Introduction
Most actions we perform in everyday life exist of series of simple movements. For example, we lace our shoes in one fluent movement while it actually consists of a series of several more simple movements. This illustrates that we can sequence simple movements in a specific order to attain fluent execution of more complex movement patterns. Recent research suggests that multiple processors may be active during the execution of a movement sequence and that each processor involves another type of representation that, in addition, develops after varying amounts of practice (Hardy et al.
1996; Park and Shea
2005; Ungerleider et al.
2002; Verwey
2003). For example, skilled movement sequences have been shown to involve spatial and nonspatial information (Bapi et al.
2000; Koch and Hoffmann
2000a; Mayr
1996) as well as effector-dependent and effector-independent components (Hikosaka et al.
1999; Verwey
2003). It is generally accepted that sequence learning develops through various learning phases, from an initial attentive phase to an automatic phase, in which no attention is needed to perform the movement. This has been described also as a transition from the declarative phase to the procedural phase (Fitts
1964; Anderson
1982). For example, without practice full attention is needed to lace a shoe, but after practice the hands seem to know how to execute the task. Yet, evidence for the different representations and their role at various stages of skill remains scattered and people may well be flexible at switching from one to another representation (Verwey
2003).
Hikosaka et al. (
1999) proposed a model in which sequence learning is acquired independently by two parallel systems; one using the spatial system and one using the motor system. The spatial system is assumed to be predominantly active at the early stages of sequence learning and involves knowledge of individual sequence elements in codes that are not effector-dependent. The motor system is assumed to be primarily active at the later stages of sequence learning and movement skill is assumed to involve effector-dependent sequence knowledge. Both systems learn the sequence independently and are assumed to be simultaneously active. However, Hikosaka et al. (
1999) propose that the level of system activation varies across practice and either sequence mechanism may have a more important contribution, depending on the behavioral context. An additional feature of their model is that during execution of a movement sequence the motor system can learn from the spatial system and visa versa.
In extension to the Hikosaka et al. (
1999) model, Bapi et al. (
2000) distinguished an effector-dependent and an effector-independent sequence representation. They suggest that the effector-dependent representation is acquired relatively slowly by the motor system and that the effector-independent representation is in visual/spatial coordinates and acquired relatively fast. In a later study, Bapi et al. (
2006) provided evidence that different cortical and subcortical networks are engaged at various stages of learning which supported the notion of different sequence representations. The Hikosaka et al. (
1999) model suggests that, in what they call the
pre-learning stage, each stimulus leads to a movement without any effect of preceding or subsequent stimuli and therefore each movement relies on an individual sensorimotor transformation. However, during repeated execution of movement patterns representations develop that code the order of the individual movements. This would occur for the spatial and for the motor system, resulting in a spatial sequence and a motor sequence. The Hikosaka et al. (
1999) model assumes that the spatial sequence is acquired relatively quickly and the motor sequence is acquired more slowly.
In order to differentiate the reliance on different types of sequence representations, Verwey (
2003) analyzed response time distributions of a sequence learning task. His analysis of response time distributions was in line with the notion that during practice various processing modes had developed and that participants can switch from one to another processing mode as a function of whether the forthcoming sequence is expected to be familiar. On basis of the response time distributions, Verwey (
2003) distinguished (at least) three processing modes, a fast sequence mode possibly involving sequence learning at the motor level, a moderately fast mode perhaps involving sequence learning at a spatial level, and a slow mode that may well involve reacting to individual key-specific cues. The fast and the moderately fast modes correspond to the two stages of the Hikosaka et al. (
1999) model and the slow processing mode corresponds to the pre-learning stage mentioned by Hikosaka et al. (
1999). In addition, some processors may simultaneously race to determine which will trigger the next response, but support for parallel racing was limited (Verwey
2003).
To make the picture more complicated, a distinction has been made between spatial representations with an egocentric (i.e., a body-based reference frame) and allocentric (i.e., a world-based reference frame) representations. Egocentric reference frames may be eye-, hand- or body-centered (Colby and Goldberg
1999). Execution of spatial tasks is probably based on a mixture of representations with different reference frames (Adam et al.
2003; Heuer and Sangals
1998; Liu et al.
2007; Deroost et al.
2006). It is likely that depending on the task at hand, there are dominant processors and representations, and that with practice the contributions of these processors to sequence execution change.
In conclusion, there is a series of findings now indicating that executing movement sequences involves at least three mechanisms that may contribute simultaneously at advanced skill levels. First, when sequence execution involves responding to key-specific cues and there is no practice, control is entirely external and involves reacting to individual key-specific cues. Second, with limited practice, sequence control is based on effector-independent spatial coordinates, which may involve various representations with different reference frames. Third, with extensive practice, effector-dependent knowledge develops at the motor level. At this stage sequence execution may be based on one processor, but also on a mixture of independent spatial and motor processors that are alternated or racing to trigger responses.
In the present study we wanted to determine whether these various components are susceptible to the spatial location at which the sequence is carried out. The contribution of effector-dependent representations can be assessed by performance with the unpracticed effector. Previous research by Verwey and Wright (
2004) provided support for the development of an effector-dependent component and for an effector-independent component during practice in the discrete sequence production (DSP) task. They showed that practiced sequences were performed faster with the practiced hand configuration than with an unpracticed hand configuration, suggesting an effector-dependent component, and that the practiced sequences were performed faster than new sequences with the unpracticed hand configuration, suggesting an effector-independent component. In a later study, Verwey and Clegg (
2005) showed that the effector-dependent component also developed during the serial reaction-time task. They suggest that this effector-dependent component developed as a result of the extended practice they had used in their experiment, which is unusual in the serial reaction-time task. However, these studies did not investigate the contributions of spatial representations to effector-dependent and effector-independent sequence learning. The contribution of the spatial representation can be examined by transferring an acquired sequencing skill from one spatial configuration to another. A study by Grafton et al. (
1998) showed that participants, executing the serial reaction-time task, are capable of transferring their skill from a normal to a large keyboard. This suggests that sequence knowledge can be represented on a relatively abstract level, independent of muscles used to respond and independent of the spatial representation. In contrast, a study by Rieger (
2004) investigated the spatial representation during skilled typing with crossed hands and showed that typing skill involves a spatial representation. The models of Hikosaka et al. (
1999) and Verwey (
2003) suggest that effector-independent sequence learning is influenced by spatial coordinates because it is not related to specific body parts, while effector-dependent sequence learning is not influenced by spatial coordinates because it is related to specific body parts. However, to our knowledge this has not yet been investigated.
In the present study we used the DSP task which is thought to stimulate the development of an effector-dependent component because a discrete sequence of limited length is practiced thoroughly (Verwey and Wright
2004). In a typical DSP task two discrete sequences are practiced by responding to fixed series of three to six key-specific stimuli. All but the first stimuli are presented immediately after the response to the previous stimulus. In the present study each participant practiced two 7-key DSP sequences with their left hand. In order to test for effector-dependent and effector-independent sequence learning, the hand used to execute the sequence was varied during test phase. In order to examine the role of spatial representations on sequence execution the position of the keyboard on which the participants responded was also varied during the test phase. During the practice phase the keyboard was either placed 90° to the left side of the body or 90° to the right side of the body while the test phase involved both positions. So, during the practice phase participants practiced two sequences with their left hand, with the keyboard either at the left or the right side of their body. The test phase involved a 2 (Hand: practiced/left vs. unpracticed/right) × 2 (Keyboard position: familiar vs. unfamiliar) × 2 (Sequence: familiar vs. random) between blocks design to examine transfer to the unpracticed hand and the unpracticed keyboard position. The independent variable Sequence was only used in Experiment 1.
In addition, the DSP is highly suitable to study sequence segmentation (Rhodes et al.
2004). Previous studies have shown that longer sequences consist of independent segments, which are thought to represent motor chunks (Verwey
2001; Verwey et al.
2002). In line with Allport (
1980), Schmidt (
1988) and Shaffer (
1991), Verwey (
2001) proposed that a cognitive and a motor component may underlie DSP. The cognitive component is thought to select a sequence (or chunk), based on a symbolic representation, and this sequence (or chunk) is read and executed by the motor component. The cognitive component additionally plans and organizes the goal structure of movements (Shaffer
1991). Based on this model it could be suggested that chunk execution is more susceptible to the spatial location at which the sequence is carried out than chunk transition, as chunk execution probably relies on a motoric representation becomes effector-dependent with practice. Therefore additional analyses were performed to investigate the contribution of a spatial representation to the different phases (chunk execution and chunk transition) of sequence execution.
In short, the purpose of the present experiments was to determine the spatial nature of effector-dependent and effector-independent representations at more advanced levels of sequence learning, by varying the hand and the position of the hand, relative to the body. Experiment 2 was conducted to replicate the results of Experiment 1 and to ascertain that the effects found in Experiment 1 had not been caused by different stimulus-response mappings in the two keyboard location conditions. That is, in Experiment 1 every key press was indicated by a cue and changing keyboard position implied a change in stimulus-response mapping too, the possible role of which was excluded in Experiment 2.
General discussion
In two experiments the influence of the position of the practiced and the unpracticed hand on DSP task performance was examined. In Experiment 1 participants learned the sequences by reacting to key-specific cues and in Experiment 2 participants learned the sequences by translating a numerical code. This difference left the eventual results unchanged, indicating that the effects found in Experiment 1 can not be explained by different stimulus-response mappings in the two keyboard location conditions and that representations that develop during practice with the DSP task are independent of the initial way of learning.
In both experiments participants executed the practiced sequences faster with the practiced than with the unpracticed hand, indicating that participants developed effector-dependent learning of the practiced sequences. This is in agreement with Hikosaka et al. (
1999) who argued that at more advanced levels of learning sequences are executed increasingly effector-dependent. Furthermore, the models of Hikosaka et al. (
1999) and Verwey (
2003) suggest that effector-independent sequence learning is influenced by spatial coordinates because it is not related to specific body parts, while effector-dependent sequence learning is not influenced by spatial coordinates because it is related to specific body parts. However, in both experiments no effect of position across keys was found on effector-dependent or effector-independent sequence learning.
Still, the obvious segmentation of the sequences gave us the opportunity to investigate the influence of the position of the hand on the different phases of sequence execution. It appeared that chunk execution of effector-dependent sequence learning was affected by the spatial position of the hand, while chunk transition was not. This suggests that slowing at T5 was indeed caused by other processes such as switching to a next chunk. So, the present experiments support the notion that at advanced skill levels sequence execution is based on several representations simultaneously, one being a representation that is both effector and position dependent and one being more general which is both effector and position independent. Furthermore, the present experiment suggests that chunk execution and chunk transition are represented by different codings, as only chunk execution was effected by the spatial position of the practiced hand. This agrees with the view that sequences are represented by different codings (Harrington et al.
2000; Hikosaka et al.
1999; Verwey
2003; Deroost et al.
2006).
Practice related shifts in representations are also mentioned in other studies. Hoffmann and Koch (
1997) and Koch (
2007) suggest that with practice sequence learning shift from a stimulus-based representation to a response-based representation. This suggests that the representation that is effector and position independent is stimulus based, while the effector and position dependent representation is response based.
Finally, the present findings suggest that chunk execution of effector-dependent learning is in a body-centered (i.e., trunk, shoulder- or head-centered) reference frame, while chunk transition of effector-dependent learning and effector-independent learning were probably not in a body-centered reference frame and perhaps in a world-based reference frame.
In conclusion, we argue that sequences can initially be learned either verbally or by responding to cues and that with additional practice an effector-dependent (perhaps motor) component develops in parallel to an effector-independent (perhaps spatial) component. We suggest that effector-dependent sequence learning consists of a location dependent component (chunk execution) and a location independent component (chunk transition).