Neurorehabil Neural Repair OnlineFirst, published on April 6, 2007 as doi:10.1177/1545968307300438
Learning Implicitly: Effects of Task and Severity After Stroke Lara A. Boyd, PT, PhD, Barbara M. Quaney, PT, PhD, Patricia S. Pohl, PT, PhD, and Carolee J. Winstein, PT, PhD
Background. Disparate results have been reported on the implicit learning ability of adults with stroke. Objective. This study aimed to elucidate the relationships between stroke severity and the task employed to test implicit motor learning. Methods. Twenty-eight patients with chronic stroke were divided according to stroke severity using the Orpington prognostic score into those with mild (n = 16, score < 3.2) or moderate stroke (n = 12, score 3.2-5.0). Seventeen healthy individuals served as matched controls (HC). All participants practiced 2 implicit learning tasks, the Serial Reaction Time (SRT) and Serial Hand Movement (SHM). Results. A group-bytask-by-block interaction (P = .000) demonstrated differences across the experimental factors. Post hoc analyses revealed differences between groups and tasks. Greater change in the speed of responding was exhibited for the SHM than the SRT task by the HC and mild groups; however, the moderate group did not demonstrate a between-task difference. Conclusion. Both stroke severity and motor task influenced the magnitude of implicit learning across acquisition, which suggests for the first time that different tasks may yield disparate implicit learning outcomes in the same population. Additionally, the impact of stroke severity may be important when assessing residual implicit motor learning capability. The combination of these 2 factors helps explain previously reported contradictory findings and may inform future studies. Key Words: Stroke—Stroke severity—Task—Motor—Implicit learning—Human. From the School of Rehabilitation Sciences, University of British Columbia, Vancouver (LAB); the Department of Physical Therapy and Rehabilitation Sciences, University of Kansas Medical Center, Kansas City (LAB, PSP); the Landon Center on Aging, University of Kansas Medical Center, Kansas City (BMQ); the Department of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles (CJW). Address correspondence to Lara A. Boyd, PT, PhD, School of Rehabilitation Sciences, University of British Columbia, T325-2211 Westbrook Mall, Vancouver, British Columbia V6T 2B5, Canada. E-mail:
[email protected]. Boyd LA, Quaney BM, Pohl PS, Winstein CJ. Learning implicitly: effects of task and severity after stroke. Neurorehabil Neural Repair XXXX;XX:xx–xx. DOI: 10.1177/1545968307300438
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he broad categories of learning and memory can be subdivided into 2 main types—explicit and implicit.1 Explicit learning may be assessed directly by testing memory for factual knowledge (eg, recognition and recall). In contrast, implicit learning is inferred through observation of changes in skilled movement relative to some baseline performance. In the case of implicit learning, improved performance is assumed to reflect the acquisition of knowledge about the task, which is then manifested as, for example, faster and/or more accurate movements. The functions of the implicit system are highly distributed, supporting multiple behaviors including skills and habits (eg, sequence learning), priming (eg, wordcompletion), associative learning (eg, classic and operant conditioning), and nonassociative learning (eg, habituation).1 The focus of this study is on implicit motor learning, which subserves the acquisition of motor skills. The hallmark of implicit motor learning is the capacity to acquire skill through physical practice without conscious awareness of what elements of performance improved.2-4 The learning of motor skills fits the criteria for implicit learning and has formed the basis for investigations in persons with unilateral brain damage.5-9
IMPLICIT LEARNING AFTER STROKE Implicit learning of subtly repeated regularities within sequences has been demonstrated in nonneurologically impaired individuals3,10-14 as well as in persons with damage in the medial temporal lobe in structures associated with explicit memory.3,10,15 Research examining implicit learning in individuals with stroke has been broad, examining generalized implicit learning capability,5,6 the impact of lesion location7-9,16-19 and lateralization,19 and explicit/implicit interactions.7,8,16 The wide range of this work largely reflects the diffuse nature of the implicit memory system. Regardless of their particular focus, the common finding from studies of implicit memory and
Copyright © The American Society of Neurorehabilitation Copyright 2007 by American Society of Neurorehabilitation.
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learning after stroke have demonstrated a preserved capability for some learning, though in many cases diminished responses are noted when comparisons are made to neurologically intact control subjects.5,7-9,16,17,19
TASK AND IMPLICIT LEARNING Classically, implicit learning has been tested using the serial reaction time (SRT) task.3,6,19-21 Although some investigators have assessed implicit learning using other tasks,5,8,9,11,12,22 very few of these studied individuals with stroke.5,8,9 Therefore, it remains unclear what impact implicit task selection has on the ability to assess capability for implicit motor learning after stroke. During SRT task practice, participants are cued repeatedly to respond as fast as possible to a light when lit by pushing a corresponding button. Unknown to the participant, she or he can be cued by a repeating sequence of stimuli. With practice, participants’ responses during the sequence become increasingly faster (as demonstrated by decreased response times [RT], compared to a random sequence). Previously, Boyd and Winstein (2001) reported that after stroke, individuals demonstrated diminished implicit motor learning of the SRT task.6 Although not a controlled factor in our analysis, examination of the group from that research report revealed that participants could be classified by stroke severity as moderately severe. In contrast, Pohl et al (2006) found preserved implicit motor learning in individuals who were classified with mildly and moderately severe stroke.23 Pohl et al’s work used a novel, functionally based serial hand movement (SHM) task.5,23 The SHM task requires responding to stimuli by moving a series of functional objects (ie, turn a key, push a button). The main purpose of the present study was to resolve these disparate results directly by including severity and implicit motor learning task in our design.
STROKE SEVERITY AND IMPLICIT LEARNING The impact of stroke severity has been discussed in the general motor learning literature.24,25 Consistently, investigations that considered severity have documented a poorer response to motor skill practice in individuals with more severe strokes. Only one study has considered stroke severity with respect to implicit motor learning. Pohl et al (2001) reported a trend that suggested when participants were stratified by stroke severity using Orpington Prognostic scores,26 those with moderate, but not mild, stroke showed implicit learning deficits.5 Thus, the second focus of this work was to confirm whether 2
stroke severity impacts implicit motor learning, regardless of task context. The Orpington Prognostic score is a clinically derived score that integrates measures of motor deficit, balance, proprioception, and cognition. It is a valid measure that has been verified as an objective predictor of outcome in stroke patients. We capitalized on the strengths of the Orpington in this work to stratify our participants into those with mild or moderately severe stroke.26 Inherently difficult in a stroke model is the necessity of disentangling motor execution impairments associated with the affected hemibody from deficient motor learning. Thus, requiring individuals with stroke to use the more involved, contralesional upper extremity for task practice can be problematic; differences between stroke and control groups might be inflated by impaired motor execution, which in turn could mask implicit motor learning. As in our previous work,5-9,16 we minimized this problem by requiring participants with stroke to practice both implicit motor learning tasks using the less involved, ipsilesional upper extremity. We matched healthy control participants for arm use. Thus, the main purpose of this study was to determine whether task selection or severity of impairment impacted implicit motor learning after stroke. We hypothesized that, consistent with prior work, participants with moderate stroke would demonstrate less benefit from implicit motor task practice than those with mild stroke and that this pattern of change would be evident regardless of task selection.
MATERIALS AND METHODS Participants Twenty-eight individuals with chronic cerebrovascular accident (CVA; at least 6 months poststroke) were studied. To increase our sample size, we recruited individuals across 2 sites (University of Kansas Medical Center, University of Southern California). To determine stroke severity, we administered the Orpington prognostic score26 at admission into this study and subdivided individuals into groups with mild (CVA-Mild; n = 16; Orpington score < 3.2) or moderate (CVA-Mod; n = 12; Orpington Score 3.2-5.0) stroke. Seventeen individuals without neurologic damage were recruited from the local community and served as age-matched controls (HC; n = 17; Table 1). All participants were over 18 years of age and right handed (as determined by the Edinburgh Inventory). Exclusion criteria for all participants included the following: (1) acute medical problems; (2) uncorrected vision loss; (3) previous history of psychiatric admission; (4) history of multiple strokes, Neurorehabilitation and Neural Repair XX(X); XXXX
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Table 1. Participant Demographics Number
Site (KU/USC)
Age
Sex
Hand Tested
MMSE
Orpington Score
HC
17
53.3 (3.3)
29.5 (.78)
2.3 (.13)
CVA-MOD
12
9R 8L 7R 9L 7R 5L
—
16
11 F 6M 6F 10 M 7F 5M
29.7 (.66)
CVA-MILD
10 KU 7 USC 10 KU 6 USC 10 KU 2 USC
28.8 (1.21)
3.4 (.16)
54.3 (3.7) 60.7 (2.7)
KU = University of Kansas Medical Center; USC = University of Southern California; F = female; M = male; R = right; L = left; MMSE = MiniMental State Examination; HC = matched controls; CVA = cerebrovascular accident; MILD = mild; MOD = moderate. Orpington score < 3.2 indicates mild stroke; score 3.2 to 5.2 indicates moderate stroke. Data are mean (standard deviation).
transient ischemic attacks, or extensive cortical white matter disease; (5) pathology of the less affected, ipsilateral to stroke upper extremity that would affect ability to perform the tasks; and (6) score of 26 or less on the Mini-Mental State Examination (MMSE).27 To protect the individual rights of participants, each signed an approved institutional informed consent form and a medical records release form prior to testing. There were no significant differences in age or MMSE scores between groups. In addition, between the stroke groups, there was no relationship between side of stroke and Orpington Score. Further description of the individuals in the CVA-Mild, CVA-Mod, and HC groups are in Table 1. Review of existing MRI, CT, or medical record confirmed that each participant in the CVA-Mild and CVA-Mod groups had a stroke. Lesion location was classified using the scheme outlined by Bamford et al28 (Table 2). This categorization strategy demonstrated that most of the individuals in the CVA-Mod group (10 of 12) had lesions that affected both the cortex and subcortex, whereas lesion location for those in the CVAMild group was more distributed (Table 2).
General Procedures All participants practiced both the SRT3 and SHM5 tasks. Participants in the CVA groups used the lessaffected or hand ipsilateral to stroke for all responses; individuals in the HC group were matched for hand use (Table 1). The order of task practice was counterbalanced across participants. The same procedures and task practice structure were followed for both tasks. Participants were instructed to respond as quickly as possible when prompted. Based on past work of implicit learning with both individuals with stroke7 and healthy controls,4,10,29 participants performed 10 blocks of sequence and 2 blocks of random practice (1200 trials; 1 block = 100 responses). Each block of responses was composed of 10 repetitions of the sequence. The beginning and end of
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Table 2. Lesion Location for 25 of the 28 Individuals With Stroke Who Participated in This Research Subject Number
Lesion Side
Stroke Severity
CVA-MILD S5 S7 S1 S2 S6 S56 S66 S51
Right Left Right Left Left Left Left Right
Mild Mild Mild Mild Mild Mild Mild Mild
S52
Right
Mild
S55
Left
Mild
Right Right Left Right Right Left
Mild Mild Mild Mild Mild Mild
Cortex Cortex Subcortex Subcortex Subcortex Subcortex Subcortex Subcortex without corticalspinal tract Subcortex without corticalspinal tract Subcortex without corticalspinal tract Subcortex + Cortex* Subcortex + Cortex* Subcortex + Cortex Subcortex + Cortex Not available Not available
Right Right Right Right Left Left Left Left Right Left Right Right
Mod Mod Mod Mod Mod Mod Mod Mod Mod Mod Mod Mod
Subcortex Subcortex + Cortex Subcortex + Cortex* Subcortex + Cortex Subcortex + Cortex Subcortex + Cortex Subcortex + Cortex Subcortex + Cortex Subcortex + Cortex Subcortex + Cortex Subcortex + Cortex Not available
S3 S21 S60 S70 S54 S67 CVA-MOD S59 S4 S23 S50 S57 S58 S61 S64 S62 S63 S68 S53
Lesion Location
Lesion location data were derived from medical records, charts, and information compiled in the University of Kansas Medical Center’s Stroke Database. Stroke severity was indexed using the Orpington Prognostic Score. All strokes were ischemic in nature unless noted with an * in the table. CVA = cerebrovascular accident; MILD = mild; MOD = moderate.
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sequences were not marked; thus, there were no pauses or breaks between repetitions of the repeated sequence within practice blocks. To control for the effects of nonspecific learning, an initial block of random-sequence responses was practiced (100 responses). Next, 9 blocks of repeated-sequence practice were performed (blocks 2-10) followed by a second random-sequence block (block 11). Last, the final block of practice consisted of the repeated sequence (block 12). Participants were not informed that they were often practicing a repeating sequence of responses. After acquisition performance of both tasks was completed, explicit knowledge was tested. The order of explicit knowledge testing was also counterbalanced across participants.
Tasks Serial Reaction Time Task. For SRT task practice, 4 different colored circles (yellow, red, blue, and green) could be displayed on the computer screen (17-inch, color) placed directly in front of the participant. A standard keyboard was placed on the table directly in front of the computer screen with the most centered letters (“v, b, n, and m”) capped with the colors yellow, red, blue, and green. Displaying 1 of the 4 colored circles on the screen generated the stimuli for movement. Only 1 colored circle appeared at a time; the other circles were transparent; however, each colored circle always appeared in the same location and maintained its relative location on the screen. Responses were made by pressing 1 of the 4 keys corresponding (in color and location) to the appropriately colored circle. As soon as the correct key was pushed, the next stimuli for movement appeared. However, the stimuli for movement remained on the screen until the correct response was made. A custom computer software program (L. Boyd, 2001, Presentation platform, Neurobehavioral Systems, Inc, Albany, Calif) controlled the appearance of the colored circles and recorded participants’ responses. Response time (RT) was stored after every key-press for later analysis. Participants were seated facing the computer screen with their hand resting on the keyboard. Minimal excursion was required for responses as participants made key-presses with 1 of 4 fingers (index through little finger), which they were allowed to lightly rest on the colored keys. All participants practiced the same 10element ambiguous repeating sequence (Blue-YellowRed-Blue-Green-Red-Blue-Red-Green-Yellow). Serial Hand Movement Task. The SHM task was performed on a custom-made hand response box (8 inches wide by 3 inches high) with 4 targets, alternating key and push button switches (Figure 1). Each type of switch was easily closed, but the object’s properties required a 4
Figure 1. Schematic drawing illustrating the serial hand movement task. Responses were made by pushing or toggling the functionally based switches (ie, push elevator-style buttons or turn key).
specific hand position, which we did not specify. No 2 adjacent switches were the same. The key-type of switch was a 1/4 inch in diameter, 1 inch in length key that was activated by turning it to the right or left. The push button−type of switch was 1/2 inch square shaped like a standard push button and was activated by depression. An LED was situated above each switch that when lit indicated the target switch. Custom computer software (R. Maletsky, Labview 7.0, National Instruments Corporation, Austin, Tex) controlled the sequencing of the stimuli and recorded RT for each response. As soon as participants closed the correct switch, the next light was illuminated to indicate the subsequent response. Participants sat facing the SHM response panel, which was placed directly in front of the arm to be used for responses. All participants practiced the same 10element ambiguous sequence, which mirrored the SRT sequence to control for difficulty (switches numbered 1-4, left to right; 1-4-2-3-2-4-3-2-1-3).
Explicit Testing Three levels of explicit knowledge were tested, (1) subjective awareness of the existence and composition of the sequence, (2) recognition memory, and (3) recall memory. Subjective memories were tested by asking participants if they noticed anything about the task. Recognition memory tests determined if participants would be able to correctly identify the repeated sequence after watching it on the screen. Recall was tested to ascertain if participants knew the repeated sequence well enough to correctly predict what element would come next when viewing a fragment of the 10 elements (ie, 4-elements; Table 3). Ten trials with 3-true and 7-foil sequences were delivered for recognition and recall testing.
Outcome Measures The same outcome measures were calculated for both the SRT and SHM tasks. Response time was the Neurorehabilitation and Neural Repair XX(X); XXXX
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Table 3. Explicit Tests Included Subjective Awareness of the Existence of a Repeating Sequence, Recognition Memory, and Recall Memory Explicit Test Condition
Explicit Test Questions
Subjective Awareness
“Did you notice anything about the task?”
If yes, “What was it?” If no, “There was a repeating sequence. Can you tell me what it was?”
Recognition Memory
Watch 10 sequences: 3 True 7 False
“Is this a sequence that you recognize?”
Recall Memory
Display 10 separate 4-element sequence fragments (ie, Y-R-B-G-?)
“What color/switch comes next?” (forced choice)
time between stimulus onset to response (key-press or turn) and was measured and stored for each trial. As is standard procedure in SRT-type task data analyses,3,5,7,13,14,30 we calculated median RT for each 10-element sequence trial. Calculation of median RT values for each sequence trial reduces the sensitivity of this measure to very large or small values. Response times were then summarized by calculating the mean median for each block of responses. This procedure was performed for both random and repeated sequences and represents absolute RT. In addition, to facilitate comparison across groups and tasks, and to eliminate the effect of grossly different RTs, a change score was calculated for each block of practice (RT change score = mean median RT from the second block of random sequence practice [block 11] minus mean median RT from repeating sequence blocks 1-10, and 12). Early in practice, RT is often very long but decreases rapidly as participants become familiar with the task. These shorter RTs are often due to learning the relationship between stimulus and response and do not relate to sequence-specific learning. We chose to use the mean median response time from the second random block (block 11) in all our change score calculations because at this time sufficient practice had occurred to greatly reduce these nonspecific learning effects. Explicit testing was evaluated by calculating a percent correct score for the subjective responses, recognition, and recall (eg, 87% or 14 of 16 participants subjectively noticed the repeating sequence).
Statistical Analyses To ascertain whether stroke affected overall response time, absolute RT was evaluated across groups with a univariate analysis of variance. Post hoc between-group Neurorehabilitation and Neural Repair XX(X); XXXX
comparisons were completed using a Bonferroni calculation. To determine whether task or stroke severity influenced implicit motor sequence learning, we performed a 3-factor repeated-measures analysis of variance (ANOVA; Group [HC, CVA-Mild, CVA-Mod] by Task [SRT, SHM] by Repeated Sequence Block [1-10]) using the RT change score as the dependent measure. In these analyses, we also considered the influence of testing site, sex, age, and the hand used for responses as covariates. Post hoc tests were performed to identify the locus of significant interactions (Task by Block ANOVA and Group by Block ANOVA) with a Bonferonni correction for repeated measures. Last, to assess between-task differences at the end of practice, we employed a Group by Task ANOVA using the RT change score from the last block of practice for each task. To better understand the relationship between stroke severity and implicit motor learning, we also correlated the amount of RT change with Orpington scores separately for each task. Explicit knowledge of the repeated sequence was assessed using a Group (HC, CVA-Mild, CVA-Mod) by Task (SRT, SHM) ANOVA with subjective awareness, recognition, and recall memory scores as the dependent measures. In all of these analyses, the level of significance was set at P ≤ .05. All statistical analyses were performed using SPSS 13.0 (SPSS Inc, Chicago, Ill).
RESULTS There were no differences in absolute RT or RT changes scores across the 2 testing sites; therefore, all data were collapsed into groups stratified only by severity using Orpington scores. Despite the stroke groups’ use of the ipsilesional, less-affected upper extremity for all task practice, absolute RTs differed for both tasks (SRT F[2,43] = 9.64, P = .000; SHM F[2,43] = 12.69, P = .000). 5
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Figure 2. Absolute response time (RT) recorded during the last sequence block (Block 12) for both tasks and each group. For both tasks, the matched controls (HC) group responded significantly faster than either stroke group. There was no significant difference in absolute RTs between the CVA-Mild and CVA-Mod groups. CVA = cerebrovascular accident; MILD = mild; MOD = moderate; SHM = serial hand movement; SRT = serial reaction time.
Post hoc analyses demonstrated that for both tasks, the HC group was significantly faster than either of the stroke groups. However, the CVA-Mild and CVA-Mod groups did not differ from one another (Figure 2).
Group and Task Effects Examination of change in RT (random sequence RT minus repeated sequence RT) demonstrated that all participants in this study generated faster responses for the repeated sequences (as compared to random sequences) for both tasks (Figure 3A-C). Furthermore, a full-factor ANOVA (with repeated measures correction) revealed a 3-way Group by Task by Block interaction (F[2, 22] = 2.95, P = .000). In addition, significant Block by Group (F[2, 39] = 4.91, P = .013) and Task by Block (F[1, 39] = 6.36, P = .016) interactions were found. Post hoc analyses revealed that the loci of this interaction centered on differences between groups and tasks. When the task factor was evaluated separately for each group (Task by Block ANOVA), the pattern of performance across the 3 groups differed. The HC group decreased RT for both tasks (as demonstrated by a Main Effect of Block, F[1, 15] = 18.64, P = .000) yet responded differently to the 2 tasks (Task by Block interaction, F[1, 11] = 7.73, P = .000; Figure 3A). This was due to the
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Figure 3. Task effects across groups were represented as change in response time (RT; random sequence RT minus repeated sequence RT). The zero line reflects random sequence performance; data below this line show faster responses. For both the matched controls (HC) (A) and CVAMild (B) groups, significant between-task differences were evident at the end of acquisition. This was not true for the CVA-Moderate group (C). SRT = serial reaction time; SHM = serial hand movement; CVA = cerebrovascular accident.
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larger amount of change made on the SHM task (RT decreased by 203 ms) than the SRT task (RT decreased by 85 ms). Similarly, those in the CVA-Mild group improved their RTs for both tasks across practice (Main Effect of Block, F[1, 13] = 11.57, P = .000). Consistent with the performance pattern of the HC group, the CVA-Mild group also showed greater reductions in RT for the SHM (125 ms) than the SRT task (84 ms; Task by Block interaction, F[1, 11] = 1.84, P = .047; Figure 3B). In contrast to the performance patterns shown by the HC and CVA-Mild groups, the CVA-Mod group appeared to be unaffected by task (Figure 3C). This was evident by the lack of a Task by Block interaction (P = .110) and was also clear in the magnitude of RT change shown for each task (SHM = −116 ms; SRT= −106 ms). The CVA-Mod group, however, did learn both tasks, as demonstrated by their significant improvements in responding to the repeating sequence (Main Effect of Block, F[1, 11] = 8.33, P = .015). Different responses across groups to the 2 tasks was confirmed by a significant Task by Group ANOVA, which considered change in RT for the last block (F[2, 41] = 3.26, P = .048). Post hoc tests revealed that the locus of this interaction centered on differences between the HC and CVA-Mild (P = .012) and HC and CVA-Mod (P = .002) groups. The CVA-Mild and CVA-Mod groups did not differ (P = .408). However, when within-group differences in task performance were assessed using paired t tests, the HC and CVA-Mild groups made substantially more change for the SHM task (HC P = .000, CVA-Mild P = .038) whereas the CVA-Mod group did not (P = .539). Because the CVA-Mod group visually appeared to make most change in RT for both tasks over the first half of practice (see Figure 3c), we conducted a subanalysis to determine whether or not this was the case. We found a Main Effect of Block (F[1, 11] = 2.98, P = .000) but no Block by Task interaction (P = .214) when we performed a Task by Block ANOVA for this group using data only from the first 6 blocks. This indicates that the group was performing similarly for both tasks, and in combination with our other data, it shows a failure to take advantage of the functional SHM task to the same degree as the HC and CVA-Mild groups. We also examined a number of potential covariate factors to determine whether they could help explain the differences between our groups and tasks. In addition to the null effect of site that we have already reported, there was no significant effect of hand used for task performance, lesion side, or age. Interestingly, we did find a significant Block by Sex interaction (F[1, 38] = 4.26, P = .046). To find the locus of this interaction, we collapsed across tasks and examined the performance of each group stratified by sex. This analysis demonstrated that the effect of sex on RTs was only evident in the HC group (P = .025), where
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Figure 4. Stroke severity stratified by Orpington Scores correlated with the magnitude of response time (RT) change for the SHM task (r = .466, P = .001) such that individuals with more severe strokes made less RT change across acquisition (A). There was no correlation between stroke severity (Orpington Score) and RT change for the SRT task (r = –.129). SHM = serial hand movement; SRT = serial reaction time.
female participants showed a significantly slower RT than the males. The effect of sex was not found in either the CVA-Mild (P = .228) or CVA-Mod (P = .498) groups. To further explore the relationship between implicit learning and stroke severity, we correlated Orpington scores and the amount of RT change shown by the end of acquisition performance separately for the SRT and SHM tasks. In this analysis, we found a significant correlation between Orpington score and the amount of change on the SHM task (r = .466; P = .001; Figure 4A) but no relationship between these factors for the SRT task (r = −.129; Figure 4B). This analysis revealed that for the SHM task there was an inverse relationship between change in RT and Orpington score (ie, individuals with less severe stroke made more RT change).
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Table 4. Explicit Knowledge Demonstrated After Acquisition Practice Subjective % Noticed
HC CVA-MILD CVA-MOD
Recognition % Correct
Recall % Correct
SRT
SHM
SRT
SHM
SRT
SHM
70.6 56.3 61.5
82.4 81.3 84.6
65.8 (18.7) 63.5 (12.0) 56.1 (21.0)
80.5 (15.9) 64.3 (29.9) 72.3 (14.8)
59.4 (22.2) 50.6 (19.1) 36.9 (14.8)
69.4 (17.4) 56.8 (13.5) 43.8 (21.8)
Data are represented as percentage correct. For subjective and recognition memory values, > 50% are above chance; for recall memory values, > 25% are above chance. SRT = serial reaction time; SHM = serial hand movement; HC = matched controls; CVA = cerebrovascular accident; MILD = mild; MOD = moderate.
Explicit Knowledge There were no between-group differences in explicit knowledge. However, within groups significantly more explicit knowledge was gained for the SHM task as compared to the SRT task (Table 4). This was evident via significant main effects of Task for subjective awareness (F[2, 43] = 6.72, P = .013), recognition memory (F[2, 43] = 8.52, P = .005), and recall memory (F[2, 43] = 3.77, P = .005). Because explicit knowledge for the repeating sequence was gained for the SHM task for most participants, we conducted a subanalysis to determine if this was a possible reason for the difference in sequence learning between the tasks. Participants were determined to have or not have acquired explicit knowledge (recognition memory) of the SHM task. Using this factor to group data, a multivariate Group (HC, CVA-Mild, CVA-Mod) by Explicit Knowledge (yes, no) ANOVA with retention test score for both tasks as the dependent variables was performed. The acquisition of explicit knowledge for the SHM task did not explain learning of either task (Main Effect of Knowledge for SRT P = .174, SHM P = .132; Group by Knowledge interaction SRT P = .853, SHM P = .109).
DISCUSSION Contrary to our initial hypothesis, both stroke severity and task selection have significant effects on implicit motor learning. These data help clarify the disparate findings reported earlier by Boyd and Winstein (2001)6 and Pohl et al (2001).5 We had predicted that the reason for our prior conflicting results was largely the result of differences in stroke severity in the studied populations. However, we were surprised to find that implicit learning is influenced by task context as demonstrated by the magnitude of RT change between experimental tasks. Our findings are important for several reasons. First, to our knowledge no other work has examined the possibility that the implicit learning task may affect outcome. 8
Although this factor may be particularly important when evaluating individuals with brain damage resulting from stroke, it was also true for our age-matched healthy control group. Second, very little attention has been focused on the impact of stroke severity1 when assessing residual implicit motor learning capability. The combination of these 2 factors helps explain previously reported contradictory findings5,6 and may inform future work that seeks to assess implicit learning in populations with neurological damage or disease.
The Effect of Task on Motor Learning Few investigations have considered the relative impact of different tasks on implicit motor learning ability.6 The HC and CVA-Mild groups clearly were able to make larger decreases in RT during practice of the SHM task as compared to the SRT task. The differential response to the 2 tasks may relate to the salience or functional nature of the movements that are required by the SHM task. It is possible that the functional nature of the SHM task strengthened both implicit and explicit memory. Although speculative at this time, it may be that the SHM task represents a more ecologically valid measure of implicit learning after stroke, as performance on this implicit learning task was closely related to stroke severity (see Figure 4A). What remains unclear is why individuals with moderate strokes were immune to the task effects displayed by the HC and CVA-Mild groups. One explanation for different RTs across tasks could be the unique workspace dimensions that characterized the SRT and SHM tasks. We find this account unlikely because less change was consistently evident for the SRT task across all groups, despite the fact that the SRT task employed a much smaller workspace than did the SHM task. Successful completion of the SHM task requires that the stimulus for movement (ie, LED illumination) be translated into a motor plan for one of several different hand postures. Because these postures could change for Neurorehabilitation and Neural Repair XX(X); XXXX
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every response, the SHM task may require a higher degree of active attentional processing than does the SRT task (which uses the same basic motor plan for every response, implemented by different peripheral effectors, the individual fingers). It is possible that the transitions between hand postures during SHM task performance required more active attentional processing, which stimulated implicit and explicit learning for the HC and CVAMild groups.29 In this case, increased attentional demands associated with the SHM task may have been achievable by the HC and CVA-Mild groups but not the CVA-Mod group. This hypothesis appears plausible, as previous studies have related the complexity of implicit sequence structure with motor performance and attentional demand.31 Our data are consistent with past work that suggested that as stroke severity increases, individuals with stroke-related brain damage are less capable of meeting high levels of attentional demand.32 An alternate explanation for the differences between the CVA-Mild and CVA-Mod groups’ performance across tasks centers on the possibility that participants with moderate stroke were less able to meet the motor planning challenges associated with the quick changes in muscle activation required by the SHM task. After stroke, individuals demonstrate substantial deviations from direct line arm movement paths, despite their use of extensive feedback control movement strategies.33,34 These deficits are noted in the less-affected ipsilesional arm,33,35-37 suggesting a central mechanism for motor planning where unilateral brain lesions have bilateral impact. It is possible that increased stroke severity leads to elevated neuromotor noise34 resulting in decreased ability to transmit motor commands. Finally, it has been suggested that using explicit recollection strategies may interfere or compete with the formation of implicit memories.29,38,39 Interestingly, participants in our study showed larger gains in both implicit and explicit memory for the SHM relative to the SRT task. Based on this result, it appears possible that tasks with some functional relevance, such as the SHM task, may shift the relationship between implicit and explicit memory systems and allow them to complement one another.40 It appears that this process facilitated implicit and explicit learning in the HC and CVA-Mild groups, but not the CVA-Mod group. It is tempting to claim that acquisition of explicit knowledge enhanced learning for the HC and CVAMild groups, whereas the CVA-Mod group was limited by their inability to take advantage of explicit strategies during practice. We believe that this is an unlikely explanation for our findings. We failed to find a relationship between retention test performance and the acquisition (or not) of explicit knowledge (recognition memory). This indicates that change in the ability to perform either motor task was related to the formation of an Neurorehabilitation and Neural Repair XX(X); XXXX
implicit memory for the repeating sequences. The expression of acquired explicit knowledge in our testing for this factor likely represents the development of this memory in conjunction with the development of implicit memory. The notion that implicit and explicit memories may develop in parallel is not new13; our data support this conceptualization of how multiple memory systems may interact both normally and after stroke-related brain damage.
Stroke Severity and Implicit Motor Learning Stroke severity is generally ignored as a factor that may influence implicit learning. However, our data suggest that severity has a large effect on the ability to learn implicit motor sequencing tasks. Whereas the performance of the CVA-Mild group closely resembled that of the HC group, the more severely affected stroke patients in the CVA-Mod group did not. Despite using the less affected, ipsilesional arm, the CVA-Mod group made significantly less change in RT with practice than did the HC and CVA-Mild groups and appeared to not be affected by the beneficial effects of the functional nature of the SHM task. It may be that the CVA-Mod group found both the SRT and SHM tasks to be equally difficult and thus made similar amounts of performance change on both. Our data are consistent with the only other report that suggested a severity effect on implicit learning in individuals with stroke.5 The finding that more severe strokes make less change in response to task practice has been noted in other investigations examining general motor learning.41 For example, Cirstea et al (2003) noted differential responses to an upper extremity task that persisted across practice and was dependent on stroke severity.24 This work relied on movements made with the hemiparetic upper extremity and is, thus, subject to stroke-related changes in motor control. Our work extends these data by demonstrating a similar severity effect even when the less-affected, nonhemiparetic arm was used for task performance. These data reveal that severity related deficits after stroke extend to both upper extremities and suggest that they are caused by a common central mechanism.
Speed of Responding Our finding of a difference in speed of responding for males versus females in the HC group is consistent with other reports of neurologically intact men demonstrating faster reaction times but making more errors than women.42-44 With our data, it is possible that the RT advantage for male healthy controls was negated by 9
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motor control deficits35,36,45,46 associated with the stroke.47 It is possible that for rapid discrete responses, such as those made during the SRT and SHM tasks, the ability to manipulate the speed-accuracy tradeoff is deleteriously affected by stroke. Alternately, it may be that the participants with stroke were already responding as fast as they could.
CONCLUSIONS Taken together, these results suggest that not all individuals with stroke are capable of demonstrating similar degrees of improvement across different implicit motor learning tasks. Despite this, it is important to point out that all individuals in this work did demonstrate implicit learning, but that level of severity and type of task greatly influenced the magnitude of change shown across practice. Thus, implicit learning and memory are not completely lost after stroke; however, as stroke severity increases, it appears that capability for implicit learning and memory may diminish. Our findings are important, as they indicate that the choice of implicit motor task has a large influence on the magnitude of change in behavior associated with acquisition practice. To our knowledge, the impact of implicit task has not been previously demonstrated for neurologically intact people or individuals with stroke. Similar to other literature on motor performance,24,41 we also document a profound severity effect on the capability for implicit motor learning after stroke. Our data extend previous work by demonstrating this severity effect when using the less involved, ipsilesional upper extremity. In sum, the choice of experimental task and population must be carefully considered before interpreting capability for implicit motor learning after stroke.
ACKNOWLEDGMENTS This work was supported by a grant from the Health Science Women’s Faculty Association at the University of Southern California awarded to CJW. Technical support was provided by George Timberlake, PhD, Joan McDowd, PhD, from the University of Kansas Medical Center, and Diane Filion, PhD, from the University of Missouri, Kansas City. In addition, the authors would like to acknowledge the assistance of Chien-ho Lin, MS, PT, and Jill Stewart, MS, PT, of the University of Southern California, and Eric Vidoni, MS, PT, of The University of Kansas Medical Center.
REFERENCES 1.
10
Squire LR. Memory and Brain. New York: Oxford University Press; 1987.
2. Polanyi M. Personal Knowledge: Towards a Post-Critical Philosophy. London: Routledge; 1958. 3. Nissen MJ, Bullemer P. Attentional requirements of learning: evidence from performance measures. Cognit Psychol. 1987;19:1-32. 4. Curran T, Keele SW.Attentional and nonattentional forms of sequence learning. J Exp Psychol Learn Mem Cogn. 1993;19: 189-202. 5. Pohl PS, McDowd JM, Filion DL, Richards LG, Stiers W. Implicit learning of a perceptual-motor skill after stroke. Phys Ther. 2001;81 (11):1780-1789. 6. Boyd LA, Winstein CJ. Implicit motor-sequence learning in humans following unilateral stroke: the impact of practice and explicit knowledge. Neurosci Lett. 2001;298(1):65-69. 7. Boyd LA, Winstein CJ. Impact of explicit information on implicit motor-sequence learning following middle cerebral artery stroke. Phys Ther. 2003;83(11):976-989. 8. Boyd LA, Winstein CJ. Providing explicit information disrupts implicit motor learning after basal ganglia stroke. Learn Mem. 2004;11(4):388-396. 9. Boyd LA, Winstein CJ. Cerebellar stroke impairs temporal but not spatial accuracy during implicit motor learning. Neurorehabil Neural Repair. 2004;18:134-143. 10. Reber PJ, Squire LR. Encapsulation of implicit and explicit memory in sequence learning. J Cogn Neurosci. 1998;10(2):248-263. 11. Shea CH, Wulf G, Whitacre CA, Park JH. Surfing the implicit wave. Q J Exp Psychol A. 2001;54(3):841-862. 12. Wulf G, Schmidt RA. Variability of practice and implicit motor learning. J Exp Psychol Learn Mem Cogn. 1997;23(4):987-1006. 13. Willingham DB, Goedert-Eschmann K. The relationship between implicit and explicit learning: evidence for parallel development. Psychol Sci. 1999;6:531-534. 14. Willingham DB, Nissan MJ, Bullemer P. On the development of procedural knowledge. J Exp Psychol Learn Mem Cogn. 1989(15): 1047-1060. 15. Glisky EL, Schacter D. Acquisition of domain-specific knowledge in organic amnesia: training for computer-related work. Neuropsychologia. 1987;25:893-906. 16. Boyd L, Winstein C. Explicit information interferes with implicit motor learning of both continuous and discrete movement tasks after stroke. J Neurol Phys Ther. 2006;30(2):46-57; discussion 58-59. 17. Gomez-Beldarrain M, Garcia-Monco JC, Rubio B, Pascual-Leone A. Effect of focal cerebellar lesions on procedural learning in the serial reaction time task. Exp Brain Res. 1998;120(1):25-30. 18. Vakil E, Blachstein H, Soroker N. Differential effect of right and left basal ganglionic infarctions on procedural learning. Cogn Behav Neurol. 2005;17 62-73. 19. Vakil E, Kahan S, Huberman M, Osimani A. Motor and nonmotor sequence learning in patients with basal ganglia lesions: the case of serial reaction time (SRT). Neuropsychologia. 2000;38 (1):1-10. 20. Hsiao AT, Reber AS. The dual-task SRT procedure: fine-tuning the timing. Psychon Bull Rev. 2001;8(2):336-342. 21. Reber AS. Implicit learning and tacit knowledge. J Exp Psychol Gen. 1989;118:219-235. 22. Green TD, Flowers JH. Implicit versus explicit learning processes in a probabilistic, continuous fine-motor catching task. J Mot Behav. 1991;23:293-300. 23. Pohl PS, McDowd JM, Filion D, Richards LG, Stiers W. Implicit learning of a motor skill after mild and moderate stroke. Clin Rehabil. 2006;20(3):246-253. 24. Cirstea MC, Ptito A, Levin MF. Arm reaching improvements with short-term practice depend on the severity of the motor deficit in stroke. Exp Brain Res. 2003;152(4):476-488. 25. Cirstea MC, Mitnitski AB, Feldman AG, Levin MF. Interjoint coordination dynamics during reaching in stroke. Exp Brain Res. 2003;151(3):289-300.
Neurorehabilitation and Neural Repair XX(X); XXXX
Implicit Learning After Stroke
26. Kalra L, Crome P. The role of prognostic scores in targeting stroke rehabilitation in elderly patients. J Am Geriatr Soc. 1993;41(4):396-400. 27. Bleecker ML, Bolla-wilson K, Kawas C, Agnew J. Age-specific norms for the Mini-Mental State Exam. Neurology. 1988;38 (10):1565-1568. 28. Bamford J, Sandercock P, Dennis M, Burn J, Warlow C. Classification and natural history of clinically identifiable subtypes of cerebral infarction. Lancet. 1991;337(8756):1521-1526. 29. Harrington DL, Haaland KY. Skill learning in the elderly: diminished implicit and explicit memory for a motor sequence. Psychol Aging. 1992;7:425-434. 30. Grafton ST, Hazeltine E, Ivry RB. Motor sequence learning with the nondominant left hand. A PET functional imaging study. Exp Brain Res. 2002;146(3):369-378. 31. Cohen A, Ivry R, Keele SW. Attention and structure in sequence learning. J Exp Psychol Learn Mem Cogn. 1990;16:17-30. 32. McDowd JM, Filion DL, Pohl PS, Richards LG, Stiers W. Attentional abilities and functional outcomes following stroke. J Gerontol B Psychol Sci Soc Sci. 2003;58(1):P45-53. 33. Debaere F, Van Asschle D, Kiekens C, Verschueren SM, Swinnen SP. Coordination of upper and lower segments: deficits on the ipsilesional side after unilateral stroke. Exp Brain Res. 2005;141 519-529. 34. McCrea PH, Eng JJ. Consequences of increased neuromotor noise for reaching movements in persons with stroke. Exp Brain Res. 2005;162 70-77. 35. Pohl PS, Winstein CJ. Practice effects on the less-affected upper extremity after stroke. Arch Phys Med Rehabil. 1999;80(6):668-675. 36. Pohl PS, Winstein CJ, Onla-Or S. Sensory-motor control in the ipsilesional upper extremity after stroke. Neurorehabilitation. 1997;9(3):245-249. 37. Velicki MR, Winstein CJ, Pohl PS. Impaired direction and extent specification of aimed arm movements in humans with strokerelated brain damage. Exp Brain Res. 2000;130(3):362-374.
Neurorehabilitation and Neural Repair XX(X); XXXX
38. Buchner A, Funke J, Berry DC. Negative correlations between control performance and verbalizable knowledge—indicators for implicit learning in-process control tasks. Q J Exp Psychol A Hum Exp Psychol. 1995;48(1):166-187. 39. Poldrack RA, Packard MG. Competition among multiple memory systems: converging evidence from animal and human brain studies. Neuropsychologia. 2003;41(3):245-251. 40. Degonda N, Mondadori CRA, Bosshardt S, et al. Implicit associative learning engages the hippocampus and interacts with explicit associative learning. Neuron. 2005;46 505-520. 41. Dancause N, Ptito A, Levin MF. Error correction strategies for motor behavior after unilateral brain damage: short-term motor learning processes. Neuropsychologia. 2002;40(8):1313-1323. 42. Buchman AS, Lergens S, Gottlieb GL, Chen CH, Almeida GL, Corcos DM. Effect of age and gender in the control of elbow flexion movements. J Mot Behav. 2000;32(4):391-399. 43. Kauranen K, Vanharanta H. Influences of aging, gender, and handedness on motor performance of upper and lower extremities. Percept Mot Skills. 1996;82(2):515-525. 44. Morrison MW, Gregory RJ, Paul JJ. Reliability of the finger tapping test and a note on sex differences. Percept Mot Skills. 1979;48:139-142. 45. Pohl PS, Luchies CW, Stoker-Yates J, Duncan PW. Upper extremity control in adults post stroke with mild residual impairment. Neurorehabil Neural Repair. 2000;14(1):33-41. 46. Winstein CJ, Pohl PS. Effects of unilateral brain damage on the control of goal-directed hand movements. Exp Brain Res. 1995;105:163-174. 47. Wolf SL, Thompson PA, Morris DA, et al. The EXCITE trial attributes of the Wolf Motor Function test in patients with subacute stroke. Neurorehabil Neural Repair. 2005;19(3):194-205.
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