Sustained attention

According to the Attention Systems Model (Posner & Petersen, Reference Posner and Petersen1990), the alerting network is a dual-level control mechanism; the low-level refers to physiological alertness, and the high-level refers to maintaining attentional focus and an optimal level of alertness during task performance, namely sustained attention (Gomes et al., Reference Gomes, Molholm, Christodoulou, Ritter and Cowan2000). Neuropsychological assessment of sustained attention requires participants to remain focused and ready to react to the presentation of rarely occurring target stimuli over a long period of time (Tucha et al., Reference Tucha, Fuermaier, Koerts, Buggenthin, Aschenbrenner, Weisbrod and Tucha2017), for example, using the Continuous Performance Test (CPT; Conners et al., Reference Conners, Staff, Connelly, Campbell, MacLean and Barnes2000).

Sustained attention is crucial for learning, for everyday activities and cognitive development (Esterman & Rothlein, Reference Esterman and Rothlein2019; Fortenbaugh et al., Reference Fortenbaugh, DeGutis and Esterman2017). Failures in sustained attention, such as vigilance decrements and fluctuations of performance, are a focus of concern, because lapses of attention in some cases, such as during car driving, can be life-threatening, and, in less extreme cases, can impair basic cognitive functions such as learning and memory (Decker et al., Reference Decker, Duncan and Finn2023; Unsworth & Robison Reference Unsworth and Robison2017).

Vigilance decrements are defined as a decline in performance in sustained attention tasks as a function of time-on-task (Thomson et al., Reference Thomson, Besner and Smilek2015) and are explained by either an overload or an underload of attentional demands. Most overload models assume that attention is a limited resource (Broadbent, Reference Broadbent and Broadbent1958; Robinson, Reference Robinson, Doughty and Long2003; Schmidt, Reference Schmidt and Robinson2001, Tomlin & Villa, Reference Tomlin and Villa1994) and that attentional resources are allocated to tasks as needed. Problems arise when task demands exceed the individual’s attentional capacity (Park & Han, Reference Park, Han and Han2008; Tomlin & Villa, Reference Tomlin and Villa1994), leading to vigilance decrements (Warm et al., Reference Warm, Dember, Hancock, Parasuraman and Mouloua1996). In contrast, underload models assume that sustained attention tasks are monotonous and boring which leads to withdrawal of attention from the task, directing it to task-unrelated mind wandering (Esterman & Rothlein, Reference Esterman and Rothlein2019; Smallwood & Schooler, Reference Smallwood and Schooler2006), and as a result, impairing performance on the primary task (Thomson et al., Reference Thomson, Besner and Smilek2015). Indeed, vigilance decrements and fluctuations of performance can be minimized in different ways, such as goal setting, feedback, and large incentives (Esterman et al., Reference Esterman, Grosso, Liu, Mitko, Morris and DeGutis2016; Robison et al., Reference Robison, Unsworth and Brewer2020).

Alerting is the primary impaired network of attention in ADHD (Marchetta et al., Reference Marchetta, Hurks, De Sonneville, Krabbendam and Jolles2008; Tucha et al., Reference Tucha, Fuermaier, Koerts, Buggenthin, Aschenbrenner, Weisbrod and Tucha2017), in both the predominantly inattentive and the combined subtypes (Huang-Pollock et al., Reference Huang-Pollock, Nigg and Halperin2006), and deficits of sustained attention are part of the diagnostic criteria for ADHD specifically for the predominantly inattentive type and the combined type (DSM-V; APA, 2013). Individuals with ADHD demonstrate difficulty in sustaining their level of arousal over time both behaviorally (Johnson et al., Reference Johnson, Robertson, Barry, Mulligan, Dáibhis, Daly and Bellgrove2008) and neurologically (see meta-analysis of Dickstein et al., Reference Dickstein, Bannon, Xavier Castellanos and Milham2006).

Individuals with ADHD consistently show worse performance in sustained attention in CPT tasks relative to controls, as indicated by significantly more omission errors and slower mean reaction times (Avisar & Shalev, Reference Avisar and Shalev2011; Tucha et al., Reference Tucha, Tucha, Walitza, Sontag, Laufkötter, Linder and Lange2009). Moreover, this decrement in performance intensifies as the task proceeds due to vigilance decrements, and more strongly so in individuals with ADHD compared to controls (Tucha et al., Reference Tucha, Tucha, Walitza, Sontag, Laufkötter, Linder and Lange2009; Reference Tucha, Fuermaier, Koerts, Buggenthin, Aschenbrenner, Weisbrod and Tucha2017). Individuals with ADHD also show increased intra-individual variability of reaction times (fluctuations of performance) (Castellanos & Tannock, Reference Castellanos and Tannock2002; Lundervold et al., Reference Lundervold, Adolfsdottir, Halleland, Halmøy, Plessen and Haavik2011; Marchetta et al., Reference Marchetta, Hurks, De Sonneville, Krabbendam and Jolles2008). Such vigilance decrements and fluctuations of performance reflect the clinical characteristics of individuals with ADHD (Fortenbaugh et al., Reference Fortenbaugh, DeGutis and Esterman2017; Tucha et al., Reference Tucha, Fuermaier, Koerts, Buggenthin, Aschenbrenner, Weisbrod and Tucha2017). These attentional patterns have important implications for classroom activities, such as reading comprehension, note-taking, and multi-step problem-solving, which require sustained attention over extended periods. Frequent attentional lapses and declining vigilance may lead to missed instructions, inconsistent engagement, and difficulties in retaining information across time.

Of relevance, individuals with ADHD may exhibit difficulty in sustained attention due to reduced motivation (e.g., Haenlein & Caul, Reference Haenlein and Caul1987; Skalski et al., Reference Skalski, Pochwatko and Balas2020; Volkow et al., Reference Volkow, Wang, Newcorn, Kollins, Wigal, Telang and Swanson2011), given that they most often present impaired performance in uninteresting repetitive tasks, but not in interesting tasks (Bush, Reference Bush2010). This reduced motivation among individuals with ADHD is associated with a disruption of the dopamine reward pathway (Volkow et al., Reference Volkow, Wang, Newcorn, Kollins, Wigal, Telang and Swanson2011).

Orienting

Under the Attentional Systems model (Posner & Petersen, Reference Posner and Petersen1990), orienting refers to the process of directing attention resources toward a specific stimulus or its features. Studies comparing children (Johnson et al., Reference Johnson, Robertson, Barry, Mulligan, Dáibhis, Daly and Bellgrove2008) and adults (e.g., Coll-Martín et al., Reference Coll-Martín, Carretero-Dios and Lupiáñez2021) with and without ADHD have not found differences in the orienting network, despite deficits of individuals with ADHD in the alerting and executive control networks. Thus, individuals with ADHD seem to have intact orienting abilities (Johnson et al., Reference Johnson, Robertson, Barry, Mulligan, Dáibhis, Daly and Bellgrove2008; Mullane et al., Reference Mullane, Corkum, Klein, McLaughlin and Lawrence2011; Fabio & Urso, Reference Fabio and Urso2014).

Executive control

When an input captures our attention, we activate the executive control attention network to voluntarily switch and direct our attention toward it (Petersen & Posner, Reference Petersen and Posner2012, Posner, Reference Posner2012, Posner & Petersen, Reference Posner and Petersen1990). Executive control are a set of general-purpose processes, which regulate one’s thoughts and behaviors, are responsible for the selective deployment of attention in a goal-driven manner (Mishra, Reference Mishra2018), and are linked to the prefrontal cortex of the brain (Miyake et al., Reference Miyake, Friedman, Emerson, Witzki, Howerter and Wager2000). Executive control includes inhibitory control, working memory, mental shifting, monitoring, planning, fluency, and problem-solving (Chan et al., Reference Chan, Shum, Toulopoulou and Chen2008). The current discussion will focus on three of the most frequently postulated executive control abilities in the literature, namely: updating of working memory, inhibition, and shifting of mental sets (Miyake et al., Reference Miyake, Friedman, Emerson, Witzki, Howerter and Wager2000; Miyake & Friedman, Reference Miyake and Friedman2012).

Working memory allows us to actively preserve a limited amount of information while performing a cognitive task or in the face of distracting information (Shah & Miyake, Reference Shah, Miyake, Miyake and Shah1999). For example, working memory is measured when individuals are asked to recall a sequence of digit names in reverse order (e.g., Morra, Reference Morra1994). Working memory is associated with academic achievements such as reading comprehension and mathematics (Best et al., Reference Best, Miller and Naglieri2011; St Clair-Thompson & Gathercole, Reference St Clair-Thompson and Gathercole2006) and contributes to many learning processes (Engle, Reference Engle, Roediger III, Nairne, Neath and Surprenant2001) including first (Carretti et al., Reference Carretti, Borella, Cornoldi and De Beni2009; St Clair-Thompson & Gathercole, Reference St Clair-Thompson and Gathercole2006) and ALL (Linck & Weiss, Reference Linck, Weiss, Sanz and Leow2011; Martin & Ellis, Reference Martin and Ellis2012).

Inhibition is the ability to suppress irrelevant dominant information deliberately. Accordingly, inhibition tasks measure the ability to suppress or withhold frequent or automatic responses (e.g., stop-signal task, Lappin & Eriksen, Reference Lappin and Eriksen1966; Logan, Reference Logan, Dagenbach and Carr1994; Stroop task, Stroop, Reference Stroop1935). Similarly to other executive control abilities, better inhibition skills are associated with better math and reading performance in preschoolers and first graders (Blair & Razza, Reference Blair and Razza2007; Espy et al., Reference Espy, McDiarmid, Cwik, Stalets, Hamby and Senn2004) and are also associated with better reading comprehension (Borella et al., Reference Borella, Carretti and Pelegrina2010), word learning (Yoshida et al., Reference Yoshida, Tran, Benitez and Kuwabara2011), and general academic achievements (St Claire-Thompson & Gathercole, Reference St Clair-Thompson and Gathercole2006).

Shifting, also known as “cognitive flexibility,” is the ability to shift consciously and efficiently between mental sets (Prior & MacWhinny, Reference Prior and MacWhinney2010) and to modify cognitive processes in response to changing environmental circumstances (Deák, Reference Deák and Kail2003). A “more flexible” person can adaptively switch attention and action for a change of task in response to feedback, whereas a “less flexible” person tends to persist on the previous task regardless of feedback (Hung & Loh, Reference Hung and Loh2020). Rule-switching paradigms are often used to assess shifting, for example, requiring individuals to respond to different dimensions of stimuli on different trials (e.g., Prior & MacWhinny, Reference Prior and MacWhinney2010). Shifting ability has been identified as particularly important for performance on complex academic tasks in math and reading (Magalhães et al., Reference Magalhães, Carneiro, Limpo and Filipe2020; Yeniad et al., Reference Yeniad, Malda, Mesman, Van IJzendoorn and Pieper2013), and it is associated with better reading comprehension performance in first and second language (Chung et al., Reference Chung, Lam and Leung2020; Hung & Loh, Reference Hung and Loh2020), as well as with better language learning (Trofimovich et al., Reference Trofimovich, Ammar, Gatbonton and Mackey2007).

Deficits in executive control are commonly observed in individuals with ADHD (Barkley, Reference Barkley1997; Castellanos & Tannock, Reference Castellanos and Tannock2002; Willcutt et al., Reference Willcutt, Doyle, Nigg, Faraone and Pennington2005), in both childhood (e.g., Willcutt et al., Reference Willcutt, Doyle, Nigg, Faraone and Pennington2005) and adolescence (Martel et al., Reference Martel, Nikolas and Nigg2007), mostly in the inattentive and combined subtypes of ADHD (Castellanos et al., Reference Castellanos, Sonuga-Barke, Milham and Tannock2006; Martel et al., Reference Martel, Nikolas and Nigg2007). Although there is some variability in the extent and nature of these impairments (Willcutt et al., Reference Willcutt, Doyle, Nigg, Faraone and Pennington2005), learners with ADHD show weaknesses in all three main components of executive control: working memory, inhibition, and shifting.

Thus, a meta-analysis of 26 studies shows impairments in working memory in children with ADHD relative to controls (Martinussen et al., Reference Martinussen, Hayden, Hogg-Johnson and Tannock2005). Further, inhibition has been identified as the most consistently impaired domain in ADHD (Nejati et al., Reference Nejati, Salehinejad, Nitsche, Najian and Javadi2020) and is claimed to be the core deficit of the disorder (Barkley, Reference Barkley1997). For example, Wodka and colleagues (Reference Wodka, Mark Mahone, Blankner, Gidley Larson, Fotedar, Denckla and Mostofsky2007) demonstrated that children with ADHD made significantly more errors across three inhibition tasks, even under low working memory demands (see also Hervey et al., Reference Hervey, Epstein and Curry2004; Lansbergen et al., Reference Lansbergen, Kenemans and van Engeland2007; Liotti et al., Reference Liotti, Pliszka, Higgins, Perez III and Semrud-Clikeman2010; Rahmi & Wimbarti, Reference Rahmi and Wimbarti2018). Finally, although it has received less research, learners with ADHD show weaker shifting abilities as well (for a meta-analysis, see Hervey et al., Reference Hervey, Epstein and Curry2004). Learners with ADHD also show reduced activation in brain regions associated with shifting and engage different brain regions to resolve the conflicts caused by task switching (Bálint et al., Reference Bálint, Bitter and Czobor2015, see also Miklós et al., Reference Miklós, Futó, Komáromy and Balázs2019). They tend to show lower performance than those without ADHD in other executive control processes as well, such as planning (Boyer et al., Reference Boyer, Geurts, Prins and Van der Oord2015; Hervey et al., Reference Hervey, Epstein and Curry2004; Kofman et al., Reference Kofman, Gidley Larson and Mostofsky2008), verbal fluency (Hervey et al., Reference Hervey, Epstein and Curry2004; Hurks et al., Reference Hurks, Hendriksen, Vles, Kalff, Feron, Kroes and Jolles2004; Takács et al., Reference Takács, Kóbor, Tárnok and Csépe2014), or monitoring (McLoughlin et al., Reference McLoughlin, Albrecht, Banaschewski, Rothenberger, Brandeis, Asherson and Kuntsi2009).

To summarize, there is abundant evidence indicating that learners with ADHD are impaired in two of the three attention networks proposed by the Attention Systems Model (Posner & Petersen, Reference Posner and Petersen1990), namely in sustained attention and executive control. We now turn to describe the role of these attention networks in language learning in order to set the stage for considering possible difficulties of individuals with ADHD in ALL.

Sustained attention

There is some evidence supporting the role of sustained attention in language learning and processing. For example, infants who are able to sustain their attention to social stimuli show larger vocabulary knowledge at an older age (Masek et al., Reference Masek, McMillan, Paterson, Tamis-LeMonda, Golinkoff and Hirsh-Pasek2021; Salley et al., Reference Salley, Panneton and Colombo2013), and sustained attention is also necessary for coordinating language production. Moreover, sustained attention is linked to better procedural/sequence learning (West et al., Reference West, Shanks and Hulme2021), which underlies implicit language learning (Granena, Reference Granena2013).

The role of sustained attention in language is further emphasized in studies on clinical populations characterized with language deficits such as learners with Developmental Language Disorder (DLD; Boerma et al., Reference Boerma, Leseman, Wijnen and Blom2017; Ebert & Kohnert, Reference Ebert and Kohnert2011; Ebert et al., Reference Ebert, Rak, Slawny and Fogg2019; Finneran et al., Reference Finneran, Francis and Leonard2009; Park et al., Reference Park, Miller, Sanjeevan, van Hell, Weiss and Mainela-Arnold2019). These studies document deficits in sustained attention in individuals with DLD who demonstrate weaknesses in vocabulary, morphosyntax, written language, and social language (e.g., Ebert & Kohnert, Reference Ebert and Kohnert2011). Accordingly, sustained attention may be one of the underlying processes supporting typical language learning and reduced sustained attention capacity could contribute to language learning difficulties.

Importantly, however, most of the research linking sustained attention to language has been conducted on first language, and the extent to which sustained attention subserves these processes in ALL is still understudied. Arguably, sustaining attention is necessary for processing complete linguistic input when learning a new language. For example, successful word learning requires the learner to sustain focus in order to form an association between an object and a label (Mueller & Tomblin, Reference Mueller and Tomblin2012), and successful grammar learning requires the learner to stay alert to different features of the language including less salient grammatical features (Ebert et al., Reference Ebert, Rak, Slawny and Fogg2019). A breakdown in sustained attention during an additional language listening task, for instance, may result in the learner missing crucial elements in a spoken sentence, such as verb endings or function words, which can hinder comprehension and reduce the opportunity to internalize correct grammatical patterns. Such momentary lapses may cause learners to overlook linguistic cues essential for successful language acquisition.

Orienting

Although not explicitly examined, one may speculate that the orienting network is relevant to language learning because learners need to be able to selectively focus on relevant linguistic input (Ebert et al., Reference Ebert, Rak, Slawny and Fogg2019). Presumably, when learners direct their attention toward the relevant language input, this may enhance and facilitate its detection (Tomlin & Villa, Reference Tomlin and Villa1994). Schmidt (Reference Schmidt and Robinson2001) proposed that orienting is related to instructional techniques, suggesting that learners’ attention can be shifted/biased towards a linguistic form increasing the likelihood of detecting formal distinctions, such as those which differ from the first language. Importantly, these are mostly theoretically motivated hypotheses, and the relationship between orienting and language learning, including ALL, has received scant study and still requires empirical validation. To move beyond speculation, future research could test whether individual differences in orienting capacity (e.g., as measured by spatial cueing tasks) predict learners’ ability to notice and internalize low-salience features in classroom instruction. Experimental designs might also manipulate orienting cues (e.g., visual or auditory highlighting of target forms) to assess whether enhancing orienting improves learning outcomes in ALL contexts.

Executive control

The executive control network has been most strongly linked to ALL. Of note, there is a large body of literature on executive control and bilingualism attempting to examine whether extensive language use or bilingualism enhances executive control abilities (Prior & Gollan, Reference Prior and Gollan2011; see Gunnerud et al., Reference Gunnerud, Ten Braak, Reikerås, Donolato and Melby-Lervåg2020 for a recent review), which is beyond the scope of the current paper. Only a few studies examined the opposite direction, relevant for current discussion, of whether better executive control abilities facilitate language learning. As elaborated below, these (relatively sparse) studies attempt to capture causality by determining order of events with executive control abilities measured prior to ALL.

The notion that executive control is relevant for ALL arises because during ALL target and non-target linguistic representations become active to some degree and compete for selection (Jared & Kroll, Reference Jared and Kroll2001). Theories emphasizing language inhibition (e.g., the inhibitory control model, Green, Reference Green1998) assume that this simultaneous activation of representations from two or more languages requires the learner to engage in conflict resolution processes, to inhibit and negotiate emerging responses and interference from the non-target language (e.g., inhibiting a certain word in English (L1) while trying to retrieve the corresponding one in Spanish (L2)). For example, in a listening task, difficulties in inhibition may result in the learner persistently activating L1 translations, making it harder to integrate the incoming L2 or L3 sentence meaningfully. Moreover, learners are expected to utilize working memory when retrieving and producing language adhering to complex grammatical rules in written or oral form, thus limited working memory capacity may make it difficult to hold sentence components in mind while applying syntactic rules, resulting in omissions or mis-ordered structures. In the same manner, additional language learners are often required to switch between their different languages and to monitor the linguistic environment in order to identify the appropriate language (Mishra, Reference Mishra2018). For example, difficulty disengaging from the prior language or rule set can lead to slower responses or inappropriate language use.

Empirically, a few studies have documented the contribution of executive control components to ALL (e.g., Chung et al., Reference Chung, Lam and Leung2020; Linck et al., Reference Linck, Osthus, Koeth and Bunting2014). For example, using an artificial language learning paradigm, Kapa and Colombo (Reference Kapa and Colombo2014) demonstrated that inhibitory control was predictive of learning in adults and that shifting and attentional monitoring predicted learning in children. Studies measuring executive control abilities before language learning have found that inhibition was significantly associated with second language learning in children in an immersion setting (Woumans et al., Reference Woumans, Ameloot, Keuleers and Van Assche2019), and that working memory was positively related to learning gains in college students studying an additional language in a classroom setting (Linck & Weiss, Reference Linck and Weiss2015).

Given the scarcity of studies directly examining the involvement of the attention networks in ALL, more research is needed to substantiate the proposed links, as exemplified by the dashed blue lines in Figure 1. Nonetheless, the overall pattern emerging is one in which the attentional processes of sustained attention and executive control are important to language learning.

Theoretical predictions

First, learners with ADHD have difficulty sustaining their attention for a long time on a specific task and struggle to complete tasks due to vigilance decrements and/or mind wandering during tasks (Tucha et al., Reference Tucha, Fuermaier, Koerts, Buggenthin, Aschenbrenner, Weisbrod and Tucha2017). Accordingly, because ALL is an effortful and resource-demanding task, it might lead to depletion of the attentional resources, in line with overload theories of sustained attention (Warm et al., Reference Warm, Dember, Hancock, Parasuraman and Mouloua1996). As a result, the deficits in sustained attention in learners with ADHD are likely to disrupt ALL and might lead to incomplete processing of the language input.

Second, we outline two possible links between the orienting abilities in ADHD and ALL. The first possibility is that because the orienting network is generally intact in learners with ADHD (Coll-Martín et al., Reference Coll-Martín, Carretero-Dios and Lupiáñez2021; Johnson et al., Reference Johnson, Robertson, Barry, Mulligan, Dáibhis, Daly and Bellgrove2008), they will show intact orientation abilities in ALL as well. The second possibility is that because ALL is a highly demanding task, orienting abilities will be taxed in such contexts. Thus, learners with ADHD will show difficulties in orienting, due to an overload of the attentional demands in ALL, which will result in missing relevant input. Studies examining the effect of task load on orienting attention have presented mixed results (see review of Santangelo & Spence, Reference Santangelo and Spence2008), suggesting either automatic orienting regardless of different task loads (Santangelo & Spence, Reference Santangelo and Spence2007), or attenuation of orienting attention in demanding tasks (Bobak & Langton, Reference Bobak and Langton2015). Thus, future studies should examine whether the challenging demands of ALL interfere with orienting abilities in learners with ADHD.

Third, the difficulties in executive control of learners with ADHD might negatively affect ALL. In particular, the limited working memory capacity of learners with ADHD (Leons et al., Reference Leons, Herbert and Gobbo2009; Martinussen et al., Reference Martinussen, Hayden, Hogg-Johnson and Tannock2005) might constrain successful language learning, because comprehending or producing language requires learners to simultaneously retrieve appropriate vocabulary, grammar, and syntactic constructions and manipulate them for further use. In addition, the impaired inhibition abilities of learners with ADHD (Hervey et al., Reference Hervey, Epstein and Curry2004) might make it more difficult for them to inhibit interference from the non-target language when learning a new language. Finally, learning an additional language requires shifting abilities, such as switching forward and backward from the target language to the previously known language/s, or shifting attention to a certain learning goal, such as from focusing on the meaning to focusing on the form of a word within the additional language. Learners with ADHD are worse at changing perspectives and switching between mental representations (Miklós et al., Reference Miklós, Futó, Komáromy and Balázs2019), which might add a further burden to the task of learning an additional language, making it more challenging for the learners with ADHD than it is for their typically developing peers.

Notably, the discussion in the current paper is framed around general trends in sustained attention and executive control challenges commonly observed in individuals with ADHD, but clearly there is substantial variability in how ADHD manifests across individuals (Nikolas, & Nigg, Reference Nikolas and Nigg2013; Willcutt et al., Reference Willcutt, Doyle, Nigg, Faraone and Pennington2005). Nonetheless, the current approach allows us to highlight patterns that are most relevant to understanding the intersection of ADHD and ALL, serving as the basis for additional future work regarding individual differences in learners with ADHD.

Empirical evidence

To understand the intersection between ADHD and ALL, we turn to available empirical studies which examine ALL in individuals with ADHD. While the current paper is not a systematic review, we conducted a comprehensive search for relevant studies, using electronic searches of databases such as PsycINFO, ERIC, and Google Scholar, as well as examination of reference lists from identified publications, in which various combinations of descriptors were used including (Additional/Foreign language learning, English additional/Foreign language learners, additional/Foreign language classroom, additional/Foreign language, Attention deficit/hyperactivity disorder (ADHD), attention). The inclusion criteria for ALL were broad, including studies involving children or adults diagnosed with ADHD and examining any aspect of ALL/processing. Studies that examined participants with other learning disabilities and neurological deficits besides ADHD were not included.

Our search identified only 11 studies published between 2004 and 2024, which are summarized in Table 1. Unfortunately, the information provided regarding the linguistic background of participants and the linguistic context in which the additional language was learned was often not comprehensive. These studies, implementing a range of methodologies including qualitative, quantitative, and position papers, provide initial evidence of difficulty in ALL among learners with ADHD.

Table 1. Summary of studies on ADHD and ALL

The extant literature examined varied aspects of ALL in learners with ADHD. Some reported on the degree to which learners with ADHD are able to complete ALL courses (Leons et al., Reference Leons, Herbert and Gobbo2009; Sparks et al., Reference Sparks, Javorsky and Philips2004), others include subjective assessments of the efficacy of various learning strategies (Kinasih & Rochmawati, Reference Kinasih and Rochmawati2020; Leons et al., Reference Leons, Herbert and Gobbo2009; Liontou, Reference Liontou2019; Paling, Reference Paling2020; Sabet et al., Reference Sabet, Farhoumand, Zafarghandi and Naseh2015), and others focused on teachers’ experiences (Indrawati, Reference Indrawati2023; Turketi, Reference Turketi2010). Importantly, scarce empirical evidence exists on the extent to which sustained attention difficulties and impaired executive control processes, specifically, might affect learners with ADHD in ALL.

One study collecting retrospective self-reports observed that learners with ADHD reported greater difficulties in focusing attention during language learning sessions (Paling, Reference Paling2020). Importantly however, such retrospective self-report measures might be biased by the participant’s diagnosis. Similarly, Turketi (Reference Turketi2010) relied on retrospective discussions based on her experience as an English as a second language teacher for children with ADHD (see Indrawati, Reference Indrawati2023, for a similar report on teachers’ experiences). Turketi observed that all four language skills (i.e., reading, writing, listening, and speaking) presented a variety of challenges for learners with ADHD. She further argued that learners with ADHD receive disrupted language input and struggle in dealing with the amount of information and distractions in their environment, leading to confusion between important and irrelevant information. The difficulty of learners with ADHD in processing input is often reflected in reading, listening comprehension, and understanding the meaning of words. In learning to read, for example, learners need to inhibit the reading rules of their previous languages in order to learn the new rules of the additional language, which is described as a confusing and frustrating experience for learners with ADHD. In addition, Turketi describes challenges of learners with ADHD in speaking and writing the additional language which are reflected in poor comprehension of language structures, poor vocabulary and pragmatics, and slow production speed of written and spoken language. Notably, though these are important insights from an experienced teacher, they should be empirically examined in the future.

Using more experimental approaches, Marashi and Dolatdoost (Reference Marashi and Dolatdoost2016) found that the severity of symptoms among individuals with ADHD who are learners of English as an additional language was linked to lower scores in speaking complexity and accuracy. The authors suggest that the learners’ limited sustained attention and working memory led to more errors in their speech. Additionally, a study by Grob et al., (Reference Groß, Bernhofs, Möhler and Christiner2023) found that adult learners with ADHD showed lower performance in a task assessing the ability to discriminate unfamiliar languages compared to learners without ADHD. The task included both simple (1 utterance) and difficult (2–3 utterances) conditions. Learners with ADHD showed lower performance in the difficult condition which required greater cognitive capacity and attentional control. Since the ability to discriminate unfamiliar speech is important for ALL (Kusumoto, Reference Kusumoto2012; Silbert et al., Reference Silbert, Smith, Jackson, Campbell, Hughes and Tare2015; Snow & Hoefnagel-Höhle, Reference Snow and Hoefnagel-Höhle1979), these findings suggest that learners with ADHD might encounter difficulties in ALL.

Taken together, these studies support an initial notion that the core cognitive deficits of ADHD might indeed negatively affect ALL (Kałdonek-Crnjaković, Reference Kałdonek-Crnjaković2018; Kinasih & Rochmawati, Reference Kinasih and Rochmawati2020; Leons et al., Reference Leons, Herbert and Gobbo2009; Turketi, Reference Turketi2010). However, much additional empirical evidence is necessary to substantiate this link and the intersection of ADHD and ALL more generally.

A call for future research

The current paper identified a significant gap in the literature and demonstrated that although there are theoretically sound arguments supporting the notion that learners with ADHD might encounter unique challenges in ALL, there is not enough empirical evidence on this issue. In what follows, we first outline general principles of a research program testing the intersection of ADHD and ALL and then illustrate two specific cases.

Most importantly, there is an urgent need for studies systematically comparing learners with and without ADHD, to examine whether there are consistent group differences in various aspects of ALL. This is necessary, because ALL is a complex endeavor, and observed group differences in one aspect of learning (e.g., grammar) will not necessarily generalize to other aspects of learning (e.g., vocabulary). Further, the possible impact of ADHD on ALL should be carefully examined in various age groups, across different stages of learning (beginner, intermediate, and advanced) and for different language combinations. Other relevant learner variables, such as prior linguistic experience, socioeconomic status, and cognitive ability, should also be systematically considered. Such broad investigations should be complemented by more targeted studies, specifically examining the possible impact of the attention network deficits in ADHD on ALL.

Such studies could adopt a variety of experimental approaches. For example, cross-sectional comparisons can be used to assess whether learners with ADHD consistently perform differently from non-ADHD peers in specific language domains, such as grammar or vocabulary. Longitudinal designs would allow researchers to track learners’ progress over time, assessing whether the learning curve differs between groups and identifying whether learners with ADHD benefit from prolonged exposure or specific types of instruction. Additionally, precise online methods like eye-tracking can help reveal real-time attentional allocation during tasks such as sentence reading or vocabulary learning, identifying whether learners with ADHD show different gaze patterns or processing speeds or increased effort (e.g., as measured via pupillometry).

One illustration of a targeted domain for research is possible differences between implicit and explicit mechanisms. Explicit language learning involves conscious hypothesis testing and rule memorization (Ellis, Reference Ellis and Ellis1994, Reference Ellis2015). In contrast, implicit learning is an unconscious process where learners acquire language elements, such as sounds and rules, based on their frequency in the input (Ellis, Reference Ellis2009, Reference Ellis2015). For example, vocabulary learning often relies on explicit processing, while grammatical structures may benefit more from implicit learning.

ADHD-related challenges may interact with these learning mechanisms. Explicit learning may place greater demands on attention and working memory resources, as it requires learners to consciously focus on rules, memorize vocabulary, and monitor their own performance—processes that can be especially effortful for individuals with ADHD. As such, purely explicit learning approaches may prove cognitively demanding for this population, particularly in the absence of external structure or support.

The role of attention in implicit learning, however, remains debated (Jiménez, Reference Jiménez and Jiménez2003). Some studies suggest that attention is necessary for implicit learning to occur (Franklin et al., Reference Franklin, Smallwood, Zedelius, Broadway and Schooler2016; Nissen & Bullemer, Reference Nissen and Bullemer1987; Staels & Van den Broeck, Reference Staels and Van den Broeck2017), while others argue that it can proceed with minimal attentional resources (Cleeremans & Jiménez, Reference Cleeremans, Jiménez, Stadler and Frensch1998; Frensch et al., Reference Frensch, Lin and Buchner1998). This debate influences predictions on how learners with ADHD manage ALL. Specifically, if some level of sustained attention is still needed to notice and internalize regularities in the input, learners with ADHD may still be disadvantaged in implicit learning if attention lapses are frequent or prolonged, whereas if minimal attention is required, their implicit learning may remain intact. The limited body of research on implicit lab-based artificial language learning in learners with ADHD has yielded mixed results. Interestingly, some studies such as Rosas et al. (Reference Rosas, Ceric, Tenorio, Mourgues, Thibaut, Hurtado and Aravena2010) found that children with ADHD excelled in implicit language learning, recognizing regularities in an artificial grammar learning task earlier than typically developing children. However, other studies showed reduced performance of learners with ADHD on artificial grammar tasks (Domuta & Pentek, Reference Domuta and Péntek2000; Laasonen et al., Reference Laasonen, Väre, Oksanen-Hennah, Leppämäki, Tani, Harno and Cleeremans2014).

Future research should aim to address these inconsistencies by exploring the conditions under which learners with ADHD succeed or struggle in implicit and explicit ALL. Such work could inform targeted instructional strategies. To wit, if evidence supports the conclusion that implicit learning requires little or no attentional resources, then implicit learning approaches such as instructional designs that rely more on repetitive and input-based exposure (e.g., reading tasks with high-frequency target structures, or repeated exposure to patterned language in context) should be incorporated in ALL instructional methods and interventions aimed at learners with ADHD. On the other hand, if implicit learning is negatively influenced by limitations of sustained attention, explicit ALL, where the structure, rules, instructions, and learning goals are overt and guided, should be emphasized among individuals with ADHD (e.g. Akbasli, Reference Akbasli, Sahin and Gürel2017). For example, structured grammar instruction with metalinguistic explanations and immediate corrective feedback or the use of visual organizers that clearly outline language rules and how these rules are different from previous linguistic knowledge may help learners maintain focus and engage their executive control.

A second example is research focusing on whether the challenges that individuals with ADHD have in executive control, specifically in inhibition, have consequences for ALL. In particular, previous linguistic knowledge might facilitate ALL due to cross-language similarities (Flynn et al., Reference Flynn, Foley and Vinnitskaya2004; Hirosh & Degani, Reference Hirosh and Degani2018; Prior, Reference Prior, Brook and Kempe2014) or might impede ALL, when learners need to inhibit previous irrelevant linguistic knowledge (Abbas et al., Reference Abbas, Degani and Prior2021; Prior et al., Reference Prior, Degani, Awawdy, Yassin and Korem2017). This process of overcoming cross-language interference is expected to be particularly challenging for learners with ADHD who show impaired inhibitory control (Barkley, Reference Barkley1997). Thus, future research should examine to what extent previous linguistic knowledge of learners with ADHD affects ALL. This can be achieved either by using group comparisons of learners with and without ADHD, or by analyzing individual differences within learners to examine the relation between inhibitory control difficulties and susceptibility to cross-language influences.

Obviously, future studies comparing ALL in individuals with and without ADHD, including those focusing on the specific attention networks involved, should also take into account additional relevant factors. For one, the population of learners with ADHD, like other clinical populations, reflects a spectrum with great individual variability (Geurts et al., Reference Geurts, Grasman, Verté, Oosterlaan, Roeyers, van Kammen and Sergeant2008). Thus, future research should take into account the particular profile of individual learners (e.g., ADHD subtypes). Additionally, learners’ age is an important domain to consider. Although symptoms of inattention remain stable across the lifespan (Hervey et al., Reference Hervey, Epstein and Curry2004), suggesting that both children and adults with ADHD may face challenges in ALL, their specific manifestations may differ across the lifespan. For instance, the involvement of implicit and explicit learning in ALL may change with age and cognitive maturity, leading to differential involvement of the attention networks.

Importantly, although the current paper has focused on the negative impacts of ADHD on ALL, there might be positive effects as well. For example, Marashi and Dolatdoost (Reference Marashi and Dolatdoost2016) found that learners with more severe symptoms of ADHD had higher fluency when speaking English as an additional language. According to the authors, the hyperactive and impulsive side of ADHD allowed these learners to produce more language output, which presumably offers more communication and learning opportunities in ALL.

Additionally, although learners with ADHD might face an additional burden when learning a new language, we should bear in mind the possible long-term benefits of bilingualism on the executive control network that is evident in a large body of literature (review in Bialystok et al., Reference Bialystok, Craik, Green and Gollan2009). To the extent that such effects are present within the population of individuals with ADHD, one might expect that ALL would in fact lead to positive consequences in terms of the attentional network of these individuals. Only a few studies examined the consequences of multilingualism for individuals with ADHD. One study found evidence for improved executive functions in bilinguals relative to monolingual individuals with high ADHD symptomology (Sharma et al., Reference Sharma, Katsos and Gibson2022). In contrast, other research did not find such improvements in executive function when comparing multilinguals to bilinguals with ADHD (Mor et al., Reference Mor, Yitzhaki-Amsalem and Prior2015). Yet other studies suggest a more nuanced relationship, indicating that the interaction between bilingualism and executive functions in individuals with ADHD may depend on various factors (Bialystok et al., Reference Bialystok, Hawrylewicz, Wiseheart and Toplak2017). These findings highlight the need for future research to explore whether and how ALL might support individuals with ADHD.

In summary, future research needs to systematically examine the effects of ADHD on ALL with more detailed information in order to map both strengths and challenges. Such nuanced understanding is necessary for developing differentiated teaching and intervention programs. Importantly, research on learners with ADHD is expected to benefit not only these learners themselves but also to contribute to more global understanding of the interplay between attention networks and ALL. Such an understanding will, in turn, allow educational systems to provide all learners with tailored services and interventions in this important learning domain.