The process of translating a word often does not result in a simple one-to-one mapping between a word in the first language (L1) and a corresponding translation in the second language (L2). In fact, previous research on English and Spanish, English and Dutch, and English and German has demonstrated that many words have a one-to-many mapping (Eddington, Degani & Tokowicz, Reference Eddington, Degani and Tokowicz2012a; Prior, MacWhinney & Kroll, Reference Prior, MacWhinney and Kroll2007; Tokowicz, Kroll, de Groot & van Hell, Reference Tokowicz, Kroll, de Groot and van Hell2002). This one-to-many mapping between a source language and target language is referred to as translation ambiguity.

One cause of translation ambiguity is near-synonymy in the target language, as in the case of the word shy, which has two equivalents in German: schüchtern and scheu. Both of these correspond to the same meaning. Translation ambiguity is also sometimes due to semantic ambiguity within the source language. For example, the English word odd means an uneven number and strange, and each of these meanings can be translated into a distinct German word (ungerade for the number sense and merkwürdig for the strange sense). We refer to the former as synonym translation-ambiguous words and to the latter as meaning translation-ambiguous words.

Unlike translation-unambiguous words for which there is only one possible translation, a speaker translating a translation-ambiguous word could activate multiple translations. This parallels within-language semantic ambiguity, which occurs when a single word has multiple meanings. Previous research on semantic ambiguity has demonstrated that context is important in resolving the meanings of ambiguous words (e.g., Duffy, Morris & Rayner, Reference Duffy, Morris and Rayner1988; Hogaboam & Perfetti, Reference Hogaboam and Perfetti1975; Onifer & Swinney, Reference Onifer and Swinney198l; Simpson, Reference Simpson1981). Additionally, other factors such as the dominance of the meanings play a significant role in meaning activation (e.g., Duffy et al., Reference Duffy, Morris and Rayner1988; Hogaboam & Perfetti, Reference Hogaboam and Perfetti1975). Further, meaning dominance and context interact in lexical ambiguity resolution such that the more dominant meaning may be primed by either the dominant or subordinate context, whereas the subordinate meaning typically is primed only by the subordinate biasing context (Duffy et al., Reference Duffy, Morris and Rayner1998; Hogaboam & Perfetti, Reference Hogaboam and Perfetti1975).

More recently it has also been noted that the semantic similarity between the various meanings of an ambiguous word may influence lexical ambiguity resolution and processing (Armstrong & Plaut, Reference Armstrong and Plaut2008; Azuma & Van Orden, Reference Azuma and Van Orden1997; Klepousniotou, Titone & Romero, Reference Klepousniotou, Titone and Romero2008; Rodd, Gaskell & Marslen-Wilson, Reference Rodd, Gaskell and Marslen-Wilson2002). For example, polysemous words, which have highly related senses (e.g., paper), are responded to more quickly in a lexical decision task than unambiguous words and homonymous words (e.g., bank), which are words with unrelated senses or meanings (Armstrong & Plaut, Reference Armstrong and Plaut2008; Azuma & Van Orden, Reference Azuma and Van Orden1997; Rodd et al., Reference Rodd, Gaskell and Marslen-Wilson2002).

Because context, meaning frequency, and semantic similarity all play a role in processing ambiguous words within a language, these factors may also influence processing translation-ambiguous words across two languages (e.g., Degani & Tokowicz, Reference Degani and Tokowicz2010b). Research to date has shown that out of context, translation-ambiguous words are translated more slowly and less accurately than translation-unambiguous words (Degani & Tokowicz, Reference Degani and Tokowicz2010a; Tokowicz & Kroll, Reference Tokowicz and Kroll2007). Degani and Tokowicz (Reference Degani and Tokowicz2010a) further demonstrated that the translation-ambiguity disadvantage is not simply due to ambiguous words being experienced less frequently because the disadvantage persisted even though they matched the token frequency of ambiguous and unambiguous words precisely in a learning paradigm.

This research has provided important insights into how these words affect processing by individuals at varying levels of proficiency ranging from beginning adult L2 learners (e.g., Degani & Tokowicz, Reference Degani and Tokowicz2010a) to highly-proficient bilinguals (e.g., Boada, Sánchez-Casas, Gavilán, García-Albea & Tokowicz, in press; see Tokowicz & Degani, Reference Tokowicz, Degani, VanPatten and Jegerski2010, for discussion of the potential changes in translation ambiguity effects that may occur with increased proficiency).Footnote ¹ However, examining how other factors such as dominance and context influence the processing of these words is critical to gaining a more complete understanding of the lexical and semantic representations of these words in the bilingual mind. Yet, to date, only a limited number of research studies have examined these factors directly (Elston-Güttler & Friederici, Reference Elston-Güttler and Friederici2005; Elston-Güttler, Paulmann & Kotz, Reference Elston-Güttler, Paulmann and Kotz2005; Elston-Güttler & Williams, Reference Elston-Güttler and Williams2008; Frenck-Mestre & Prince, Reference Frenck-Mestre and Prince1997; Laxén & Lavaur, Reference Laxén and Lavaur2010).

Laxén and Lavaur (Reference Laxén and Lavaur2010, Experiment 3) used a translation recognition task to examine how translation dominance and the semantic similarity between the translations of translation-ambiguous words affect translation recognition. The translation-ambiguous words that had less semantically similar translations (meaning translation-ambiguous words) were responded to more slowly overall compared to words with more semantically similar translations (synonym translation-ambiguous words) and to unambiguous words. Additionally, dominant translations were responded to more quickly than subordinate translations, and the dominance effect was greater for words with less semantically similar translations than for words with more semantically similar translations. The authors suggested that this was due to greater shared representations at the semantic level of representation in memory for the more semantically similar translation-ambiguous words. Nevertheless, this study examined only bilinguals’ decisions on translation-ambiguous words out of context, where ambiguity effects may be exaggerated in comparison to effects in context.

A study by Elston-Güttler et al. (Reference Elston-Güttler, Paulmann and Kotz2005, Experiment 2) compared how less and more proficient German–English bilinguals processed homonyms in a sentence context. They also examined the effects of dominance of the homonyms’ meanings captured by the English translations (e.g., the German word Kiefer means both jaw and pine in German). In the experiment, a sentence biased one meaning of the homonymous word (e.g., jaw), which served as the prime. The prime appeared in the final position of the sentence. After a delay, the target word, which corresponded to the other sense of the homonym (e.g., pine), was presented. Then, participants made a lexical decision to the target word. There were two conditions: (i) a sentence that biased the dominant meaning of the word (e.g., “The sticky candy stuck together his jaw”) with a target that represented the subordinate meaning (e.g., pine), and (ii) a sentence that biased the subordinate meaning (e.g., “The beautiful table was made of solid pine”), with a target that represented the dominant meaning (e.g., jaw). Dominant targets were responded to more quickly than the subordinate targets, demonstrating dominance effects across translated senses of semantically-related words.

A study by Elston-Güttler and Williams (Reference Elston-Güttler and Williams2008) examined how German learners of English process translation-ambiguous words within sentential context. They used polysemous German words such as Blase, which translates to both blister and bubble. Participants read a sentence and decided whether the final word made sense in the context. The German learners of English responded more slowly to the polysemous translations that biased the alternative sense (e.g., “His shoes were uncomfortable due to a bubble”) than to the same words in control sentences (e.g., “She was very hungry because of a bubble”). These findings suggest that all meanings of the ambiguous word were activated, thereby leading to the interference effect. Because this interference effect appeared even though only one translated sense was presented, it suggests that on viewing the English word form (bubble) the bilinguals activated the German word form (Blasen), which in turn activated both meanings and corresponding translations (bubble and blister) (but see Degani, Prior & Tokowicz, Reference Degani and Tokowicz2010, for an alternative explanation).

Although Elston-Güttler and Williams (Reference Elston-Güttler and Williams2008) showed that both meanings of a polysemous word are activated even in biasing context, they did not directly examine the role of translation dominance, which may also influence these effects. Dominance influences ambiguous word processing (Elston-Güttler et al., Reference Elston-Güttler, Paulmann and Kotz2005; Laxén & Lavaur, Reference Laxén and Lavaur2010) as does semantic similarity between the translations (Laxén & Lavaur, Reference Laxén and Lavaur2010), and these factors may interact with each other. The current study examines these three factors: word context, semantic similarity/source of translation ambiguity, and translation dominance/frequency.Footnote ² Thus, this study provides an investigation of how each of these elements affects processing of translation-ambiguous words, and how these factors interact with one another, within a word translation recognition task.

Despite the fact that translation ambiguity consistently affects processing, the majority of bilingual models (e.g., Dijkstra & van Heuven, Reference Dijkstra and van Heuven2002; Jacquet & French, Reference Jacquet and French2002) have not been adapted to take translation ambiguity effects into consideration. One such adaptation was presented by Laxén and Lavaur (Reference Laxén and Lavaur2010), who described a modified version of the Distributed Conceptual Feature Model (DCFM; de Groot, Reference de Groot1992; van Hell & de Groot, Reference van Hell and de Groot1998) to explain their findings. According to the original DCFM, a word's semantic information is distributed across nodes in a shared (L1/L2) semantic level of representation. The speed and accuracy of translating a word is influenced by how many shared nodes there are between a word in L1 and its corresponding translation in L2. Concrete words and cognate translations are thought to share more nodes at the semantic level across languages than abstract words and noncognate translations, respectively. Specifically, the DCFM predicts that the greater the number of shared nodes between the L1 and L2, the faster and more accurate processing will be. The model therefore provides an explanation of why concrete words and cognates are translated more quickly and accurately than abstract words and noncognates, respectively (e.g., de Groot, Reference de Groot1992; van Hell & de Groot, Reference van Hell and de Groot1998; see also Schoonbaert, Duyck, Brysbaert & Hartsuiker, Reference Schoonbaert, Duyck, Brysbaert and Hartsuiker2009, for a similar explanation within the context of translation priming). Laxén and Lavaur's (Reference Laxén and Lavaur2010) extension of the DCFM included translation-ambiguous words so that more than one translation could be selected at the lexical level of representation. According to this model, the more semantically similar the possible translations are (as for synonym translation-ambiguous words), the greater the number of shared nodes will be active, leading to faster processing (see Figure 1a). When translations are less semantically similar (as for meaning translation-ambiguous words) there will be less shared activation at the semantic level and therefore processing will be slower (see Figure 1b).

Figure 1.

Laxén and Lavaur's (Reference Laxén and Lavaur2010) extension of the Distributed Conceptual Feature Model (de Groot, Reference de Groot1992; van Hell & de Groot, Reference van Hell and de Groot1998). Panel (a) depicts synonym translation-ambiguous words, and panel (b) depicts meaning translation-ambiguous words.

The Revised Hierarchal Model (RHM; Kroll & Stewart, Reference Kroll and Stewart1994) has also been extended to include translation ambiguity (Kroll & Tokowicz, Reference Kroll, Tokowicz and Nicol2001; see Figure 2). The original RHM makes two key assumptions. First, bilinguals have stronger lexical links from L2 words to their translations in L1 than the reverse. Second, there are stronger bidirectional links between concepts and corresponding words in L1 than between concepts and corresponding words in L2. As a result of these asymmetries, translation from L2 to L1 is thought to be mediated via lexical associations. By contrast, translation from L1 to L2 is conceptually mediated, such that access to the translation is through concepts or meanings (from the L1 word, to the concept, to the L2 word).

Figure 2.

The Revised Hierarchical Model adapted to account for translation-ambiguous words (Kroll & Tokowicz, Reference Kroll, Tokowicz and Nicol2001).

Kroll and Tokowicz (Reference Kroll, Tokowicz and Nicol2001) modified the RHM to include translation-ambiguous words, but did not distinguish synonym and meaning translation-ambiguous words. Here, we incorporate these two elements into the RHM and refer to the adapted model as the Revised Hierarchical Model of Translation Ambiguity (RHM-TA). The RHM-TA predicts different outcomes for translation-ambiguous words derived from different sources (synonym vs. meaning). In Figure 3, we portray this model indicating translation ambiguity in the L1 to L2 direction. For synonym translation-ambiguous words, there is a single L1 lexical representation and a single conceptual representation, but this conceptual representation is connected to multiple L2 lexical representations (see Figure 3a). For meaning translation-ambiguous words, there is again a single L1 lexical representation, but more than one conceptual representation; each conceptual representation is connected to a different L2 lexical representation (see Figure 3b). Additionally, for meaning translation-ambiguous words with one highly dominant translation, there would be a stronger connections to the concept for the dominant meaning and to the L2 lexicon for the dominant translation; here, we make the simplifying assumption that the dominant translation across languages corresponds to the dominant meaning within a language, although this may not necessarily be the case because the meaning distribution experienced in L1 will not precisely mirror that in L2.

Figure 3.

The Revised Hierarchical Model of Translation Ambiguity. Panel (a) depicts synonym translation-ambiguous words, and panel (b) depicts meaning translation-ambiguous words.

Based on prior research (e.g. Elston-Güttler et al., Reference Elston-Güttler, Paulmann and Kotz2005; Frenck-Mestre & Prince, Reference Frenck-Mestre and Prince1997; Laxén & Lavaur, Reference Laxén and Lavaur2010) and the RHM-TA framework, we predict that dominant translations will be processed more quickly than subordinate translations. Furthermore, once the meaning is established for meaning translation-ambiguous words, there will be a more one-to-one connection from the L1 word to the L2 word via concepts. This is because the ambiguity for meaning translation-ambiguous words is due to the multiple connections from the L1 lexical level to the conceptual level, but there is a direct mapping from a single concept to a single word from the conceptual level to the L2 lexical level. For example, if the context biased the strange sense of the word odd then the only possible translation would be merkwürdig. In contrast, a context for the word fruit would not limit the possible translations available to a single translation in German; Obst and Frucht would both remain options. Therefore, meaning translation-ambiguous words may have an advantage over synonym translation-ambiguous words in tasks that establish some form of context because the latter will maintain a one-to-many mapping regardless of context. Consistent with this idea, Degani and Tokowicz (Reference Degani and Tokowicz2010a) found that when teaching monolinguals translation-unambiguous and ambiguous Dutch words, the meaning translation-ambiguous words were learned more easily than synonym translation-ambiguous words.

Because in this experiment we directly compare two sources of translation ambiguity in a categorical rather than continuous manner, we chose to demonstrate these two types of ambiguity in the RHM-TA in a more localized fashion. However, this representation may be oversimplified. Instead, this model could be adapted to have a more distributed representation as in the DCFM. For example, polysemous translation-ambiguous words in which the translations are more highly related, the meanings at the conceptual level would overlap to a higher degree.

The goals of the current study are: (i) to better understand how L2 learners process translation-ambiguous words; (ii) to understand how word context, semantic relatedness, and dominance of the translations interact with processing of these words; and (iii) to examine whether word context can disambiguate translation-ambiguous words. We therefore examined differences between the processing of the synonym vs. meaning types of translation-ambiguous words compared to translation-unambiguous words, as well as the differences in processing for dominant and subordinate translations. Because the semantic similarity between different senses of ambiguous words influences processing within a language (Armstrong & Plaut, Reference Armstrong and Plaut2008; Azuma & Van Orden, Reference Azuma and Van Orden1997; Rodd et al., Reference Rodd, Gaskell and Marslen-Wilson2002), in a post-hoc analysis we also examined how the perceived semantic similarity between the multiple translations of the translation-ambiguous words correlates with processing speed.

To examine these issues, we developed a modification of the translation recognition task (e.g. de Groot & Comijs, Reference de Groot and Comijs1995), which we have termed the primed translation recognition task. In this task, bilinguals decide if pairs of words are correct translations; the word pairs are preceded by an unrelated or related prime word. We focused our analyses on the correct translation pairs that were preceded by unrelated vs. related primes; faster reaction times on related than unrelated trials was taken to indicate priming. Initially, we were additionally interested in how the matching of the related prime to the meaning of the translation, for the meaning translation-ambiguous items (e.g., a prime that matched the dominant translation paired with that dominant translation vs. paired with the subordinate translation) may affect processing. However, the manipulation of prime match (match RT: 1259 ms vs. mismatch RT: 1248 ms) did not yield any significant effects, F < 1. Therefore, we examine only the effects of primes that matched the translation meaning and excluded data from trials on which mismatched primes were presented.

Based on the findings of previous studies (e.g., Tokowicz & Kroll, Reference Tokowicz and Kroll2007) and the RHM-TA, we predicted an overall ambiguity disadvantage such that translation-ambiguous words (e.g., trunk) would be responded to more slowly and less accurately than unambiguous words (e.g., art). Based on previous semantic priming studies (see Neely, Reference Neely, Besner and Humphreys1991, for a review), we expected target-translation pairs preceded by related primes (e.g., prime: design; target–translation: ART–Kunst) to result in faster response times than target–translation pairs preceded by unrelated primes (e.g., prime: fiscal; target–translation: ART–Kunst). In line with the RHM-TA, we also predicted that related primes would disambiguate meaning translation-ambiguous words (e.g., prime: paper; target–translation: SHEET–Blatt) and make them easier to process, such that response times for semantically primed meaning translation-ambiguous words would be similar to primed translation-unambiguous words. Based on previous studies within a language (Duffy et al., Reference Duffy, Morris and Rayner1998; Hogaboam & Perfetti, Reference Hogaboam and Perfetti1975) and across languages (Laxén & Lavaur, Reference Laxén and Lavaur2010) we predicted further that dominant translations (e.g., SHY–schüchtern) would result in faster response times than subordinate translations (e.g., SHY–scheu) for both synonym and meaning translation-ambiguous words. We expected that synonym translation-ambiguous words would be responded to more quickly overall than meaning-translation ambiguous words based on the predictions of the modified DCFM and previous research (Laxén & Lavaur, Reference Laxén and Lavaur2010). Lastly, based on the RHM-TA, we expected a translation ambiguity type by relatedness interaction such that related primes would facilitate translation-recognition speed more for meaning translation-ambiguous words than for synonym translation-ambiguous words. This is because a related prime could disambiguate the meaning-translation ambiguous word by restricting the selection to one translation but should not disambiguate a synonym translation-ambiguous word.

Method

Design

This study used a 3 word type (unambiguous, synonym translation-ambiguous, meaning translation-ambiguous) by 2 prime relatedness (related, unrelated) by 2 translation dominance (dominant, subordinate) within-subjects design.

Task order

Following two tasks that did not yield interesting findings, participants completed the primed translation recognition task.Footnote ³ Lastly, participants completed a language history questionnaire (Tokowicz, Michael & Kroll, Reference Tokowicz, Michael and Kroll2004) that details their demographic information, previous language exposure, and self-reported ratings of L1 and L2 proficiency. Additionally, a subset of participants completed form (spelling and sound) and meaning similarity ratings for the German translations of the translation-ambiguous English words.

Participants

Data were analyzed from 26 native English-speaking participants from the University of Pittsburgh and The Pennsylvania State University. An additional 17 participants took part in the study but did not contribute data to the final analyses because they failed to reach 50% accuracy on all three word types in the translation recognition task (unambiguous, meaning translation-ambiguous, and synonym translation-ambiguous words; n = 7) or had exposure to a language other than English before age 10 (n = 10). Participants included in the final analyses had an overall mean accuracy of 85%. All but one of the participants were right handed. Language history questionnaire data are shown in Table 1.

Table 1.

Language history questionnaire data.

Note: The L1 and L2 reading, writing, conversation ability and speech comprehension ability are based on a 1–10 scale, on which 1 indicates the lowest ability level and 10 indicates the highest ability level.

Stimuli

The critical word stimuli consisted of 448 prime–target–translation triplets (see Table 2 for example stimuli; for the complete list of items see Supplementary Materials accompanying this article online, via the journal's webpage at http://journals.cambridge.org/BIL). The critical target words consisted of 32 translation-unambiguous words, 32 meaning translation-ambiguous words, 32 synonym translation-ambiguous words, and 92 filler targets. Translations and their dominance were obtained from English–German/German–English number-of-translations norms obtained by Eddington et al. (Reference Eddington, Degani and Tokowicz2012a), who used the same method to elicit and code translations as Tokowicz et al. (Reference Tokowicz, Kroll, de Groot and van Hell2002). Specifically, individuals provided the first translation they thought of for each word, and the number of correct translations across individuals was used to determine translation ambiguity. Words were considered translation ambiguous if they elicited two or more German translations and translation unambiguous if they elicited a single German translation. The most frequently provided translation was considered the dominant translation. Using dictionaries (Biographisches Institut GmbH, 2012; LEO GmbH, 2012), we classified words as synonym translation ambiguous if they had German translations with similar senses; we classified words as meaning translation ambiguous if they had German translations with dissimilar meanings.Footnote ⁴

Table 2.

Example stimuli.

For each critical target, there was a corresponding related and unrelated prime. All translation-ambiguous targets (meaning and synonym types) had two corresponding translations. For the meaning-ambiguous targets, there were related and unrelated primes that corresponded to each distinct meaning/translation. However, because the synonym translation-ambiguous targets essentially only have one meaning (although there may be slight differences in usage of the words) there was only one related and unrelated prime for each target. Translation-unambiguous targets were paired with a single translation and also had one related and unrelated prime. Prime and target pairs had been normed previously for relatedness by a group of native English speakers (Eddington, Reference Eddington2009). Stimulus characteristics by condition are given in Table 3, prime characteristics are given in Table 4, and relatedness ratings are given in Table 5.

Table 3.

Characteristics of targets and translations.

Note: English word frequencies are the log of the SUBTL frequency norms per million (Brysbaert & New, Reference Brysbaert and New2009) downloaded from the English Lexicon Project (Balota et al., Reference Balota, Yap, Cortese, Hutchison, Kessler, Loftis, Neely, Nelson, Simpson and Treiman2007). English orthographic neighborhood (ortho. N) size values were obtained from the English Lexicon Project (Balota et al., Reference Balota, Yap, Cortese, Hutchison, Kessler, Loftis, Neely, Nelson, Simpson and Treiman2007). German word frequencies are the log of the SUBTLEX-DE word frequency norms per million (Brysbaert et al., Reference Brysbaert, Buchmeier, Conrad, Jacobs, Bölte and Böhl2011). Form similarity ratings between the English and German word pairs were computed using the normalized for word length Levenshtein metric as described in Schepens et al. (Reference Schepens, Dijkstra and Grootjen2012).

Table 4.

Characteristics of related and unrelated primes by type.

Note: English word frequencies are the log of the SUBTL frequency norms (Brysbaert & New, Reference Brysbaert and New2009) downloaded from the English Lexicon Project (Balota et al., Reference Balota, Yap, Cortese, Hutchison, Kessler, Loftis, Neely, Nelson, Simpson and Treiman2007). English orthographic neighborhood (ortho. N) size values were obtained from the English Lexicon Project (Balota et al., Reference Balota, Yap, Cortese, Hutchison, Kessler, Loftis, Neely, Nelson, Simpson and Treiman2007). German word frequencies are the log of the SUBTLEX-DE word frequency norms (Brysbaert et al., Reference Brysbaert, Buchmeier, Conrad, Jacobs, Bölte and Böhl2011). Form similarity ratings between the English and German word pairs were computed using the normalized for word length Levenshtein metric as described in Schepens et al. (Reference Schepens, Dijkstra and Grootjen2012).

Table 5.

Mean semantic similarity ratings for prime–target pairs.

Note: Semantic similarity ratings were based on a scale from 1–7 on which 1 indicates that the two words are highly unrelated and 7 indicates that the words are highly related. These ratings were obtained from Eddington (Reference Eddington2009). Twenty-four participants rated prime–target pairs for relatedness. There were two related and unrelated primes that correspond to each distinct meaning for the meaning translation-ambiguous target words. Only one related and unrelated pair was presented for the filler, unambiguous, and synonym translation-ambiguous targets.

However, because of the limited selection of items there were some differences in word characteristics of the prime–target–translation triplets across conditions. Item characteristic differences were regressed out of the analyses by items. We address these differences further in the results section.

The conditions were counterbalanced across list versions such that each target word was presented only once to each participant. All the critical targets were paired with their corresponding correct translation and therefore constituted “yes” trials. The filler targets consisted of targets paired with incorrect translations and therefore constituted “no” trials. There were equal numbers of related and unrelated primes paired with the filler and critical targets within each list. Data from only the “yes” trials were analyzed.

Procedure

In the primed translation recognition task, participants were asked to decide if English–German word pairs were translation equivalents. Each English–German pair was preceded by a related or unrelated prime. Participants first saw a fixation cross for 1000 ms, then a prime for 250 ms, followed immediately by a target–translation pair. Participants had up to 3500 ms to respond using a button box (leftmost key for “no” and rightmost key for “yes”). Participants had four breaks during the task to rest their eyes. The E-prime software package (Schneider, Eschman & Zuccolotto, Reference Schneider, Eschman and Zuccolotto2010) was used to present the stimuli and to record reaction time (RT) and accuracy.

Results

Data trimming

One word was removed from the data analysis because it was mislabeled as being a synonym type rather than meaning type ambiguous word.Footnote ⁵ Following this, response latencies that were shorter than 300 ms or longer than 3000 ms, and all response latencies 2.5 standard deviations above or below each participant's mean were excluded from analyses. These procedures led to the removal of 3.32% of the data.

Analysis approach

We conducted three sets of analyses to examine different aspects of our results. We first explored overall ambiguity effects with 2 word type (ambiguous vs. unambiguous) by 2 prime relatedness (related vs. unrelated) ANOVAs. We then investigated word type effects using 3 word type (synonym, meaning, unambiguous) by 2 prime relatedness (related vs. unrelated) ANOVAs. Finally, we investigated translation dominance effects using 2 ambiguity type (synonym vs. meaning) by 2 translation dominance (dominant vs. subordinate) by 2 prime relatedness (related vs. unrelated) ANOVAs. Mean RT and accuracy data are analyzed by participants (F ₁) and by items (F ₂).

To assess the matching of prime, target, and translation characteristics across conditions, we examined: word length; log SUBTL English word frequency (per million words; Brysbaert & New, Reference Brysbaert and New2009) for primes and targets; log SUBTLEX-DE German word frequency (per million words; Brysbaert, Buchmeier, Conrad, Jacobs, Bölte & Böhl, 2011) for translations; prime and target orthographic neighborhood sizes; and target–translation form similarity (Schepens, Dijkstra & Grootjen, Reference Schepens, Dijkstra and Grootjen2012) in three analyses. These three analyses are congruent with our three sets of primary participant and items analyses. Any factor that differed across conditions in main effects or interactions using a conservative alpha level of .15 was regressed out of the item RT and accuracy data; the saved residual values were treated as the dependent measures in the analyses by items (see Table 6).Footnote ⁶

Table 6.

Analyses of item characteristics.

Note: F and p values and means are reported for all effects significant at the p < .15 level. All other ps ≥ .224.

Analyses

To examine overall ambiguity effects, both meaning and synonym type translation-ambiguous words were collapsed in 2 word type (ambiguous vs. unambiguous) by 2 prime relatedness (related vs. unrelated) repeated measures ANOVAs (see means in Table 7).

Table 7.

Means and Standard Deviations (SDs) for the reaction time (RT) and accuracy analyses.

Note: Negative priming values indicate reverse priming.

Overall, translation-ambiguous words were recognized more slowly (1284 vs. 1017 ms) (F ₁(1,25) = 173.93, MSE = 10645.95, p < .001; F ₂(1,298) = 21.46, MSE = 69781.10, p < .001) and less accurately (77 vs. 94%) (F ₁(1,25) = 156.21, MSE = 0.004, p < .001; F ₂(1,310) = 16.89, MSE = 0.061, p < .001) than translation-unambiguous words.Footnote ⁷ Target–translation pairs that were preceded by related primes were responded to more quickly than target–translation pairs preceded by unrelated primes (1119 vs. 1182 ms), F ₁(1,25) = 11.88, MSE = 8714.16, p < .01; F ₂(1,298) = 5.85, MSE = 69781.10, p < .016. No main effects of relatedness were found in the accuracy analyses, F ₁(1,25) = 2.01, MSE = 0.009, p = .169; F ₂ < 1. The ambiguity by relatedness interaction was not significant for the analyses of RT (Fs < 1) or accuracy, F ₁(1,25) = 2.88, MSE = 0.008, p = .102; F ₂ < 1.

To examine word type effects, we further analyzed the three word types in 3 word type (synonym, meaning, unambiguous) by 2 prime relatedness (related vs. unrelated) ANOVAs on RT and accuracy. Again, translation-ambiguous words were recognized more slowly (M _synonym = 1284 ms, M _meaning = 1268 ms, M _unambiguous = 1017 ms) (F ₁(2,50) = 54.45, MSE = 21902.58, p < .001; F ₂(1,292) = 9.90, MSE = 69052.70, p < .001) and less accurately than translation-unambiguous words (M _synonym = 79%, M _meaning = 75%, M _unambiguous = 94%), F ₁(2,50) = 41.21, MSE = 0.012, p < .001; F ₂(1,304) = 9.34, MSE = 0.061, p < .001. Examination of the 95% confidence intervals for RT and accuracy revealed that the means for the form and meaning ambiguous words were similar, and were both different than those for the translation-unambiguous words. Also, processing was faster following related than unrelated primes (1151 vs. 1228 ms) (F ₁(1,25) = 12.23, MSE = 18920.18, p = .002; F ₂(1,292) = 8.85, MSE = 69052.70, p < .01), however this priming effect was not significant in the accuracy analyses, F ₁(1,25) = 1.83, MSE = 0.024, p = .189; F ₂(1,304) = 1.15, MSE = 0.061, p = .29. Word type and relatedness did not interact in either analysis, for RT: F ₁(2,50) = 1.18, MSE = 16162.06, p = . 316; F ₂ < 1; for accuracy: Fs < 1.

In the following analyses, we directly compare the ambiguous word types and the differing effects of priming and dominance on synonym and meaning translation-ambiguous words. To accomplish this, we tested only the translation-ambiguous words, and used 2 ambiguity type (synonym vs. meaning) by 2 translation dominance (dominant vs. subordinate) by 2 prime relatedness (related vs. unrelated) repeated measures ANOVAs on the RT and accuracy data. Missing cells (n = 3) were replaced with the mean.

We observed no RT differences between the meaning translation-ambiguous words and the synonym translation-ambiguous words (1285 vs. 1274 ms), F ₁ < 1; F₂(1,231) = 1.14, MSE = 78548.17, p = .286. However, synonym translation-ambiguous words were responded to marginally more accurately than the meaning translation-ambiguous words (79 vs. 74%), F ₁(1,25) = 3.21, MSE = 0.035, p = .085; F ₂(1,243) = 3.35, MSE = 0.069, p = .069. Dominant translations were responded to more quickly than subordinate translations in the analysis by participants (1246 vs. 1314 ms) (F ₁(1,25) = 9.19, MSE = 26325.51, p < .01), but not in the analysis by items, F ₂(1,231) = 1.13, MSE = 78548.17, p = .288. Dominant translations were also responded to more accurately than subordinate translations (83 vs. 70%), F ₁(1,25) = 34.56, MSE = 0.024, p < .001; F ₂(1,243) = 14.23, MSE = 0.069, p < .001. Target–translation pairs preceded by related primes were responded to more quickly than target–translation pairs preceded by unrelated primes (1232 vs. 1327 ms), F ₁(1,25) = 8.21, MSE = 56754.05, p = .008; F ₂(1,231) = 7.41, MSE = 78548.17, p = .007. There was no significant relatedness effect in the accuracy analyses, F ₁(1,25) = 1.70, MSE = 0.072, p = .204; F ₂(1,243) = 1.67, MSE = 0.069, p = .197. The interaction between type and dominance was not significant by participants or items in the reaction time analyses, F ₁ < 1; F ₂(1,231) = 2.64, MSE = 78548.17, p = .106. We did observe a significant type by dominance interaction in the accuracy analysis by participants, F ₁(1,25) = 6.23, MSE = 0.033, p = .02, which was marginally significant by items, F ₂(1,243) = 3.19, MSE = 0.069, p = .075. To gain a better understanding of the significant interaction for the accuracy analysis, we conducted Bonferroni-corrected post-hoc t-tests (value to reach significance = .0125), which revealed that the effect of translation dominance was greater for meaning translation-ambiguous words (M _dominant = 84%, M _subordinate = 64%), t(25) = 5.23, p < .001, d = 1.35, than for the synonym translation-ambiguous words (M _dominant = 82%, M _subordinate = 76%), t(25) = 2.39, p = .025, d = 0.44. Additionally, there was no significant difference between dominant translations for the meaning and synonym translation-ambiguous words, t(25) = 0.602, p = .55, d = 0.08, but there was a significant difference between the subordinate translations for the synonym and meaning translation-ambiguous words such that subordinate synonym translation-ambiguous words had a higher accuracy than subordinate meaning translation-ambiguous words (76 vs. 64 %), t(25) = 2.86, p = .009, d = 0.84.

No ambiguity type by relatedness interaction was observed in the RT and accuracy analyses, Fs < 1. Although only significant interactions are typically probed further, the RHM-TA makes specific predictions regarding differences in priming for the two types of ambiguous words. Therefore, we conducted planned t-tests (using the Bonferroni correction for multiple comparisons; critical value to reach significance = .025) examining the RT priming effects for synonym and meaning translation-ambiguous words. These revealed significant priming for meaning translation-ambiguous words (M _related = 1215 ms, M _unrelated = 1333 ms), t(25) = 2.67, p = .013, d = 0.42, but not for synonym translation-ambiguous words (M _related = 1250 ms, M _unrelated = 1321 ms), t(25) = 1.91, p = .068, d = 0.24. Again, because the interaction between ambiguity type and relatedness was not statistically significant, these findings must be interpreted with caution. No dominance by relatedness interaction or three way interactions were observed.