performance reflects the need for attentional resources. With simple sequences, learning has low attentional demands (Cleeremans, 1993; Cohen et al., 1990; Keele & Jennings, 1992): performance differs little or not at all between single task and dual task conditions. Attention is closely tied with awareness, however, in that explicit learning requires greater attention than implicit (Cleeremans, 1993; Hazeltine et al., 1997; Jimenez & Vazquez, 2005). Attentional demands also increase with sequence complexity (Cohen et al., 1990; Rowland & Shanks, 2006), and between deterministic and probabilistic sequences (Jimenez & Vazquez, 2005).

However, alternative accounts of dual task paradigms exist. Some argue that a secondary task may introduce additional variance and/or a more qualitatively different learning task (Hsiao & Reber, 2001; Jimenez & Vazquez, 2005; Keele, Ivry, Mayr, Hazeltine, & Heuer, 2003; Rah, Reber, & Hsiao, 2000). For instance, Keele and colleagues (2003) posit that the learning impairment observed in dual task paradigms is due to the increase in uncorrelated events that occur in dual task condition, rather than because of a limitation of attentional resources; Rah et al. (2000) similarly suppose that under dual task conditions, individuals perform a single, more complex task rather than two discrete tasks. Consistent with this theory, implicit sequence learning that takes place under different attentional demands exhibit different patterns of sleep-dependent consolidation (Spencer, Sunm, & Ivry, 2006). Given the theoretical complexities of dual task paradigms, exploration of the attentional demands of sequence learning may require new approach in the future.


Much of human behavior relies on our ability to correctly sequence actions. On the surface, sequencing appears to be a simple process; however, consider the sophisticated patterns described above (i.e., probabilistic and higher-order sequences) which the brain is capable of learning and reproducing. In addition, the brain must not only execute sequences in the proper order but must also detect such patterns in our behavior, often without conscious awareness. Perhaps it is not surprising, then, that motor sequence learning is one of the most ubiquitous arenas in the field of motor skill learning. In addition to considerable gains in our knowledge of the sequencing process, sequence learning is also a ubiquitous prototypical motor skill by which we may understand broader processes of consolidation, sleep, awareness, and various clinical states.

Research over the last decades indicates that no single process supports sequence learning; rather, we are capable of learning multiple parallel features of environmental and behavioral regularities. For instance, parallel representations are formed in multiple reference frames, both motoric and abstract. We are apparently adept at learning multiple structural features of sequences as well, highlighting the capacity of the motor system to exploit both simple and complex stimulus regularities. There is much we do not understand in this process, however, such as the roles of various reference frames and attentional requirements. Expansion of our understanding in this field will undoubtedly yield further payout.


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