Segmentation of short keying sequences does not spontaneously transfer to other sequences.

Abstract

Previous research suggested that highly practiced discrete 6-key sequences are spontaneously segmented, sometimes even differently for different persons. This suggests there is some limit in the length of motor chunks that are assumed to underlie the segments in the sequence. The present experiment examined whether a segmentation pattern induced in one 6-key sequence (the prestructured sequence) determines segmentation in other 6-key sequences. The results are in line with segmentation, but showed neither transfer from the prestructured to a concurrently practiced unstructured sequence, nor to two new sequences that were carried out in a subsequent phase. Moreover, segmentation of these two new sequences was mutually different. Hence, while segmentation seems a phenomenon affecting all 6-element keying sequences, the exact segmentation pattern is not determined by that of a familiar keying sequence. Another result of the present research is that using different fingers of the same hand did slow execution rate (thus indicating effector-specific sequence learning), but the rate reduction was clearly smaller than in a previous study in which transfer to fingers of the other hand was assessed (Verwey & Wright, 2004). This is more in line with effector-specific learning being a result of sequence learning in terms of a hand-based reference frame than learning to directly trigger particular effectors (i.e., the fingers).

Full text

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Segmentation of short keying sequences
does not spontaneously transfer to other sequences
Willem B. Verweya,*, Elger L. Abrahamsea, Luis Jiménezb
aCognitive Psychology and Ergonomics, Faculty of Behavioral Sciences, Universiteit Twente, Postbus 217,
7500 AE Enschede, The Netherlands
bFacultad de Psicología, Universidad de Santiago de Compostela, Santiago de Compostela, Spain
a r t i c l ei n f o
Article history:
Available online 8 January 2009
PsycINFO classification:
2330
2340
Keywords:
Motor skills
Movement sequences
Keying sequences
a b s t r a c t
Previous research suggested that highly practiced discrete 6-key
sequences are spontaneously segmented, sometimes even differ-
ently for different persons. This suggests there is some limit in
the length of motor chunks that are assumed to underlie the seg-
ments in the sequence. The present experiment examined whether
a segmentation pattern induced in one 6-key sequence (the pre-
structured sequence) determines segmentation in other 6-key
sequences. The results are in line with segmentation, but showed
neither transfer from the prestructured to a concurrently practiced
unstructured sequence, nor to two new sequences that were car-
ried out in a subsequent phase. Moreover, segmentation of these
two new sequences was mutually different. Hence, while segmen-
tation seems a phenomenon affecting all 6-element keying
sequences, the exact segmentation pattern is not determined by
that of a familiar keying sequence. Another result of the present
research is that using different fingers of the same hand did slow
execution rate (thus indicating effector-specific sequence learn-
ing), but the rate reduction was clearly smaller than in a previous
study in which transfer to fingers of the other hand was assessed
(Verwey & Wright, 2004). This is more in line with effector-specific
learning being a result of sequence learning in terms of a hand-
based reference frame than learning to directly trigger particular
effectors (i.e., the fingers).
? 2008 Elsevier B.V. All rights reserved.
0167-9457/$ - see front matter ? 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.humov.2008.10.004
* Corresponding author. Tel.: +31 53 489 4764.
E-mail address: w.b.verwey@utwente.nl (W.B. Verwey).
Human Movement Science 28 (2009) 348–361
Contents lists available at ScienceDirect
Human Movement Science
journal homepage: www.elsevier.com/locate/humov

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1. Introduction
1.1. Controlling sequential action
Compared to most animals, humans excel in the ability to acquire sequential skills. We are less suc-
cessful in understanding the mechanisms underlying these skills though we did develop some theo-
ries about them. The classic notion is that the execution of movement sequences involves the
combining of elementary, sometimes innate, motor patterns or motor chunks (e.g., Book, 1908; Lash-
ley, 1951; Miller, Galanter, & Pribram, 1960; Paillard, 1960). For example, Gallistel (1980) argued that
‘‘individual movements that comprise the skill are first perfected to the point where they can be made
more rapidly and accurately with little variation. Then they become welded together into ‘chunks’ ”(p.
367). Chunking is generally used to refer to a strategy to code multiple items in a relational structure,
and goes beyond motor control in that the concept is used for central knowledge representations too.
The benefit of chunking is that it allows circumventing limitations in human information processing as
more elaborate representations can be used (e.g., Anderson, 1996; Halford, Wilson, & Phillips, 1998).
We argue that motor chunks are the memory representations that allow familiar movement se-
quences to be selected and executed as a single response (Verwey, 1999). Given the notion that move-
ment control involves several hierarchically ordered levels (e.g., MacKay, 1982; Rosenbaum, Kenny, &
Derr, 1983), we argue also that motor chunks are the lowest level of movement organization, straight
above the individual movements, controlling only relatively brief sequences (i.e., of up to about five
elementary movements; Verwey, 2003a). Longer sequences would, in turn, be organized by using
more abstract, higher level action representations (e.g., Engelkamp & Jahn, 2003; Hard, Lozano, & Tver-
sky, 2006; Schack & Mechsner, 2006). According to the dual processor model, these representations
are being used by two independent, parallel processors (Verwey, 2001). The motor processor executes
the few more tightly coupled elements of a motor chunk. The cognitive processor either concatenates
these chunks into longer sequences, or it may increase execution rate in parallel to the motor proces-
sor by using explicit knowledge to prepare and executing the individual elements when there is noth-
ing to concatenate (either because the sequence is short, or when the last chunk has been initiated; cf.
Verwey & Eikelboom, 2003).
In the discrete sequence production (DSP) task participants usually execute two discrete, fixed ser-
ies of three to six key presses, each of which is practiced for about 500 trials (see Rhodes, Bullock, Ver-
wey, Averbeck, & Page, 2004, for an overview of among others the DSP task). This task is especially
suited for studying the mechanisms underlying sequential motor skills because, as compared to se-
quences requiring more complex movement elements, executing a key press takes so little time that
individual interkey intervals (IKIs) are likely to reflect exclusively the action of the underlying control
mechanisms. Also, given the high execution rates in the DSP task, studying IKIs is likely to reveal prop-
erties of the fastest mechanism at a specific level of practice because slower mechanisms (that might
be active in parallel, like verbal ones, see Verwey, 2003b) are not likely to contribute much then.
1.2. Rhythm, segmentation, and motor chunks
It has been clear for many years that even with little practice certain properties, such as regularities
in element order (like 12321), familiarity with certain parts, and temporal segmentation during prac-
tice may cause sequences to be carried out as a few more closely integrated segments (e.g., Povel &
Collard, 1982; Restle, 1970; Sakai, Hikosaka, & Nakamura, 2004; Sakai, Kitagucki, & Hikosaka, 2003;
Verwey, 1996). A particularly interesting finding is that even without such regularities, longer keying
sequences appear to always include one or more relatively long response times (Kennerley, Sakai, &
Rushwordth, 2004; Sakai et al., 2004; Verwey, 2003a; Verwey & Eikelboom, 2003; de Kleine & Verwey,
2008). As the sequential positions of these slow responses were found to differ across participants,
these sequences may have included individually different segmentation patterns. Due to the individ-
ual differences, this had not been recognized before across groups of participants other than as a gen-
eral effect on mean element execution time of sequence length (Verwey, 2003a; i.e., the rate effect,
Sternberg, Monsell, Knoll, & Wright, 1978). Indications that sequences of over about 4-key presses
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are spontaneously segmented led to the notion that there is some neural limitation in the number of
elements represented in a single motor chunk (Verwey, Lammens, & van Honk, 2002).
Verwey (1996) argued that the segmentation pattern used at the start of practice will eventually
also determine chunk boundaries. This notion was based on a study by Summers (1975), which
showed that including pauses of 500 ms during practice at particular locations appeared to later cause
these locations to be marked by longer response times when the pauses were no longer imposed (also
see Summers, Sargent, & Hawkins, 1984). Summers (1975) argued that this was caused by a temporal
pattern – a rhythm – that is closely associated with the sequence at hand. However, (Verwey 1996;
Verwey & Dronkert, 1996) reported indications that, rather than a developing temporal pattern, the
pauses in Summers (1975) practice phase may well have determined the chunk boundaries that de-
velop with practice. So, when later the pauses were removed the IKIs at these positions remained rel-
atively long because they involved a transition from one to the next motor chunk, rather than a
rhythm that was maintained. These chunks appeared so robust that when participants had carried
out two successive 2-key segments separated by a pause during practice, they stuck to this pattern
even though a single 4-key segment would have been more efficient over the transfer phase (Verwey,
1996, 2001).
One research issue in the present study concerns the possibility that training a 6-key sequence with
a particular temporal pattern determines the way in which participants structure sequences that are
trained either simultaneously or successively. This idea was triggered by the unexpected observation
in Verwey et al. (2002) that an unstructured 6-key sequence showed the same segmentation pattern
as that found in a simultaneously practiced sequence that consisted of a repeated 3-key segment. In
the present study, participants practiced two 6-key sequences. One, the prestructured sequence, im-
posed a temporal structure that, for one group (the 2-2-2 group) included pauses preceding the onset
of the third and of the fifth key press, and for another group (the 3-3 group) included a single pause
preceding the fourth key press. Simultaneously, participants practiced a second, unstructured sequence,
that did not involve any pauses, and that may therefore be segmented in individually different ways
(because it is assumed to be too long to be carried out as a single whole). The first research question
concerned whether the temporal structure of the prestructured sequence would emerge in the simul-
taneously practiced unstructured sequence as well as in two new successively executed sequences.
1.3. Effector-specific learning
In addition to investigating the temporal determinants of chunk learning, we were also interested
in the representational status of these chunks. A slowdown in execution rate has been found when the
(same) keys of the sequence were pressed with different fingers than during practice (Verwey &
Wright, 2004). This has been taken as evidence for effector-specific sequence learning, but it remains
unclear what this exactly means. Despite explicit search, no evidence could be obtained for effector
(i.e., finger) specific triggering in response selection tasks (Proctor & Dutta, 1993). In contrast, there
are several indications with aiming tasks (Bock & Eckmiller, 1986; Darling & Gilchrist, 1991; Gordon,
Ghilardi, & Ghez, 1994; Krakauer, Pine, Ghilardi, & Ghez, 2000) and response selection tasks (Cho &
Proctor, 2002; Lippa, 1996) for the development of spatial hand- and forearm-based reference frames
used for coding target locations. So, one may wonder whether a slowdown similar to that found by
Verwey and Wright (2004) will be observed if other fingers of the same hand are assigned to each
key. Motor learning in terms of automatic triggering of particular fingers predicts that performance
will reduce substantially because automatic response tendencies need to be suppressed and replaced
by controlled selection of other fingers. Conversely, if a spatial hand-specific reference frame underlies
sequencing skill, little or no performance reduction is predicted of using adjacent fingers of the same
hand because the existing hand-based reference frame is still applicable.
In short, the aim of the present study was twofold. First, it aimed at determining whether the tem-
poral pattern imposed in prestructured sequence influences segmentation of a simultaneously prac-
ticed, unstructured sequence, and of two new sequences that are practiced subsequently. Second,
this study examined whether evidence for effector-specific sequence learning is observed also when
adjacent fingers of the same hand are used to distinguish between finger-specific sequence learning
and hand-based spatial learning.
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2. Method
2.1. Participants
Forty-eight (24 female and 24 male) students from the University of Twente participated in ex-
change for course credits. They were aged between 18 and 26 and signed informed consent before
the start of the experiment.
2.2. Task
The computer screen displayed black outlines of six square placeholders on a white background in
the same spatial arrangement as the assigned keys on a QWERTY keyboard, i.e., D F G J K L. Stimuli
consisted of the area enclosed by a placeholder becoming homogeneously green. Participants re-
sponded by depressing with three fingers from their left and three fingers from their right hand the
spatially compatible key on a computer keyboard. Each participant was trained with two 6-key se-
quences, resulting in a gradual development of keying skill. Immediately after depression of the cor-
rect key the content of the square turned white again and the next placeholder was filled. As only the
latencies for depression of the keys were registered, it was acceptable to release a key after the ensu-
ing one had been depressed. The last key press of each sequence was followed by blanking the entire
screen for 500 ms, then the placeholders were redrawn, and 1000 ms later the first key-specific cue of
the next sequence was presented.
The visual angle was between about 1–2 ? for each placeholder (at 60 cm face-display distance).
The distance between adjacent placeholders was about 0.5 ?, with the exception that the distance be-
tween the third and fourth placeholder was about 1 ? (corresponding to the larger distance between
left and right hand fingers). Errors resulted in the message ‘‘wrong key” (in Dutch) for 500 ms, and the
trial only advanced after the correct key had been pressed.
For all participants, one sequence in the practice phase did not contain a pause and was called the
unstructured sequence. The second sequence in the practice phase involved one or two nonzero re-
sponse stimulus intervals (RSIs) in order to impose a structure. For half the participants, this prestruc-
tured sequence included a relatively long RSI between response three and four (3-3 group). For the
remaining participants that sequence included two RSIs, one between the second and third key, and
one between the fourth and fifth key (2-2-2 group). These RSIs consisted of non aging intervals of at
least 200 ms (starting at 200 ms, 5 ms was added in successive iterations, after each of which there
was a 1% chance that this interval lengthening process was halted). Non aging intervals imply that
shorter intervals have a higher probability than longer intervals. This reduces the predictability of
the interval duration (see Gottsdanker, Perkins, & Aftab, 1986). In the test phase, none of the se-
quences included RSIs exceeding 0 ms.
2.3. Procedure
The experiment started off with an instruction on the screen after which participants performed
seven 148-trial blocks (each trial including a 6-key sequence) as a practice phase. The instruction in-
cluded information on which fingers to use: half of the 2-2-2 and the 3-3 groups practiced with the left
little, ring, and middle fingers (thus excluding the left index finger), and the right index, middle, and
ring fingers (excluding the right little finger). The other half practiced with the left ring, middle, and
index finger (excluding the little finger), and the right middle, ring, and little fingers (excluding the
index finger). Halfway each block there was a 7 s break during which performance was presented
in terms of mean sequence execution time and error rate. At the end of each block, participants were
informed also of their mean sequence execution time and error rate. Between successive blocks, par-
ticipants rested for 7 min. Faulty key presses in the practice and test phases were immediately
repeated.
Block 8 was the test block and included four 40-trial subblocks. These were separated by 7 s breaks
during which, again, mean RT and error rates were presented. These four subblocks involved manip-
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ulation of sequence familiarity and of the fingers used. This implied that two new and the two familiar
sequences were carried out once with the familiar finger setting (e.g., left little, ring, and middle fin-
gers and right index, middle, and ring fingers), and once with unfamiliar finger settings (e.g., the left
ring, middle, and index fingers, and the right middle, ring, and little fingers). Importantly, the unfamil-
iar finger setting changed the mapping between fingers and the corresponding keys, but largely main-
tained the hand-based reference frame, so that the keys assigned to each hand remained the same. The
computer screen each time instructed participants which fingers to use. In contrast to the practice
phase, none of the test block sequences included a pause.
In order to be able to compare execution rate of key presses as a function of their location in the
sequences, it is imperative that finger-specific effects are not associated with sequential positions
(due to some fingers being faster; Coover, 1923; Kornblum, 1965; Lahy, 1924). This was accomplished
by counterbalancing across participants fingers over sequential positions. This involved rotating keys
at each sequential position (D?F?G?J?K?L?D?F?G?...). For example, one participant executed
KFGDJL and FKLJDG, a second participant carried out LGJFKD and GLDKFJ, and a third DJKGLF and
JDFLGK. This yielded a set of six versions of each sequence. Each participant practiced one sequence
with one or two pauses (which was removed during testing). Across participants, each particular se-
quence was practiced equally often with (one or two) pauses as without, while the order of presenta-
tion of these sequences within blocks was random. Also, the two new sequences in test Block 8 were
taken from the same set of six sequences implying that across participants each sequence was as often
a practiced and a new sequence.
Following the experiment, participants filled out a paper questionnaire. It started by asking
whether participants could reproduce in writing the two sequences they had been trained with
(‘reproduction’). Then they were to select their two sequences out of a set of 18 alternatives (‘forced
choice’). Eventually they were asked to indicate how they had reproduced the sequences in the first
and second parts of the questionnaire: (a) by remembering the order of the letters on the keys, (b)
by ticking the sequence with their fingers on the table, (c) by ticking the sequence in memory, (d)
by remembering the order of the stimuli on the screen, and (e) in another way.
2.4. Apparatus and setting
Stimulus presentation, timing, and data collection was achieved using the E-prime? 1.1 experi-
mental software package on a standard Pentium? IV class PC. Stimuli were presented on a 17 inch
Philips 107T5 display running at 1024 by 768 pixel resolution in 32 bit color, and refreshing at
85 Hz. The viewing distance was approximately 60 cm, but not strictly controlled.
2.5. Dependent measures
The serial positions of the six responses in each sequence are denoted by their index: R1 through
R6. The interval between two successive key presses is denoted interkey interval (IKI), and the position
of a specific IKI is indicated by a T with an index denoting the response it precedes. So, T2–T6 indicate
the IKIs preceding R2–R6. As cue onset coincides with depression of the previous key, T2–T5 are also
response times to their corresponding cues. T1 denotes the interval between the onset of the first cue
and pressing the first key.
3. Results
3.1. Practice phase
Apart from providing participants with training on their two 6-key sequences, the practice phase
allowed also examining whether practicing the prestructured sequence affects segmentation of the
unstructured sequence. This was investigated with a 2 (Group: 3-3 vs. 2-2-2) ? 2 (Sequence: with
vs. without pause) ? 2 (Block 6 & 7) ? 6 (Key) ANOVA on response times with group as between-sub-
ject variable.
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All main effects were significant in this ANOVA, as were the Group ? Sequence, Group ? Key, Se-
quence ? Key, and Group ? Sequence ? Key interactions, ps < .01. Closer examination of the data
showed the expected long T4 in Group 3-3 and the long T3 and T5 in Group 2-2-2 for the prestructured
sequence (Fig. 1). The sequences that were practiced without any pause appeared to show a minor car-
ry-over effect in the 3-3 group of the other, segmented, sequence in that T4 was longer than the mean
of T2, T3, T5, and T6, F(1, 46) = 7.6, p < .01. However, a similar interaction was not found in Group 2-2-
2 when comparing the means of T2, T4, and T6 with the means of T3 and T5, F(1, 46) = 0.5, p > .20. So,
as the effect in 3-3 may be attributed also to a natural tendency for a 3-3 segmentation pattern, the
absence of such a transfer in 2-2-2 indicates that there is little evidence that the rhythm of the pre-
structured sequence gets transferred to the unstructured sequence.
Across both groups, the presence of pauses also slowed T1 relative to the unstructured sequence,
F(1, 46) = 18.7, p < .001. This effect tended to be larger in 2-2-2 than in 3-3, F(1, 46) = 3.8, p = .06,
and the effect of the pauses on T1 was significant in just Group 2-2-2, F(1, 46) = 19.8, p < .001, and
not in Group 3-3, F(1, 46) = 2.8, p = .10.
Error rates in the practice phase are shown in Fig. 1 and were subjected to arcsin transformations
and analyzed with an ANOVA using the same design as with response times. Key presses in the
unstructured sequence involved more errors than key presses in the prestructured sequence, 2.4%
vs. 1.4%, F(1, 46) = 29.7, p < .001. This higher error rate occurred especially at the fourth response in
the unstructured sequences of both groups, 4.5%, again suggesting a natural chunking tendency with
unstructured sequences. Error rate of the fourth key appeared higher than that of each of the other five
keys, Fs(1, 46) > 5.6, ps < .05. In the 3-3 prestructured sequence error rate was highest with the fifth
response, 4.0%. In the 2-2-2 sequence all responses had error rates below 2.1%. This difference was
indicated by a Group ? Sequence ? Key interaction, F(5, 230) = 4.7, p < .001.
3.2. Test phase
Fig. 2 shows the main results of the test phase. The design in the test phase involved a 2 (Group: 3-3
vs. 2-2-2) ? 2 (Segmentation during practice: prestructured vs. unstructured sequence) ? 2 (Sequence
familiarity: practiced vs. new) ? 2 (Finger setting: practiced vs. unpracticed) ? 6 (Interval) ANOVA
with group as between subject variable. To determine possible differences between the three DSP
phases, initiation (T1), transition (2-2-2: T3 and T5; 3-3: T4), and execution (2-2-2: T2 T4 T6; 3-3:
Fig. 1. T1 and the interkey intervals T2–T6 separately for the 2-2-2 and 3-3 groups in practice sessions 6 and 7 as a function of
key position and presence of a pause. Bars indicate error percentages for unstructured and prestructured sequences (open and
filled, resp.).
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T2 T3 T5 T6), an additional 2 (Group) ? 2 (Segmentation) ? 2 (Sequence Familiarity) ? 2 (Finger set-
ting) ? 3 (Phase) ANOVA was carried out when necessary.
3.3. Segmentation of the prestructured sequence
When transition and execution phases were pooled across the 2-2-2 and 3-3 groups (for 2-2-2: T3
and T5 vs. T2 T4 T6; for 3-3: T4 vs. T2 T3 T5 T6), planned comparison of the times associated with the
alleged start element of each segment and with the remaining (non-initial) elements appeared highly
significant, F(1, 46) = 69.6, p < .001. This indication for persistent segmentation was significant also
when tested for the two groups separately, Fs(1, 46) > 27.8, ps < .001. In line with the results in the
practice phase, the T1 difference between the two familiar sequences was greater in the 2-2-2 than
in the 3-3 group, F(1, 46) = 5.6, p < .05. While in the 2-2-2 group the segmented sequence was initiated
55 ms more slowly than the unstructured sequence, F(1,46) = 7.6, p < .01, in the 3-3 group the seg-
mented sequence was not initiated significantly slower, F(1, 46) = 0.3, p > .20.
3.4. Segmentation of the unstructured sequence
Like in the practice phase, the unstructured sequence of the 3-3 condition showed a marginally sig-
nificant indication across all unstructured sequences that segmentation had transferred from the seg-
mented sequence in that T4 (200 ms) was marginally longer than T2, T3, T5, and T6 (178 ms), F(1,
46) = 3.5, p = .07. However, such transfer had again not developed in 2-2-2 as evidenced by comparing
T2, T4, T6 with T3 and T5 for the unstructured sequence, F(1, 46) = 0.1, p > .20. A further analysis
assessing segmentation transfer for each of the six individual sequence versions making up the group
of prestructured and unstructured sequences did not show any transfer either.
In order to examine individual segmentation differences, we then classified each participant as a
function of her or his longest interkey interval in the unstructured sequence (relative to the other
interkeys intervals of that participant). This allowed classification of participants in five groups (i.e.,
with longest R2–R6). Fig. 3 shows the mean segmentation pattern per subgroup and the number of
Group 222
time (ms)
100
200
300
400
500
600
KEY1
Group 33
new 1
new 2
unstructured
prestructured (during practice)
23456
KEY1
23456
Fig. 2. T1 and the interkey intervals T2–T6 separately for the 2-2-2 and 3-3 groups in the test phase as a function of key number
and sequence.
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participants in that subgroup (this appeared unaffected by use of familiar or unfamiliar fingers). These
data demonstrated large individual differences in how sequences were carried out. Importantly, the
segmentation patterns in the unstructured sequence were not systematically affected by the temporal
pattern of the accompanying prestructured sequence: Tests of whether there were more 3-3 than 2-2-
2 participants with a longest T4 in the unstructured sequence (7 vs. 5), and more 2-2-2 than 3-3 par-
ticipants with a longest T3 and T5 in the unstructured sequence (6 vs. 3; 4 vs. 1), were far from sig-
nificant, X2s(d.f. = 1) > .39, ps > .20.
Arcsin transformed errors were subjected also to a 2 (Group) ? 2 (Segmentation during
practice) ? 2 (Sequence Familiarity) ? 2 (Finger setting) ? 6 (Interval) ANOVA. It showed for unfamil-
iar sequences that the fifth sequence element had the highest error rate, 7.1%, whereas for the familiar
sequences this was the third element, 5.2%, F(5, 230) = 3.0, p < .05.
In short, the response data showed clear indications of segmentation in the unstructured sequence,
but this did not consistently reflect the temporal pattern of the accompanying prestructured se-
quences, neither across all sequences, nor for the individual sequence versions of the unstructured
group, nor in the number of participants in the 2-2-2 and 3-3 group using a particular segmentation.
3.5. Segmentation of the new sequences
The two new sequences had not been practiced and, hence, were basically unstructured. In order to
determine whether segmentation of the prestructured sequence would make participants use that
same segmentation pattern with the two new sequences that were executed after segmentation
had developed, planned comparisons were carried out on both new sequences in the 2-2-2 and the
3-3 groups. As indicated in Fig. 2, there seemed to be transfer of the 2-2-2 segmentation pattern to
the new sequences in that T3 and T5 were longer than T2, T4, and T6, F(1, 46) = 13.7, p < .001, but
transfer of the 3-3 segmentation pattern to the new sequences was not supported by a larger T4 than
the average of T2, T3, T5, and T6, F(1, 46) = 0.2, p > .20. In fact, for the 3-3 group, planned comparison
suggested the same segmentation as in the 2-2-2 group, F(1, 46) = 6.2, p < .05. The error analyses
showed that unfamiliar sequences had more errors than familiar sequences, 5.0 vs. 3.8%, F(1,
46) = 19.9, p < .001.
Even if there is no transfer from the prestructured to the new sequences, participants may still have
developed a common segmentation pattern just for their two new sequences in which no segmenta-
tion was imposed at all. We tested with separate ANOVAs segmentation patterns using T2–T6 for each
Fig. 3. Average interkey intervals for the unstructured sequence in the test phase as a function of participant subgroup with a
different longest interkey interval (across the familiar and unfamiliar hand conditions). The numbers of participants in each
subgroup are indicated.
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of the three subgroups (each with 16 participants) that had the same two new sequences. Each of
these three ANOVAs had a 2 (Group: 2-2-2 vs. 3-3) ? 2 (Sequence) ? 2 (Finger setting) ? 5 (Key T2–
T6) design. Fig. 4 shows the movement patterns for each subgroup (middle column) along with the
interkey intervals (right column). The indication in this figure that the new sequences were all seg-
mented in some way was supported by significant differences amongst T2–T6 in each subgroup,
Fs(4, 56) > 10.4, ps < .001. Furthermore, the upper and lower subgroups in Fig. 4 appeared to have
two different segmentation patterns for each of their sequences, Sequence ? Key interactions: Fs(4,
56) > 6.8, ps < .001, whereas the segmentation pattern for each sequence was not different for the mid-
dle group, F(4, 56) = 1.6, p > .18. This difference between the three subgroups was significant in an AN-
OVA also including subgroup as a variable, F(8, 168) = 6.1, p < .001. So, it seems that with certain
sequence combinations a common temporal pattern may be used, but that the use of a common tem-
poral pattern is certainly not general. Rather, there appears to be a tendency to break the sequence at
specific joints depending on each sequence, often coinciding with a transition of fingers of one to fin-
gers of the other hand. Indeed, six out of the eight identified transitions (i.e., dotted lines in the middle
panel of Fig. 4) coincide with such movements, and within-hand transitions are only identified in se-
quences which also present another transition between hands. The segmentation patterns were not
different for the 2-2-2 and 3-3 participants in the three ANOVAs, F(5, 70) < 1, ps > .20, and not different
for the two finger settings either, F(5, 70) < 1, ps > .20.
3.6. Finger-specific sequence learning
The analyses involving the three phases of the sequence (initiation, execution, transition) indicated
the development of an effector-specific sequence component in the familiar sequences. This is shown
Fig. 4. Average interkey intervals for each individual new sequence (right column), along with the segmentation of these
sequences (left column), and a graphical overview of the temporal-spatial key press orders in each of three subgroups (middle
column). The letters on top indicate the key positions on the keyboard. Keying order in the middle column for each sequence is
from top to bottom. Dashed lines indicate relatively slow transitions.
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by the Finger Setting ? Sequence Familiarity interaction, F(1, 46) = 5.9, p < .05. Planned comparisons
confirmed that with the familiar sequences the unfamiliar finger setting yielded 18 ms slower re-
sponses, F(1, 46) = 4.44, p < .05, whereas with the new sequences finger setting did not significantly
affect response times, ?8 ms, F(1, 46) = 1.4, p > .20.
A subsequent planned comparison, examining the three DSP phases separately, confirmed that the
unfamiliar finger settings did not affect any of the phases of the new sequences, while it did affect the
phases in the familiar sequences differently, F(1, 46) = 4.1, p < .05. A further planned comparison con-
firmed for the familiar sequences that transition and execution intervals suffered more from using the
unfamiliar finger settings (22 ms) than initiation (?2 ms), F(1, 46) = 4.4, p < .05.
When examining just the prestructured sequences, the use of other fingers than during practice
slowed execution by 23 ms, F(1, 46) = 5.7, p < .05, but not the transition intervals T3 and T5 in 2-2-
2 and T4 in 3-3 (0 ms difference), F(1, 46) = 0.0, p > .20. This distinction between transition and execu-
tion intervals was significant, F(1, 46) = 5.0, p < .05. So, changing fingers in prestructured sequences
slowed execution but not initiation and transition intervals.
This was different with unstructured sequences. On the assumption that the longest interkey inter-
val (from T2–T6) in the unstructured sequence comprised a transition, we compared this longest
interval with the mean of the remaining intervals using a 2 (Group) ? 2 (Peak vs. other intervals) ? 2
(Finger Setting) ANOVA. This did not show that effector-specific learning was less for the longest inter-
val (24 ms) than for the remaining intervals (19 ms), F(1, 46) = 1, p > .20. Instead, the effector-specific
effect gradually increased with position (T1–T6: 0, 15, 20, 31, 44, 31 ms, resp.), F(5, 180) = 2.3, p < .05.
So, in contrast to the prestructured sequences, the slowing observed when other fingers were used
than during practice was not different for the longest and the remaining intervals of the unstructured
sequences, but increased with sequential position.
The error analysis showed a Sequence Familiarity ? Finger Setting interaction indicating that famil-
iar sequences carried out with the familiar finger setting involved less errors than when the unfamiliar
sequence, the unfamiliar finger setting, or both were involved (3.2% vs. 4.4% and higher, F(1, 46) = 8.8,
p < .01). Examination of the error rates at the various keys showed no clear patterns. Familiar unstruc-
tured sequences carried out with the practiced finger settings still showed their highest error rates at
the fourth key (suggesting spontaneous segmentation), but this was no longer significantly higher
than the rest.
In short, using other fingers than during practice slowed execution of familiar 2-2-2 and 3-3 (pre-
structured and unstructured) sequences, but this did not affect their initiation. In prestructured se-
quences the finger change did not slow the relatively slow transition intervals either. In
unstructured sequences the effect of the finger change increased with position, while there was no dif-
ference between the effect produced in the longest interkey interval and the effect produced in the
remaining intervals. The new sequences never showed an effect of using unpracticed fingers, thus sup-
porting the conclusion that the effector-specific effects were sequence specific, and not due to the fact
that some fingers had become more skilled.
3.7. Awareness
The questionnaire results showed that the prestructured sequence was reproduced correctly by 42
(88%) participants while the unstructured sequence was reproduced correctly by only 28 participants
(58%), X2(d.f. = 1) = 10.3, p < .01. In the recognition phase, 44 (92%) participants recognized their pre-
structured, and 45 (94%) their unstructured sequence. Thirty-six participants indicated that they had
reproduced the sequences in writing by ticking the sequence (either on the table or in memory; of
these 36 participants, 10 indicated to have used just ticking on the table, and 2 used just ticking in
memory). Twenty-five participants indicated to have used remembering of stimulus order on the
screen as one of the reproduction cues (for 5 it was the only strategy). The amount of explicit sequence
knowledge (reproduction and recognition performance) in familiar sequences appeared not to be sig-
nificantly correlated to execution rate, rs < ?.40. Together these findings suggest that the sequences
were represented primarily in a code that is tailored for executing these keying sequences, and that
the need to write down (or recognize) a sequence involved strategies to derive explicit from implicit
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sequence knowledge (cf. Heuer & Schmidtke, 1996). With the unstructured sequence this strategy
proved harder than with prestructured sequences.
4. Discussion
4.1. No transfer of segmentation
Data confirmed that inclusion of pauses during practice in a structured sequence results in a con-
tinuation of that segmentation pattern after pause removal (cf. Verwey, 1996). The present study
showed that this segmentation pattern is sequence specific in that there was no clear transfer to other
sequences in various tests: First, the transfer of the segmentation pattern from the prestructured to
the unstructured sequence, observed in the 3-3 group, was not found in the 2-2-2 group. This indicates
that the apparent transfer in 3-3, found in the practice and test phases, was probably due to a natural
segmentation tendency and is independent of the accompanying prestructured sequence. A natural
segmentation tendency in 3-key chunks was corroborated by the error data of the unstructured se-
quences in both groups. So, the impetus for the present study, namely the observation that an unstruc-
tured 6-key sequence showed the same segmentation pattern as in the simultaneously practiced 2 ? 3
sequence (Verwey et al., 2002), seemed to have been caused by a tendency to segment 6-key se-
quences in threes, rather than that the 2 ? 3 segmentation pattern in that study transferred to the
1 ? 6 sequence.
Second, the unstructured sequence in the test phase showed some systematic differences between
2-2-2 and 3-3 groups that could have indicated transfer from the prestructured sequence, but the
associated segmentation patterns appeared to differ across the various versions of the unstructured
sequence, and did not reflect the accompanying 2-2-2 or 3-3 segmentation patterns either.
Third, classification of participants as a function of the position of longest interkey interval in
unstructured sequences showed large individual differences as to how the unstructured sequences
were segmented in the test phase (Fig. 3). Importantly, the groups with a long T3 or T5 did not include
significantly more 2-2-2 participants, and the group with a longest T4 did not include significantly
more 3-3 participants. Inspection of segmentation for each particular sequence version did not pro-
vide any indication that segmentation had been influenced by the use of specific fingers in a particular
sequential position (e.g., because the little finger was slower).
Fourth,in new sequences a 2-2-2 segmentationpattern seemed dominantacross both2-2-2 and 3-3
groups (Fig. 2), again refuting transfer from the prestructured sequences. Separate analyses on each of
three subgroups carrying out a particular set of new sequences in the test phase showed that segmen-
tation of new sequences was not necessarily the same within the two new sequences either. One group
(middle group in Fig. 4) had an almost identical 2-2-2 segmentation pattern for both new sequences
(even though half the participants had practiced in the 3-3 group). For another group (lower group
in Fig. 4) the interkey interval patterns were quite different though they may still reflect a similar seg-
mentationpatternforthetwonewsequences.However,theuppergroupinFig.4showedsegmentation
patternsthatwereclearlydifferentforbothnewsequences.NoticethattheresultsinFig.4indicatealso
that the relatively slow interkey intervals were not caused so much by a slow finger (e.g., the little fin-
ger), but rather by a combination of segment length, a switch between hands, and perhaps position in
the sequence of the index finger. As explained below, the finding that the first key press following an
inter-hand transition was relatively often quite slow, is in line with hand-based sequence coding.
In short, while confirming that unstructured sequences are spontaneously segmented (Verwey,
2003a; Verwey & Eikelboom, 2003), the data did not show indications that the spontaneously devel-
oped segmentation patterns in the unstructured and the new sequences are influenced by the imposed
segmentation of the accompanying prestructured sequence. Likewise, the new sequences do not nec-
essarily involve the same segmentation pattern.
4.2. Transfer to new finger settings
The data also demonstrated an effector-specific sequence learning component in that using other
fingers of the same hand caused execution rates to reduce. Nevertheless, the size of this component
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was quite small (18 ms) compared to the (65 ms) effect size found with transfer between fingers of
one and two hands, and vice versa (Verwey & Wright, 2004). This small rate reduction seems hard
to reconcile with the notion in the introduction that changing fingers implies that automatic triggering
of individual fingers is suppressed and replaced by controlled triggering of other fingers. The relatively
small performance reduction when other fingers of the same hands are used is more in line with the
notion that effector-specific learning is based on the development and use of a spatial component with
a hand-based reference frame. At least for the serial RT task, recent results confirm that implicit as
well as explicit sequence learning is based on spatial learning (Liu, Lungu, Waechter, Willingham, &
Ashe, 2007). However, that study did not involve the extensive practice of the present experiment,
and can therefore not be taken as immediate evidence that spatial learning is dominant with extensive
practice too as practice may well involve development of another type of coding (Hikosaka et al.,
1999). Still, the present results are in line with the notion that effector-specific sequence learning is
spatial. In terms of the dual processor model, these spatial components might all be a function of
the motor processor. Hand-based sequence learning can explain also why in the present data seg-
ments often seem to start following an inter-hand transition in that another reference frame needs
to be activated after such a transition (Fig. 4). Obviously, the notion that effector-specific sequence
learning is actually based on a spatial, hand-based frame of reference has to be tested more directly.
4.3. Phases of sequence execution
Though not directly anticipated, the present data confirmed the theoretical distinction between
execution of a motor chunk and the transition from one to the next motor chunk within the prestruc-
tured sequence as postulated by the hierarchically operating dual processor model (Verwey, 2001).
That is, using adjacent fingers lengthened intervals that were within the predefined segments (i.e., in-
tra-chunk intervals), but not those that started a segment (i.e., inter-chunk intervals). This extends re-
cent findings supporting the distinction between execution of chunks and transition between chunks
in that chunk execution was slowed when the hand position was changed relative to the body while
initiation and transition phases were not (de Kleine & Verwey, 2008). This endorses the distinction be-
tween a cognitive processor responsible for concatenating motor chunks, and a motor processor car-
rying out motor chunks that represent individual key presses in terms of various spatial reference
frames.
The observation in the unstructured sequences that the effector-specific effect seemed to increase
with sequential position may be explained by the dual processor model too (Verwey, 2001). As the
buffer used to prepare short keying sequences (sometimes denoted motor buffer) is assumed to have
a limited capacity (Rosenbaum, 1990; Sternberg et al., 1978), one could argue that the increasing
effector-dependence with position in the unstructured sequence can be attributed to a simultaneous
contribution of the (effector-independent) cognitive processor to the (effector-dependent) motor pro-
cessor solely at the start of the sequence (Verwey, 2003b).
5. Conclusions
The main findings are (a) that the temporal pattern found with keying sequences is sequence-
dependent and does not influence other sequences, and (b) that execution slows when other fingers
of the same hand are being used. This slowing seems smaller than when fingers of the other hand
are used (as in Verwey & Wright, 2004), which can be explained by the notion that effector-specific
sequence learning involves the development of a hand-based reference frame. Additional results are
(c) a confirmation that different processes are involved in concatenating and executing motor chunks,
and that (d) spontaneous segmentation is highly variable within and between participants. (e) The dif-
ficulty some participants have in explicitly reproducing discrete sequences (here and in earlier stud-
ies) confirms that even though participants are aware they are producing two fixed 6-key sequences
their motor chunks are not directly accessible to processes used to reproduce the sequences in another
mode (like verbal expression), and that in order to verbally reproduce the sequence they may revert to
playing back their motor chunks.
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Acknowledgment
We would like to thank Malou Bolscher, Janine den Hertog, Matthias Kampermann, Milou Kievik,
Malto Risto, Judith Wagner, Mike Walpuski, and Noshik Zeko for running the experiment. This re-
search was supported in part by a visiting researcher grant to the first author from the Max Planck
Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, and by Grant No. 461-04-
620 from the Netherlands Organization for Scientific Research NWO. Jiménez was supported by grants
SEJ2005 25754-E and SEJ2006 27564-E from the Spanish Ministerio de Educación y Ciencia.
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