The cognitive architecture of human language processing has been studied for decades, but using computational modeling for such studies is a relatively recent topic. Indeed, computational approaches to language processing have become increasingly popular in our field, mainly due to advances in computational modeling techniques and the availability of large collections of experimental data. Language learning, particularly child language learning, has been the subject of many computational models. By simulating the process of child language learning, computational models may indeed teach us which linguistic representations are learnable from the input that children have access to (and which are not), as well as which mechanisms yield the same patterns of behavior that are found in children's language performance.

The computational BIA+ model (Dijkstra & Van Heuven, 2002) has provided a useful account for bilingual word recognition, while the verbal (pre-quantitative) RHM (Kroll & Stewart, 1994) has often served as a reference framework for bilingual word production and translation. According to Brysbaert and Duyck (2010), a strong need is felt for a unified implemented account of bilingual word comprehension, lexical-semantic processing, and word production. With this goal in mind, we built a localist-connectionist model, called Multilink, which integrates basic assumptions of both BIA+ and RHM. It simulates the recognition and production of cognates (form-similar translation equivalents) and non-cognates of different lengths and frequencies in tasks like monolingual and bilingual lexical decision, word naming, and word translation production. It also considers effects of lexical similarity, cognate status, relative L2-proficiency, and translation direction. Model-to-model comparisons show that Multilink provides higher correlations with empirical data than both IA and BIA+ models.

The contribution that Ton Dijkstra has made to the field of bilingualism, with his colleagues over the years, is beyond measure. He has advanced our field with the thoughtful and thought-provoking models of the bilingual lexicon he has put forward, and with the vast empirical data he and his colleagues have collected from numerous bilinguals, using a variety of experimental methods. This paper by Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (2018) is no exception. It comprises a thoughtful and detailed description of a new model, Multilink, and provides relevant information regarding the context in which the model was developed, its assumptions, its successes and challenges. The model is the first to consider aspects of both word production and word recognition, as well as of word translation, and to explicitly address how translation equivalents that share both form and meaning – cognates – may be processed. Also addressed are word-frequency concerns, including differences in word frequency that are dependent on language proficiency and exposure. The assumptions that are made within the model are clearly laid out, and less-than-ideal decisions that needed to be made are acknowledged. The authors conclude that a model like Multilink could be the basis for a general model of the mental lexicon. In that, they promote the view taken in Libben, Goral, and Libben (2017) that the bilingual – not the monolingual – lexicon should be considered as the default.

The Multilink model that Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (2018) present is an excellent example that connects empirical patterns obtained from behavioral studies with mechanisms that can be implemented in computational models. We have previously argued that implementation of computational models is important because it forces the researchers to be explicit about assumptions and to specify parameters and variables that may be absent in verbal models. The Multilink model, along with BIA/BIA+ and many other models, provides concrete hypotheses regarding the role of variables such as word frequency, word length, orthographic similarity, and phonological neighborhood for researchers to test and verify against empirical data (see examples in the special issue on computational modeling published in this journal; Li, 2013).

How are the two lexicons of a bilingual represented and how do they interact during language processing? These questions are central to bilingualism and have been the topics of a large number of studies. Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, De Korte & Rekke (in press) put forward a model that tries to capture people's behavior in several tasks mostly involving reading, with the exception of word translation. A praise-worthy feature of the model is that it is computationally implemented following a local-connectionist architecture. The model is then used to quantitatively account for reaction time patterns on various tasks, with considerable success.

A major benefit of computational models is their ability to demonstrate which theoretical assumptions are truly necessary to explain a pattern of data. Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (in press) have impressively shown with Multilink that it is possible to account for a range of findings from bilingual lexical decision, word naming, and forward and backward translation tasks with an integrated lexicon, without lateral connections between translation equivalents, and without inhibition. In this commentary, we consider the applicability of the current model to other multilingual language production tasks, and note where the model's assumptions might need revision as its scope is expanded.

In their keynote paper, Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (2018) present a computational model of bilingual word recognition and translation, Multilink, that integrates and further refines the architecture and processing principles of two influential models of bilingual word processing: the Bilingual Activation Model (BIA/BIA+) and the Revised Hierarchical model (RHM). Unlike the earlier models, Multilink has been implemented as a computational model so its design principles and assumptions can be compared with human processing data in simulation studies – which is an important step forward in model development and refinement. But Multilink also leaves behind an important theoretical advancement that was touched upon in extending BIA to BIA+ (Dijkstra & Van Heuven, 2002): how linguistic context influences word processing. In their presentation of BIA+, Dijkstra and Van Heuven (2002) hypothesized that syntactic and semantic aspects of sentence context may affect the word identification system. Theoretically, this was an important step forward, as none of the bilingual word processing models (and few monolingual word processing models, for that matter) had incorporated linguistic context, and at that time only a handful of empirical studies had examined how linguistic context affects bilingual word processing. However, in the past 15 years a significant body of empirical work has been published that examines how semantic and syntactic information in sentences impacts word processing in bilinguals. These important insights are not incorporated in the Multilink model.

In their keynote article, Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, De Korte & Rekke (2018) propose a new model that aims to integrate and take care of the possible shortcomings of both the Revised Hierarchical Model (RHM, Kroll & Stewart, 1994) and Bilingual Interactive Activation model (BIA and several of its later versions, Dijkstra & Van Heuven, 1998). They begin their proposal by examining the objections/issues raised by Brysbaert and Duyck (2010) on RHM. It is well known that RHM is a developmental, production-based model which mostly predicted performance on translation-based tasks albeit with different predictions for translation recognition vs. production with regard to second language proficiency; BIA being a connectionist model of bilingual word recognition majorly emphasized on language non –selective selection and parallel language activation. Having been developed in and around Dutch (English as the second language), it took cognate status and orthographic similarity between words very seriously. Cognate status and orthographic similarity as factors won't count much as theoretical constructs around which a hypothesis could be developed if we look round the diverse types of orthographies and phonologies we find around the world.

Multilink developed by Dijkstra et al. (2018) is a computational model of monolingual and bilingual lexical access in comprehension and production. The non-selective activation of a bilingual's two languages is handled in the model by assuming that bilinguals have an integrated lexicon and that orthographic overlap between the input and the lexical representation drives lexical activation. Hence, an orthographic unit such as the letter T may activate words from multiple languages that contain the letter, resulting in simultaneous activation of multiple languages. This basic mechanism for explaining non-selective activation is similar between Multilink and its predecessors BIA and BIA+.

Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (2018) present in their keynote article a promising computational model of word recognition and word production in monolinguals and bilinguals, called Multilink. We agree with the authors that the model is a “basis for the development of a more general computational model of word retrieval” (Dijkstra et al., 2018). However, it is also important that such a model is universal.

Multilink moves the field a step forward in being able to make clear predictions about how a bilingual's languages interact in a variety of tasks. It addresses some shortcomings of the BIA (Dijkstra, van Heuven & Grainger, 1998) and BIA+ (Dijkstra & van Heuven, 2002) models, which did not explain performance from bilinguals of varied proficiency levels or on translation tasks. However, there are still gains that can be made – Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (2018) themselves point out that there are additional ways in which they plan to expand the model.

One of the cool aspects of the original implementation of the BIA model (van Heuven, Dijkstra & Grainger, 1998) was the discovery that inhibitory connections between language nodes and lexical representations was a necessary feature for the model to be able to simulate the target data set at that time. This demonstrates the importance of computational modeling, a key point of the present target article, since inhibitory connections were postulated to occur only between representations at the same level in the conceptual model (Grainger & Dijkstra, 1992). Top-down inhibition was subsequently dropped in the BIA+ model (Dijkstra & van Heuven, 2002), and the Multilink model of the present target article (Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte & Rekké, 2018) goes one step further by removing all kinds of inhibitory connections, both between and within levels. Instead, the authors of the model propose that bilingual language processing relies on bidirectional excitatory connections between representations at different levels. This is curious given that even more evidence has accumulated in favor of inhibition since the original implementation of the BIA model, both between neighboring lexical representations (i.e., lateral inhibition) and from language membership representations (e.g., language nodes and tags) down to lexical representations. In this commentary, we focus on whether the exclusion of these two inhibitory processes is warranted, and how the inclusion of these processes might benefit future developments of the model.

In their keynote article, Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (2018) introduce the Multilink model representing an integrated bi/multilingual lexicon. This proposal builds upon both previous and recent research on an integrated cognitive architecture underlying the language faculty (for a summary, see e.g., Putnam, Carlson & Reitter, 2018). In our view, the adjustments proposed by the authors are an improvement on previous instantiations of similar models such as those discussed in the present article. In our remarks we explicate how the Multilink model may be further enhanced, by making any appeal to language-specific nodes or representations epiphenomenal. To achieve this, we propose a novel approach to representing language membership as the result of gradient emergent principles that builds upon the integrated lexicon underlying the Multilink model.

Like the BIA model (Dijkstra & van Heuven, 1998; van Heuven, Dijkstra & Grainger, 1998) and the BIA+ model (Dijkstra & van Heuven, 2002), the Multilink model is a symbolic, localist-connectionist, interactive model for lexical processing in the visual domain. In our view, the symbolic nature of Multilink makes it attractive and easily interpretable, even in relation to brain activity (Page, 2000, p. 501; 2017). Symbolic localist-connectionist models have a long tradition and have been applied to many different areas of cognitive research (e.g., Grainger & Jacobs, 1998). As a consequence, a lot is known about their properties and limitations (e.g., Bowers, 2009). These models can also easily be organized hierarchically in a cognitive functional way, and they have a reasonable degree of flexibility while still being falsifiable. Thus, despite the availability of other sophisticated frameworks for modeling language processes, a lot can still be gained from localist models.

Evidence suggests that cultural experiences and learning multiple languages have measurable effects on children's development of executive function (EF). However, the precise impact of how bilingualism and culture contribute to observed effects remains inconclusive. The present study aims to investigate how these factors shape the development of early EF constructs longitudinally, between monolingual and bilingual children at ages 3, 3½ and 4 years, with a set of EF tasks that are uniquely relevant to the effects of bilingualism and cultural practices. We hypothesize that the effects of bilingualism and cultural backgrounds (i.e., Eastern) are based on different, though related, cognitive control processes associated with different EF constructs. Results revealed a significant bilingualism effect on cognitive control processes measuring selective attention, switching, and inhibition; while an effect of culture was most pronounced on behavioral regulation/response inhibition. Contributions of bilingualism and cultural experiences on individual EF constructs across development are discussed.

We investigated whether bilingual language experience over the lifespan impacts women and men in a manner that differentially buffers against age-related declines in executive control. To this end, we investigated whether executive control performance in a lifespan sample of adult women and men were differentially impacted by individual differences in bilingual language experience, assessed using an unspeeded measure of executive control: the Wisconsin Card Sort Test. The results suggested that women showed both the greatest degree of age-related decline across WCST measures, and a greater likelihood than men to express improved performance as a function of increased bilingual experience. We consider implications of this finding for advancing our understanding of the relation between bilingualism and cognition, and also the effects of biological sex on cognitive aging.

We asked whether bilinguals’ benefits reach beyond the auditory modality to benefit multisensory processing. We measured audiovisual integration of auditory and visual cues in monolinguals and bilinguals via the double-flash illusion where the presentation of multiple auditory stimuli concurrent with a single visual flash induces an illusory perception of multiple flashes. We varied stimulus onset asynchrony (SOA) between auditory and visual cues to measure the “temporal binding window” where listeners fuse a single percept. Bilinguals showed faster responses and were less susceptible to the double-flash illusion than monolinguals. Moreover, monolinguals showed poorer sensitivity in AV processing compared to bilinguals. The width of bilinguals’ AV temporal integration window was narrower than monolinguals’ for both leading and lagging SOAs (Biling.: -65–112 ms; Mono.: -193 – 112 ms). Our results suggest the plasticity afforded by speaking multiple languages enhances multisensory integration and audiovisual binding in the bilingual brain.

Studies of the effects of bilingualism on cognition have given results that do not consistently replicate, reflecting at least in part wide differences in criteria for bilingualism and heterogeneity of language combinations within studied samples. We examined the bilingual advantage in attention, working memory and novel-word learning in early sequential Hindi–English bilinguals. We sought to clarify the aspects of cognition that benefit from bilingualism by using multiple measures and a sample sufficiently well-defined to permit independent replication. Bilinguals outperformed monolinguals on response inhibition, novel-word learning and almost all working memory tasks. In contrast, both groups performed comparably on selective attention. Analyses of individual differences showed that bilingual novel-word learning was related to their verbal working memory and ability to inhibit an ongoing action, whereas this was not the case for monolinguals. Results indicate a specific bilingual advantage that is confined to some but not all aspects of cognition.

Language acquisition is characterized by progressive use of inflectional morphology marking verb tense and agreement. Linguistic milestones are also linked to left-brain lateralization for language specialization. We used neuroimaging (fNIRS) to investigate how bilingual exposure influences children's cortical organization for processing morpho-syntax. In Study 1, monolinguals and bilinguals (n = 39) completed a grammaticality judgment task that included English sentences with violations in earlier-acquired (verb agreement) and later-acquired (verb tense/agreement) structures. Groups showed similar performance and greater activation in left inferior frontal region (IFG) for later- than earlier-acquired conditions. Bilinguals showed stronger and more restricted left IFG activation. In Study 2, bilinguals completed a comparable Spanish task revealing patterns of left IFG activation similar to English. Taken together, the findings suggest that bilinguals with linguistic competence at parity with monolingual counterparts have a higher degree of cortical specialization for language, likely a result of enriched linguistic experiences.

This study examines possible crosslinguistic influence on basic word order processing in a second language (L2). Targeting Swedish V2 word order we investigate adult German learners (+V2 in the L1) and English learners (-V2 in the L1) of Swedish who are matched for proficiency. We report results from two offline behavioural tasks (written production, metalinguistic judgements), and online processing as measured by event-related potentials (ERPs). All groups showed sensitivity to word order violations behaviourally and neurocognitively. Behaviourally, the learners differed from the native speakers only on judgements. Crucially, they did not differ from each other. Neurocognitively, all groups showed a similar increased centro-parietal P600 ERP-effect, but German learners (+V2) displayed more nativelike anterior ERP-effects than English learners (-V2). The results suggest crosslinguistic influence in that the presence of a similar word order in the L1 can facilitate online processing in an L2 – even if no offline behavioural effects are discerned.