Neuropsychology and Neuroanatomy of Code Switching: Test Development and Application

Abstract

Code switching has been studied in the context of a side effect of neurological pathology. However, little is known about how to apply code switching to the assessment of neuropathology and test development. This study reviewed and identified cognitive and linguistic theoretical underpinning, and neuropsychological and neuroanatomical aspects of code switching in order to use code switching to activate interference. These provide valuable information to consider for developing code switching-based neuropsychological tests. Overall, this study found that code switching is a diverse and multi-dimensional process. Theories mainly denote activation of the retrieval and organization of lexicolinguistic knowledge. Neuropsychological domains that are involved include executive functions, lexical retrieval, and working memory. Brain regions implicated in code switching heavily rely on frontosubcortical structures and, to a lesser extent, temporal and parietal cortical regions. These areas mainly involve executive control and language functions, which act to select relevant and inhibit irrelevant language networks. Specifically, these regions include the anterior cingulated cortex, the left dorsolateral prefrontal cortex, the bilateral supramarginal gyri, left caudate, left putamen, left globus pallidus, left thalamus, and middle temporal areas. The paper outlines the application of code switching tests using common executive functioning, lexical fluency, and working memory neuropsychology instrument. These include code switching versions of phonemic fluency, semantic fluency, written and oral Trail Making Test, and digit span. Interference, dual task, and other formulas are used to determine the degree of language interference and switching.

Introduction

There are approximately 6000 languages that exist across the world, and the majority of the world speaks two languages or more than two languages (Grimes, 1992). This ability creates a multilingual situation whereby individuals have command of more than one languages as they communicate in different speech situations. This results into mixing two or more languages at the same time. There is rarely any consensus on the terminology about mixed language use. Wei (1998) considers alternations between languages at or above clause levels as code mixing. Romaine (1995) refers to both inter-sentential and intra-sentential switches as code switching. Bilingual speakers may shift from one language to another entirely (Poplack, Sankoff, & Miller, 1988), or they mix languages partially within the single speech (Gumperz, 1982a, b).

Code switching is a cognitive process by which an individual alternates and modulates two languages. Generally speaking, code switching is the process by which an individual reacts in a different language, or alternates between one or more languages simultaneously (Bhatia & Ritchie, 2008).

This cognitive- and language-based process is an important topic of study in neuropsychology and neurolinguistics. It is essential to identify the theoretical underpinnings, neuroanatomical structures, as well as cognitive functions associated with code switching in order to develop practical tools to better assess bilinguals with neurocognitive changes (e.g., dementia, traumatic brain injury, tumor, stroke, movement disorders, etc.). There have been some cases of pathological code switching. For example, a patient with a frontal lobe tumor showed difficulty being aware of mixing between the Italian and Friulian languages. This was in the context of preserved neuropsychological abilities (Fabbro, Skrap, & Aglioti, 2000). It is believed that bilinguals perform better than monolinguals on executive control in one language (Bialystok, 2009). Therefore, engaging a bilingual in code switching may generate useful information on executive control and may provide a separate indicator of neurocognitive functions.

Aims and Objectives

The aim of this article is to review and define the various types of code switching; review and identify theoretical underpinnings, neuroanatomical aspects, and neuropsychological aspects of code switching; and apply such information to neuropsychological test development. This helps to address the clinical and research need to outline the multiple dimensions of code switching and to use the process of code switching as a measurement that provides an indicator of brain pathology, neurocognitive degradation, and linguist control abilities.

Definition and Classification of Code Switching

Myers-Scotton (1993) described the term code switching as “using the alternation of varieties of linguistics in a single conversation” (Myers-Scotton, 1993). In 1995, Milory and Muysken defined code switching as the alternative use of two or more languages in a dialogue by an individual (Milroy & Muysken, 1995). There are many types of code switching. Milroy and Muysken (1995) introduced code switching as a term under which different kinds of bilingual behavior are categorized. Intra-sentential refers to a type of code switching that happens within a sentence, whereas the inter-sentential type of code switching occurs between sentences. Muysken, Díaz, and Muysken (2000) later used the term code mixing instead of intra-sentential code switching when both grammatical and lexical features of two languages are uttered in one sentence (Muysken et al., 2000). Myers-Scotton (1989) also described switching between sounds within a word, termed intra-word switching (Myers-Scotton, 1989).

Code switching occurs not only between two languages but also between dialects of the same language (Gardner-Chloros, 1991). Code switching has also been described as exchanging of the passage of speech that belong to two or more different grammatical systems or subsystems in a single conversation (Gumperz, 1982a, b). Auer (1995) used the term alternation in the same sense, meaning that one language replaces another half way through a particular utterance (Auer, 1995). In contrast, insertion occurs when a single lexical item of one language is incorporated into the structure of the other language (Muysken et al., 2000).

Weinreich (2010), the pioneer of contact linguistics, has defined code switching as instances of the language deviation, which occur in the form of the language of bilingual people as the result of their familiarity with more than one language, and he called it interference (Weinreich, 2010). According to Weinreich’s definition, interference can be observed at different levels, for example the lexical level (Weinreich, 2010). Weinreich’s viewpoint of code switching was generally composed of interruptions of sustaining in one language as opposed to those opinions of other researchers. For example, Clyne, Clyne, and Michael (2003) used the term of transference to refer to what Weinreich described as interference (Clyne et al., 2003).

Myers-Scotton (1993) explained that code switching is not necessarily a complete change to another language. He presented his model as the Matrix Language Frame model, in which the base or first language plays a more dominant role in code switching by distinguishing the grammatical form of speech; the other is the second or guest language. This model is mainly influenced by psycholinguistic theories of code switching (Myers-Scotton, 1993).

According to Myers-Scotton (2002), classic code switching and composite code switching, which are two types of intra-sentential code switching, have distinct features (Myers-Scotton, 2002). “In classic code switching, only one of the participating languages is the source of the morphosyntactic structure of the bilingual clause, whereas the morphosyntactic structure consists of two languages in composite code-switching” (Myers-Scotton, 2002: 8). Myers-Scotten further proposed that the base languages have their own internal structure, yet still depend on the matrix language as the source of morphosyntactic frame of the bilingual clause, termed classic code switching. In contrast, composite code switching describes an interaction in which the second (guest) language shares some of the grammatical structures of the first (base) language, suggesting the part of the abstract structure underlying surface configurations comes from more than one language (Myers-Scotton, 1993, 2006). In Matrix Language Frame model, code switching phenomena are explained through psycholinguistics whereby human linguistic behavior is explained. Another type of code switching is called congruent lexicalization (Haugen, 1950), which often occurs when two languages are lexically similar, and the syntactic structure of one language cannot be applied to the other one. In fact, it is usually challenging to attribute the syntactic structure of the language to other languages. In this regard, Haugen (1950) suggested that in congruent lexicalization, there is a syntactic frame, which is shared between two languages and is filled by lexical items that come from both languages. Muysken et al. (2000) also introduced insertional code switching, which occurs when lexical items of one language are placed into the morphosyntactic structure of the other language (Muysken et al., 2000). The aforementioned types of code switching are summarized in Table 1.

Table 1 Types of code switching

Theories and Approaches of Code Switching

In addition to the various types of code switching, there have been multiple theoretical frameworks that have been proposed. Theoretical approaches that denote the reason behind code switching have been placed in six categories. These include linear order, subcategorization, theory based, matrix language, nonspecific and minimalist approaches (Naseh, 2002). The linear order approach not only considers similarities but also dissimilarities in the two languages, which are involved in the code switching process. Sankoff and Poplack (1981) introduced the equivalence constraint approach in order to explain code switching when the structures of the two languages coincide in such a manner that the languages seem quite similar both in written and in oral forms (Sankoff & Poplack, 1981). It may be postulated that the closer the languages’ structure is, the more likely the code switching will occur. Bentahila and Davies (1983) explain that in the subcategorization model, code switching occurs when subcategories are similar. However, code switching does not violate the rules of the languages, especially when they have different structures (Bentahila & Davies, 1983). The theory-based model explained code switching as an up-to-date version of the theory of Universal Grammar (the genetic component of the language), which was introduced by Chomsky (1980). This concept suggests that a large portion of grammar in any particular language is common to all languages (Chomsky, 2000). Therefore, code switching may be a product of cross talk between languages that share a universal structure.

The Matrix Language Frame model claims that one of the languages (the Matrix Language) plays a more dominant role in verifying the morphosyntactic structure of the speech. The second language (the Embedded Language) plays a less active role. Based on this framework by Myers-Scotton (1993), there are two principles in code switching. The first principle is the Morpheme Order Principle. This principle states that “The Matrix Language + Embedded Language constituents, which are consisting of singly occurring Embedded Language lexemes and any number of Matrix Language morphemes, surface morpheme order (reflecting surface syntactic reactions) will be that of the Matrix Language.” The second principle, System Morpheme Principle, states that “in Matrix Language + Embedded Language constituents, all system morphemes which have grammatical relations external to their head constituent, will come from the Matrix Language” (Myers-Scotton, 1993).

Another approach to code switching, the nonspecific congruence model, presented by Winford (2003) and Treffers-Daller (2009), differentiates between phrases and individual words. This process occurs when two languages are similar in their phrase structures, with no consideration of word order differences between the two languages. The limitations on single-word switching and multiword switching are similar (Winford, 2003; Treffers-Daller, 2009). Any embedded language component, whether in a single-word code switch or as a part of a lengthier code switching with more words, can serve as a functional replacement for a matrix language counterpart (Winford, 2003).

The minimalist approach in code switching states that a sentence is derived from mixing items from lexicons of multiple languages. One type of the minimalist approach is the intra-sentential model of code switching, developed by MacSwan (2000). This approach indicates that items are drawn from two or more languages to introduce into a lexical array. The minimalist approach indicates that the items from different languages may have similar words that linguistically work together (MacSwan, 2000). In 1995, Mahootian used null theory of code switching, which indicates that “code switching sequences are not subject to structural constraints beyond the general principles of phrase structure that govern monolingual sequences” (Santorini & Mahootian, 1995). According to the null theory, code switching occurs because language parameters or characteristics, which are broader than language structures, are similar (Mahootian & Santorini, 1994).

Paradis (1993) introduced the activation threshold hypothesis, which suggests that a stored lexicon requires a certain level of activation for the information to be retrieved. At the same time, the threshold for the lexicon of another language is inhibited (Paradis, 1993). When the threshold of both languages is low, it is likely that code switching will occur as individuals are in a bilingual mode (Grosjean, 1995). Later on, Paradis also indicated that brain lesions affect the threshold of lexicons, which explains why one language may be more affected than another after an injury (Paradis, 1998) (Table 2).

Table 2 Theories of code switching

Sociolinguistics Aspect of Code Switching

In sociolinguistics, it is suggested that code switching may actively happening mostly unconsciously in the society among people who know more than one language. However, code switching does not occur independent from social factors. Thus, the notion that code switching happens unconsciously should be considered more as a possible aspect of the phenomenon and needs to be more closely examined. According to Holmes and Wilson (2017), different factors such as the situation, the setting of an interaction, the interactants, and topic of conversation influence the patterns of code switching, which ultimately determines the social context of the interaction.

In sociolinguistics domain, the reasons why people code switch in various social settings carry important values (Holmes & Wilson, 2017). Holmes and Wilson (2017) believe that people code switch in order to show their solidarity, change the topic, quote someone, show affection, emphasize meaning, be rhetorical, and mark an ethnic identity. Moreover, it is believed that code switching creates a kind of prestige for the interlocutors who are able to switch from a language to another language; in particular if the second language is considered higher compared to the first language (Auer, 2005).

Neuroanatomical Substrates

Neuroimaging studies on bilingualism have focused on the specific regions of brain that are responsible for the processing of the first and second languages, while a very limited number of studies specifically investigated the brain regions for language switching.

In the neurocognitive model of bilingual language switching, Abutalebi and Green (2008) denote five brain regions that are involved when code switching occurs between two languages. These include the anterior cingulated cortex (ACC), the left dorsolateral prefrontal cortex (DLPFC), the bilateral supramarginal gyri (SMG), and the caudate nucleus. In this study, the researchers assumed that the cognitive demand of managing two languages is sustained by a subcortical–cortical circuitry (Abutalebi & Green, 2008). They added that these parts of the brain are responsible for facilitating the selection of language, while the irrelevant language is suppressed. Also, Abutalebi and Green (2008) argued that the same areas of the brain are responsible for cognitive control and executive functioning. In particular, the bilateral SMG and left DLPFC are parts of the frontoparietal network of attention (Toro, Fox, & Paus, 2008). Researchers have also mentioned that the ACC and the caudate nucleus are two regions of the brain that are involved in cognitive control (Kerns, Cohen, & MacDonald, 2004; van Schouwenburg, den Ouden, & Cools, 2010; Westly, Grydeland, Walhovd, & Fjell, 2010). For instance, the ACC region in general is associated with error detection, which is assumed to be part of a salience network. This network is believed to assign neural resources to external stimulation or internal processing (Seeley et al., 2007). The role of the caudate nucleus has been demonstrated in research on motor response control (Boehler, Appelbaum, Krebs, Hopf, & Woldorff, 2010) and goal-oriented behavior (Grahn, Parkinson, & Owen, 2008). The caudate also acts as a significant mediator for the role of cortical activity in the ACC prefrontal regions to modulate code switching (Hedden & Gabrieli, 2010). Therefore, the brain regions responsible for code switching in bilingual people, as presented by Abutalebi and Green (2008), are similar to brain regions that control attentional and executive networks in cognitive function. This fact supports the hypothesis that language control in bilingual people, more specifically language switching, is a demanding task that shares many features with other types of cognitive control centers. For example, a study on the effect of location and size of the lesion on the recovery of bilingual aphasia patients showed that a lesion of the left caudate nucleus results in problematic language control, which will lead to impairment in language switching or language mixing. Therefore, the caudate nucleus is more active when the person is using two languages at the same time (Abutalebi & Green, 2008).

In contrast to the model, which was presented by Abutalebi and Green (2008), a study by Luk, Green, Abutalebi, and Grady (2012) demonstrated activation in the pre-Supplementary Motor Area (SMA) instead of the ACC. Also, they reported a set of stimulations in the right precentral gyrus and in the left medial temporal gyrus, while there was no activity in the bilateral SMG (Luk et al., 2012). In a separate study by Velanova, Wheeler, and Luna (2008) the researchers argued about the possibility of ACC activation. They believed that the ACC has a significant role in error and detection trials, but low activation in correct trials (Velanova, Wheeler, & Luna, 2008). Some other researchers also argued about the role of the ACC and confirmed the activation of the pre-SMA in the performance role of response control in everyday tasks, error detection, related response, feedback, and performance monitoring (Bush, Luu, & Posner, 2000; Hester, Foxe, Molholm, Shpaner, & Garavan, 2005; Nachev, Kennard, & Husain, 2008). As Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis (2004), Ridderinkhof, Van Den Wildenberg, Segalowitz, and Carter (2004) mentioned, the rostral cingulate area is a region where sometimes the pre-SMA combines with the dorsal ACC (Ridderinkhof et al., 2004; Ridderinkhof et al., 2004). Luk et al. (2012), in a meta-analysis study, showed that the rostral cingulate area is found mostly in studies focused on language switching. This suggests that the role of the pre-SMA in executing and initiating speech generation is still controversial, especially when it comes to the matter of language conflict (Liu, Hu, Guo, & Peng, 2010).

There have been cases reported where individuals without aphasia and without cognitive or intellectual decline pathologically mix two or more languages. Pathological language switching and the process of uncontrollable code switching in an utterance have been implicated in explaining a deficit in sustaining cognitive set in one language (Fabbro et al., 2000). Multiple neuroanatomical regions have shown to be related to this condition. Grossly, left temporal–parietal lesions and frontal lobe lesions have been implicated (Fabbro et al., 2000, Fabbro, 2013), as have white matter tracks connecting the left inferior (Fabbro et al., 2000), middle, and superior frontal gyri; the left caudate (Abutalebi, Miozzo, & Cappa, 2000); the left thalamic region (Mariën, Abutalebi, Engelborghs, & De Deyn, 2005); and the left globus pallidus and putamen (Abutalebi, Della Rosa, Tettamanti, Green, & Cappa, 2009).

Studies tend to show convergence of specific neuroanatomical networks. Taken the aforementioned studies into account, it appears that frontosubcortical networks and left hemisphere are largely at play, while some support the role of the temporal and parietal lobes, in code switching. See Table 3 for a summary of neuroanatomical regions involved in code switching.

Table 3 Neuroanatomy of code switching

Dual language context is required to change the network including in basal ganglia and frontal regions connected to conflict monitoring and interference suppression, and partial regions connected to changes between tasks. In frequent code switching, there is no control over the language code switching but needs more planning and collaboration between the different language schemes. Green and Abutalebi (2013) mentioned that in fact the context which code switching is happening on that is influencing the connectivity of the right cerebellar and left inferior frontal regions (Green & Abutalebi, 2013).

Gold, Johnson, and Powell (2013) and Luk, Bialystok, Craik and Grady (2011) in two different researches demonstrated that there are no differences on neuropsychological tests including executive control tasks. However, both studies interpreted that despite the absence of behavioral changes, brain mechanisms underlain cognitive benefits for bilinguals (Luk et al., 2011; Gold, Johnson, & Powell, 2013).

Casaponsa, Carreiras, & Duñabeitia (2015) supported this finding and added that although the cognitive process is involved in code switching among bilinguals, no direct mapping is observed between a certain area of brain and this cognitive process (Casaponsa et al., 2015). Green & Abutalebi (2016) also supported their idea by elaborating that language switching and cognitive control are linked with a wide range of brain areas which mostly includes the frontal, parietal and subcortical areas of the brain (Abutalebi & Green, 2016). Therefore, it can be concluded that when bilingual and monolingual differences are detected in any related area of brain (frontal, parietal or subcortical), we cannot make sure whether that was related to cognitive control, language control, or both.

Abutalebi et al. (2011) in a study which compared the brain activity in bilinguals and monolinguals as they performed a nonverbal conflict-monitoring task (Bialystok, Craik, & Luk, 2012) revealed that both monolinguals and bilinguals having activity in anterior cingulate cortex, a region responsible for cognitive control, although bilinguals were more efficient than monolinguals and required less activation to resolve the same level of conflict (Abutalebi et al., 2011; Bialystok et al., 2012).

Multiple cognitive tasks underlie the process of code switching. The task taps into executive functioning via a switching component, working memory, and executive language control via the alternation between the two languages component, and language availability and inhibition via the use of the natural process of language inhibition/interference. There are standard neuropsychological tests that measure the aforementioned cognitive domains and subdomains. These include Trail Making Test, FAS verbal fluency, anima fluency or naming, stroop color-word interference, and digit span tasks. The methodology and administration framework of such tests can be used to test the degree of code switching. Please see Table 4 for a list of cognitive domains, subdomains, and tests.

Table 4 Neuropsychological domains and tests related to code switching

Impaired performance on a specific executive language task may be more sensitive to impaired executive control problems as bilinguals typically outperform monolinguals on executive control tasks (Bialystok, 2009). Table 4 also shows a summary of cognitive domains involved in code switching.

Clinical Application of Code Switching

Standard neuropsychological tests can be used to purposely activate the level of interference between two languages. Please see Table 4 for a list of tests. The methods of test administration make such tests ideal to adapt to code switching tasks. Similar to most executive function tests, in all code switching tasks there are three trials: (1) baseline performance of first language (e.g., English); (2) baseline performance of second language (e.g., Farsi or Persian language); (3) switching performance in first and second languages. For example, using a lexical retrieval framework, phonemic and semantic word fluency tests (e.g., FAS and animal fluency), the patient with suspected neurocognitive deficit is asked to: (1) say as many animals in English, (2) say as many animals in Persian, (3) and switch between animals in Persian and English language, each within 60 s.

The result from code switching tasks, used in the neuropsychological tests, can be analyzed by either existing cognitive neuroscience formulas of interference and dual task model formulas, or comparison of performance using normative data. Currently, there are no existing normative data on standard neuropsychological tests that incorporate code switching tasks. More research is needed in this area to develop normative data. However, there are formulas that calculate the level of interference and task demands when performing a dual task. Table 5 shows a list of the general formulas. Following this section is a description of the formulas, as well as step-by-step method to calculate these formulas.

Table 5 Formulas

When translating interference calculation by Golden (1978) to code switching, the following calculations can be made:

Original interference formula:

  1. 1.

    (Word Reading Baseline (W) × Color Naming Baseline (C)/W + C) − Actual Interference Trial.

Code Switching Adaptation:

  1. 1.

    Baseline performance of first language (L1).

  2. 2.

    Baseline performance of second language (L2).

  3. 3.

    Switching performance in first and second languages (Sw L1&L2).

  4. 4.

    Predicted Interference Score = (L1 × L2)/(L1 + L2)

  5. 5.

    Actual Interference Score = Sw L1&L2 − Predicted Interference Score

Interference is a measure of inhibition of information, suppression of information, and control of an automatic response. To calculate this formula, the baseline performances (L1, L2) are used to determine the Predicted Interference Score. This score is then subtracted from the person’s switching performance. The difference between switching performance and Predicted Interference highlights, by way of negative values and positive values, the degree of Actual Interference between two languages when compared to the expected/predicted interference.

When translating the Dual Task Mu Calculation by Baddeley, Della Sala, Papagno, and Spinnler (1997) to code switching, the following calculations can be made:

Original Mu Formula:

  1. 1.

    First Task Performance (1TP).

  2. 2.

    Second Task Performance (2TP).

  3. 3.

    Dual Task Performance (DTP)

  4. a.

    DTP First Task = (1TP − DTP)/1TP

  5. b.

    DTP Second Task = (2TP − DTP)/2TP

  6. 4.

    Mu percentage = (1 − (DTP First Task + DTP Second Task)/2) × 100

Code Switching Adaptation:

  1. 1.

    Baseline performance of first language (L1)

  2. 2.

    Baseline performance of second language (L2)

  3. 3.

    Switching performance in first and second languages (Sw L1&L2)

    1. a.

      Sw L1 = (L1 − Sw L1&L2)/L1

    2. b.

      Sw L2 = (L2 − Sw L1&L2)/L2

  4. 4.

    Mu percentage = (1 − (Sw L1 + Sw L2)/2) × 100; percentage of interference

Mu is a multimodal measure that evaluates the level of working memory load, the amount of resources dedicated to one task or both tasks, and degree of interference of a task on another task. To calculate this formula, baseline performance is subtracted from the switching task (or the dual task trial). This is divided by the baseline performance to get a proportion of one language.

Lastly, the following basic algebraic calculation can account for language dominance when calculating code switching tasks.

Removing the non-dominant language (L-non) influence on L1&L2 Switching:

  1. 1.

    Determine which language (e.g., L1 or L2) the person is more proficient. The lowest score on any baseline performance will be the non-dominant language (L-non) and the highest score will be the dominant language (L-dom).

  2. 2.

    Switching performance in first and second languages (Sw L1&L2).

  3. 3.

    Percentage of difference between languages = (L-dom − L-non/L-dom − L-non) × 100.

  4. 4.

    Percentage of switching performance after removing the difference between L-dom and L-non = ((L-dom − L-non) − Sw L1&L2).

This measure helps to remove the interference of a language that is the non-dominant language in order to get a better gage of the dominate language interference effect. In situation where an individual is less proficient or has less usage of a second language, this formula can assist in determining the degree of switching in on language alone. When there is low proficiency in the non-dominant language or the clinician suspects a large discrepancy between L1 and L2, this formula maybe useful.

Considering the same methodology from verbal fluency, written and oral Trail Making Test, and digit span, the following are a few examples of code switching tests, administration directions, and recording sheet that integrate similar methodology.

Code Switching Phonemic Fluency Test

  1. 1.

    Say as many Persian words that start with F in 1 min.

  2. 2.

    Say as many English words that start F in 1 min.

  3. 3.

    Switch between Persian words and English words that start with F in 1 min.

Recording Form

First language (L1) words that start a letter (e.g., F) Second language (L2) words that start with same letter as L1 Switching between L1 and L2 words
1. 1. 1.
2. 2. 2.
3. 3. 3.
4. 4. 4.
5 5 5
6 6 6
7. 7. 7.
8. 8. 8.
9. 9. 9.
10. 10. 10.
11. 11. 11.
12. 12. 12.
13. 13. 13.
14. 14. 14.
15. 15. 15.
16. 16. 16.
17. 17. 17.
18. 18. 18.
19. 19. 19.
20. 20. 20.
21. 21. 21.
22. 22. 22.
23. 23. 23.
L1 Total: L2 Total: Sw L1&L2 Total:
Interference
  1. Predicted Interference Score = (L1 × L2)/(L1 + L2)
  2. Actual Interference Score = Sw L1&L2 − Predicted Interference Score
Dual Task Mu Working Memory Load
  1. Sw L1 = (L1 − Sw L1&L2)/L1
  2. Sw L2 = (L2 − Sw L1&L2)/L2
  3. Mu = (1 − (Sw L1 + Sw L2)/2) × 100
Removing non-dominant langue
  1. L2 − Sw L1&L2

Code Switching Semantic Fluency Test

  1. 1.

    Name as many animals in Persian as you can in 1 min.

  2. 2.

    Name as many animals in English as you can in 1 min.

  3. 3.

    Switch between Persian and English animals in 1 min. You can repeat names you said before.

Recording form

First language (L1) animal words Second Language (L2) animal words Switching between L1 and L2 animals
1. 1. 1.
2. 2. 2.
3. 3. 3.
4. 4. 4.
5 5 5
6 6 6
7. 7. 7.
8. 8. 8.
9. 9. 9.
10. 10. 10.
11. 11. 11.
12. 12. 12.
13. 13. 13.
14. 14. 14.
15. 15. 15.
16. 16. 16.
17. 17. 17.
18. 18. 18.
19. 19. 19.
20. 20. 20.
L1 Total: L2 Total: Sw L1&L2 Total:
Interference
  1. Predicted Interference Score = (L1 × L2)/(L1 + L2)
  2. Actual Interference Score = Sw L1&L2 − Predicted Interference Score
Dual Task Mu Working Memory Load
  1. Sw L1 = (L1 − Sw L1&L2)/L1
  2. Sw L2 = (L2 − Sw L1&L2)/L2
  3. Mu = (1 − (Sw L1 + Sw L2)/2) × 100
Removing non-dominant langue
  1. L2 − Sw L1&L2

Code Switching Trail Making Tests (Letters and Visuomotor Version)

  1. 1.

    Connect the letters of the Persian alphabet in order, starting with Alef (ا) and ending with Re (ر). Work as fast as you can without making errors.

  2. 2.

    Connect the letters of the English alphabet in order, starting with A and end with L. Work as fast as you can without making errors.

  3. 3.

    Switch between letters of the Persian and English alphabets in order. Start with Alef and end with L. Work as fast as you can without making errors. Administer a short practice trial that includes three switches prior to administration of each trail.

    Note Similar methodology from semantic and phonemic fluency test is used to calculate code switching across the three trials.

Code Switching Trail Making Tests (Letters and Oral Version)

  1. 1.

    Say the letters of the Persian alphabet in order, starting with Alef (ا) and ending with Re (ر). Work as fast as you can without making errors.

  2. 2.

    Say the letters of the English alphabet in order, starting with A and end with L. Work as fast as you can without making errors.

  3. 3.

    Switch between letters of the Persian and English alphabets in order, out loud. Start with Alef (ا) and end with L. Work as fast as you can without making errors. Administer a short practice trial that includes three switches prior to administration of each trail.

    Note Similar methodology from semantic and phonemic fluency test is used to calculate code switching across the three trials.

Code Switching Trail Making Tests (Number and Visuomotor Version)

  1. 1.

    Connect the Persian numbers in order, starting with Yek (۱) and ending with davazdah (۱۲). Work as fast as you can without making errors.

  2. 2.

    Connect the English numbers in order, starting with one and end with twelve. Work as fast as you can without making errors.

  3. 3.

    Switch between Persian and English numbers in order. Start with Yek (۱) and end with twelve. Work as fast as you can without making errors. Administer a short practice trial that includes three switches prior to administration of each trail.

    Note Similar methodology from semantic and phonemic fluency test is used to calculate code switching across the three trials.

Code Switching Trail Making Tests (Number and Oral Version)

  1. 1.

    Say the Persian numbers in order, starting with Yek (۱) and ending with davazdah (۱۲). Work as fast as you can without making errors.

  2. 2.

    Say the English numbers in order, starting with one and end with twelve. Work as fast as you can without making errors.

  3. 3.

    Switch between Persian and English numbers in order, out loud. Start with Yek (۱) and end with twelve. Work as fast as you can without making errors. Administer a short practice trial that includes three switches prior to administration of each trail.

    Note Similar methodology from semantic and phonemic fluency test is used to calculate code switching across the three trials.

Code Switching Digit Span Test

  1. 1.

    I will say some numbers in Persian and I want you to repeat them after me.

  2. 2.

    I will say some numbers in English and I want you to repeat them after me.

  3. 3.

    I will say some numbers in Persian and English. I want you to repeat them after me.

Recording form

Item Number of switches Response Points
Sample: 5 ٢ 1   
1. 2 ٦ 5 2   
2. 7 ٣ 9 ٥ 3   
3. 3 ٧6 ٤ 8 4   
4. 1 ٢ 6 ٩ 3 ٨ 5   
5. 4 ٩ 1 ٨ 3 ١ 7 6   
6. 8۴6 ٣ 7٥ 2 ٤ 7   
7. 5 ٦6٤1٥8٢4 8   
  1. Similar methodology from semantic and phonemic fluency test is used to calculate code switching across the three trials

Methodological Considerations

In code switching, methodology depends heavily on the objectives of the research. In addition, the languages which are involved in the code switching process need to be taken into account. It is very important in the code switching analysis, and on the basis of methodology, to make a distinction between the data which is obtained from typologically similar languages, or from two very distant languages (Cantone, 2007). For instance, some languages may have two letters with the same sound in one language. The letter selected for the fluency task must be a single sounding letter in both languages to avoid confusion or mismatch of an unwanted letter that sounds the same as the targeted letter. To avoid language proficiency issues, the use of high-frequency words and letters will be easier for bilinguals. Furthermore, the Mu calculation is vulnerable to language proficiency as the calculation equally weighs both languages; when in practice, individuals have varying degree of proficiency in each language.

Individuals from particular racial backgrounds and education systems do not learn the alphabet in a sequential manner (unlike the English alphabet). While some individuals will remember high-frequency letters or words, they may not recall low-frequency letters or words. Therefore, the verbal fluency and Trail Making tests may be impacted by such factors.

Other factors to consider when interpreting code switching test results include the influence of the dominate language, the context in which the language is used, and the level of proficiency as a function of dominant/non-dominant language and context. For instance, an individual can use English the majority of the time at work (e.g., reading, teaching, writing), while using very little of English at home when communicating with family members. The same individual may use Dari or Pashto at home and when in a personal environment. This further became complicated by the level of proficiency, as mentioned earlier. The individual may have higher or lower proficiency in the work context while having another level of proficiency in the home context. This area needs further research to scientifically understand how context would impact code switching. The following are a few ways to consider the influence of such factors in code switching.

Interviewing and understanding the individuals’ background for language dominance, context usage, and proficiency can assist with the interpretation of code switching tests. Another way to take into consideration the aforementioned factors is to examine the first and second trials of the code switching test to determine the level of proficiency and inquiring about the context the individual’s answers are often used in. For example, if the individual generates a word list in English and upon inquiry the professional learns that the words are often used in the work environment or in education system (e.g., work items such as a computer, email, etc.), while other words generated in Dari or Pashto are words used in the home (e.g., house furniture, etc.), then it can be reasonably assumed that context is a notable factor. The number of contexts (e.g., home, work, school, personal relationships, family, friends, professionals, religious associations) can be one factor to consider. Statistically the number of contexts can be controlled for.

For instance, after the individual generate a list of words in L1 and L2, the professional can see whether there is a large percentage of difference between the two list of words (e.g., L1 has 15 words while L2 has 5 words, a difference of 10, which is 67% of the maximum words generated). Although there is no cutoff for percentage as more scientific research in this area is needed; theoretically, if the difference is greater than 50% of the maximum words generated, then the influence of proficiency within a single or multiple context needs to be considered in the code switching calculation.

Clinical judgment and research should be considered when making diagnostic or treatment decisions in these cases. Thus, these results should be correlated with other neuropsychological and language tests to help clarify any possible existing type of cognitive or language deficits. For example, if executive functioning tests are normal but code switching is notably impaired, the methodological, cultural and bilingual factors should be highly considered with interpretation of results to help ensure these results are not used to indicate possible deficits when they do not exist.

Summary and Discussion

Code switching occurs when an individual switches between two or more languages. There has been little research on the topic of code switching as it relates using how this process can be applied to the assessment of neuropathology and test development. The aim of this study was to provide a review the literature in order to provide a comprehensive view of code switching as well as the cognitive and linguistic theoretical underpinning, and neuropsychological and neuroanatomical aspects of code switching, and apply such information to neuropsychological test development.

This study found that the types of code switching are highly diverse and can occur within words, phrase or sentence, inserted within one language, between sounds, and within grammar or syntax structure of one language (e.g., Intra-word, Intra-sentential, Inter-sentential Code Switching, Insertion, Interference/transference, Composite, Congruent, etc.). Theories mainly denote activation of the retrieval and organization of lexicolinguistic knowledge (e.g., Activating Threshold Hypothesis). Neuroanatomical regions that were associated with code switching involve frontal, temporal, and parietal cortical and subcortical areas involving executive control and language functions, which act to select relevant and inhibit irrelevant language networks. Specifically, these regions include the anterior cingulated cortex, the left dorsolateral prefrontal cortex, the bilateral supramarginal gyri, left caudate, left putamen, left globus pallidus, and left thalamus. Salient network has also been implicated. Regarding neuropsychological functions, codes switching appeared to have more influence on executive functions, lexical retrieval, and working memory domains. The research on neuroanatomical functions of code switching and neuropsychological tests appears to be consistent as executive functions, lexical retrieval, and working memory domains are commonly associated with frontosubcortical as well as temporal function. The switching element of code switching may be linked to the frontosubcortical regions, while the language element of code switching may be linked to temporal structures.

Standard neuropsychological tests can be adapted to code switching tasks as they provide a standardized method of measuring and recording code switching tasks associated with executive functions, lexical retrieval, and working memory domains. Integrating standard neuropsychological tests and administration methods can assist in scoring and interpreting code switching tasks. Code switching tasks have three trials to systematically engage a person in the cognitive process of code switching. These include the following: (1) baseline performance of first language (e.g., English); (2) baseline performance of second language (e.g., Persian language); (3) switching performance in first and second languages. For example, using a lexical retrieval neuropsychological testing method, such as phonemic and semantic word fluency tests (e.g., FAS and animal fluency), the patient with suspected neurocognitive deficit is asked to: (1) list as many animals in English, (2) say as many animals in Persian, (3) and switch between animals in Persian and English languages, each within 60 s. The test that can be adapted to code switching versions includes phonemic fluency (FAS verbal fluency), semantic fluency (animal verbal fluency), written and oral Trail Making Test, and digit span. These code switching versions of neuropsychological tests can assist clinicians and researchers to records, assess, and diagnose clinical syndrome that produce unintentional code switching errors. Since these tests are sensitive to brain disease/injury and cognitive impairment, it is hypothesized that such test can also help detect impairments at an early on prior to moderate-to-severe stages of cognitive impairment, therefore acting as a prevention measure.

Another implication of using code switching neuropsychological tests is to understand the various neurological conditions that present either cortically or subcortically. As we found, code switching impacts various parts of the brain, including frontoparietal, subcortical, and, to a lesser extent, mid-temporal regions. Though it is unclear to what extent code switching affects cortical and subcortical sites (due to the wide variability of the studies), clinicians and researchers can use such instruments to study executive and linguistic control. Conditions that have subcortical challenges may show a particular and distinct profile or level of impairment on code switching tasks as compared to cortical conditions. Subcortical conditions include frontostriatal/HIV dementia, Huntington disease, subcortical stroke, and Parkinson’s disease. It is worthwhile to compare such performance across cortical conditions such as Alzheimer’s and frontotemporal disease when considering the cortical activation of code switching. Nevertheless, the profiles in code switching will likely differ as a function of cortical versus subcortical neurological conditions.

Furthermore, the review highlights various formulas to calculate the degree of interference, working memory load, and removing the effects of a non-dominant language (e.g., Interference, Mu, and subtraction of L2 or non-dominant). These calculations can serve clinicians and researchers to further extrapolate the degree of interference and working memory load. It is hypothesized that the use of such calculations as well as future normative data can assist with diagnosis and treatment planning of individuals who are bilingual/multilingual and experience cognitive impairments (e.g., Alzheimer’s disease, traumatic brain injury, seizure disorders, etc.).

This article provides a comprehensive collection of different aspects of code switching with the aim to assist the studies where code switching is evaluated and neuropsychological test development is involved. Code switching tasks play a critical role in clinical assessment. The clinical instrument that the researchers would like to utilize in their assessment can be borne out of code switching process. Considering code switching process carefully in clinical assessment would create a significant difference in making sound decisions and suitable treatment for the patients with cognitive impairments, because it provides valuable information for the psychologist, neuropsychologist, neurologist, and psychiatrist, which will assist them in their assessment and treatment planning in a culturally congruent manner. When code switching process is considered comprehensively in the clinical assessment procedure, identifying cognitive impairment will be an explicit task. Moreover, it makes the path clearer for healthcare provider to identify brain disorders or damages. In the assessment, not only the cognitive skills should be considered but also language skills and their interactions must be taken into account. We have also emphasized the importance of considering all languages that a patient uses to code switch from different analytical dimensions. The other dimension that researchers need to consider is the methodology which should commit to the needs of their studies. In neuropsychological test development, it is important to identify the elements under assessment from various perspectives because each dimension may leave a different effect on the code switching process. Also, in some cases social factors cannot be separated from code switching. Therefore, it is essential to have a clear objective in mind of the factors which might influence code switching process psychologically, neurologically and linguistically because it ultimately exert influence on assessment, diagnosis and treatment. Specifically, the code switching process may impact data collected during an assessment, which can later interfere with the ultimate results/conclusions drawn and the further use of that data in diagnosing. Ultimately, not considering or understanding the impact of these factors can jeopardize any conclusions made from the information obtained by individuals engaging in code switching.

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Ramezani, A., Alvani, S.R., Mohajer, L. et al. Neuropsychology and Neuroanatomy of Code Switching: Test Development and Application. Psychol Stud 65, 101–114 (2020). https://doi.org/10.1007/s12646-019-00548-5

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Keywords

  • Code switching
  • Neurocognitive
  • Executive language
  • Bilingual
  • Cultural neuropsychology