The Role of Sleep in Memory Consolidation and Reading in Dyslexia

Abstract

Dyslexia is a neurodevelopmental disorder characterized by reading difficulty, which has long been attributed to a phonological processing deficit. However, recent research suggests that general difficulties with learning and memory, but also in memory consolidation, may underlie disordered reading. This review article provides an overview of the relationship between learning and memory, memory consolidation during sleep, and reading and explores the emerging literature on consolidation during sleep in individuals with dyslexia. We consider evidence that sleep appears to be less effective for memory consolidation in children with dyslexia and how this may be related to their deficits in reading. This discussion highlights the need for further research to determine the extent to which atypical sleep patterns may contribute to learning deficits associated with disordered reading.

INTRODUCTION

In recent years, there has been an increased interest in the role of memory consolidation during sleep in the development of language (Dumay & Gaskell, 2007; Gómez, Bootzin, & Nadel, 2006; Fenn, Nusbaum, & Margoliash, 2003; Gaskell & Dumay, 2003; see Brain and Language special issue, Rasch, ed.). A logical extension of sleep-related mechanisms in the development of reading is an emerging literature (e.g., Williams & Earle, 2022; Sengottuvel, Vasudevamurthy, Ullman, & Earle, 2020). The bulk of this work has been conducted on typical learners; however, examining sleep in individuals such as those with dyslexia may provide unique insight into the nuanced relationship between sleep and reading development.

Dyslexia is a neurodevelopmental disorder characterized by persistent difficulties in accurate and fluent word decoding, which cannot be attributed to intellectual limitations or lack of educational opportunities (Lyon, Shaywitz, & Shaywitz, 2003). Phonological processing deficits, particularly challenges with phoneme awareness, have been identified as a key factor contributing to reading difficulties in dyslexia (Hulme, Bowyer-Crane, Carroll, Duff, & Snowling, 2012; Vellutino, Fletcher, Snowling, & Scanlon, 2004; Ehri et al., 2001). For the past four decades, the dominant theory regarding its etiology has looked to problems in sensory processing to account for weaknesses in phonological skill (Gabrieli, 2009). More recently, a set of theoretical perspectives that have been gaining empirical support has pointed to domain-general deficits in learning and memory—specifically, in procedural memory—as accounting for the deficits observed in developmental dyslexia (Ullman, Earle, Walenski, & Janacsek, 2020; Nicolson & Fawcett, 2011; Ullman, 2004).

This hypothesis has been investigated through many different measures of procedural learning, albeit with mixed interpretations (see West, Melby-Lervåg, & Hulme, 2021; Lum, Ullman, & Conti-Ramsden, 2013, for metaanalyses). Procedural learning has often been examined through a nonlinguistic, implicit learning task in those with dyslexia, such as through a serial RT task (SRTT; Nissen & Bullemer, 1987), or some similar procedure involving the learning of sequences. Furthermore, statistical learning (SL), which is often thought to rely on the procedural memory system depending on how it is trained, has been found to be impaired in those with dyslexia (Qi, Sanchez Araujo, Georgan, Gabrieli, & Arciuli, 2019; Gabay, Thiessen, & Holt, 2015). We note, however, that the procedural learning deficit in dyslexia remains a highly debated, and polarizing, perspective and places a heavy burden of interpretation on how procedural learning is defined (e.g., Schmalz, Moll, Mulatti, & Schulte-Körne, 2019; Vicari et al., 2005). At the very least, a difficulty in the learning of sequences appears to be a consistent finding in children with dyslexia, regardless of whether these nonlinguistic deficits are thought to be causal to the reading difficulties that define this disorder (West et al., 2021).

Beyond a deficit in procedural learning, the other core tenet of a procedural deficit in those with dyslexia is that declarative memory is thought to be relatively unaffected (Hedenius, 2013) and that it may play a compensatory role for a weakness in procedural memory (Ullman & Pullman, 2015). This assumption has opened the procedural deficit framework(s) to further criticism, in that children with dyslexia are observed to experience difficulties in learning aspects of language thought to depend on declarative memory (e.g., words; Alt et al., 2017). Moreover, a reduced growth trajectory in vocabulary development is associated with increased familial risk for dyslexia (van Viersen et al., 2017). As vocabulary learning is thought to depend on the declarative memory system, we would not expect to see such differences in children with dyslexia under the procedural deficit frameworks. Moreover, vocabulary size has a closer conceptual relationship to reading than performance on nonlinguistic procedural learning tasks.

A potential way in which we might resolve these disparate findings might be to consider not only the initial learning behaviors of those with dyslexia but also the sleep-mediated consolidation of both declarative and procedural learning in this population. In recent years, there has been an increased interest in the role of sleep in the consolidation of speech and language learning (e.g., Earle & Myers, 2015a, 2015b; Dumay & Gaskell, 2007; Fenn et al., 2003; see Gomez, Newman-Smith, Breslin, & Bootzin, 2011, for a review). By extension, there has been interest in examining sleep as playing a mechanistic role in various disorders of language and reading (Sengottuvel et al., 2020; Ullman et al., 2020; Earle, Landi, & Myers, 2018; McGregor & Alper, 2015). Interestingly, there is a small but growing collection of findings that point to both atypical sleep and consolidation behaviors in children with dyslexia (Ballan, Durrant, Manoach, & Gabay, 2023; Ben-Zion, Gabitov, Bitan, & Prior, 2023; Smith et al., 2018; Carotenuto, Esposito, Cortese, Laino, & Verrotti, 2016; Gabay, Schiff, & Vakil, 2012; Bruni, Ferri, Novelli, Finotti, et al., 2009; Bruni, Ferri, Novelli, Terribili, et al., 2009). As with the literature on procedural learning and dyslexia, however, the findings are mixed. Moreover, there is not yet a clear connection between a potential deficit in consolidation and difficulties in reading.

The purpose of the following review was to provide a cohesive narrative on issues surrounding learning and memory in those with dyslexia by considering the retention profiles after initial learning. Below, we briefly summarize the literature that describes the basic stages of reading acquisition and the role of different types of memory in reading acquisition. This is followed by a description of the various frameworks that posit a selective weakness in procedural learning in those with dyslexia ( Ullman et al., 2020; Nicolson & Fawcett, 2007). We will build on this framework by a brief review of the role of sleep and memory consolidation, followed by presenting reports of atypical sleep and consolidation in children with developmental dyslexia. Through this review, we will highlight the challenges to learning and memory in children with dyslexia that may not be limited to initial learning but also the subsequent consolidation of memory.

THE ROLE OF MEMORY IN READING

Reading acquisition is a complex process that develops in overlapping phases. Initially, children depend on visual cues and memorization during the prealphabetic phase. As they learn letter–sound relationships, they enter the alphabetic phase, which facilitates decoding (Ehri, 2002, 2005, 2017). Automaticity develops with practice, enabling skilled readers to quickly recognize familiar words, which in turn enhances reading fluency (Castles & Nation, 2008, 2010; Nation & Castles, 2017).

There has been recent interest in the hypothesis that disordered reading stems from a selective weakness in certain memory systems over others. This has prompted a narrative that successful reading acquisition may depend on the interplay between two distinct memory systems: declarative and procedural memory (Earle et al., 2020; Ullman, 2016). Declarative memory, also often referred to as explicit memory, relies on structures (e.g., hippocampus) in the medial temporal lobe and refers to the conscious memory for facts, events, and information that can be consciously recalled. Procedural memory, often associated with implicit learning, is associated with the “slow learning” network that relies on regions like the striatum and neocortex and involves the learning and retention of skills, habits, and motor sequences. How these memory systems map onto specific facets of reading ability in typical readers is not entirely clear. One hypothesis is that early learning might be related to declarative memory, with a shift toward procedural dominance as skills become automated (e.g., Earle et al., 2020); however, this is a highly nuanced literature.

In a parallel literature, SL has emerged as a potential factor influencing reading outcomes. SL refers to the ability to extract patterns and regularities from the environment, including those in written language. SL is thought to support reading development by facilitating one’s sensitivity to transitional probabilities of letter sequences (Sawi & Rueckl, 2019; Harm & Seidenberg, 2004). Many have suggested that common SL tasks, such as artificial grammar learning and SRTT, depend on the procedural memory system (Conway & Christiansen, 2006; Newport & Aslin, 2004). These tasks tend to be implicitly learned, and the SRTT is one of the most often used performance-based measures of procedural learning (Nissen & Bullemer, 1987). We note that despite this common use of SL ability as a performance measure of procedural memory, the association should be treated with caution, as certain task manipulations during an SL task (e.g., explicit identification of recurring patterns/associations) have been found to engage the medial temporal lobe (Schapiro & Turk-Browne, 2015; Turk-Browne, Scholl, Chun, & Johnson, 2009). Moreover, there may be age-related differences on how SL relies on different memory systems. For example, before the hippocampal–pFC circuit is fully developed, SL may primarily involve the neocortex and striatal networks (e.g., Witt, Puspitawati, & Vinter, 2013). SL is therefore not a monolithic skill, and thus, the relationship between SL and reading abilities may depend on the specific tasks used to assess each skill and the overlap in underlying cognitive processes.

As mentioned above, various accounts have suggested that selective weaknesses in procedural over declarative memory can explain the various behaviors observed in dyslexia (Ullman et al., 2020; Nicolson & Fawcett, 1990, 2007). For example, the procedural circuit deficit hypothesis (PDH) posits that abnormalities of procedural memory brain structures may contribute to disorders such as dyslexia ( Ullman et al., 2020; Ullman & Pullman, 2015). This hypothesis is supported by evidence that individuals with dyslexia struggle with implicit learning tasks that are related to procedural memory, such as SL, sequence learning, and category learning (Clark & Lum, 2017; Gabay et al., 2015; Lum et al., 2013). Within this framework, Ullman and Pullman (2015) have hypothesized that declarative memory remains largely intact in dyslexia and that this memory system may be leveraged to compensate for procedural memory deficits. In dyslexia, this compensation may involve memorizing common orthographic strings rather than decoding letter–sound correspondences as well as relying on semantic context to understand written passages. Studies showing increased brain activity associated with declarative memory during reading tasks in dyslexic individuals support this notion of compensatory activation (Gebauer et al., 2012; Eden et al., 2004; Temple et al., 2003; as cited in Ullman & Pullman, 2015). We do however reiterate that this a highly debated perspective.

SLEEP AND MEMORY CONSOLIDATION

Memory consolidation is a dynamic process that involves the ongoing transformation and stabilization of newly acquired information as well as new instances of activating existing information. Under the current view, memory representations are not fixed but rather evolve over time, even after initial consolidation and subsequent reconsolidation (Dudai, 2012; Winocur & Moscovitch, 2011). The transition from initial (or newly reactivated) traces to distributed representations is facilitated by interactions during offline periods, such as sleep and quiet rest (McNaughton, 2010; Born, Rasch, & Gais, 2006; Buzsáki, 1989). Sleep appears to play an important role in both declarative and procedural memory consolidation (Fogel, Ray, Binnie, & Owen, 2015; Ackermann & Rasch, 2014; Born et al., 2006).

Specific electrophysiological indexes of sleep patterns, including slow oscillations, spindles, and sharp-wave ripples, have been linked to the consolidation process (see Girardeau & Lopes-Dos-Santos, 2021; Klinzing, Niethard, & Born, 2019; Rasch & Born, 2013, for reviews). In 1993, Steriade and colleagues discovered a rhythm that operates in the frequency range of 0.5–1.5 Hz, called the slow oscillation. By contrast, delta waves represent a broader category of slow-wave activity that encompasses a frequency range from 1 to 4 Hz. Although delta waves include the frequency band of slow oscillations, they also cover a somewhat faster range of activity that can be present in lighter stages of nonrapid eye movement (NREM) sleep as well as during slow-wave sleep (SWS). In addition, brief bursts of activity in the thalamus and neocortex, known as sleep spindles (7–14 Hz), are linked to memory consolidation, by transferring information across structures and strengthening connections between neurons (Steriade, McCormick, & Sejnowski, 1993; Steriade, Nuñez, & Amzica, 1993). Finally, sharp-wave ripples are brief events (30–120 msec) occurring in the hippocampus (Buzsáki, 2015). These events are thought to play a crucial role in memory reactivation, a process where previously activated neural sequences are “replayed” during sleep or other offline periods.

There has been more work done to investigate the mechanism of consolidation during sleep in declarative, over procedural, memory. It is suggested that, during offline periods, especially during NREM sleep, the synchronized reactivation of memory traces within the hippocampus and an array of cortical and subcortical structures occurs. This reactivation facilitates the reconfiguration of neural connections and the formation of cortico-cortical connections (i.e., independent of connections to the subcortical structures that initially encoded the trace; Johnson, Euston, Tatsuno, & McNaughton, 2010; Wilson & McNaughton, 1994). There is robust evidence to support that hippocampal memory reactivation facilitates the memory transfer (Ji & Wilson, 2007), which in turn improves declarative memory recall (see Gais & Born, 2004, for a review).

Investigations into procedural memory consolidation have shown that performance on procedural tasks may also benefit from sleep (Korman et al., 2007; Fischer, Hallschmid, Elsner, & Born, 2002; Walker, Brakefield, Morgan, Hobson, & Stickgold, 2002). Traditionally, rapid eye movement (REM) sleep was believed to be essential for procedural memory consolidation (Smith, 2001; Plihal & Born, 1997). Ackermann and Rasch (2014) however demonstrated that an increase in spindle activity during NREM sleep was correlated with performance gains on a procedural task. In addition, Fogel, Smith, and Cote (2007) observed an enhancement in spindle power in sleep that followed a simple procedural (motor skill learning) task. Moreover, an examination of EEG power in the spindle (12–16 Hz) and slow-wave frequency range (0.1–4 Hz) during NREM sleep was conducted to explore its correlation with overnight memory consolidation in declarative and procedural memory (Holz et al., 2012). The study showed that both EEG spindle power and slow-wave activity were positively correlated with the changes between before and after sleep, for performance in both declarative (word list) and procedural (mirror-tracing) tasks.

The mechanism of consolidation for procedural memory is less well understood than that for declarative memory; however, there is some neuroimaging work to suggest similarities with the hippocampal network, in a transfer of information between brain structures overnight (Durrant, Cairney, & Lewis, 2013). This study examined the relationship between the consolidation of auditory tone sequences during sleep and the amount of SWS. Participants underwent a consolidation period of either 30 min or 24 hr that was followed by a delayed test session during which brain activity was monitored using fMRI. Participants showed greater improvement in recognizing the learned patterns after a 24-hr consolidation period compared to a 30-min period. Furthermore, the amount of SWS obtained during the delay period predicted the extent of this improvement. The fMRI results suggested a gradual shift in activation from the hippocampus to the striatum, mediated by the amount of SWS obtained. Moreover, the connectivity between the striatum and the parahippocampus was weaker, and the connectivity between the putamen and the planum temporale was stronger, after sleep. Together, the authors suggested that whereas the task relied on the hippocampus for initial encoding, sleep transferred the information to the striatum. The reduced reliance on the hippocampal memory system may indicate that the memory trace changed from an episode-bound to a more generalized and abstract representation of the input.

There is evidence that sleep is beneficial for memory consolidation as early as infancy (Gómez et al., 2006) and in early childhood (Ashworth, Hill, Karmiloff-Smith, & Dimitriou, 2014). The literature on children’s consolidation of memory is relatively small. Although this claim is subject to debate, there is evidence to suggest that the mechanisms underlying sleep-mediated changes to behavior in children are not all that different to that of adults (Lokhandwala & Spencer, 2021; Kurdziel, Duclos, & Spencer, 2013). This observation is qualified by the differences between children and adults in characteristics of sleep: Infants and small children require more sleep than adults, distributing their sleep bouts throughout the 24-hr cycle. Moreover, sleep architecture is characterized by a higher proportion of NREM (and SWS in particular) early in development, reducing its relative duration with respect to REM over time (Mason & Spencer, 2022; Peiffer, Brichet, De Tiège, Peigneux, & Urbain, 2020; Kurth et al., 2010; Gaudreau, Carrier, & Montplaisir, 2001). Interestingly, overall sleep duration in children appears to reduce as hippocampal volume increases (Riggins & Spencer, 2020). Distributed sleep (i.e., naps in addition to overnight sleep) has therefore been hypothesized to support memory retention while the hippocampus matures (Mason & Spencer, 2022). Mason and Spencer (2022), in their cross-sectional review of the available literature on memory consolidation during childhood, have suggested that the effects of sleep on declarative memory consolidation may be relatively stronger in middle childhood (age 6–12 years). This may implicate sleep as being particularly important during the acquisition of literacy.

MEMORY CONSOLIDATION DURING SLEEP SUPPORTS LANGUAGE

There has been a proliferation of work conducted in the last two decades regarding the role of sleep in learning language (see Gómez & Esterline, 2019; Gomez et al., 2011, for reviews). For example, the finding that offline consolidation supports the integration of novel word forms in the mental lexicon is a robust phenomenon (Dumay & Gaskell, 2007, 2012; Tamminen, Payne, Stickgold, Wamsley, & Gaskell, 2010; Davis, Di Betta, Macdonald, & Gaskell, 2009). Sleep has been shown to facilitate the learning of various elements of grammar as well. For example, naps appear to promote the abstraction of grammatical elements in artificial grammar (Gómez et al., 2006). The basic units of language learning that are necessary for reading are supported by sleep.

Of course, reading skill is also closely associated with speech-sound representations. There is an emerging literature on sleep’s role in speech (Earle & Myers, 2014). Previous studies have shown that, after acoustic–phonetic training, sleep enhances and generalizes perceptual performance (Qin & Zhang, 2019; Xie, Earle, & Myers, 2018; Earle & Myers, 2015a, 2015b; Fenn, Margoliash, & Nusbaum, 2013; Fenn et al., 2003). Importantly, the magnitude of perceptual improvement during the offline period varies widely among individuals (Earle et al., 2018; Earle, Landi, & Myers, 2017). Moreover, the magnitude of consolidation of speech-sound information has been found to predict orthographic decoding (but not sight word recognition) skill in adult readers, suggesting that habitual, nightly updates to the speech-sound representation may be important for reading skill, even in adulthood (Williams & Earle, 2022).

Taken together, there is a robust literature that supports the role of sleep in consolidating linguistic information in ways that are likely to support reading. Direct investigations into the relationship between individual differences in reading and consolidation, however, are sparse (although see discussion of group-level differences in populations with reading disorders below). Given the robust evidence supporting its relevance in learning speech and language, however, we may surmise that sleep plays a vital role in reading development as well. Beyond the role of sleep in building language knowledge (e.g., phonological and lexical representations), sleep may directly support reading ability by strengthening the connections between written symbols and their corresponding sounds. Moreover, consolidation of procedural learning may support the increased automaticity and fluency in decoding ability. In these ways, habitual differences in sleep may provide mechanistic insights, at least in part, into the variability in skill across readers.

CHARACTERISTICS OF SLEEP IN DYSLEXIA

The above discussion considered the potential relevance of consolidation processes during sleep to language learning and, by extension, reading. Recently, there have been reports that suggest that children with dyslexia are observed with heightened levels of sleep disturbances, raising the possibility that sleep may play some role in the deficits observed in reading (Carotenuto et al., 2016). Below, we examine the documented characteristics of sleep in children with reading disability and/or dyslexia as well as how this may relate to atypical patterns of overnight consolidation. We will highlight the specific ways in which consolidation appears to be impaired in this population, which may bridge the gap between the findings on sleep in this population and broader theoretical frameworks regarding the etiology of dyslexia.

The few findings on the proportions of time spent in different sleep stages in children with dyslexia are mixed. Mercier, Pivik, and Busby (1993) reported that children with reading disabilities had a sleep pattern with more SWS, less REM sleep, and longer REM sleep latency. In contrast, Bruni, Ferri, Novelli, Terribili, and colleagues (2009) found that, rather than SWS, it was the N2 stage of NREM sleep that had a significantly higher percentage in children with dyslexia. Moreover, it was observed that children with dyslexia had a significantly lower number of stage shifts per hour of sleep. Smith and colleagues (2018) found a marginal (p < .05, but not after correction for multiple comparisons) difference between children with and without dyslexia on total sleep time (dyslexia > control); however, they observed no significant group differences in the percent time spent in any sleep stage. These disparate findings do not offer a consensus narrative regarding how children with dyslexia differ from typical readers with respect to duration of sleep stages, other than that they appear to be different from that of typical children.

Beyond time spent in each stage, there are atypical characteristics of NREM sleep reported in children with dyslexia. For example, Mercier and colleagues (1993) found an increased number of sleep spindles in children with dyslexia, as compared to typical readers. Similarly, Bruni, Ferri, Novelli, Terribili, and colleagues (2009) found sleep spindle density during Stage N2 to be significantly higher in children with dyslexia. In addition, children with dyslexia were observed with an increased power in the frequency bands of 0.5–3 and 11–12 Hz. Moreover, during SWS, the power in the frequency band of 0.5–1 Hz increased. Similarly, Smith and colleagues (2018) observed numerical, but not statistically significant, increases in spindle power, spindle density, and slow-wave activity in children with dyslexia as compared to typical readers. Interestingly, increased power in the delta band during SWS, as well as increased sleep spindle density, is associated with better declarative memory consolidation in typical individuals (Gais & Born, 2004). However, slow-wave activity and spindle power were found to be unassociated with overnight consolidation of cued recall in children with dyslexia, unlike typical readers (Smith et al., 2018). Moreover, higher spindle density has been correlated with severity of dyslexia (Bruni, Ferri, Novelli, Terribili, et al., 2009). In other words, the same electrophysiological events during sleep that facilitate memory consolidation in typical children do not appear to help children with dyslexia learn, and in some cases, they seem to worsen symptoms.

To summarize, children with dyslexia appear to have sleep characteristics that differ from those of children with typical reading development. How the duration of sleep, or proportion of total sleep spent in different stages, appears to differ between those with and without dyslexia seems to vary across studies. Interestingly, children with dyslexia are observed with heightened electrophysiological parameters that are typically associated with memory consolidation (e.g., sleep spindle density, spindle power), and yet these parameters appear unrelated to gains in behavior. Below, we review the available behavioral findings on offline memory consolidation in individuals with dyslexia.

SLEEP AND MEMORY CONSOLIDATION IN DYSLEXIA

The vast majority of studies that have investigated memory consolidation in those with dyslexia have examined behavior over time on tasks that are often aligned with procedural learning. Building from the PDH framework, some have claimed that those with dyslexia simply have difficulty with certain types of learning, such that poor consolidation is subsequent to poor initial encoding. For example, Ben-Zion and colleagues (2023) claim that deficits in consolidation observed in those with dyslexia may be attributed to an initial failure to extract statistical regularities from the input. The authors examined both the learning and consolidation of a motor sequence and of artificial grammar. In the motor task, there were no significant differences between those with dyslexia and controls in learning a new sequence of finger movements; however, those with dyslexia struggled to generalize the sequence to an untrained hand. In the artificial grammar learning task, those with dyslexia demonstrated impaired learning initially and, furthermore, did not make offline gains. In a similar finding, Gabay and colleagues (2012) investigated sequence learning under a divided attention condition. Both groups struggled to learn when performing a secondary task simultaneously; however, the control group showed improvement (faster RT), whereas those with dyslexia did not. In addition, the control group improved after a 24-hr period, whereas those with dyslexia did not, suggesting a deficit in initial learning under increased attentional demands. To note, as the authors acknowledge, individuals with dyslexia often exhibit higher rates of comorbid attention difficulties (Boada, Willcutt, & Pennington, 2012). Thus, even while those with ADHD were excluded from their sample, there may have been attention-related difficulties that influenced performance. In any case, these authors have collectively argued against the idea that those with dyslexia have a consolidation deficit. Rather, they propose that the deficits in consolidation are likely epiphenomenal to weaknesses in initial learning.

Others have observed deficits in consolidation in those with dyslexia, even after typical (or typical-like) learning. For example, on an auditory SL task, Ballan and colleagues (2023) found that adults with and without dyslexia both demonstrated initial learning of simple sound patterns. However, whereas typical readers retained this learned knowledge after sleep, adults with dyslexia deteriorated in their performance. In another example, Nicolson, Fawcett, Brookes, and Needle (2010) found that, 24 hr after motor sequence learning, those without dyslexia performed more quickly on keying a learned motor sequence, whereas those with dyslexia performed more slowly and with more errors. In a follow-up study, Needle, Nicolson, and Fawcett (2015) investigated the consolidation of procedural learning in the same motor sequence task as before, but with a slow, paced training to ensure that the lack of consolidation observed in those with dyslexia was not attributable to poor initial learning. Researchers then examined their performance immediately after learning and again 24 hr later. Whereas the performance of the two groups did not differ immediately after learning, the control group significantly outperformed the dyslexia group 24 hr later. Together, these findings suggest that adults with dyslexia may have difficulty consolidating newly learned sequences.

Similarly, Hedenius and colleagues (2013) tracked changes in performance on an alternating SRTT in children with and without dyslexia. The results showed no differences between the developmental dyslexia (DD) and typically developing (TD) groups in initial learning, and yet the control group outperformed the dyslexia group after 24 hr. The same authors recently investigated if prolonged training might facilitate procedural memory consolidation in children with DD (Hedenius, Lum, & Bölte, 2021). Again, the authors found that there was no significant difference between the TD and DD groups at the end of Session 1 (p = .797), indicating that both groups acquired a similar level of sequence knowledge. However, during the follow-up session 24 hr later, the performance of the DD group was significantly lower than that of the TD group (p=.003). Moreover, in both the 2013 and 2021 studies, performance at the 24-hr follow-up session was found to be a significant predictor of reading fluency. This relationship held after accounting for factors such as children’s phoneme awareness and inattention symptoms (Hedenius et al., 2021). The authors suggest that children with DD may not struggle with initial learning, but with the offline consolidation of procedural memory.

If a deficit in consolidation is attributable to disruptions in sleep, then it would stand to reason that deficits in consolidation would not be bound to a particular memory system (i.e., procedural memory) but would be observed across all types of memory that undergo offline consolidation. However, there are relatively fewer investigations into the consolidation of declarative memory in this population. In one example, Smith and colleagues (2018) tracked performance on cued recall in children with and without dyslexia. Although children with dyslexia showed overnight improvements similar to control participants after an overnight interval, overnight changes in cued recall were uncorrelated with sleep measures (such as slow-wave activity, spindle power, and other sleep parameters). Interestingly, consolidation of cued recall was found to be reduced in children with dyslexia after 1 week. In another investigation, Sengottuvel and colleagues (2020) tracked performance on an object recognition task in children with and without reading disabilities in India. Despite comparable learning observed between the two groups initially, only the children without dyslexia were observed to improve their performance on the next day. Together, these results may suggest that potential deficits in consolidation in this population may not be limited to procedural memory.

In conclusion, the findings from various studies demonstrate that those with dyslexia exhibit impairments in the consolidation of both declarative and procedural memory. This position is complicated by findings that point to a deficit in initial learning that hinder the interpretation of performance at a later time point. Although declarative memory, in some tasks, do appear to enhance over time in those with dyslexia, these enhancements appear unrelated to sleep, and moreover, their performance remains weaker to those without dyslexia. In procedural tasks, performance has often been observed to worsen after sleep in individuals with dyslexia. Taken together, it appears that offline consolidation, in both procedural and declarative memory, may be affected in those with dyslexia. Interestingly, whereas the reports on the electrophysiological characteristics of sleep focus on children, the bulk of the behavioral consolidation literature (with the exception of Smith et al., 2018; Hedenius et al., 2013) focus on adults with dyslexia. Given the age-related differences in sleep, a longitudinal and/or cross-sectional investigation may be necessary for mapping how observed differences in sleep inform consolidation behaviors.

CONCLUSION

Reading is a complex skill that relies on a network of brain functions, including the ability to learn and remember new information. Beyond initial learning, research has shown that sleep plays a crucial role in consolidating both procedural and declarative memories. Frameworks (e.g., PDH) that implicate learning weaknesses in the etiology of dyslexia currently do not account for how consolidation behaviors contribute to the memory profile associated with this disorder. The aim of this review was therefore to examine the state of knowledge surrounding sleep and consolidation behaviors, contextualized in the broader narrative surrounding procedural and declarative memory function, in dyslexia.

Regardless of the memory type, the current evidence indicates that sleep does not seem to improve memory consolidation in children with dyslexia as it does in typical children. Most studies that have examined consolidation in dyslexia have examined procedural memory. All of them have found performance to be weak in those with dyslexia as compared to typically developing controls in the day(s) after the initial learning. Some have claimed that the poor retention of procedural information may be attributable to weak initial learning; however, others have observed impaired performance even after initial learning that appeared unimpaired. In addition, limited research points toward potential impairments in declarative memory consolidation in dyslexia. Together, this suggests that sleep and/or memory consolidation might be disrupted in dyslexia, across both procedural and declarative memory.

How sleep may contribute to the deficits observed in dyslexia is yet unclear. Poor sleep may lead to impoverished linguistic representations; however, this would lead to an expectation that dyslexia would more often co-occur with a disorder in language. Moreover, that children with dyslexia often demonstrate increases in electrophysiological markers of memory consolidation, such as increased sleep spindle power and density, is intriguing. Perhaps these markers represent compensatory mechanisms that are maladaptive for the functional outcomes. As Smith and colleagues (2018) suggest, even if sleep problems may not be the direct cause of the disorder, sleep might worsen the difficulties experienced by some children with dyslexia.

In summary, this article reviewed the characteristics of sleep in dyslexia and how it relates to memory consolidation. The findings suggest that these children often have atypical sleep patterns and struggle with memory consolidation. The exact relationship between sleep and memory consolidation in dyslexia is still unclear, and more research is needed to understand the relative contributions of initial learning difficulties and consolidation problems in dyslexia. In any case, it seems clear that the learning and memory profiles of this population are incomplete without description of retention behavior.

Diversity in Citation Practices

Retrospective analysis of the citations in every article published in this journal from 2010 to 2021 reveals a persistent pattern of gender imbalance: Although the proportions of authorship teams (categorized by estimated gender identification of first author/last author) publishing in the Journal of Cognitive Neuroscience (JoCN) during this period were M(an)/M = .407, W(oman)/ M = .32, M/W = .115, and W/W = .159, the comparable proportions for the articles that these authorship teams cited were M/M = .549, W/M = .257, M/W = .109, and W/W = .085 (Postle and Fulvio, JoCN, 34:1, pp. 1–3). Consequently, JoCN encourages all authors to consider gender balance explicitly when selecting which articles to cite and gives them the opportunity to report their article’s gender citation balance. The authors of this article report its proportions of citations by gender category to be M/M = .337, W/M = .229, M/W = .145, and W/W = .289.

Data Availability Statement

This is a review article and does not include original data.