Procedural learning is impaired in dyslexia: Evidence from a meta-analysis of serial reaction time studies^☆

1.1. Dyslexia and procedural memory impairments

Similarly, there has been ongoing interest in whether one or more functions of the procedural memory system also contribute or underlie the reading impairments in dyslexia (Nicolson & Fawcett, 1990, 2007; Nicolson, Fawcett, Brookes, & Needle, 2010; Ullman, 2004). This memory system underlies the learning, knowledge, and execution of motor and cognitive skills and habits (Gabrieli, 1998; Packard & Knowlton, 2002; Ullman, 2004). The system underlies a range of types of knowledge, including context-dependent sequential or probabilistically structured information. Learning and knowledge in this system seems to be implicit (not available to conscious awareness), and the learned skills can be processed automatically and rapidly. Learning the skills is relatively slow, with a fair amount of repetition or practice required in order for them to be processed rapidly and automatically. The neural substrates of the procedural memory system are also reasonably well understood, with the basal ganglia, cerebellum, and motor-related areas all playing roles (Kandel, Schwartz, & Jessell, 2012; Packard & Knowlton, 2002; Parent & Hazrati, 1995; Ullman, 2004).

It has been hypothesized that the reading impairments in dyslexia may be at least partly explained by problems with the procedural memory system. Nicolson and Fawcett (2007, 2011) argue that the reading difficulties in dyslexia are, in part, related to parts of the procedural memory system that support language, in particular phonology. Specifically, it is claimed that the reading problems in dyslexia are linked to problems with learning and/or adapting phonological knowledge and automatizing skills necessary to support reading. Nicolson and Fawcett particularly implicate the cerebellum in dyslexia. Ullman (2004) also posits the presence of procedural memory impairments in dyslexia, but suggests that the underlying neural abnormalities may encompass various brain structures underlying procedural memory, including the basal ganglia. Consistent with these views, neural abnormalities have been reported in various structures underlying procedural memory, including the cerebellum (Brambati et al., 2004; Kronbichler et al., 2008), the basal ganglia (Eckert et al., 2005; Pernet, Poline, Demonet, & Rousselet, 2009), and motor areas (Silani et al., 2005). Finally, both Nicolson (Nicolson & Fawcett, 1990, 2007) and Ullman (Ullman, 2004; Ullman & Pullman, 2013) consider that the declarative memory system plays a compensatory role for at least some of the procedural memory deficits in dyslexia.

A key prediction of the proposal that procedural memory impairments are found in and may underlie dyslexia, is that individuals with dyslexia should in fact have worse procedural learning abilities than control individuals with typical reading skills. A number of studies have examined learning in procedural memory in dyslexia, using a variety of paradigms, including artificial grammar learning (Pavlidou, Louise Kelly, & Williams, 2010; Rüsseler, Gerth, & Münte, 2006), alternating serial reaction time task (Howard, Howard, Japikse, & Eden, 2006), as well as the classic serial reaction time (SRT) task first described by Nissen and Bullemer (1987). Indeed, many of these studies have reported procedural learning impairments in the disorder (e.g., Vicari et al., 2005; Vicari, Marotta, Menghini, Molinari, & Petrosini, 2003). However, this finding has not always been replicated (e.g., Bussy et al., 2011; Deroost et al., 2010; Menghini et al., 2010), leaving open the question as to whether procedural learning deficits are indeed found in dyslexia. Moreover, the heterogeneity of findings suggests the possibility that participant level variables (e.g., the age of tested individuals) or methodological factors (e.g., the amount of training in the learning tasks) might help explain the pattern of results.

Qualitative reviews cannot easily synthesize this literature, while accounting for study-specific features such as effect size, sample size, and task related methodological differences. Rather, a rigorous quantitative approach using meta-analytic techniques is more appropriate. In meta-analysis, the results from similar individual studies with similar methodologies are pooled, permitting population parameters to be estimated with greater precision (Borenstein, 2009; Hunter & Schmidt, 1990).

1.2. The serial reaction time (SRT) task

This report used meta-analysis and meta-regression to evaluate and synthesize existing evidence and determine whether procedural learning is affected in dyslexia and what factors may influence the observed inconsistencies in the literature. We focused on the SRT task because this task has been the most widely used to examine procedural learning in dyslexia, with fully 14 studies to our knowledge, and indeed, is the best-studied procedural learning task more generally (for a brief review of findings related to the SRT task see Robertson, 2007). Thus, it is now appropriate to summarize this literature using meta-analysis.

In the SRT task, which was initially developed by Nissen and Bullemer (1987), participants are seated in front of a computer display, on which a visual stimulus repeatedly appears in one of four locations. In the implicit version of the task, on which we focus here, the only instructions provided are to press one of several (typically four) buttons that matches the location of a visual stimulus on the screen. For example, if the second of four stimuli in a row light up, the participant must press the second of four buttons in a row as quickly and accurately as possible. The primary dependent variable of interest is reaction times (RTs) that measure how rapidly participants are able to press the response button following presentation of each visual stimulus. The task is typically divided into blocks of stimulus presentations. Within each block there may in the range of 50 to over 100 stimulus presentations, depending on the study. Unknown to participants, in most blocks the visual stimulus follows a predefined sequence that is typically 6–12 items in length. This sequence is repeated multiple times within the ‘sequence blocks’. Following training on the sequence blocks, a ‘random block’ is presented in which the visual stimulus appears randomly.

In neurologically intact children and adults (e.g., Lum, Kidd, Davis, & Conti-Ramsden, 2010; Thomas et al., 2004), RTs decrease (i.e., becomes faster) over the course of training on the sequence blocks, but then increase on the random block. This increase in participants’ RTs (i.e., RTs become slower) that is observed when the visual stimulus begins to appear randomly, is taken to indicate that information about the sequence has been learned. Note that if no information about the sequence had been obtained, RTs should continue to decrease or reach asymptote, presumably as participants become proficient at pressing the response buttons. This latter result is often found in individuals with neurodegenerative diseases or lesions affecting basal ganglia or cerebellum (Knopman & Nissen, 1991; Mayor-Dubois, Maeder, Zesiger, & Roulet-Perez, 2010; Molinari et al., 1997; Pascual-Leone et al., 1993; Siegert, Taylor, Weatherall, & Abernethy, 2006) or who have prefrontal lesions (Beldarrain, Grafman, Pascual-Leone, & Garcia-Monco, 1999; Schmidtke, Manner, Kaufmann, & Schmolck, 2002).

1.3. Studies examining SRT in dyslexia

At present it is unclear whether individuals with dyslexia are impaired at the SRT tasks, as compared to typically developing (TD) control individuals. The key comparison in these studies is whether the difference in RTs between sequence and random blocks is significantly larger in TD individuals than in participants with dyslexia. Although a number of studies have indeed reported such findings (Jimenez-Fernandez, Vaquero, Jimenez, & Defior, 2011; Menghini, Hagberg, Caltagirone, Petrosini, & Vicari, 2006; Stoodley, Harrison, & Stein, 2006; Stoodley, Ray, Jack, & Stein, 2008; Vicari et al., 2005, 2003), others have not (Bussy et al., 2011; Deroost et al., 2010; Gabay, Schiff, & Vakil, 2012; Kelly, Griffiths, & Frith, 2002; Menghini et al., 2010; Rüsseler et al., 2006; Yang & Hong-Yan, 2011).

A number of explanations could account for the inconsistent pattern of findings. One possibility, of course, is that procedural learning impairments are in fact not present in dyslexia, contrary to the predictions of Nicolson and Fawcett (2007, 2011) and Ullman (2004). On this view, the pattern of results of impaired and unimpaired procedural learning in dyslexia is due to random chance, and a meta-analysis of SRT studies should not reveal a reliable impairment.

Second, procedural learning impairments might indeed be reliably present across individuals in the disorders, and the heterogeneity of results is due to noise or to insufficient power (e.g., small sample sizes) in some studies. On this perspective, a meta-analysis of SRT studies should reveal a deficit on this task, but no other variables (e.g., age, methodological factors) should account for any of the variability of findings across studies.

Third, procedural impairments may occur primarily in certain dyslexic subgroups, and/or under certain testing conditions. For example, it has previously been suggested that inconsistent findings pertaining to other cognitive and motor impairments in dyslexia might be due to deficits restricted largely to certain subgroups (e.g., Rosen, 2003; White et al., 2006). Additionally, the likelihood of compensation in the SRT task by declarative memory, which is primarily supported the medial temporal lobes, may change with age. Declarative memory supports learning, storage and retrieval of information (Squire, 1992; Squire, Stark, & Clark, 2004; Tulving & Markowitsch, 1998). Learning via the declarative memory system is occurs via binding arbitrarily related pieces of information together (Mayes, Montaldi, & Migo, 2007). Learning via the declarative memory system can be fast; learning can take place after a single exposure. However, with repeated exposures to the information the propensity learning takes places increases along with the efficiency stored information can be retrieved (Alvarez & Squire, 1994).

Research indicates declarative memory improves throughout childhood (Lum et al., 2010; Ofen et al., 2007; Ullman & Pierpont, 2005). In concert with behavioral data, the medial temporal have a comparable developmental trajectory, matuaring from childhood and into adolescence (Giedd et al., 1999; Ofen et al., 2007). Although the implicit version of the SRT task is designed to minimize the involvement of the declarative memory system, several studies show declarative memory and medial temporal involvement in participants who have neurodevelopmental or degenerative conditions affecting the basal ganglia and/or cerebellum (Beauchamp, Dagher, Panisset, & Doyon, 2008; Dagher, Owen, Boecker, & Brooks, 2001; Moody, Bookheimer, Vanek, & Knowlton, 2004; Rauch et al., 2007; Ullman & Pullman, 2013). In these groups, levels of implicit or perhaps even some explicit learning on the SRT task that are comparable to controls is achieved via activation of the medial temporal lobes. That is, declarative memory may be able to compensate for procedural memory impairments on the SRT task. These findings have form part of a larger literature suggesting that medial temporal lobe, underlies the learning of implicit as well as explicit knowledge (Chun, 2000; Poldrack & Rodriguez, 2003; Rose, Haider, Weiller, & Buchel, 2002; Ullman, 2008). The protracted developmental trajectory of declarative memory may mean compensation and subsequently smaller differences between dyslexic and control groups may be observed to a greater extent in samples comprising older children or adults. Thus the age of participants might account for differences in findings in the SRT task/dyslexia literature. Indeed, it is interesting to note that the age of participants in past research investing SRT task performance in dyslexia varies substantially ranging from a mean of less than 10 years to over 35 years (Jimenez-Fernandez et al., 2011; Menghini et al., 2006).

Variability in methodological conditions could also help explain learning variability in the SRT task in dyslexia. Multiple variants of this task have been employed in studies of dyslexia. The task varies in a number of respects. For instance, the length of training, for example, as measured by the number of exposures to the sequence (number of blocks times number of sequences per block), has varied substantially between studies, from as little as 10 (Stoodley et al., 2006) to as much as 108 (Deroost et al., 2010). Learning via the procedural memory system requires practice or repeated exposures to information (Packard & Knowlton, 2002). It could be that procedural learning takes place in individuals with dyslexia, however, more practice or exposure to information is required relative to age-matched controls. That is, individuals with dyslexia are ‘slow procedural learners’. Another possibility is that with extended training comes greater declarative memory-based compensation. Thus studies that provide more exposures to the sequence in SRT tasks might observe a smaller difference between dyslexic and control groups.

Also variable between studies is the length of the sequence used in the SRT task. In the dyslexia literature, this ranges from as few as five items (Vicari et al., 2003) to as many as 12 (Deroost et al., 2010; Rüsseler et al., 2006). Importantly, previous evidence suggests that shorter sequences are easier to learn than longer ones (Howard & Howard, 1989). Thus, differences between individuals with dyslexia and controls may only be observed for longer sequences.

Another potential methodological influence on study findings is the type of sequence. First order conditional (FOC) sequences and second order conditional (SOC) sequences have been used in past research into dyslexia (Deroost et al., 2010; Jimenez-Fernandez et al., 2011). In FOC sequences, the spatial location of the visual stimulus on the screen can be predicted from its preceding location. For example, if the visual stimulus appears in Position 1, there might be an 80% probability it will then appear in Position 2. In contrast, in SOC sequences the location of the visual stimulus cannot be predicted by its preceding location. That is, there is an equal probability between transitions from one spatial location to the next. Rather, the location that the visual stimulus will appear, can only predicted by multiple previous transitions.

A number of studies have shown implicit learning of SOC (and other higher order conditional) sequences involves the medial temporal lobes in addition to basal ganglia and cerebellum (Ergorul & Eichenbaum, 2006; Schendan, Searl, Melrose, & Stern, 2003). In accounting for these findings Poldrack and Rodriguez (2003) suggest the medial temporal lobes are necessary for representing information displaced over time or space, irrespective of whether learning or retrieval is implicit or explicit. In SOC sequences, since first order transitions occur with equal probability, learning can only take place if multiple preceding transitions are represented. This suggestion may explain why individuals with medial temporal lobe damage but intact basal ganglia and cerebellum can learn FOC sequences, but not SOC sequences (Curran, 1997). Also, fMRI studies investigating SRT task performance have shown medial temporal lobe activation when participants implicitly learning a SOC conditional sequence (Schendan et al., 2003) but not FOC conditional sequence (Thomas et al., 2004). Thus there is some evidence that implicit learning of SOC sequences may additionally be supported by the medial temporal lobe; a structure assumed to be intact in dyslexia (Hedenius, Ullman, Alm, Jennische, & Persson, 2013; Ullman & Pullman, 2013).

To understand whether SRT task performance is indeed at least partly explained by the above factors, they need to be systematically examined. In this report we first used meta-analysis to systematically synthesize the evidence related to performance of individuals with dyslexia on SRT tasks. The purpose of this analysis was to investigate whether individuals with dyslexia have poorer procedural learning compared to typically developing control participants who did not have dyslexia. We then used meta-regression to investigate whether there were systematic influences that might account for inconsistent findings in the literature examining SRT in dyslexia. Specifically, we investigated whether participants’ age and characteristics of the SRT task (number of exposures to the sequence, sequence length, and sequence type) could account for discrepancies between studies.

2.1. Study design

We followed the methods used by previous meta-analyses of SRT task performance in other clinical groups, such as individuals with Parkinson's disease (Siegert et al., 2006; Siegert, Weatherall, & Bell, 2008). Articles were identified following searches in ERIC (hosted by EbscoHost), MEDLINE (hosted by OvidSP), EMBASE, CINAHL (hosted by EbscoHost), PsycInfo (hosted by EbscoHost) electronic databases to June 2013. The search strategy aimed to identify studies undertaken with samples comprising children or adults with developmental dyslexia who were administered a version of the SRT task. Details of all keywords and fields search are presented in Appendix A.

2.2. Study inclusion criteria

The inclusionary criteria used in this meta-analysis were based on the protocols used by Siegert et al. (2006, 2008). First, since Nissen and Bullemer's (1987) original description of SRT task was first published in 1987, studies published before this date were excluded. Second, studies included in the meta-analysis were required to be published in a peer-review journal (written in any language) reporting on an original piece of research. Third, the study was required to have administered a version of Nissen and Bullemer's (1987) SRT task. That is, it was an implicit version of the task (no indication was given to the participant that there was any sort of sequence), and the structure of the task needed to involve presenting a series of blocks comprising sequenced spatial visual stimulus presentations that were followed by at least one block comprising random spatial visual stimulus presentations. Fourth, the study needed to have presented the SRT task to at least one group comprising individuals (children or adults) identified with developmental dyslexia (not alexia following adult-onset brain damage) and one control group comprising individuals of comparable age that did not have a developmental disorder (i.e., typically developing controls). These criteria served to identify studies with similar methodologies. Fig. 1 summarizes studies removed following application of each criterion according to PRISMA guidelines (Moher, Liberati, Tetzlaff, Altman, & The, 2009).

2.3. Study selection

After the removal of duplicate entries, one reviewer assessed all the abstracts. A random sample of 10% of all abstracts was assessed by a second reviewer. Any disagreements were resolved by discussion. Finally, the reviewers independently retrieved and screened full-text articles accordingly to the eligibility criteria. Inter-rater reliability was strong. For 42 out of the 43 articles (Cohen's kappa = .988) the reviewers independently agreed upon the suitability of each article for inclusion in the meta-analysis. Agreement about the suitability of one article was reached through consensus. A total of 14 published studies were included, and their data was extracted for the meta-analysis. A summary of each of study's participants and SRT task structure are summarized in Tables 1 and 2 respectively.

2.4. Effect size calculations and data extraction procedures

The standard method for comparing the performance of two groups on an SRT task tests whether the difference in RTs between the final random block and preceding block comprising sequenced stimulus presentations differs between the study and control group (e.g., Nissen & Bullemer, 1987). From each study, data was extracted to allow an effect size to be computed along with its variance that quantified this effect. A standardized mean difference (SMD) was used as the effect size measure. This metric describes differences between groups in standard deviation units. For this meta-analysis, the SMD was computed so that positive values indicated that the control group evidenced higher levels of procedural learning on the SRT task, as compared to the study group of individuals with dyslexia.

Following Siegert et al. (2006) the general formula to compute SMD for this interaction value is shown in Eq. (1) and variance in Eq. (2).

where $\overline{x}$ is mean difference in RTs between the final random block and preceding sequence block (Table X showed blocks from each study used to compute the SMD).

SD_pooled is within-group standard deviation of the difference between the final random block and preceding block, pooled across the control and study group.

The result from each study included in the meta-analysis was described using a single effect size. For 11 studies, a single effect size was extracted for each study (Gabay et al., 2012; Jimenez-Fernandez et al., 2011; Kelly et al., 2002; Menghini et al., 2010, 2006; Rüsseler et al., 2006; Stoodley et al., 2006, 2008; Vicari et al., 2005, 2003; Yang, Bi, Long, & Tao, 2013). For three studies, it was necessary to average two sets of effect sizes reported. In the study by Bussy et al. (2011) effect sizes from analyses comparing two dyslexic subgroups to a control group were averaged to create a single effect size. In the Deroost et al. (2010) study, effect sizes were averaged from separate analyses that compared the dyslexic and control group on FOC and SOC sequence learning. In the study by Yang and Hong-Yan (2011) effect sizes were averaged from analyses that compared the dyslexic and control group performance on a SRT task which was completed by the left hand and then right hand. For six studies, it was necessary to impute a value for var(SMD) based on participants performance on other blocks of the SRT task (Deroost et al., 2010; Gabay et al., 2012; Jimenez-Fernandez et al., 2011; Kelly et al., 2002; Rüsseler et al., 2006; Yang & Hong-Yan, 2011). Comprehensive Meta-Analysis Software Package (Borenstein, Rothstein, & Cohen, 1999) was used to convert the extracted data to a common effect size and variance. Description of the data extracted from the studies is presented in Appendix B.

2.5. Meta-analytic procedures

To address the first question, that is, whether there was a difference between individuals with dyslexia and TD controls on sequence learning in SRT tasks, effect sizes were pooled and a weighted averaged effect size was computed using a random effects model (Hedges & Olkin, 1985). A significance test for the weighted average effect size was computed using an alpha level of .05. The use of a random effects model indicates we are assuming differences between study level effect sizes are the sum of sampling error (referred to as within-study variance) and ‘true’ effect size differences (referred to as between-study variance).

The second question addressed in this report was whether different methodological characteristics accounted for differences in study level effect sizes. In undertaking this analysis we first measured the total amount of heterogeneity using the I² statistic (Higgins & Thompson, 2002). I² describes the amount of heterogeneity in effect sizes (as a percentage or proportion) that is attributable to between-study effects (e.g., participant or methodological characteristics). Alternatively stated, the I² statistic measures variability in effect sizes not attributable to sampling error. As a guideline Higgins, Thompson, Deeks, and Altman (2003) suggest that values of 25%, 50% and 75% correspond to low, moderate and high levels of heterogeneity respectively.

Finally, meta-regression (Greenland, 1987) was used to investigate whether participants’ age, sequence type, sequence length and number of exposures to the sequence accounted for variability in effect sizes.

3.1. Evaluation of publication bias of included studies

Preliminary analyses investigated the presence of publication bias using a funnel plot, which plots a measure of study precision (using standard error) against individual study effect sizes (Egger, Smith, Schneider, & Minder, 1997). These data are presented in Fig. 2. Using this approach, bias is considered to be present if effect sizes are asymmetrically distributed around the overall effect size when the study precision is low. When the study precision is high, there is less variability in study effect sizes. Egger's test of asymmetry was not found to be significant (Intercept = 1.793, t (12) = 1.32, p = .212). This suggests the distribution of effect sizes is adequately symmetrical and therefore that publication bias is unlikely.

3.2. Procedural learning in dyslexia

The first research question addressed whether or not individuals with dyslexia are worse at procedural learning than TD control individuals. The effect sizes computed for each study and the weighted average effect size are presented in Fig. 3. Positive SMD values indicate that the TD control group performed better than the dyslexia group, that is, that the control group showed more sequence learning as indicated by a larger RT difference between sequence and random blocks. The weighted average effect size was found to be .449 and highly significant (p < .001). This indicates that on average, individuals with dyslexia perform around half a standard deviation worse than controls of a comparable age on sequence learning in SRT tasks. According to Cohen's (1988) taxonomy this corresponds to a medium effect size.

Despite the finding that the overall effect size is statistically significant, inspection of Fig. 3 shows substantial variability in study level effect sizes. For example, the largest effect size observed was 1.172 and the smallest −.710 (which indicates that control individuals actually performed worse on the SRT task than individuals with dyslexia). Calculation of the I² statistic indicated that 53.1% of variability between effect sizes represents true heterogeneity (i.e., differences between effect sizes not accounted for by sampling error). According to the guidelines by Higgins et al. (2003), this indicates that in this collection of studies there is moderate levels of heterogeneity. That is, there may be systematic influences that account for differences in study results.

3.3. What accounts for the heterogeneity in the findings?

Random-effects model meta-regression was used to investigate the second research question: whether the mean of age of participants in each study and methodological factors account for differences in study findings. The methodological factors examined were sequence type, sequence length and number of exposures to the sequence (see Table 2). In the study by Kelly et al. (2002) half the participants were tested on a sequence that was 9-elements long and the other half with an 8-element sequence. For the meta-regression analyses a value of 8.5 (the average of the two sequence lengths) was used to describe the sequence length in that study. Similarly, in that same study half of the participants were exposed to the sequence 96 times and the other half 84 times. The value used to describe the Number of Exposures to the Sequence in the meta-regression in that study was 90 (the average of the two values). Finally, for all studies Sequence Type was dummy coded so that FOC = 0 and SOC = 1.

To have sufficient statistical power in meta-regression an effect size to covariate ratio of 10:1 is suggested (see Borenstein, 2009). Since there were only 14 effect sizes (one from each study), separate meta-regressions were undertaken that tested one covariate at a time. In addition, we also investigated whether interactions between predictor variables accounted for the heterogeneity in effect sizes. The interaction term for continuous variables was created by centering and then multiplying variables. The interaction term using Sequence Type (which is a dichotomous variable) was created by multiplying this variable with other continuous variables. To preserve statistical power, only the interaction term was entered into the model. In these analyses the influence of the main effects were removed from the interaction term by regressing the interaction term on to the main effects and saving the standardized residuals using ordinary least squares regression. The residuals were then entered into the analysis as the interaction term. For example, to create the “Age × Sequence Length” interaction term, participants’ age and sequence length for each study were multiplied. The ensuing values were then regressed onto “Age” and “Sequence Length” and standardized residuals were saved and used as the covariate in the analysis.

The outcome variables used in the following meta-regressions were the effect sizes reported in Fig. 3. The exception was for analyses testing whether sequence type influenced effect sizes. As noted earlier, Deroost et al. (2010) tested participants on both FOC and SOC sequence types. To increase the number of data points for SOC sequences, only the effect size pertaining to the results for the SOC conditional sequences in that study was used in analyses investigating sequence type. Note that including both sets of results in the analyses would bias the results by treating dependent sets of results an independent (Tramèr, Reynolds, Moore, & McQuay, 1997). A summary of the results from the meta-regressions is presented in Table 3.

Models 1–4 tested the contribution of age and methodological characteristics of the SRT task as main effects in accounting for differences in effect sizes; none were found to fit the data. However, two out of the six models testing an interaction were found to account for significant amounts of heterogeneity. Model 5 which tested the Age × Number of Exposures to the Sequence interaction term was found to be a significant predictor of effect sizes. This model accounted for 37% of between-study heterogeneity. This result indicates effect sizes become smaller in studies where participants are exposed to the sequence more times, but only when participants are older in age. In other words, the difference between the dyslexic and control groups on the SRT task decreases when more training to the sequence is provided and when participants are older. This model plotted against observed effect sizes is presented in Panel A in Fig. 4.

Model 6, which tested the Age × Sequence Type interaction term, was also found to be a significant predictor of effect sizes. This model accounted for 28% of between-study variance. This indicates that effect sizes become smaller for studies that presented SOC sequence, but only when participants were older. That is, the difference between dyslexic and control groups on the SRT task is smaller for SOC sequence than for FOC sequences, but only for older participants. Panel B in Fig. 4, plots this model against observed effect sizes.

4.1. Limitations of meta-analysis and meta-regression

Two limitations need to be taken into account when interpreting the results presented in this report. First, results from the meta-analysis showing poorer procedural learning in dyslexia (see Fig. 2) cannot address the issue of causality between procedural learning and reading problems. All studies identified in the systematic search of the literature used a correlational research design. Therefore, the findings from our review should be interpreted to indicate that, at any particular single point in time, reading and procedural learning problems are evident in dyslexia. Second, interpreting results from meta-regression does require some caution (see Thompson & Higgins, 2002). This is because there might be measured or non-measured variables that correlate with the covariates, which in turn can lead to spurious claims about the relations between predictor and outcome variables. This is particularly important to note given the differences in participant characteristics noted in Table 1. For example, some studies ruled out the presence of ADHD in participants with dyslexia whereas others did not. The presence of ADHD or other comorbid disorder may also have an influence on effect sizes. It therefore needs to be noted that our discussion is speculative, necessarily tentative in nature and requires further investigation.

4.2. Clinical implications of meta-analyses findings

In the ongoing effort to improve detection rates and remediation of reading difficulties, the results from the current study suggest procedural memory is worthy of further consideration. With respect to detection, results presented in this report indicate poor procedural memory might be a risk factor for reading problems. Further research is needed to develop a practical, time-efficient assessment of procedural memory that can be used in clinical and other remediation contexts. The extent to which focusing on procedural memory in the context of remediation can lead to improved reading outcomes is crucially dependent on whether a causal link exists between poor procedural learning and reading problems; a topic for future research. The findings of these meta-analyses also raise issues regarding the focus for remediation-related research. Should remediation harness the compensatory potential of declarative memory, should intervention be directed to procedural memory, or both? More generally, the interactions observed between age and task performance on study findings, serve as a timely reminder that neurodevelopment needs to be taken into account to better understand the underlying nature of dyslexia (see Karmiloff-Smith, 1988).