Interactions between language and inhibitory control: Evidence from a combined language switching and Stroop paradigm

Abstract

Aims and objectives:

This study assessed bilinguals’ language control (LC) and inhibitory control (IC) performance (switch costs and Stroop effects) simultaneously in the same participants to investigate how these processes influence each other.

Design:

Seventy-four Turkish-English bilinguals were presented with Turkish (L1) or English (L2) color words printed either in congruent or incongruent ink color and instructed to name the ink color of these words in the presentation language. Stimuli’s language and congruency were either the same as in the previous trial or different.

Data and analysis:

Reaction times (RTs), switch costs (mean RTs on language repetition subtracted from switch trials), and the Stroop effects (mean RTs on congruent subtracted from incongruent trials) were analyzed using the linear mixed-effect model.

Findings:

The switch costs were larger on incongruent than congruent, and the Stroop effects were larger on language switch than repetition trials. This means that the LC performance decreased while resolving conflict, and the IC performance decreased during switching language, indicating that these two share a common IC mechanism. However, the switch costs and Stroop effects across L1 and L2 were symmetrical in all conditions, leaving the previous interpretation uncertain. Besides, the Stroop effects were larger when followed by congruent than incongruent trials during language repetition, whereas they were equal during switching. This means that the ability to adjust performance by previous experience was disrupted during language switching. Moreover, for the high-L2 proficiency group, this ability was diminished in language repetition trials too. These results indicate that rather than inhibition, other processes may primarily mediate bilinguals’ LC.

Originality:

This study provides evidence for how language and inhibitory control influence each other by combining language switching and Stroop paradigms. Furthermore, it investigates the sequential congruency effects (SCE).

Significance/implications:

This study shows that SCE investigation may provide significant theoretical implications.

Keywords

Language control inhibitory control language switch cost Stroop effect sequential congruency effect

Previous studies have shown that bilinguals’ two languages are simultaneously active during language use (see Kroll et al., 2012). This means that bilinguals need a mechanism to control the potential interference of the non-target language while using the target language, that is, language control (LC). It has been suggested that LC is mediated by the domain-general executive control (EC) system; thus, the practice of LC in bilinguals leads to enhancement in EC (see Bialystok, 2017; Bialystok et al., 2009 for reviews).

A well-known experimental procedure for examining the underlying cognitive mechanisms of bilingual LC is the language-switching paradigm. In this task, bilingual participants are instructed to name pictures or digits in one of the two languages based on a language cue, on switch trials (stimuli in a different language compared to the previous trial), or repetition trials (stimuli in the same language as in the previous trial; for example, Costa & Santesteban, 2004; Meuter & Allport, 1999). Responses on switch trials are usually slower and more error-prone than on the repetition trials. This difference has been referred to as “language switch cost” (see Declerck & Philipp, 2015, for a review), and a larger magnitude of the language switch cost is assumed to reflect higher difficulty in language control (e.g., Declerck et al., 2017; Verhoef et al., 2009). Critically, many studies have found larger magnitude of the switch costs for the native language (L1) than for the second language (L2; for example, de Bruin et al., 2014; Jackson et al., 2001; Linck et al., 2012; Macizo et al., 2012; Meuter & Allport, 1999). This difference has been referred to as “asymmetrical switch cost” (see Figure 1) and is assumed to be a marker for the role of inhibition during language control (see Declerck & Philipp, 2015, for a review).

Figure 1.

Hypothetical examples of language switch cost asymmetries and sequential congruency effect.

Specifically, the asymmetrical switch cost receives an explanation within the Inhibitory Control (IC) model of bilingual LC (Green, 1998). According to the IC model, the non-target language is inhibited while processing the target language. Furthermore, the inhibition applied to a given language is reactive, that is, proportional to the strength of that language’s activation. This means that a greater magnitude of inhibition is needed to suppress the stronger L1 than the weaker L2; therefore, L1 is inhibited to a greater extent during L2 processing than vice versa. In this regard, asymmetrical switch costs indeed indicate that switching into L1 involves greater difficulty needed to overcome the strong inhibition of the L1 on previous trial(s), a finding consistent with the IC model (but see Christoffels et al., 2007; Costa & Santesteban, 2004; Gollan & Ferreira, 2009; Verhoef et al., 2010).

Inhibitory control (IC), which is assumed to be a core part of EC (Diamond, 2013; Miyake et al., 2000), refers to goal-directed regulation of attention, behavior, thoughts, and/or emotions in the face of conflict. Among many other tasks, the so-called congruency tasks are well-known experimental procedures to measure IC. In these tasks, stimuli include a relevant and an irrelevant feature. Participants are asked to respond to a relevant feature, and the congruency between the relevant and irrelevant feature is manipulated. As a prime example, the Stroop task (e.g., Stroop, 1935; see MacLeod, 1991, for a review) requires participants to identify the ink color (relevant feature) of a color word while ignoring the meaning (irrelevant feature) of the word itself. On congruent (C) trials, the meaning of the word and ink color match (e.g., the color word “red” presented in red ink color); whereas on incongruent (I) trials, the meaning of the word and ink color mismatch (e.g., the color word “red” presented in green ink color). Since the irrelevant feature on I trials draws attention and requires inhibition, its influence is reflected as slower and more error-prone performance. Thus, a larger difference between I and C trials, that is, “congruency effect” (or Stroop effect for this task), is assumed to reflect worse inhibitory control (e.g., Linck et al., 2012).

Evidence for LC being linked to inhibition comes from studies showing that bilinguals’ IC ability modulates their switch cost asymmetries. For example, several studies found symmetrical switch costs for bilinguals with high performance on tasks that require IC, but asymmetrical switch costs for those with low performance on tasks that require IC (Liu et al., 2014, 2017, 2018). These studies suggest that higher IC is associated with a greater ability to overcome the strong L1 inhibition, as demonstrated by the switch cost symmetries. One problem with these findings, however, is the perspective that the IC model’s (Green, 1998) predictions could also be interpreted to be the opposite. That is, bilinguals with higher IC abilities could inhibit L1 more strongly (see de Bruin et al., 2014; Linck et al., 2012), thus showing larger language asymmetries. Therefore, rather than comparing group performances, investigating how the need for IC influences the need for LC and vice versa in the same participants is of theoretical interest.

An effort to more directly examine how the need for inhibition affects language-switching performance, have been made by Liu et al. (2019). The authors instructed bilinguals to switch between L1 and L2 in a non-conflicting context or a conflicting context (induced by Stroop interference). The results showed that the switch costs and the language asymmetry of the costs were larger in the conflicting context than the non-conflicting context. The authors’ interpretation posited that in the conflicting context, the need to resolve conflict reduced the availability of IC for language switching (see also Liu et al., 2020). However, when participants with high and low IC abilities were separated into two groups, the larger language asymmetry of the costs in the conflicting context was present only for the high-IC group. Even though the authors’ interpretation posited in a different direction, one may argue that this result indicates that higher IC abilities are associated with stronger L1 inhibition, that is, larger switch cost asymmetries.

To summarize, it is still unclear how IC performance interacts with LC performance. In particular, investigating how the need for IC influences the ability to process L1 and L2 is of theoretical interest. Therefore, to further investigate this topic, rather than comparing different groups or comparing the performance of a single group in different tasks, in this study, we set out to simultaneously assess measurements of LC and IC on a single task and in the same participants. Furthermore, besides IC, we also set out to assess another index of executive control, that is, the “sequential congruency effect” (SCE; also named as congruency sequence effect or Gratton effect).

SCE refers to the observation that congruency effects in congruency tasks are typically smaller following I compared to following C trials (e.g., Gratton et al., 1992; see Egner, 2014). Specifically, SCE is the interaction between previous and next trials’ congruency. That is, previous I trials lead to enhanced performance on next I trials (I-I trial sequences) and impaired performance on next C trials (I-C trial sequences). In contrast, previous C trials lead to impaired performance on next I (C-I trial sequences) and enhanced performance on the next C trials (C-C trial sequences; see Figure 1).

There are several accounts for the underlying cognitive mechanisms of SCE (see Duthoo et al., 2014; Egner, 2007). Briefly, according to the repetition expectancy account (Gratton et al., 1992), SCEs are triggered proactively, based on participants’ expectancy regarding the upcoming trial. The Conflict Monitoring Theory (CM theory; Botvinick et al., 1999, 2001), however, posits that SCEs are triggered reactively by conflict on the previous trial(s). On the other hand, the feature integration account (Hommel et al., 2004; Mayr et al., 2003) argues that SCEs are episodic memory effects deriving from retrieval of partial or complete overlap of stimulus and response features from trial to trial. The former two accounts assume that SCEs are the result of top-down attentional control applied from trial to trial, whereas the latter account assumes that SCEs are the result of bottom-up retrieval. An intermediate position by Egner (2014) argues that both top-down and bottom-up processes contribute to the magnitude of the SCE. Nevertheless, whatever the underlying basis of the SCE is, a consensus among these accounts is that the SCE is an expression of learning by previous experience (see Egner, 2014). Thus, a larger SCE reflects greater adjustment of current processing by previous experience.

Since previous studies have shown some evidence that SCE magnitudes are modulated by language groups (e.g., Grundy et al., 2017, see Green, 2018), in this study, we also aimed to explore how the SCEs are modulated by language and language switching. For this, we designed a task in which the Stroop and language-switching paradigms were combined. Participants were presented with congruent/incongruent color words in their L1/L2 and instructed to name the ink color of these words in the presentation language. Stimuli’s language and congruency were either the same as in the previous trial or different.

We assessed, bilinguals’ language switch costs as an index of language control, and the effect of language on switch costs (i.e., language switch cost asymmetries) as an index of differences between inhibition for L1 and L2 during language control. Also, we assessed Stroop effect as an index of inhibitory control, and the effect of previous trials’ congruency on Stroop effects (i.e., sequential congruency effects [SCE]) as an index of the ability to learn from previous experience. Our predictions are listed below.

Predictions for the switch costs

We predicted to observe switch cost, that is, impaired performance on language switch than language repetition trials (see Declerck & Philipp, 2015).

Assuming language control and inhibitory control compete for the same cognitive resources (e.g., Liu et al., 2019), we predicted to observe larger switch costs on I than C trials.

We predicted to observe switch cost asymmetries, that is, larger switch cost for the L1 than for the L2 (see Declerck & Philipp, 2015).

Assuming language control and inhibitory control compete for the same cognitive resources (e.g., Liu et al., 2019), we predicted larger asymmetries of the switch costs on I than C trials.

The magnitude of the switch costs and their asymmetries on different levels of congruency and previous trials’ congruency was a question to be explored in this study.

Predictions for the Stroop effects

We predicted to observe Stroop effect, that is, impaired performance on I than C trials (see MacLeod, 1991).

Assuming language control and inhibitory control compete for the same cognitive resources (e.g., Liu et al., 2019), we predicted larger Stroop effects on language switch than language repetition trials.

Assuming processing the L2 requires stronger inhibition of the non-target L1 (e.g., de Bruin et al., 2014), we predicted that the need to inhibit language would compete for the inhibitory control required for resolving Stroop interference. Therefore, we predicted larger Stroop effects on L2 than L1 trials.

We predicted to observe SCE, that is, larger Stroop effect preceded by C than I trials (see Egner, 2007).

The magnitude of the SCEs on different levels of language switch type and language was a question to be explored in this study.

Method

This study was approved by the Hacettepe University Ethics Commission.

Participants

Ninety-two Turkish-English bilinguals, recruited from several universities in Ankara, participated in this study. All participants were studying in departments where the teaching language is English. They all completed a self-rating questionnaire about their language experience and proficiency (LEAP-Q: Marian et al., 2007). For screening participants executive function skills, we administered the Turkish versions of Trail Making Test (Türkeş et al., 2015), Wechsler Adult Intelligence Scale-IV Digit Span subtest (Sezgin et al., 2017), and Raven Standard Progressive Matrices Test (Karakaş, 2006).

To keep the sample homogeneous regarding their language profiles, we excluded seven participants who declared that their L1 differed from Turkish and/or L2 differed from English. Four participants who declared having a neuro or psychological disorder and/or using medication were also excluded. Six participants were excluded for having 25% or higher rate of data to be eliminated in Reaction Time (RT) analyses (i.e., error trials, trials followed by an error, and trials with RT larger or smaller than 2.5 standard deviations [SDs] from the mean). In addition, one participant was excluded for technical issues.

The final sample consisted of 74 participants (66.2% female) with a mean age of 21.65 (SD = 1.77, range = 7), whose L1 was Turkish and L2 was English. They all were with normal or corrected-to-normal vision and suffered no neuro or psychological disorder. The means and SDs for their self-rated language proficiency skills and their performance on the executive function tests are presented in Table 1.

Table 1.

Means (and SDs) of participants’ language skills and executive function tests.

Language proficiency
	L1	L2
AoA	–	9.39 (2.16)
Reading*	9.24 (.97)	8.46 (1.17)
Listening*	9.23 (.94)	8.41 (1.03)
Speaking*	8.92 (1.14)	7.46 (1.28)
Mean proficiency*	9.13 (.88)	8.1 (.94)
TMT
A time	22.30 (7.09)
B time	51.94 (15.13)
Digit span
Forward	10.13 (1.89)
Backward	9.56 (2.43)
Sequencing	10.63 (2.27)
RSPM
54.18 (3.52)

Note. AoA: age of acquisition; L1: first language; L2: second language; RSPM: Raven standard progressive matrices; TMT: trial making test.

Asterisks indicate significant differences between L1 and L2 scores.

Task and procedure

The experiment was designed using PsychToolbox (Version 3.0.15; http://psychtoolbox.org/) and MATLAB (Version 2018a; The MathWorks, Inc., Natick, MA). The experimental task combines a language-switching paradigm with the Stroop paradigm.

The experimental stimuli were eight color words, four of them in Turkish (“kırmızı,” “yeşil,” “sarı,” and “mavi”) and four their English translations (“red,” “green,” “yellow,” and “blue”). The color words were presented either in a congruent ink color (e.g., the word “red” presented in red) or in an incongruent ink color (e.g., the word “red” presented in yellow, green, or blue). In total, there were four Turkish and four English congruent, and 12 Turkish and 12 English incongruent stimuli.

Participants were instructed to name the ink color of the printed Turkish color words in Turkish and the ink color of the English color words in English. The stimuli were presented so that the language of the stimulus was either the same as in the previous trial (language repetition) or different (language switch). Also, the congruency of the stimulus was either the same as in the previous trial (C-C and I-I trial sequences) or different (C-I and I-C trial sequences).

The experimental task consisted of eight blocks, and each block consisted of 21 experimental trials, with the first trial being the filler trial. There were 10 trials for each trial type (e.g., L1 incongruent trials followed by L2 congruent). In each trial, the experimental stimuli were randomly selected and presented; for example, on L1 incongruent trials followed by L2 congruent, a stimulus among 12 Turkish incongruent stimuli was randomly selected and presented.

Critically, the order of presentation of each trial type was counterbalanced. The complete counterbalancing was achieved in eight blocks (see Table A1 in the online Appendix A for more detailed information). The blocks were presented in a partial-counterbalanced order across participants. Before the experimental task, there was a practice block of 21 trials.

The color words were presented on a 15.6-inch white screen in 60 pt., Times New Roman font. Every trial began with a fixation cross presented at the center of the screen for 500 milliseconds, followed by a color word presented for 2,000 milliseconds and a blank screen presented for 500 milliseconds (see Figure 2). The verbal responses made by the participants were recorded and checked for accuracy post-experiment.

Figure 2.

Experimental task.

All participants signed a written informed consent form before participating. Before the experimental task, they completed the questionnaire about their language experience and proficiency. Afterward, they performed the experimental task and were allowed to rest between each block. Following the experiment, they completed the executive function tests, which were applied in a partial-counterbalanced order across participants. The study lasted approximately 80 minutes.

Results

The Reaction Times (RTs) were analyzed using the Linear Mixed Effects (LME) model. Analyses were performed using the lmer command of the lme4 package (Bates et al., 2015b) in RStudio. The lmerTest package (Kuznetsova et al., 2017) was used to compute p-values and to inform significant interactions.

The first trial of each block, the error trials and the trials followed by an error were removed from analyses. RTs larger or smaller than 2.5 SDs from the mean (per each trial type, such as switch to L1 switch to incongruent) were also removed (Liu et al., 2019). These criteria led to the exclusion of 12.11% of the data (ranging from 5% to 20.13% for different conditions). The structure of the data is presented in Table 2.

Table 2.

Mean RTs in the L1 and L2 trials decomposed in language switch type, congruency, and previous congruency.

Language	Language switch type	Congruency	Previous congruency	Mean (SD)
L1	Switch	Congruent	Congruent	987 (218)
		Congruent	Incongruent	1,006 (216)
		Incongruent	Congruent	1,199 (264)
		Incongruent	Incongruent	1,206 (252)
	Repeat	Congruent	Congruent	906 (206)
		Congruent	Incongruent	944 (196)
		Incongruent	Congruent	1,122 (234)
		Incongruent	Incongruent	1,091 (242)
L2	Switch	Congruent	Congruent	993 (213)
		Congruent	Incongruent	1,035 (229)
		Incongruent	Congruent	1,199 (258)
		Incongruent	Incongruent	1,233 (253)
	Repeat	Congruent	Congruent	906 (202)
		Congruent	Incongruent	983 (209)
		Incongruent	Congruent	1,135 (228)
		Incongruent	Incongruent	1,105 (234)

Note. L1: first language; L2: second language; SD: standard deviation.

The response variable defined for the model was raw RT. The fixed effects were language (L1, L2), language switch type (switch, repetition), congruency (congruent, incongruent), and previous congruency (congruent, incongruent). The random effects were trial and participant. The LME model was constructed in line with the suggestion to follow the random-effects structure that best represents the experimental design and use a parsimonious model (Bates et al., 2015a). Thus, the effect of trial was defined as random intercept nested within the fixed effects and participant was defined as another random intercept. The LME model was specified as: RT ~ (language × language switch type × congruency × previous congruency)/(1|trial) + (1|participant). The model results are presented in Table 3 (see Table B1 in the online Appendix B for model summary). Accuracy scores were ignored due to the high accuracy rate (93.7%).

Table 3.

Output of LME model assessing participants’ RT performance.

	F	df(n)	df(d)	p	Pairwise comparison
	F	df(n)	df(d)	p		β	SE	df	CI lower	CI upper	t	p
Language	14.05	1	10,308	.000	L1 < L2	−.015	0.003	10,308	−0.023	−0.008	–4.053	.000
Language switch	454.31	1	10,308	.000	S > R	.084	0.003	10,308	0.076	0.091	21.75	.000
Congruency	2,515.50	1	10,308	.000	C < I	−.193	0.003	10,308	−0.201	−0.185	–49.96	.000
Previous congruency	34.66	1	10,308	.000	pC < pI	−.022	0.003	10,308	−0.029	−0.014	–5.74	.000
Language × language switch	.009	1	10,308	.923	–
Language switch × congruency	13.05	1	10,308	.000	S_C > R_C	.069	0.005	10,308	0.058	0.079	12.87	.000
					S_I > R_I	.098	0.005	10,308	0.088	0.109	17.81	.000
					S_C < S_I	−.208	0.005	10,308	−0.218	−0.197	–37.62	.000
					R_C < R_I	−.178	0.005	10,308	−0.189	−0.167	–32.99	.000
Language × previous congruency	8.49	1	10,308	.003	L1_pC ≈ L2_pC	−.004	0.005	10,308	−0.015	0.005	−.85	.393
Language × previous congruency	8.49	1	10,308	.003	L1_pI < L2_pI	−.026	0.005	10,309	−0.037	−0.015	–4.80	.000
Language switch × previous congruency	2.87	1	10,308	.090	S_pC < S_pI	−.029	0.005	10,310	−0.040	−0.018	–5.29	.000
Language switch × previous congruency	2.87	1	10,308	.090	R_pC < R_pI	−.015	0.005	10,309	−0.025	−0.004	–2.80	.005
Congruency × previous congruency	42.01	1	10,308	.000	C_pC < C_pI	−.046	0.005	10,308	−0.057	−0.036	–8.70	.000
Congruency × previous congruency	42.01	1	10,308	.000	I_pC ≈ I_pI	.002	0.005	10,309	−0.008	0.013	.43	.665
Language switch × congruency × previous congruency	27.07	1	10,308	.000	SC_pC < SI_pC	−.212	0.007	10,308	−0.227	−0.197	–27.68	.000
					SC_pI < SI_pI	−.203	0.007	10,308	−0.219	−0.188	–29.76	.000
					RC_pC < RI_pC	−.223	0.007	10,308	−0.238	−0.208	–25.58	.000
					RC_pI < RI_pI	−.133	0.007	10,308	−0.149	−0.118	–17.15	.000

Note. C: congruent; I: incongruent; L1: first language; L2: second language; pC: previous congruent; pI: previous incongruent; S: language switch; R: language repetition.

The remaining interaction effects were not statistically significant (p > .05).

To test the remaining predictions and better illustrate the interaction effects, we calculated the switch costs by subtracting the mean RT on language repeat from language switch trials. Afterward, we conducted an LME model specified as language switch cost ~ (language × congruency × previous congruency) + (1|participant) (see Table B2 and Table B3 in the online Appendix B for more detailed information). We also calculated the Stroop effects by subtracting the mean RT on congruent from incongruent trials. Afterward, we conducted an LME model specified as: Stroop effect ~ (language × language switch type × previous congruency) + (1|participant) (see Table B4 and Table B5 in the online Appendix B for more detailed information). The results of these analyses are presented in Table 4.

Table 4.

Output of LME model assessing participants’ language switch cost and Stroop effect performance.

Switch cost
	F	df(n)	df(d)	p	Pairwise comparison
	F	df(n)	df(d)	p		β	SE	df	CI lower	CI upper	t	p
Language	0.027	1	511	.868	–
Congruency	12.41	1	511	.000	C < I	−.028	0.008	511	−0.044	−0.012	–3.52	.000
Previous congruency	2.559	1	511	.110	–
Congruency × previous congruency	22.10	1	511	.000	C_pC ≈ I_pC	.009	0.011	511	−0.013	0.032	.833	.405
					C_pI < I_pI	−.067	0.011	511	−0.089	−0.044	–5.81	.000
Stroop effect
	F	df(n)	df(d)	p	Pairwise comparison
	F	df(n)	df(d)	p		β	SE	df	CI lower	CI upper	t	p
Language	0.715	1	511	.262	–
Language switch	12.35	1	511	.000	S > R	.028	0.008	511	0.012	0.044	3.515	.000
Previous congruency	36.17	1	511	.000	C > I	.049	0.008	511	0.033	0.066	6.014	.000
Language switch × previous congruency	22.01	1	511	.000	S_pC ≈ S_pI	.010	0.011	511	−0.011	0.033	0.935	.350
					R_pC > R_pI	.087	0.011	511	0.064	0.110	7.571	.000

Note. C: congruent; I: incongruent; L1: first language; L2: second language; pC: previous congruent; pI: previous incongruent S: language switch; R: language repetition.

The remaining interaction effects were not statistically significant (p > .05).

The results showed a significant main effect of all variables (see Table 3). However, the interaction effect of language and language switch type was not significant. Neither this effect was qualified by any other three-way interaction effect, indicating that the language switch costs were symmetrical across all conditions (see Table 4).

On the other hand, the results showed a significant two-way interaction between language switch type and congruency, indicating that the switch costs were larger on incongruent than on congruent trials, and the Stroop effects were larger on language switch than on language repetition trials (see Tables 3 and 4).

Also, we observed a significant two-way interaction between congruency and previous congruency, as well as a marginally significant interaction between language switch type and previous congruency. These effects were qualified by a significant three-way interaction between language switch type, congruency, and previous congruency (see Table 3 and Figure 3(a)). The switch cost analyses showed that the switch costs were not different on congruent and incongruent trials when the previous trial was congruent; whereas they were larger on incongruent than congruent trials when the previous trial was incongruent (see Table 4, Figure 3(b)). The Stroop effect analyses showed that the Stroop effects on language switch trials were not different when the previous trial was congruent and incongruent; whereas the Stroop effects on language repetition trials were larger when the previous trial was congruent than incongruent (see Table 4, Figure 3(c)).

Figure 3.

The interaction effect between language switch type, congruency, and previous congruency on RT (a), switch cost (b), and Stroop effect (c).

Interestingly, the results showed a significant two-way interaction between language and previous congruency. This effect showed that the RTs on L1 and L2 trials were not different when the previous trial was congruent; whereas the RTs on L2 trials were higher than L1 when the previous trial was incongruent (see Table 3). However, this effect was not qualified by any other interaction.

Further analyses

To better account for the missing language switch cost asymmetries, we divided the sample into two groups by their mean L2 proficiency. One group comprised the participants on the highest quarter on L2 proficiency (total 18 participants, 61.1% female) and the other group comprised the participants on the lowest quarter on L2 proficiency (total 18 participants, 61.1% female). The independent-samples t-tests confirmed that the groups significantly differed regarding their L2 proficiency scores and did not differ regarding their performance on executive function tests (see Table 5).

Table 5.

Means (and SDs) of groups’ language skills and executive function tests.

High L2 proficiency group			Low L2 proficiency group
Language proficiency			Language proficiency
	L1	L2		L1	L2
AoA	–	8.94 (1.74)_*	AoA	–	10.06 (1.4)
Reading	9.67 (.76)_*	9.72 (.57)_*	Reading^*	8.61 (1.03)	7.22 (.87)
Listening	9.83 (.70)_*	9.67 (.48)_*	Listening^*	8.56 (.85)	7.33 (.76)
Speaking^*	9.67 (.67)_*	8.83 (.92)_*	Speaking^*	8.16 (1.09)	6.28 (.75)
Mean proficiency	9.72 (.61)_*	9.40 (.41)_*	Mean proficiency^*	8.44 (.77)	6.94 (.46)
TMT time			TMT time
A	21.71 (6.78)		A	22.78 (9.49)
B	52.70 (15.47)		B	54.77 (19.57)
Digit span			Digit span
Forward	10.00 (2.16)		Forward	9.89 (1.77)
Backward	9.68 (3.00)		Backward	8.88 (2.32)
Sequencing	10.75 (2.32)		Sequencing	11.00 (2.74)
RSPM			RSPM
54.24 (3.83)			53.61 (3.79)

Note. AoA: age of acquisition, L1: first language; L2: second language. TMT: trial making test; RSPM: Raven standard progressive matrices.

Superscript asterisks indicate significant within-group differences between L1 and L2 scores, whereas subscript asterisks indicate significant between-group differences.

We re-performed the RT analyses adding the L2 proficiency group as another fixed effect (see Table C1 and Table C2 in the online Appendix C for more detailed information). The results revealed a non-significant main effect of the group, F (1, 34.0) = 0.186, p = .66. Even though the switch costs were numerically larger for the low-L2 proficiency group, the interaction between language and language switch type did not reach statistically significant level, F (1, 5004.7) = 0.77, p = .38. However, the two-way interaction between group and congruency was significant, F (1, 5004.5) = 16.17, p = .000, and this effect was further qualified by a marginally significant four-way interaction between language switch type, congruency, and previous congruency, F (1, 5004.4) = 2.97, p = .084 (see Figure 4(a)).

Figure 4.

The interaction effect between language switch type, congruency, and previous congruency.

To better illustrate the interaction effect, we analyzed the Stroop effects and SCEs by subtracting the Stroop effects followed by an incongruent than Stroop effects followed by a congruent trial. The Stroop effect analyses showed a marginally significant three-way interaction between language switch type, and previous congruency, F (1, 238) = 3.46, p = .063 (see Figure 4(b)). This effect indicates that for both groups the Stroop effects on language switch trials were not different when the previous trial was congruent or incongruent (high-L2 proficiency: β = .038, SE = 0.024, CI[−0.00, 0.08], t = 1.609, p = .108; low-L2 proficiency: β = −.013, SE = 0.024, CI[−0.06, 0.03], t = −0.573, p = .566). Whereas the Stroop effects on language repetition trials were larger when the previous trial was congruent than incongruent, and this difference was slightly larger for the low-L2 proficiency group, β = .097, SE = 0.024, CI[0.05, 0.14], t = 4.066, p = .000, than for the high-L2 proficiency group, β = .060, SE = 0.024, CI[0.01, 0.10], t = 2.525, p = .012. The significant interaction between group and language switch type, F (1, 136) = 4.161, p = .043, on SCE analyses confirm this, indicating larger SCEs on language repetition than switch trials for the low-L2 proficiency group, β = −.022, SE = 0.031, CI[−0.17, −0.05], t = −3.594, p = .000, while non-different SCEs on language switch or repetition trials for the high-L2 proficiency group, β = −.111, SE = 0.031, CI[−0.08, 0.04], t = −0.709, p = .479 (see, Figure 4(c)).

Discussion

In this study, we set out to investigate the interactions between bilinguals’ language control and inhibitory control, by measuring the language switch cost and Stroop effect performance simultaneously in the same participants. Moreover, we also set out to investigate the differences between inhibition for L1 and L2 during language control by assessing the language switch cost asymmetries as well as the ability to readjust behavior in response to previous experience by assessing the SCE performance. For this, we designed a task that combines the language and Stroop paradigms, and manipulated the switch or repetition status of language and congruency.

In line with the previous language-switching studies, we observed a main effect of language switch type indicating that the performance on language switch trials was lower than repetition. Also, in line with the previous Stroop studies, we observed a main effect of congruency indicating that the performance on incongruent trials was lower than congruent. Critically, consistent with Liu et al. (2019), we observed that the switch costs were larger on incongruent trials than on congruent trials as well as the Stroop effects were larger on language switch than repetition trials. This means that the LC performance decreased while resolving Stroop interference, as well as the Stroop-related interference resolution performance decreased during language switching.

This result may indicate that LC and Stroop-related interference resolution are both mediated by inhibitory control; thus, the RT performance on trials including both language switch and Stroop interference, decreased due to the competition between language and Stroop interference for common inhibitory control processes. However, it is important to note that the magnitude of the L1 and L2 language switch costs were symmetrical across congruent and incongruent stimuli. Given the assumption that switching to L1 requires greater inhibitory control than switching to L2, we predicted larger L1 switch costs than L2 switch costs. Furthermore, given the assumption that LC and conflict resolution are mediated by inhibition, we predicted that the need to switch to L1 on incongruent trials would require even greater inhibitory control; thus, language switch cost asymmetries would be larger on incongruent than congruent trials.

The lack of language switch cost asymmetries, leaves open the possibility that the reduced RT performance on trials requiring language and conflict resolution has no connection with shared inhibitory control mechanism between LC and IC. Instead, one may link this result to dual-task demands.

Liu et al. (2019) used a very similar task and reported asymmetrical switch costs for Chinese-English bilinguals in the conflicting context. There might be several reasons why we did not observe similar results. On one hand, the lack of asymmetries could mean that equal inhibition was applied to both languages. In particular, the IC model (Green, 1998) suggests that unbalanced bilinguals with a higher level of proficiency in L1 would experience asymmetrical switch costs due to inhibiting the L1 to a greater extent during L2 processing. Whereas, balanced bilinguals with similar proficiency levels in L1 and L2 would experience symmetrical switch costs since comparable levels of inhibition are required in both languages. Therefore, the symmetrical switch costs in our study, could be accounted to participants’ relatively high proficiency level in the L2, consistent with the switch cost literature based on L2 proficiency (e.g., Costa et al., 2006; Declerck et al., 2012; Jin et al., 2014; Linck et al., 2012; Schwieter & Sunderman, 2008; Verhoef et al., 2009). To investigate this possibility, we divided the sample into high-L2 and low-L2 proficiency groups and we made sure that the groups did not differ in their executive function performance which could possibly complement the inhibitory control need for LC (e.g., Liu et al., 2014, 2016, 2017, 2018). Again, the results of this group analysis showed no group differences in the language switch cost asymmetries.

Moreover, considering that processing the target language requires inhibiting the non-target language (de Bruin et al., 2014; Green, 1998), we predicted that the need to inhibit language would compete for the inhibitory control required for resolving Stroop interference. Specifically, since processing the L2 requires stronger inhibition of the non-target L1 (de Bruin et al., 2014; Green, 1998), we predicted larger Stroop effects on L2 than L1 trials. However, our results showed no Stroop effect asymmetries between the languages. Neither, the results of the group analyses showed any Stroop effect asymmetries between the languages. To summarize, the results of this study showed no evidence for different levels of L1 versus L2 inhibition, that is, reactive inhibition (Green, 1998) during language control. Therefore, the lack of language switch cost asymmetries could mean that the observed switch costs are due to different mechanisms than inhibition (see Bobb & Wodniecka, 2013).

Other results found in this study could shed some light on this possibility. In line with the SCE literature (see Egner, 2007), we observed an interaction between congruency and previous congruency, indicating that the Stroop effects were larger when followed by congruent than incongruent trials. Critically, this effect was modified by the language switch type. Namely, the typical SCE pattern was present on language repetition trials, and disrupted on language switch trials. This result indicates that the ability to adjust performance in response to previous experience was disrupted during language switching. The same effect was mirrored in language switch costs as an impaired ability to switch language on incongruent trials especially when the previous trial was incongruent.

These results indicate that, whatever the underlying processes of SCE are, they play a role in language control. For example, Grundy et al. (2017) interpreted SCEs as the ability to disengage attention from previous trial information. That is, a smaller magnitude of SCE reflects an enhanced ability to disengage attention. Using a flanker task, they compared the SCE magnitudes in bilinguals and monolinguals. Their results demonstrated that SCEs were smaller for bilinguals than monolinguals, indicating that bilinguals had a higher ability to disengage attention from the previous trial (see Goldsmith & Morton, 2018). The authors posited that bilingual LC is mediated by the ability to disengage attention, rather than inhibition.

Furthermore, the group analyses in this study showed diminished SCE magnitude on language repetition trials as well. Specifically, the group analyses showed an interaction between group and language switch type on SCEs. Similar to the findings on the main analyses, the typical SCE pattern was not present on language switch trials for both groups. Whereas, on language repetition trials, the typical SCE pattern was present only for the low-L2 proficiency group, while diminished for the high-L2 proficiency group. As a result, for the low-L2 proficiency group the SCEs were larger on language repetition than switch trials, whereas for the high-L2 proficiency group the SCEs were non-different on language repetition or switch trials.

Considering smaller SCEs as an index of greater ability to disengage attention from previous experience, our results are in a sense consistent with Grundy et al. (2017). Since the authors posit that the ability to disengage attention has a major role in LC, our results showed that this ability was greater in the high-L2 group which theoretically is expected to be more practiced in LC. However, it is important to note that interpreting smaller SCE magnitude as some form of enhanced processing sounds contrary to almost every other account of SCE (see Goldsmith & Morton, 2018).

Other ways of interpreting the interaction between the language and SCEs concerns the Adaptive Control Hypothesis (Green & Abutalebi, 2013). In the Adaptive Control Hypothesis, Green and Abutalebi (2013) expanded the IC model (Green, 1998) to provide a more detailed description of the processes involved in bilingual LC and the implications for cognition. They identified the following eight LC processes: goal maintenance, conflict monitoring, interference suppression, salient cue detection, selective response inhibition, task disengagement, task engagement, and opportunistic planning. According to this model, these control processes adapt to the demands placed on them by the interactional context for language use. The model described the following three such contexts: single-language, dual-language, and dense code-switching. Critically, the model predicts how resolving an interference affects the ability of switching language on the next trial. It is suggested that resolving a conflict would have different effects on the language switch costs for bilinguals from different interactional contexts. For example, the authors predict that bilinguals in a single-language context may not be able to adapt to the control dilemma induced by a language switch trial immediately followed by an interference trial.

Considering that this study’s sample consisted of Turkish-English bilinguals studying in departments where the teaching language is English, presumably, they were using English only at school and Turkish at home and other contexts (since Turkish is the community language in Turkey). Thus, one may evaluate them as single-language bilinguals. One potential interpretation of these findings is that participants’ language control processes associated with the single-language interactional context reduced their flexibility to overcome the control dilemma between resolving conflict and language switching. For this reason, the most impaired language-switching performance was observed on incongruent trials especially when the previous trial was incongruent.

Furthermore, Green (2018), in his extended control process model (see also Green & Wei, 2014), discusses the differences in the breadth of attention associated with different LC states. He suggests to investigate the SCEs as an indicator of the breath of attention (Green, 2018, p. 9–10). He offers different LC regimes or states for different language contexts and critically, predicts how SCEs interact with LC states. In particular, he argues how bilinguals adopted to different LC regimes are susceptible to previous interference. For example, a single-language or a dual-language context requires a competitive control regime to control the languages. Competitive control leads to narrowing of attention that would result in a greater influence of the previous trial, that is, larger SCE. In contrast, a dense code-switching context requires a cooperative control regime to control the languages. In a dense code-switching context, bilinguals should be more susceptible to interference. Therefore, increased susceptibility to immediate interference in a dense code-switching may be compensated by a greater facility in disengaging from it, leading to an increased breadth of attention. This would result in a smaller influence of the previous trial on the next trial, that is, smaller SCE.

The differences in the SCE magnitudes between groups observed in this study may indicate differences in the breath of attention as suggested by Green (2018). Thus, future studies investigating how the results found in this study interact with bilingual groups from different interactional contexts would help clarify the results.

Conclusion

The bilingual advantage on EC tasks has been linked to their constant need to control the interference of the non-target language. In this sense, the IC model (Green, 1998) which assigns a prominent role to inhibition during LC has received great attention for interpreting the bilinguals’ EC advantage. That is, the bilingual advantage is attributed to the use of reactive inhibitory processes for conflict resolution so that their cognitive system reacts in a more efficient manner to the presence of conflict. This notion has received support by several studies reporting that bilinguals with higher inhibitory control abilities show smaller language switch cost asymmetries.

However, the IC model’s (Green, 1998) predictions could also be interpreted to be the opposite. That is, bilinguals with higher inhibitory control abilities could inhibit L1 more strongly, thus showing larger language asymmetries than those with low inhibitory control abilities. Therefore, in this study, we investigated bilinguals’ language and inhibitory control performance simultaneously in the same participants, and we also examined the language asymmetries and SCE patterns.

Our results showed that when combined with conflict resolution, language switching does not require reactive inhibition, thus we observed no direct evidence for inhibition during LC. In contrast, it seems other EC processes (e.g., attentional processes) may have a prominent role in bilingual LC. Therefore, the bilingual advantage on EC tasks should not be solely linked to the inhibition demands of language control.

To summarize, this study showed that investigating the SCE pattern of congruency effects may be very informative in determining the underlying mechanisms of LC. Thus, there is a need for future studies investigating how the results found in this study interact with different bilingual language interactional contexts.

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Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Mevla Yahya

Supplemental material

Supplemental material for this article is available online.

Author biographies

Mevla Yahya is a Ph.D. student at Istanbul University, Psychology Department. At the same time, she is a Teaching Assistant at International Balkan University, Psychology Department. Her research interest focuses on underlying cognitive mechanisms of bilingual language control.

Arzu Özkan Ceylan is an Assistant Proffesor at Hacettepe University, Psychology Department. She mainly conducts ERP/behavioral research on attention, inhibition and lately on bilingual language control.

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