Article
Reference
The "implicit" serial reaction time task induces rapid and temporary adaptation rather than implicit motor learning
TROFIMOVA, Olga, et al.
Abstract
The serial reaction time task (SRTT) has been widely used to induce learning of a repeated motor sequence without the participants' awareness. The task has also been of major influence for defining current concepts of offline consolidation after motor learning. The present study intended to replicate previous findings in a larger population of 53 healthy individuals. We were unable to reproduce previous results of online and offline implicit motor learning with the SRTT. Trials with a repeated sequence rapidly induced shorter reaction times compared to random trials, but this improvement was lost in a post-test obtained a few minutes after the training block. Furthermore, no offline consolidation was observed as there was no change in sequence specific reaction time gain between the post-test immediately after training and a re-test obtained 8 h after training. Online or offline learning remained absent when we modulated the number of sequence repetitions, the error levels, and the structure of random sequences. We conclude that the SRTT induces a rapid and temporary adaptation to the sequence rather than learning, since [...]
TROFIMOVA, Olga, et al . The "implicit" serial reaction time task induces rapid and temporary adaptation rather than implicit motor learning. Neurobiology of Learning and Memory , 2020, vol. 175, p. 107297
DOI : 10.1016/j.nlm.2020.107297 PMID : 32822865
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Neurobiology of Learning and Memory
journal homepage:www.elsevier.com/locate/ynlme
The “implicit” serial reaction time task induces rapid and temporary adaptation rather than implicit motor learning
Olga Trofimova
a, Anaïs Mottaz
a, Leslie Allaman
a, Léa A.S. Chauvigné
a, Adrian G. Guggisberg
a,b,⁎aImaging-Assisted Neurorehabilitation Laboratory, Department of Clinical Neurosciences, University of Geneva, Geneva, Switzerland
bDivision of Neurorehabilitation, Department of Clinical Neurosciences, University Hospitals Geneva, Geneva, Switzerland
A R T I C L E I N F O Keywords:
Serial reaction time task (SRTT) Implicit learning
Memory consolidation Motor skills
A B S T R A C T
The serial reaction time task (SRTT) has been widely used to induce learning of a repeated motor sequence without the participants’ awareness. The task has also been of major influence for defining current concepts of offline consolidation after motor learning. The present study intended to replicate previous findings in a larger population of 53 healthy individuals. We were unable to reproduce previous results of online and offline implicit motor learning with the SRTT. Trials with a repeated sequence rapidly induced shorter reaction times compared to random trials, but this improvement was lost in a post-test obtained a few minutes after the training block.
Furthermore, no offline consolidation was observed as there was no change in sequence specific reaction time gain between the post-test immediately after training and a re-test obtained 8 h after training. Online or offline learning remained absent when we modulated the number of sequence repetitions, the error levels, and the structure of random sequences. We conclude that the SRTT induces a rapid and temporary adaptation to the sequence rather than learning, since the repeated motor sequence does not seem to be encoded in memory.
1. Introduction
The acquisition of motor behaviors involves explicit and implicit aspects. Explicit aspects are typically conscious and can be described with words, for example when explaining the goal of a motor task («reach the target with your finger as fast as possible»), or by decom- posing a complex motor behavior into simple movements that have to be performed in a certain order. Conversely, practice is required for implicit aspects (Fitts & Posner, 1967). Through a series of trials and errors, movements are corrected and refined until they reach optimal accuracy and timing. Implicit adjustments are often not accessible to consciousness.
There is a long history of research focused on separating implicit from explicit components of motor learning based on temporal stages, behavioral (Moisello et al., 2011;Taylor, Krakauer, & Ivry, 2014) or neuroanatomical components (Liew et al., 2018; Schendan, Searl, Melrose, & Stern, 2003;Steele & Penhune, 2010;Vidoni & Boyd, 2007).
Early stages of learning usually involve explicit processes related for instance to the conscious learning of the sequence order (Fitts & Posner, 1967). On the other hand, implicit aspects intervene in later steps of fine tuning and automatization (Fitts & Posner, 1967; Kal, Prosée, Winters, & Van Der Kamp, 2018;Logan, 1988).
It has been postulated that learning of a movement sequence can also be achieved without explicit awareness (Albouy et al., 2006; Nissen
& Bullemer, 1987). One of the most frequently used paradigms to test such implicit sequence learning is the serial reaction time task (SRTT) (Nissen & Bullemer, 1987;Robertson, 2007). In the “implicit” version of this paradigm, a repeated sequence is inserted between random blocks without the participants’ knowledge. The sequence repetition leads to a decrease in response time (RT) during sequence compared to random trials. Subjects are usually not aware of the repeated sequence, and the gain in response time is thus thought to result from implicit learning (Chrobak, Siuda-Krzywicka, Siwek, Tereszko, Janeczko, Starowicz- Filip, & Dudek, 2017; Deroost & Soetens, 2006; Martini, Furtner, &
Sachse, 2013; Unsworth & Engle, 2005; Willingham & Goedert- Eschmann, 1999). More recently, the implicit SRTT has also been used to study retention and consolidation of motor learning, which has led to influential concepts related to daytime consolidation of learned motor skills (Brown, Robertson, & Press, 2009;Cohen, Pascual-Leone, Press, &
Robertson, 2005;Press, Casement, Pascual-Leone, & Robertson, 2005;
Robertson, Pascual-Leone, & Press, 2004; Sami, Robertson, & Miall, 2014).
In the SRTT, the motor action itself (pressing on a button with one of four fingers) is not very difficult; indeed a very steep learning curve is
https://doi.org/10.1016/j.nlm.2020.107297 Received 22 July 2020; Accepted 10 August 2020
⁎Corresponding author at: Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospitals Geneva, Avenue de Beau-Séjour 26, 1211 Geneva 14, Switzerland.
E-mail address:aguggis@gmail.com(A.G. Guggisberg).
Available online 18 August 2020
1074-7427/ © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
T
observed within the first few trials. Thus, the focus of the SRTT is on the sequential nature of the movement, a normally explicit aspect made implicit in this task.
The present study aimed at replicating previous findings of a day- time offline consolidation of implicit sequence learning with the SRTT (Brown, Robertson, & Press, 2009;Cohen et al., 2005;Press et al., 2005;
Robertson et al., 2004;Sami et al., 2014) in order to further investigate mechanisms at play in this type of learning. We used the same ex- perimental design and methodology as those studies. However, we were not able to replicate their results and hence we investigated possible reasons. Some issues arising from the interpretation of the effects ob- served in the widely used “implicit” SRTT are presented and discussed here.
2. Materials and methods 2.1. Participants
A total of 63 healthy right-handed volunteers (30 males and 33 females, 27.7 ± 6.4 (mean ± SD) years old) participated in the study after signing informed consent. The Ethics Committee of the Canton of Geneva approved all study procedures (PB_2016-02172 (04-081)) that were conducted in accordance with the declaration of Helsinki. The participants had normal or corrected to normal vision with no color vision deficiency. Ten participants were excluded from the data: eight of them showed a partial or total explicit awareness of the sequence, correctly recalling five or more consecutive elements, as assessed at the end of the experiment; one participant slept between the two experi- mental sessions despite the given instructions; the last one performed the task with exceptionally long and stable response times compared to the other participants (mean 5.5 SD above the group mean), showing that he was not responding as fast as he could contrary to the other participants. Participants received a fixed monetary compensation for their participation plus, in some cases, a bonus as reward for good performance (see below).
2.2. Task
A serial reaction time task (SRTT) (Nissen & Bullemer, 1987) was designed using the E-Prime 2.0 software (Psychology Software Tools, Pittsburgh, PA) (Schneider, Eschman, & Zuccolotto, 2010). Four grey squares (3 × 3 cm spaced by 1.5 cm) appeared horizontally over a black background on a computer screen placed approximately 60 cm from the participants' eyes. For each trial one of the squares became orange, indicating that it was the target for the response. A Chronos box (Psychology Software Tools, Pittsburgh, PA; https://pstnet.com/
products/chronos/) with four horizontally arranged buttons was placed in front of the participants, who were instructed to keep their right hand on it (index to little finger on the four buttons) and to press the button corresponding to the target square as fast as they could without making errors. In case of error, the target square remained orange until the correct button was pressed. A 500 ms interval preceded the next target. The targets were either presented in a random order, or following a 12-item pattern that was repeated several times in a row (sequence).
2.3. Experimental design and measurements
The experiment (Fig. 1) consisted of two sessions administered the same day. Session 1 (between 9AM and 10AM) was composed of three blocks. Each block – pretest, training, and posttest – contained random items followed by a repeated sequence and random items again, without breaks. Between the blocks, the screen indicated to the parti- cipants that they could take a short break if they wanted to. Session 2 (between 5:30 PM and 6:30 PM) contained only one retest block. In the 8-hour interval that separated the two sessions, the participants were
not allowed to sleep.
Random and sequence parts were created as previously described (Brown, Robertson, & Press, 2009;Cohen et al., 2005;Press et al., 2005;
Robertson et al., 2004; Sami et al., 2014). The following 12-item se- quence was used in the experiment: 2–3–1–4–3–2–4–1–3–4–2–1 (the four squares are numbered left to right). The random parts followed these rules: the four targets appeared an equal number of times; a target could not be repeated twice in a row; no segment of four or more consecutive elements of the sequence could be used; and all random parts were different from each other but did not differ from one par- ticipant to the other. Other regular patterns such as interleaved re- petition (e.g. 232, 414) or runs (e.g. 123, 432) were present an equal number of times in every random part.
Response time (RT) was measured from the moment the colored target appeared on the screen to the moment the correct button was pressed. For each participant, all RTs ≥ 3 SD above the mean of the block were excluded from the data. Trials with errors were kept in the data as long as RT did not exceed 3 SD above the mean. Implicit se- quence learning (Skill) was calculated in each block by taking the dif- ference between the mean RT in the last (non-excluded) 36 sequence trials and the 36 following random trials, similarly to previously pub- lished studies where a subset of trials at the end of sequence parts is compared to subsequent random trials (Brown, Robertson, & Press, 2009; Carvalho et al., 2018; Cohen et al., 2005; Press et al., 2005;
Robertson et al., 2004; Sami et al., 2014). The purpose of SkillPre (pretest) was to give a pre-learning baseline, of SkillPost (posttest) to measure the amount of implicit learning at the end of session 1, and of SkillRe (retest) to measure offline consolidation of implicit learning.
SkillTr (training) is an additional indicator of the amount of learning at the end of the training, even if it was not designed as a test block.
We used a questionnaire to make sure that participants did not gain explicit awareness of the sequence at the end of session 2. The parti- cipants were asked whether they noticed a repeated sequence during the experiment, and if so, to try writing it using the numbers 1 to 4 corresponding to the four buttons. Participants who correctly recalled five or more consecutive elements of the sequence were excluded from the study, as they probably used explicit learning. The participants who recalled less than five consecutive elements were considered not aware of the sequence (Brown, Robertson, & Press, 2009;Press et al., 2005;
Robertson et al., 2004; Sami et al., 2014; Willingham & Goedert- Eschmann, 1999).
In order to obtain implicit sequence learning and offline con- solidation as expected, we conducted several different versions of the experiment. In each version, different parameters were used with the aim of reaching an optimal setup for implicit sequence learning and offline consolidation. A total of five versions of the experiment were created and administered. Each participant was included in one version only, since the repeated sequence was the same in all versions.Table 1 summarizes the differences between the five experimental versions.
For the first version, the three test blocks (pretest, posttest, and retest) were composed of 50 random trials followed by 180 sequence trials and 50 random trials, whereas the training block had 50 random, 300 sequence, and 50 random trials. This first version corresponded exactly to the studies we aimed to replicate (Brown, Robertson, & Press, 2009;Cohen et al., 2005;Press et al., 2005;Robertson et al., 2004;Sami et al., 2014). The participants were told to respond as fast as possible without making too many errors.
In the second version, the number of trials in the test blocks was reduced in order to decrease the amount of learning during pretest. The test blocks had the following structure: 60 random trials, 90 sequence trials, 60 random trials. The training block had 60 random, 300 se- quence, and 60 random trials.
The number of sequence trials in the test blocks was reduced even more in the third version, down to 60 random trials, 60 sequence trials, and 60 random trials, while the training block stayed the same.
Furthermore, in order to decrease the error percentage to levels
O. Trofimova, et al. Neurobiology of Learning and Memory 175 (2020) 107297
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reported previously (reported error < 1–2%) (Sami et al., 2014), the participants were instructed to concentrate on making as few errors as possible (thus, “respond as fast as possible” was removed from the in- structions).
In the fourth version, the structure of the blocks and the instructions were the same as in version 3. In addition, the participants in this group received a feedback regarding their error percentage after pretest (po- sitive feedback if < 2% of error) and a monetary reward of 10 Swiss francs (equivalent to ca. 10 US dollars) at the end of the second session if the error percentage was lower than 2% for the entire experiment.
The fifth version was designed to minimize structural differences between random and sequence parts. All random parts were organized in segments of four items where each digit was used once (ex: 4213, 3214, 1234, etc.) similarly to the sequence (2314–3241–3421). Thus, the random parts followed additional rules: the three four-digit per- mutations present in the sequence were not used, each of the other existing four-digit permutations was used only one or two times per random part to avoid learning of one permutation over another, con- secutive repetitions of one digit (ex: 22), two digits (ex: 2424), and three digits (ex: 243243) were not allowed. The structure of the blocks, the instructions and the feedback were the same as in version 4, except there was no monetary reward for low error percentages.
The first session (pretest, training, and posttest) took 12–17 min to complete and the second session (retest) took 3–4 min.
2.4. Statistical tests
One sample t-tests were conducted to determine if each of the four Skills was significantly different from zero, since the values met the assumption of normality (Shapiro-Francia test, p = 0.61). Paired sample t-tests and one-way repeated measures ANOVAs were per- formed to evaluate if there were within-group differences between the four Skills. Unpaired sample t-tests and one-way ANOVAs were used to compare the different experimental groups. Spearman correlation was used to evaluate the link between the number of sequence repetitions and the magnitude of Skill, given the non-normal distribution of the numbers of sequence repetitions (Shapiro-Wilk test, p < 0.001).
Ordinary least squares regression was applied to test the effect of age and sex on learning indices.
3. Results
In all experimental versions, the participants had lower RTs on re- peated sequence trials than on random trials on average (meanSkill values are positive, seeFig. 2). However, when analyzing differences in Skills between blocks, we noted an absence of the two expected effects:
online implicit sequence learning (posttest-pretest) and offline con- solidation of implicit sequence learning (retest-posttest). By comparing the different experimental versions, we investigated possible reasons for this lack of effect.
3.1. Effect of the number of sequence repetitions on Skills
From the first experimental version, it was striking to note that SkillPre – supposedly a baseline – was much higher than zero. A see- mingly plausible reason for that was that the sequence was already learned in the pretest block where it was repeated 15 times (180 se- quence trials). By reducing the number of repetitions of the sequence in the test blocks from 180 trials to 90 and then to 60 trials, we wanted to contain sequence learning within the training block. In this manner, we Fig. 1.Experimental design.
Table 1
Summary of the five experimental versions.
Version 1 Version 2 Version 3 Version 4 Version 5
Number of participants after exclusion of data (age range) 10
(22–42 yrs) 10
(20–50 yrs) 10
(20–43 yrs) 11
(21–35 yrs) 12
(20–36 yrs)
Number of trials in test blocks (random-sequence-random) 50–180-50 60–90-60 60–60–60 60–60–60 60–60–60
Number of trials in training block (random-sequence-random) 50–300–50 60–300–60 60–300–60 60–300–60 60–300–60
Instructions: emphasis on making few errors No No Yes Yes Yes
Feedback on error after pretest No No No Yes Yes
Monetary reward if error < 2% No No No Yes No
Structure of random parts matched to structure of sequence No No No No Yes
Fig. 2.Mean Skills for each of the five experimental versions. Skill represents the RT difference between random trials and sequence trials in each block (36 random trials following the sequence − 36 last trials of the sequence). Error bars indicate SD.
aimed at obtaining a SkillPre around zero, i.e. no difference in RT be- tween random and sequence trials. SkillPre was lower when the se- quence was repeated fewer times (see Skill at pretest in version 1 vs 2 vs 3–5 inFig. 2). In fact, there was a significant correlation between the number of sequence repetitions and the Skill when considering all the blocks (ρ = 0.24, p < 0.001). In other words, the RT difference be- tween random and sequence was smaller when the sequence was re- peated fewer times (Fig. 3). However, even with only 60 sequence trials (the sequence repeated five times), SkillPre was significantly higher than zero (t32= 3.41, p = 0.002). Thus, the participants responded significantly faster in sequence trials than in random trials, even after executing the sequence only five times. Furthermore, reducing the number of sequence repetitions in the test blocks did not result in sig- nificant online sequence learning (t32= 0.16, p = 0.87) or offline consolidation (t32= 0.42, p = 0.67).
3.2. Effect of random parts structure on Skills
A possible cause for the RT difference after only five sequence re- petitions is the structure of the sequence itself. The repeated sequence was chosen for our study in order to remain comparable with the ma- jority of previous trials with the SRTT. However, a particular pattern can be noticed in this sequence: its 12 items can be divided into three segments of four where each digit is used once.
2314 3241 3421
The criteria used for random sequences described in previous stu- dies did not guarantee a similar target distribution across the four fin- gers. To test the hypothesis of a lower difficulty with this pattern as compared to a random pattern, we modified the random parts to im- pose the same structure as during the sequence. This way, the overall structure of the task was homogenized, and the only difference between random and sequence lay in the absence of repetitions in the random parts. In this version, the difference between sequence and random trials during the pretest block (SkillPre) still tended to be different from 0 (t11= 2.04, p = 0.07) and SkillPre was not significantly lower than in the other versions (F(4, 48)= 0.51, p = 0.73). Moreover, like in the other versions, there was no difference between the four Skills (F(3,
33)= 0.29, p = 0.84), meaning that even when the structure of random and sequence parts was homogenized, there was still no online learning or offline consolidation of the sequence.
3.3. Effect of instructions and reward on error percentage and mean RT Error percentage is another parameter that might influence RT and Skills. Indeed, if many errors occur, the sequence is correctly produced
fewer times, potentially resulting in weaker sequence learning. To re- duce response error percentages, some adjustments were made from one version to another: the instructions given to the participants were modified (emphasis on making few errors rather than fast response), a feedback was given after pretest (positive feedback if error < 2%), and monetary reward was added (10 CHF bonus if error < 2%).Table 2 shows the mean error percentages in the different versions. The error rate varied significantly in the different versions (F(3, 49) = 7.76, p < 0.001). Error was significantly lower with the changed instruc- tions, feedback, and reward, as compared to the initial setup (t30= 3.9, p < 0.001). However, despite the low error percentage obtained with those modifications (mean error of 0.86%), there was no online (t10= 0.97, p = 0.36) or offline consolidation (t10= 0.96, p = 0.36) of sequence learning.
3.4. Implicit sequence learning and its offline consolidation
Hence, the adjustments made between the experimental versions have not lead to the learning and consolidation effects we expected with the SRTT. Despite the differences in block length, instructions, feedback, reward, and random part structure between the five versions, they all seemed to lead to the same pattern of result, i.e. no online learning or offline consolidation. Since results were very similar in all the versions, the five groups were assembled and analyzed together in order to confirm the effects observed in each version separately, but with a larger sample size.Fig. 4shows mean Skills in pretest, training, posttest, and retest across all 53 participants. All four Skills were higher than zero (SkillPre: t52 = 5.86, p < 0.001; SkillTr: t52 = 7.53, p < 0.001; SkillPost: t52= 5.77, p < 0.001; SkillRe: t52= 6.11, Fig. 3.Skill against number of trials in the sequence part of the block. Each dot
represents one participant's Skill in one block. Skill represents the RT difference between random trials and sequence trials in each block (36 random trials following the sequence − 36 last trials of the sequence). The line shows a linear regression model fit.
Table 2
Mean error percentages throughout the experiment across subjects in each modality.
Instructions A Instructions B
No feedback No feedback Feedback No reward
(n = 20) No reward
(n = 10) No reward
(n = 12) Reward
(n = 11) Mean error
(%) 5.86 4.16 1.32 0.86
Instructions A: “Answer as fast as you can without making too many errors”;
Instructions B: “Concentrate on making as few errors as possible”; Feedback after pretest: positive if error < 2%, negative if error ≥ 2%; Reward: 10 CHF bonus at the end of the experiment if overall error < 2%.
Fig. 4.Mean Skills of the 53 participants. Skill represents the RT difference between random trials and sequence trials in each block (36 random trials following the sequence − 36 last trials of the sequence). Error bars indicate SD.
P-values were obtained by one samplet-test for each bar and paired samplet- test between bars. * = p < 0.05, *** = p < 0.001.
O. Trofimova, et al. Neurobiology of Learning and Memory 175 (2020) 107297
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p < 0.001). There was no difference between SkillPre and SkillPost (t52= 0.15, p = 0.88) or between SkillPost and SkillRe (t52= 0.16, p = 0.87), indicating that the participants showed neither online im- provement in Skill from pretest to posttest nor offline consolidation from posttest to retest. This confirmed the results that were observed in each group individually. Furthermore, there was a drop in Skill from training to posttest (t52= 2.32, p = 0.02).
We tested potential confounding effects of age and sex by regressing them on online (SkillPost – SkillPre) and offline (SkillRe – SkillPost) learning indices. Neither age (online: Beta = −1.00, p = 0.30; offline:
Beta = −0.32, p = 0.71) nor sex (online: Beta male = 9.98, p = 0.42;
offline: Beta male = 12.05, p = 0.28) were significant predictors.
3.5. Non-specific improvement
We noted a general RT decrease in both random and sequence trials, which seemed to indicate an unspecific improvement in the task. To explore a possible sequence-independent learning in the SRTT, along with an offline consolidation of this learning from morning to evening, we compared the mean RTs of the test blocks (Fig. 5). RT tended to decrease from pretest to posttest, although not significantly (t52= 1.76, p = 0.08) and it significantly decreased from posttest to retest (t52= 3.64, p < 0.001), indicating a non-specific improvement at the task in the 8-hour interval between the two sessions.
4. Discussion
The SRTT has been widely used as an implicit motor learning paradigm. Different variants of the task exist (implicit vs explicit, de- terministic vs probabilistic, unimanual vs bimanual) and it is generally assumed that the RT rebound effect observed in the implicit variants reflects implicit motor sequence learning. However, whether the term
“learning” should be used in this context is far from clear. Many defi- nitions of learning have been proposed (de Houwer, Barnes-Holmes, &
Moors, 2013; Gagne, 1977; Lachman, 1997; Mayer, 1982; Schunk, 2011). While there is no consensus on a single definition, and processes involved in learning are still debated, three elements seem present in every definition: learning produces a change; there is a long-term per- sistence of that change; and learning is a consequence of interaction with the environment.
In the case of the SRTT, a decrease in RT is observed when a motor sequence is repeated. Furthermore, when that sequence stops (random trials begin), an increase in RT is consistently observed, indicating that the previous RT decrease is specific to the sequence. The observed change is a consequence of a visuo-motor interaction with the en- vironment, precisely with the computer screen and the response box.
What is less clear is the long-term nature of that change. Indeed, the bulk of previous studies on the SRTT used an experimental design without baseline measures or assessment of the stability in time of the
sequence knowledge. In that widely used design, several blocks of the repeating sequence are followed by one block of random items (Chrobak et al., 2017; Gómez-Beldarrain, García-Moncó, Rubio, &
Pascual-Leone, 1998;Lum et al., 2018), sometimes followed again by sequence blocks (Deroost & Soetens, 2006; Grundey, Thirugnasambandam, Amu, Paulus, & Nitsche, 2018;Martini, Furtner,
& Sachse, 2013;Nissen & Bullemer, 1987;Unsworth & Engle, 2005). In these cases, if an immediate effect was consistently observed (RT dif- ference between sequence and random blocks), the encoding of the sequence in memory was not tested, as there was no distinction be- tween training (long sequence block with multiple repetitions) and testing (short sequence block with few repetitions). On the other hand, when training and testing were separated, with a pre-training baseline test and a post-training test (Brown, Robertson, & Press, 2009;Carvalho et al., 2018;Press et al., 2005;Robertson et al., 2004;Sami et al., 2014;
Schendan et al., 2003), results were similar to those found in our data:
the RT rebound was already present in the pretest baseline (SkillPre, often referred to as Skill0), was higher or stable in the training (SkillTr), and close to the baseline level again in the posttest (SkillPost, often referred to as Skill1). Therefore, we observed a quick sequence-specific RT change in pretest, an improvement during training, but an absence of long-term retention, illustrated by the drop back to baseline level at posttest. In other words, there was no online improvement in Skill across session 1 of the experiment. Often, authors interpret the RT difference at posttest (SkillPost) as learning due to training, without considering the level at pretest.
By contrast, in the explicit version of the task, i.e. when the parti- cipants were told at the beginning that there would be a repeated se- quence, the RT difference between sequence and random increased during training (SkillTr > SkillPre) and persisted at posttest (SkillPost > SkillPre), indicating learning with retention (Sami et al., 2014).
In the present study, the absence of improvement between pretest and posttest was unlikely to be explained by fatigue, considering the short length of the test blocks (3–4 min) and the overall RT decrease.
Furthermore, it could not be attributed to variability in age or sex differences, given the absence of effect of those variables on Skill dif- ferences as assessed by linear regressions.
4.1. Motor sequence adaptation
In the implicit versions of the SRTT, if the RT rebound effect after a repeated sequence does probably not reflect learning, it is nonetheless existent, as it is consistently found across the literature. Part of the RT differences between sequence and random reported in the literature and found in our study may have been due to specific characteristics of the sequence, which differed with regards to the characteristics of random trials in terms of target distribution across the four fingers.
However, we demonstrate here (in version 5 of the experiment) that even when these characteristics were matched, a rapid though transient adaptation of RTs occurred.
Rather than learning as defined above, there seemed to be a quick and unconscious adaptation to the sequence, already during the pretest block (even when the sequence was repeated only five times), and through the entire experiment. Moreover, at least in our experiment, this adaptation was proportional to the number of sequence repetitions, in view of the positive correlation between the number of sequence repetitions and the magnitude of Skill. Hence, longer exposure to the sequence produced proportionally larger gains in RT, although they remained transient and disappeared after the next random block. This is consistent with the higher Skill found during training in some studies (Brown, Robertson, & Press, 2009;Sami et al., 2014).
Evidence suggests that the brain rapidly detects statistical regula- rities in the environment in different modalities (Barlow, 2001;
Cecchetto & Lawson, 2017; Garrido, Sahani, & Dolan, 2013;
Tervaniemi, Maury, & Näätänen, 1994). This happens implicitly and Fig. 5.Mean RT in the three test blocks for the 53 participants. Error bars
indicate SD. P-values were obtained by paired samplet-test. *** = p < 0.001.
automatically, even when a high cognitive load is imposed upon the participants (Garrido, Teng, Taylor, Rowe, & Mattingley, 2016; Lv et al., 2010). In the SRTT, the motor regularity seems to be detected after only a few sequence repetitions, resulting in RT decrease. When the sequence stops and unexpected items (i.e. random) appear, the motor response is slowed down, resulting in RT increase. The motor sequence, however, does not seem to be encoded in memory, at least not with the amount of training used in common experimental designs.
Therefore, the RT difference between sequence and random (Skills) is interpreted here as a form ofadaptationto the motor sequence. In fact, similar conclusions can be drawn not only for the classical SRTT task, but also for other tasks of implicit learning such as the Continuous Pursuit Tracking Task and in perceptual versions of the task (Al- Sharman & Siengsukon, 2014; Ewolds, Broker, de Oliveira, Raab, &
Kunzell, 2017;Gheysen, Gevers, De Schutter, Van Waelvelde, & Fias, 2009;Goschke & Bolte, 2007).
4.2. Offline consolidation
An offline improvement of the adaptation to the sequence has been previously found, in the absence of sleep, such that the RT difference between sequence and random trials was larger in the evening session than at the end of the morning session (SkillRe > SkillPost) (Brown, Robertson, & Press, 2009; Cohen et al., 2005; Press et al., 2005;
Robertson et al., 2004;Sami et al., 2014). Despite the effort put into reproducing the experimental design used in those studies, we were unable to replicate the offline consolidation of the sequence-specific implicit adaptation effect. In particular, our attempts to modulate the length of pre- and post-test blocks, adjust instructions and impose few errors, and homogenize the structure of random and sequence trials were not successful in reproducing significant offline consolidation.
It is then unclear if explicit and implicit components of the SRTT consolidate differently, and why results regarding sequence-specific consolidation are inconsistent. Moreover, even studies reporting a consolidation did not report online sequence-specific performance im- provement through the course of session 1 (Brown, Robertson, & Press, 2009;Cohen et al., 2005;Press et al., 2005;Robertson et al., 2004;Sami et al., 2014). Offline performance improvements without preceding online learning are difficult to interpret.
In SRTT studies investigating offline consolidation after a period of sleep, findings are again inconsistent. In a review article, Lerner and Gluck (Lerner & Gluck, 2019) report results from 14 studies using the implicit SRTT out of which five studies with positive results, six with negative results, and three with mixed results. Thus, offline con- solidation of SRTT effects, be it with or without sleep, have not shown robust reproducibility. Partial positive results could be due to un- controlled explicit aspects in the task or circadian factors.
Although we could not elicit actual sequence-specific offline con- solidation, we found a non-specific offline improvement between the two sessions indicating that some form of motor learning did occur.
Average RTs in the task were lower in the evening session compared to the morning session, in the absence of sleep or additional practice be- tween sessions. These results are consistent with other studies, where the authors have found no sequence-specific offline consolidation but an offline consolidation of the general motor skill (i.e. an RT decrease in both sequence and random items) (Carvalho et al., 2018;Meier & Cock, 2014;Nemeth & Janacsek, 2011;Nemeth et al., 2010). Hence, despite the occurrence of unspecific online and offline improvements in reac- tion times, no lasting sequence-specific improvements could be ob- served.
4.3. Implicit nature of the task
One possible reason for the differing results concerning offline consolidation across studies might be an uncontrolled explicit compo- nent in the implicit SRTT (Moisello et al., 2009;Shanks & Johnstone,
1999; Shanks & St John, 1994; Wilkinson & Shanks, 2004). It is common practice to assess the level of explicit awareness of the se- quence with a questionnaire and to exclude data from participants who correctly recalled a number of consecutive elements of the sequence above a fixed cutoff (Brown, Robertson, & Press, 2009; Press et al., 2005;Robertson et al., 2004;Sami et al., 2014;Willingham & Goedert- Eschmann, 1999). However, this approach can be problematic because the remaining participants potentially form a heterogeneous group. In particular, it could include participants that had explicit knowledge just below the cutoff, participants in whom the memory of the sequence faded by the end of the experiment, or participants with intermediate levels of awareness between implicit and explicit learning, i.e. fringe consciousness (Norman, Price, & Duff, 2006;Shanks & St John, 1994;
Wilkinson & Jahanshahi, 2007). Furthermore, in some studies using the SRTT, participants who became aware of the sequence were not sepa- rated from those who did not (Martini, Furtner, & Sachse, 2013;Nissen
& Bullemer, 1987; Willingham & Goedert-Eschmann, 1999). Thus, studies with more participants having some explicit awareness of the sequence may find greater consolidation effects, which are then not implicit.
Other studies used alternative ways of testing explicit knowledge.
Those include sequence completion (Stefaniak, Willems, Adam, &
Meulemans, 2008;Wilkinson & Shanks, 2004), inclusive and exclusive production (Dennis, Howard, & Howard, 2006) and sequence recogni- tion (Sanchez, Gobel, & Reber, 2010;Willingham, Greeley, & Bardone, 1993) combined with confidence level assessment (Dennis, Howard, &
Howard, 2006;Rose, Haider, Salari, & Büchel, 2011;Sanchez, Gobel, &
Reber, 2010). Above chance level performance at sequence completion or production was interpreted as evidence of implicit sequence knowledge when the participant concurrently failed at sequence re- cognition or reported low levels of confidence. However, as these as- sessments were presented right at the end of the task and often after a sequence block (i.e. without interference of random trials between se- quence and knowledge assessment), adaptation effects could still have been operating. Thus, while those methods are efficient at detecting participants with explicit knowledge, they do not guarantee that ob- served effects are due to implicit learning with memory encoding.
5. Conclusions
Together, these results raise concerns on the usage of the “implicit”
version of the SRTT as implicit motor sequence learning paradigm, given the temporary adaptation effect it produces, and the possible confounds between implicit, partially explicit and totally explicit con- ditions. While procedural learning certainly has implicit components, whether a sequence can be implicitly learned remains unclear. To test motor learning, we thus recommend to always assess stability in time, to avoid misinterpretation of results.
The SRTT is suitable for inducing robust temporary motor adapta- tion which can be used to study the mechanisms behind this effect.
However, as long as online improvement of Skill from a baseline to a post-training test is not demonstrated, the RT rebound effect observed in the SRTT should not be interpreted as implicit learning.
Acknowledgements
Sources of funding: AGG was supported by the Swiss National Science Foundation (grant 320030_169275).
Declaration of Conflicting Interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influ- ence the work reported in this paper.
O. Trofimova, et al. Neurobiology of Learning and Memory 175 (2020) 107297
6
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