Semantic-affective interaction

The interaction between semantic and affective priming has been debated in psycholinguistics for over a decade (Hu & Liu, Reference Hu and Liu2019; Jiang et al., Reference Jiang, Xiao and Liu2012; Storbeck & Clore, Reference Storbeck and Clore2008). Traditionally, priming effects have been associated with semantic priming, where response times are faster when targets (e.g., dog) follow semantically related word primes (e.g., cat) than unrelated primes (e.g., table) (Altarriba, Reference Altarriba1992; Neely, Reference Neely, Besner and Humphreys1991). Semantic priming operates on the basis of shared semantic features, independent of emotional attributes.

Fazio et al. (Reference Fazio, Sanbonmatsu, Powell and Kardes1986) were the first to observe that a faster response can occur when the prime and target have the same emotional valence (positive or negative), even in the absence of semantic relatedness. This finding introduced the concept of affective priming.

Research generally supports the notion that semantic and emotional access operate independently and exhibit unique characteristics. For instance, Blair et al. (Reference Blair, Richell, Mitchell, Leonard, Morton and Blair2006) found that individuals with psychopathy showed lower levels of affective priming than a control group, while both groups showed similar levels of semantic priming. Studies employing Event-Related Potential (ERP) and functional Magnetic Resonance Imaging (fMRI) have identified distinct neural regions associated with semantic and affective priming, supporting their separate processing (Eder et al., Reference Eder, Leuthold, Rothermund and Schweinberger2012; Liu et al., Reference Liu, Hu, Peng, Yang and Li2010).

Regarding the causal relationship between semantic and affective meaning, hypotheses propose either semantic priming or affective response priming as the cause of affective priming (Eder et al., Reference Eder, Leuthold, Rothermund and Schweinberger2012; Hu & Liu, Reference Hu and Liu2019). Proponents of semantic priming dominance argue that the spreading activation in a semantic network or a distributed memory system leads to affective priming (Fazio, Reference Fazio2001; Spruyt et al., Reference Spruyt, Houwer, Hermans and Eelen2007). In contrast, proponents of affective response priming believe that the automatic activation of affective attributes themselves underlies affective priming (Houwer et al., Reference Houwer, Hermans, Rothermund and Wentura2002; Klinger et al., Reference Klinger, Burton and Pitts2000).

Studies have demonstrated subliminal affective priming, suggesting that emotional processing may occur prior to semantic processing (Fazio, Reference Fazio2001; Houwer et al., Reference Houwer, Hermans, Rothermund and Wentura2002; S. S. Li & Li, Reference Li and Li2007; Liao & Tao, Reference Liao and Tao2004). This perspective is referred to as the ‘affective primacy hypothesis’, which posits that emotional information is retrieved at the initial stage of word processing (Klauer & Musch, Reference Klauer, Musch, Musch and Klauer2003; Zajonc, Reference Zajonc2000). However, opposing views, such as the ‘cognitive primacy hypothesis’, suggest that semantic processing precedes and is essential for affective processing (Lazarus & Folkman, Reference Lazarus and Folkman1984; Storbeck & Robinson, Reference Storbeck, Robinson and Mccourt2006). For example, Nummenmaa et al. (Reference Nummenmaa, Hyönä and Calvo2010) found faster semantic categorization responses than affective categorization in their experiments, suggesting a cognitive precedence.

Furthermore, some studies argue that both semantic processing and affective processing constitute affective priming effects, as in Eder et al. (Reference Eder, Leuthold, Rothermund and Schweinberger2012). However, the research suggested that semantic and affective variables should be manipulated independently in future studies.

Notably, the ‘cognitive primacy hypothesis’ often depicts affective priming as subordinate to, and much weaker than, semantic priming (Hu & Liu, Reference Hu and Liu2019; Nummenmaa et al., Reference Nummenmaa, Hyönä and Calvo2010). Storbeck and Robinson (Reference Storbeck and Robinson2004) found that affective priming was relatively weak and only occurred when the research stimuli belonged to the same semantic category. In contrast, semantic priming occurred despite variations in task type, stimuli, and stimulus-onset asynchrony (SOA). However, in a replication experiment, Storbeck and Clore (Reference Storbeck and Clore2008) obtained contrary results, showing that only affective priming was observed in the evaluation task. The strength of affective and semantic priming effects is strongly dependent on task type.

In summary, researchers acknowledge the distinctiveness of affective and semantic priming, but there is a lack of consensus as to which takes precedence or is more robust. Furthermore, discussions have primarily focused on monolingual contexts, with limited exploration of cross-language semantic-affective interactions (Cao & Wang, Reference Cao and Wang2018; Jiang et al., Reference Jiang, Xiao and Liu2012; Sianipar et al., Reference Sianipar, Middelburg and Dijkstra2015).

Cross-language lexical representation

Various models have been proposed in bilingual studies to unravel the complexities involved in cross-language lexical processing. Among these models, the Revised Hierarchical Model (RHM) provides insights into the dynamic development of the cognitive system concerning L2Footnote ² acquisition (Kroll & Sholl, Reference Kroll, Sholl and Harris1992; Kroll & Stewart, Reference Kroll and Stewart1994). The RHM delineates critical components, including the lexical and conceptual levels in both languages, the connections between them, and the varying strengths of these connections, represented by solid and dotted lines (see Figure 1). It posits that forward translation (from L1 to L2) can be slower than backward translation (from L2 to L1), primarily due to the greater challenge of mapping L1 words to their L2 equivalents (indicated by the dotted line) compared to the reverse process (indicated by the solid line).

Figure 1.

Revised Hierarchical Model (Adapted from Kroll and Stewart, Reference Kroll and Stewart1994)

Numerous studies have supported the principles of the RHM, consolidating its status as one of the most widely accepted models of bilingual memory representation (Heredia & Altarriba, Reference Paivio, Heredia and Altarriba2014). Nevertheless, the RHM exhibits conspicuous limitations when it comes to explaining affective-semantic interactions. It was originally conceived to address translation asymmetries observed in bilingual individuals. In terms of semantic priming effects, stronger priming is typically observed from L1 to L2 than from L2 to L1, contrary to translation asymmetries. Furthermore, the RHM primarily emphasizes conceptual links between L1 and L2 words. However, associations between words in different languages at the affective level do not follow a linear pattern, as is the case for semantic associations. Several studies on affective priming have reported emotion effects of equal or even greater intensity in L2 than in L1 (Ayçiçeği & Harris, Reference Ayçiçeği and Harris2004; El-Dakhs & Altarriba, Reference El-Dakhs and Altarriba2018; Ferré et al., Reference Ferré, García, Fraga, Sánchez-Casas and Molero2010; Ponari et al., Reference Ponari, Rodríguez-Cuadrado, Vinson, Fox, Costa and Vigliocco2015). Consequently, the RHM proves inadequate for interpreting priming effects associated with affective influences.

Another prominent model of bilingual memory representation is the Distributed Feature Model (DFM). The DFM effectively explains the differences in processing effects across different word types. In particular, concrete words tend to be translated more rapidly than abstract words. According to Paivio (Reference Paivio1986), concrete translation pairs exhibit a higher degree of semantic similarity than abstract pairs, with the former sharing more conceptual nodes in the DFM. Similarly, Li (Reference Li2018) reported that, among the three word categories (nouns, verbs, and adjectives), nouns elicited stronger semantic priming effects than verbs, which, in turn, elicited stronger effects than adjectives. These findings support the notion of a descending order of concreteness among these categories.

The DFM, with its concreteness effect phenomenon, underscores that concrete words are generally easier to encode, store, retrieve, and translate than abstract words (Altarriba, Reference Altarriba, Heredia and Altarriba2014). However, our particular interest lies in the application of the DFM to emotion words, or more specifically, the emotion effect. Intriguingly, emotion words, despite receiving low concreteness ratings (Altarriba et al., Reference Altarriba, Bauer and Benvenuto1999), consistently exhibit processing advantages over neutral words, including faster translation between languages. This observation raises questions about whether the representation of emotion words in L1 and L2 is consistent with the DFM. Furthermore, emotion words have been shown to induce backward priming effects (from L2 to L1) or to evoke emotions with similar intensity in L2 as in L1 among unbalanced bilinguals and late L2 learners (Ayçiçeği & Harris, Reference Ayçiçeği and Harris2004; El-Dakhs & Altarriba, Reference El-Dakhs and Altarriba2018; Ferré et al., Reference Ferré, García, Fraga, Sánchez-Casas and Molero2010; Ponari et al., Reference Ponari, Rodríguez-Cuadrado, Vinson, Fox, Costa and Vigliocco2015). These findings challenge the predictions of current models.

In summary, although the RHM and the DFM are prominent theoretical models of bilingual memory representation, they do not fully account for the unique characteristics of emotion word representation. The RHM fails to differentiate between word types, while the DFM, although providing a more comprehensive perspective than the RHM, falls short in capturing the nuanced features associated with emotion words. Both models primarily focus on the extent of semantic activation and overlap between words in different languages. However, the processing of emotion words is complex due to the intricate interplay between semantic effects and affective influences. As Pavlenko (Reference Pavlenko2005) points out, emotion words necessitate the separation of conceptual and semantic representations.

Models of emotion word processing, such as the Spreading Activation Model (SAM, Fazio et al., Reference Fazio, Sanbonmatsu, Powell and Kardes1986), primarily explore the psychological mechanisms underpinning the affective aspects of words within a monolingual mental lexicon. However, none of these models has effectively incorporated the complex interaction between semantic effects and affective influences in cross-language emotion word representation.

In conclusion, existing theoretical models do not comprehensively address the issue of cross-language emotion word representation. The interplay between semantic and emotional access in mixed-language contexts challenges the predictability of effects produced by emotion primes, thus complicating the applicability of these models. Therefore, it is imperative to distinguish the emotion category within the bilingual mental lexicon and to investigate its distinctive patterns in cross-language processing.

Cross-language emotion word processing

Research on emotion processing in bilinguals has yielded a complex and multifaceted picture. Bilinguals often show a preference for using their second language (L2) when discussing sensitive or anxiety-provoking topics, as observed in classic studies such as Bond and Lai's (Reference Bond and Lai1986), where Chinese undergraduates gave more elaborate responses in L2 to embarrassing questions. Dewaele's (Reference Dewaele2004) investigation into emotional responses to swear and taboo words among bilinguals showed that such words elicited stronger emotions in the native language (L1), which gradually decreased in later-acquired languages. Similar trends were found for the phrase “I love you” among adult multilinguals. Eilola and Havelka (Reference Eilola and Havelka2011) and Caldwell-Harris et al. (Reference Caldwell-Harris, Tong, Lung and Poo2011) studied skin conductance responses (SCRs) in different linguistic contexts. They found higher SCRs for negative/taboo words in L1 than in L2. These findings are consistent with the Foreign Language Effect, which suggests that words in a less familiar language (e.g., L2) tend to carry less emotional weight (Altarriba, Reference Altarriba, Heredia and Altarriba2014; Rosselli et al., Reference Rosselli, Velez-uribe, Ardila, Ardila, Cieślicka, Heredia and Rosselli2017).

However, the existing literature is not entirely consistent. Some studies, such as Anooshian and Hertel (Reference Anooshian and Hertel1994), found higher recall rates for emotion words than neutral words exclusively in the native language, suggesting an L1 advantage in memory tasks. Conversely, Ayçiçeği and Harris (Reference Ayçiçeği and Harris2004) reported a stronger emotion-memory effect for negative words in the L2. They argued that L2 words retain rich emotional associations for bilinguals.

The variability in research findings can be attributed to numerous factors, as highlighted by Rosselli et al. (Reference Rosselli, Velez-uribe, Ardila, Ardila, Cieślicka, Heredia and Rosselli2017). These factors include the age of language acquisition, the frequency of language use, the context in which each language was acquired, experimental task types, language proficiency, language dominance, the linguistic distance between languages, differences in orthographic systems, bilingual processing strategies, and the nature of emotional stimuli. Investigating the differences in emotional processing between L1 and L2 is a challenging task that requires careful consideration and control of these variables.

The present study

This study delves into the intriguing domain of semantic-affective priming effects, particularly in cross-language conditions. The aim is to determine if and how changes in semantic-affective interaction occur when switching languages. Two specific research questions will be addressed:

1. (1) Do semantic effects and affective influences manifest themselves in single language priming (L1→L1 & L2→L2)? If so, how do they function? In particular, do they function in parallel or interactively?

2. (2) Do semantic effects and affective influences persist in cross-language priming (L1→L2 & L2→L1)? If so, how do they function? In particular, do they function in parallel or interactively?

Based on the insights gained from these questions, we delve deeper into the representation of emotion words in cross-language scenarios. We seek to answer pressing questions such as whether Chinese EFL learners maintain shared or separate storage for the conceptual representation of emotion words in their bilingual mental lexicon. We also investigate whether emotion words in the bilingual mental lexicon are organized hierarchically or exhibit distributed features. Furthermore, we aim to identify additional characteristics related to the cross-language representation of emotion words.

Four experiments were designed to investigate the interaction between semantic and affective priming. Each experiment employed a Lexical Decision Task (LDT) paradigm with a 3 (prime valence: positive, negative, neutral) × 2 (semantic relatedness: related, unrelated) mixed design. The LDT was chosen due to its proven effectiveness in eliciting both semantic and affective priming effects (Eder et al., Reference Eder, Leuthold, Rothermund and Schweinberger2012; Hu & Liu, Reference Hu and Liu2019; Sianipar et al., Reference Sianipar, Middelburg and Dijkstra2015). For example, Storbeck and Robinson (Reference Storbeck and Robinson2004) observed the semantic priming effect using the LDT, and Storbeck and Clore (Reference Storbeck and Clore2008) extended this to include both semantic and affective priming. It is noteworthy that both studies were conducted in English (L1 for the participants).

In the present study, the first experiment used Chinese primes and targets (L1 for the participants), while the second experiment used only English materials (L2 for the participants). In contrast, Experiment III involved priming from L1 primes to L2 targets, and Experiment IV examined the reverse, from L2 primes to L1 targets, with the explicit aim of investigating cross-language priming effects.

Experiment I: L1 (Chinese)

Design

This experiment was designed to examine whether L1 emotion words exerted a priming effect on semantically related or unrelated L1 target words. It was designed and launched using E-prime version 3.0 on laptops. All participants were tested individually in a quiet laboratory. Instructions in Chinese were displayed on the computer screen and were verbally reinforced by the experimenter, who remained in the room during the experiment and provided guidance to ensure that participants maintained a fixed head position, focused on the fixation point, and maintained a consistent distance from the computer screen, etc.

The whole procedure consisted of two phases, with the formal phase containing 72 trials representing 12 repetitions of 6 conditions. In the training phase, participants completed 12 trials with stimuli that were not present in the formal phase, allowing them to familiarize themselves with the procedure. Participants’ accuracy rates and reaction times (RTs) were recorded for analysis. Access to the formal phase was granted upon full comprehension of the task.

On each trial, a fixation cross (“+”) appeared for 300 ms to center participants’ attention. Subsequently, a Chinese prime was displayed for 350 ms, followed by a 150 ms blank screen interval. A Chinese target word then appeared at the same location and remained until the participant responded or up to a maximum of 1000 ms. The SOA was fixed at 500 ms. An inter-trial interval of 1000 ms separated each trial (see Figure 2). Participants were instructed to perform an LDT by pressing “1” for a word or “9” for a nonword.

Figure 2.

Single Trial Demonstration for Experiment I

Results

Mean RTs and accuracy rates were calculated for each participant in each condition. Accuracy rates below 70% were trimmed from the remaining dataFootnote ³. RTs were trimmed by removing responses that were more than 3.0 standard deviations from the overall mean. Thirty-six participants’ data were retained for further analysis (see Table 1).

Table 1.

Mean RTs (in ms) and Accuracy Rates (in brackets) in Experiment I

Two-factor repeated-measure ANOVAs were conducted to analyze accuracy rates. The results indicated a marginally significant interaction between the valence of prime stimuli and prime-target relatedness (F (2,34) = 2.993, p = .064, η² = .150). No significant main effect was observed for valence (F (2,34) = 2.260, p = .120) or relatedness (F (1,35) = .016, p = .899).

Results from repeated-measure ANOVAs on RTs revealed a significant main effect for relatedness (F (1,35) = 14.848, p < .001, η² = .298), with RTs for related conditions being significantly faster than unrelated ones. No significant main effect was found for valence (F (2,34) = .224, p = .800), and no significant 3 (valence) × 2 (relatedness) interaction was detected (F (2,34) = 2.003, p = .150).

Experiment II: L2 (English)

Design

Experiment II replicated the procedure of Experiment I with one key difference – the materials were replaced by English words. A single trial demonstration is shown in Figure 3.

Figure 3.

Single Trial Demonstration for Experiment II

Results

Mean RTs and accuracy rates were computed and calculated for each participant in the same manner as in Experiment I. Data from 35 participants were retained for further analysis (see Table 2).

Table 2.

Mean RTs (in ms) and Accuracy Rates (in brackets) in Experiment II

Two-factor repeated-measure ANOVAs were conducted to analyze accuracy rates. The results revealed a significant main effect for valence (F (2, 33) = 4.509, p = .019, η² = .215). Although no significant main effect was found for relatedness (F (1, 34) = .260, p = .613), the interaction between valence and relatedness was significant (F (2, 33) = 18.655, p < .001, η² = .531).

Results from repeated-measure ANOVAs on RTs revealed a significant main effect for valence (F (2, 33) = 5.584, p = .012, η² = .370). Specifically, RTs for emotion primes were significantly shorter than for neutral primes. No significant main effect was found for relatedness (F (1,34) = .448, p = .511), nor was there any significant 3 (valence) × 2 (relatedness) interaction (F (2,33) = 1.070, p = .363).

Experiment III: L1 (Chinese)→L2 (English)

Design

Experiment III replicated the procedures of the previous experiments, except for the materials. In this experiment, all primes were Chinese words, and all targets were English. A single trial demonstration is shown in Figure 4.

Figure 4.

Single Trial Demonstration for Experiment III

Results

Mean RTs and accuracy rates were computed and calculated for each participant, following the same methodology as in the previous experiments. Data from 36 participants were retained for further analysis (see Table 3).

Table 3.

Mean RTs (in ms) and Accuracy Rates (in Brackets) in Experiment III

Two-factor repeated-measure ANOVAs were conducted to analyze accuracy rates. The results revealed a significant interaction between the valence of the prime stimuli and the prime-target relatedness (F (2,34) = 9.892, p < .01, η² = .368). No statistically significant main effects were found in any of the analyses.

Results from repeated-measure ANOVAs on RTs revealed a significant main effect for relatedness (F (1,35) = 120.285, p < .001, η² = .775). Specifically, RTs for related conditions were shorter than those for unrelated conditions. No significant main effect was found for valence (F (2,34) = .001, p = .999), but the 3 (valence) × 2 (relatedness) interaction was significant (F (2,34) = 37.277, p < .001, η² = .687). In related conditions, RTs were faster for negative than for positive word pairs (p = .042), and faster for positive than for neutral word pairs (p = .037). When the prime and the target were unrelated, RTs were faster for neutral than for positive word pairs (p = .031), and faster for positive than for negative word pairs (p = .004).

Experiment IV: L2→L1

Design

Experiment IV replicated the procedures of the previous experiments, except for the materials. In this experiment, all primes were English words, and all targets were Chinese words. A single trial demonstration is illustrated in Figure 5.

Figure 5.

Single Trial Demonstration for Experiment IV

Results

Mean RTs and accuracy rates were computed and calculated for each participant, using the same methodology as in the previous experiments. Data from 35 participants were retained for further analysis (see Table 4).

Table 4.

Mean RTs (in ms) and Accuracy Rates (in Brackets) in Experiment IV

Two-factor repeated-measure ANOVAs were conducted to analyze accuracy rates. The results revealed a significant main effect for valence (F (2,33) = 4.531, p = .018, η² = .215), indicating that accuracy rates were significantly higher for the positive and neutral groups than for the negative groups. While no significant main effect for semantic relatedness was found (F (1,34) = 2.806, p = .103), the interaction between valence and relatedness was pronounced (F (2,33) = 19.555, p < .001, η² = .542).

Results from repeated-measure ANOVAs on RTs revealed a significant main effect for relatedness (F (1,34) = 48.263, p < .001, η² = .587), with RTs for related conditions being significantly shorter than those for unrelated conditions. No significant main effect was found for valence (F (2,33) = .104, p = .902), but the 3 (valence) × 2 (relatedness) interaction was significant (F (2,33) = 31.395, p < .001, η² = .655). In related conditions, RTs were significantly faster for positive than for negative word pairs (p = .001), and significantly faster for negative than for neutral word pairs (p = .005). When the prime and the target were unrelated, RTs were significantly faster for neutral than for negative word pairs (p = .002), and significantly faster for negative than for positive word pairs (p < .001). In semantically related conditions, RTs were significantly faster in both positive (p < .001) and negative (p = .002) groups than in unrelated conditions, whereas the reverse was true in neutral conditions (p = .001).

Difference tests on Experiments III and IV

To evaluate cross-language priming effects in different directions, we compared the results of Experiments III (Chinese to English) and IV (English to Chinese). The experiments investigated how the direction of priming (from L1 to L2 and vice versa) and the valence of the primes (positive, negative, neutral) influenced the size of the priming effects. To compare Experiments III and IV, the size of priming effects was calculated by subtracting related RTs from unrelated RTs. These values were then analyzed using a 3 (prime valence: positive/negative/neutral) × 2 (priming direction: L1 to L2/L2 to L1) two-factor mixed ANOVA (see Table 5).

Table 5.

Mean Difference on Size of Priming Effects (in ms) and Standard Deviations (M ± SD) between Experiments III and IV

The ANOVAs revealed significant main effects for both valence (F (2,68) = 40.894, p < .001, η² = .372) and priming direction (F (1,69) = 20.297, p < .001, η² = .227). Notably, the magnitude of the effect was significantly larger for emotion primes than for neutral primes. Besides, the effect magnitude of Experiment III (Chinese to English) was significantly larger than that of Experiment IV (English to Chinese).

In addition, a significant interaction between prime valence and priming direction was found (F (2,68) = 14.630, p < .001, η² = .175). In Experiment III, the effect magnitude of negative primes was significantly larger than that of positive primes (p = .001), which was larger than that of neutral primes (p < .001). In Experiment IV, the effect magnitude of positive primes was significantly larger than that of negative primes (p < .001), which was larger than that of neutral primes (p < .001).

These results indicate that cross-language priming effects were significant for all types of primes in Experiment III (from L1 to L2), regardless of valence. However, in Experiment IV (from L2 to L1), only emotion words, as opposed to neutral words, elicited priming effects.

The observed asymmetric magnitudes of priming effects between Experiments III and IV suggest a hierarchical representation of semantic access between L1 and L2, consistent with the findings of Experiments I and II. While L1 priming facilitated the semantic processing of L2 words, the reverse direction, from L2 to L1, revealed that affective attributes in L2 priming served as facilitative factors in the processing of L1 words.

Furthermore, the larger magnitude of the effects induced by emotion primes than by neutral primes suggests that emotion words may access conceptual storage before neutral words. Although semantic access appeared to be hierarchical in the association of L1 and L2 lexicons, emotion words were able to elicit cross-language priming effects in both directions, suggesting that affective attributions are stored, activated, and processed in a distributed manner.

General discussion

The results of this series of experiments provide insights into the complex interplay between semantic and affective priming in a bilingual context and shed light on the dual-access nature of cross-language priming. The results revealed a fascinating pattern: priming within the L1 (Chinese) produced primarily a semantic priming effect, whereas priming within the L2 (English) induced primarily an affective priming effect. However, the dynamics shifted when cross-language priming was examined: L1→L2 priming led to both semantic and affective priming, whereas L2→L1 priming showed cross-language effects primarily for emotion words, where semantic and affective priming interacted and influenced each other.

Several noteworthy findings can be generalized from the analysis of the results.

First, semantic and affective priming can occur independently, challenging the widely held ‘cognitive primacy hypothesis’, which posits that semantic processing is a prerequisite for affective processing. The findings challenge the hypothesized causal relationship between semantic and affective priming and the notion of a rigid sequential order. Furthermore, the results indicate that neither semantic priming nor affective priming is consistently stronger or more stable than the other, suggesting that the relative strength of these priming effects may depend on the direction of priming in cross-language conditions.

Moreover, the present study is consistent with previous research in suggesting that the processing of emotion words differs between the native language (L1) and the second language (L2). In particular, we observed no affective priming effect when the LDT was performed in participants’ dominant language (Chinese), whereas such an effect was detected in a similar task carried out in their second language (English). According to Ayçiçeği and Harris (Reference Ayçiçeği and Harris2004), the “unexpectedness and novelty” of the L2 (English) items may facilitate deeper processing than otherwise. In other words, L2 words, including emotion words, may be perceived as more novel or amusing than L1 words. However, our study suggests that novelty alone does not account for the L2 processing advantage observed for emotion words; the effect appears to be exclusive to emotion words and not a general feature of L2 processing. Consequently, it seems that emotion words somehow enhance the “unexpectedness and novelty” of L2, amplifying the contrast between emotion and neutral words in L2 compared to L1. This finding is further supported by Basnight-Brown (Reference Basnight-Brown2009), who found that emotion words are processed more rapidly in L2 than in L1.

In particular, our findings resonate with the well-documented Foreign Language Effect, which typically refers to a sense of detachment in the perception of L2 words. The effect involves a direct comparison between L2 emotion words and their L1 counterparts, suggesting that L2 emotion words generally convey less emotional weight than their L1 counterparts. However, this study introduces a fresh perspective by comparing the contrast between emotion words and neutral words in L2 with that in L1, and suggests that the distinction between L2 emotion words and L2 neutral words may be more pronounced, successfully triggering affective priming effects.

From the perspective of the bilingual mental lexicon, the observations of semantic priming effects are consistent with the descriptions of RHM. Apparently, in our experiments, conceptual associations appeared to be more robust in L1 than in L2. For example, semantic priming effects were evident in the Chinese experiment (Experiment I), but not in the English experiment (Experiment II). Although both cross-language experiments (Experiments III and IV) showed semantic priming effects, the effects seemed to be conditional in the L2→L1 direction (English to Chinese), especially when the materials were emotion words. In addition, the results of the difference tests showed that the effect magnitude of Experiment III (Chinese to English) was significantly larger than that of Experiment IV (English to Chinese). This asymmetrical cross-language priming effect is consistent with the assumption that access to the L2 (target) from the L1 (prime) is conceptually mediated. In contrast, the reverse process of accessing the L1 (target) from the L2 (prime) occurs primarily at the lexical level for non-proficient learners, as suggested by the RHM (Dimitropoulou et al., Reference Dimitropoulou, Duñabeitia and Carreiras2011; Keatley et al., Reference Keatley, Spinks and Gelder1994).

However, the display of affective associations deviated from the patterns predicted by the RHM. Instead, it appeared to conform to the principles of the DFM. While the DFM is commonly used to explain processing effects related to concrete words, we found that emotion words, despite being perceived as less concrete, exhibited processing advantages over neutral words. This observation led us to infer that cross-language affective attributes are interconnected in a manner similar to conceptual nodes, as depicted in the DFM. When semantic meanings traverse hierarchical links across languages, affective nodes associated with emotion words are activated and propagate along a distinct path, enhancing the affective priming effect.

To some extent, this conclusion is consistent with the Spreading Activation Model (SAM), which depicts a shared network composed of affectively related concepts (Fazio et al., Reference Fazio, Sanbonmatsu, Powell and Kardes1986). However, the SAM primarily addresses monolingual issues and does not explain why the effect magnitude of emotion words from L1 to L2 is not consistently larger than that from L2 to L1, as reported in various studies (Ayçiçeği & Harris, Reference Ayçiçeği and Harris2004; Chan et al., Reference Chan, Ybarra and Schwarz2006; El-Dakhs & Altarriba, Reference El-Dakhs and Altarriba2018; Ferré et al., Reference Ferré, García, Fraga, Sánchez-Casas and Molero2010; Klauer & Musch, Reference Klauer, Musch, Musch and Klauer2003; Ponari et al., Reference Ponari, Rodríguez-Cuadrado, Vinson, Fox, Costa and Vigliocco2015).

In general, prevailing models of the bilingual mental lexicon primarily emphasize semantic activation across languages, often neglecting the impact of affective factors. Conversely, models dedicated to affective priming tend to focus on monolingual aspects, thereby overlooking the interplay of cross-language influences and semantic-affective interactions. In light of these limitations, the present study introduces a dual-access construct to illustrate how semantic access and emotional access interact in a mixed-language context.

The construct underscores that semantic access and emotional access operate independently, yet concurrently, and interact with each other. It is therefore aptly referred to as a ‘dual-access’ construct. Within this framework, semantic access exhibits hierarchical properties, with L1 priming consistently showing greater strength than L2 priming, and L1→L2 priming being stronger than L2→L1 priming, illustrating the hierarchical nature. At the same time, emotional access is depicted as distributed, as indicated by the presence of affective priming effects even when code-switching to a foreign language.

Furthermore, it is important to note that the interaction between semantic priming and affective priming within this construct is mutually facilitative. According to the results of Experiment III, related-emotion pairs yielded significantly faster response times than unrelated-emotion pairs, indicating a cross-language congruency effect in L1 to L2 priming for emotion words. Although the effect was also found for neutral words, it appeared to be much weaker. In Experiment IV, the difference in effect magnitude between emotion words and neutral primes was further amplified, with the congruent priming effect being observed exclusively in emotion words. Given that the primes in this case were in L2, this suggests a greater disparity between emotion words and neutral words in L2 than in L1 in cross-language priming conditions. Meanwhile, the semantic priming effects of emotion words suggest that affective influences facilitate semantic access. Difference tests supported this observation, showing that the effect magnitude of semantic priming was significantly larger for emotion primes than for neutral primes, providing evidence for affective facilitation.

In conclusion, the results of the present study showed that both semantic effects and affective influences can manifest themselves in cross-language priming. It appears that semantic and affective features interact in a facilitatory manner, reinforcing the congruency effects produced by emotion words. Existing classical models fall short in comprehensively explaining the complexity of cross-language priming of emotion words. To address this shortcoming, our study introduces the dual-access construct, which lays emphasis on the mutually facilitative relationship between semantic priming and affective priming.