Idiomatic expressions and their processing

The mechanisms underlying the processing of figurative language have been widely debated. Idiomatic expressions are one of the most common and popular forms of figurative language used in daily interaction. Idioms are multi-word expressions whose interpretation is non-literal, meaning it cannot be derived by using regular compositional processes. As such, they require the language user to go beyond what is stated literally in order to infer the communicative intent of the speaker [7]. In addition, idioms have attracted attention both on theoretical and empirical grounds. The reason is that they cannot be placed immediately within a grammar-lexicon dichotomy of language. On the one hand, it can be assumed that they belong in, and are retrieved from, the mental lexicon, following the assumption that they need to be acquired and stored in the same way as lexical items. On the other, their (complex) structure suggests that they may be processed like structures generated by grammar [8–9].

Idiom processing has been addressed extensively in recent research, but mainly, two different types of theoretical approaches have emerged regarding the processing and comprehension of idioms [10]. Representative of the first type of approach is the Lexical Representation Hypothesis. Originally proposed by Swinney & Cutler [11] (see also Bobrow & Bell [12] and Gibbs [13]), this hypothesis claims that idioms are stored as lexical items, and understanding an idiom involves two parallel processes, a retrieval process, and a literal compositional computation process based on decomposing every element separately [8–10]. Further, it is assumed that the retrieval process is faster, while the compositional process demands longer time.

In a further development, Hamblin & Gibbs [14] suggest that idioms are essentially decomposable. For these authors, idiomatic interpretation rests on identifying the individual constituents, mainly because of the decomposability of the idiomatic expressions. Along similar lines, some authors have claimed that the processing and understanding of idioms cannot be reduced to lexical access or lexical retrieval only, and an additional interpretation process might be necessary [15–17]. Thus, Gibbs [16] suggests that idioms invoke complex meanings which are motivated by independently existing conceptual metaphors which are part of everyday thought.

The second type of approach, labelled the Configuration Hypothesis, aims to capture the dynamic process involved in idiom interpretation. The authors in favour of this position argue that idioms are represented in a concrete and distributed way and are processed as complex expressions [15]. This latter approach can be described as a “compositional” approach whereby each constituent is assumed to contribute semantically to the idiom interpretation.

In a comprehensive review of approaches to idioms, Titone & Connine [7] suggest that either view fails to account adequately for findings in research or capture the nature of idioms. These authors thus suggest a “hybrid” model, which acknowledges both the arbitrary “word” nature and the compositional aspect of idioms. Their model takes into account an important parameter on which idioms vary: degree of decomposability. A similar view, albeit from a slightly different perspective, is defended in Sanford [18] who describes idioms as the intersection of conceptual and syntactic schemas. Another factor which has been shown to impact on idiom processing is the mode of presentation. Tabossi, Fanari & Wolf [19] found that idiom identification differs depending on whether the expression was presented auditorily or visually (as text). This clearly implies that the different modalities in which the idioms are presented, demand different forms of processing.

Another related figurative and formulaic language category are proverbs. Proverbs are fixed sentential expressions which invoke well-received wisdom, social or moral norms [20–21]. They reflect themes from everyday life and often refer to general principles of reasonable human behaviour and action [21–22], and, as such, may seem to express self-evident truths. However, like idioms, proverbs display a wide range of interpretations depending on the type of mechanism involved: they can be metaphorical, as You can’t judge a book by its cover, or based on personification, hyperbole or a paradox (No news is good news). Unlike idioms, however, proverbs are typically based on some kind of cause-effect reasoning (Once bitten, twice shy) or reasoning by analogy, but may also reflect a certain type of cultural practice (Never look a gift horse in the mouth). Although proverbs may have prototypical figurative meanings, they are very much vital due to the necessity to draw precise inferences in each specific context of encounter [21]. Thus, the correct interpretation of a proverb requires the ability to navigate between their concrete, literal meaning and their figurative meaning by establishing a metaphorical connection between these two interpretations. These properties of proverbs make them an interesting category to study, especially in comparison to idioms. In the current study we refer to this type of figurative expression as “instructive expressions” reflecting their communicative role in language use.

Unlike idioms and proverbs, metaphors are a category which is often less conventionalised and more transparent. It is often assumed that metaphors are a device which maps concepts from two distinct domains, which are not otherwise conceptually linked to each other [23]. Like idioms, however, metaphors vary in degree of novelty/conventionality which affects the way they are processed. In the current study we use novel metaphors as a category which does not depend on stored vocabulary knowledge, but rather invites active processing based on analogy. Thus, the processing of novel metaphors can act as a base-line for the processing of novel expressions, in contrast to all other idiomatic categories included in the study.

Figurative language in High Functioning Autism (HFA)

Despite the long-standing interest in idioms, their nature and processing in typical populations, research on idiom comprehension in atypical populations overall, and in autism, in particular, has been surprisingly scanty.

Mashal & Kasirer [24] conducted a study where individuals with HFA, children with learning disorder and typically developing controls were compared. The authors used 11 subtests, ranging from interpretation of figurative expressions, including visual metaphors, idioms, conventional metaphors, novel metaphors, to comprehension of homophones and semantic tests (synonymy, similarity). Their results showed that children with learning deficit scored significantly lower than typically developing peers on comprehension of conventional metaphors and idioms, but no significant differences were observed between the two groups on comprehension of visual and novel metaphors. Furthermore, both deficit groups displayed a dissociation between the visual modality and the auditory modality, supporting the assumption that modality is a salient factor in figurative language processing. In contrast, the control group demonstrated an association between idioms and conventional metaphors. This result supports the idea that idioms and conventional metaphors share a common ground, in that both are relatively frozen conventionalised expressions and most likely rest on similar mechanisms for their processing. This is consistent with findings in a recent study of metaphor processing in highly verbal individuals with autism [25].

Vogindroukas & Zikopoulou [26] conducted a study aimed at the developmental trajectory of idiom comprehension in Asperger’s syndrome (AS) and HFA. The authors compared children with HFA/AS to their typically developing peers and to control adults. They found that children with AS/ HFA performed more poorly on the idiom task compared to the other two groups, while the two typically developing groups did not display any differences in terms of performance. In addition, the authors did not find a significant correlation between IQ scores and performance on the task for the HFA group, but IQ did correlate with idiomatic comprehension and age for the two control groups. It is important to mention that this study used idioms out of context. The fact that the idioms were presented without a supportive context means that the participants had to rely exclusively on a semantic analysis or retrieval. Thus, the study design might have introduced an additional problem and a potential confound between expression familiarity/frequency and decomposability. In that study, Vogindroukas & Zikopoulou [26] confirm that children with AS/HFA have a tendency to make literal interpretations, but that the difficulty in idiom comprehension is definitely not due to an intellectual deficit.

Whyte et al [27] addressed what factors predict performance on idiom comprehension in highly-verbal children with autism in comparison to an age-matched control group and a control group matched on grammar-age equivalence. This study focused specifically on the contribution of syntax competence and advanced Theory of Mind (ToM) abilities to idiom comprehension in autism. The results document that children with autism indeed perform more poorly compared to age-matched controls, but that there is no significant difference with the syntax competence matched group. Furthermore, while advanced ToM ability did not predict performance in the control groups, it correlated with idiom comprehension in the participants with autism.

Lee et al [28] tested Korean high-functioning children with autism compared to similarly aged children with Attention Deficit Hyperactivity Disorder (ADHD) and a control group of typically developing children. The test design included matching idioms to a picture, which either described the figurative meaning of the expression or the literal interpretation. Non-figurative sentences paired with their literal interpretation were used as a base-line condition. This study found that both the children with autism and the ADHD group performed significantly worse than controls on the two idiom conditions, and especially in the condition where the idiom was paired with its literal interpretation. No differences were observed on the non-figurative expressions.

Norbury [29] investigated idiom comprehension in context in children with communication disorders and age-matched peers. Children belonging to the communication disorders group were selected to represent a spectrum of communication difficulty, including Specific Language Impairment (SLI), Pragmatic Language Impairment (PLI) (with or without presence of autistic symptoms), autistic disorder, and Asperger syndrome. An idiom definition task was chosen to measure idiom comprehension in context. The main finding of that study was that children with language and communication impairments benefit from context in interpreting unfamiliar idioms. However, children with deficits in structural language did not benefit from context as much as typically developing peers. Furthermore, children with autism spectrum disorder (ASD) symptoms and pragmatic impairments, but without concomitant structural language deficits, were not impaired to the same degree in their idiom understanding and were able to use context as effectively as their peers. Importantly, language ability was one of the most significant predictors of idiom understanding. These findings highlight the role of language skills as a key determinant in the use of context to aid idiom comprehension, and further suggest that contextual cues can provide sufficient information to derive at least a cursory understanding of new idiomatic expressions.

Factors in idioms processing

Previous research has documented that idioms are more easily processed in the presence of supportive context ([16]; see Vulchanova et al [10] for a review). Findings from several studies suggest that the main role of context is to provide semantic support for decoding the target (appropriate) meaning of a sentence or an expression [17, 30–33]. In contrast, it is widely argued that individuals with autism are impaired in processing ambiguous linguistic information in context [34–36]. In addition, they often fail to attach context to their memories, and are specifically impaired in processing the social aspects of contextual information [37], while Ozonoff & Miller [38] demonstrate deviant use of context. However, the evidence concerning a contextual deficit is somewhat controversial. Thus, other studies have shown that competence at making use of context depends on language status, and children with higher verbal age can perform at the level of controls [39]. Also, success in that group may depend on the exact position of the ambiguous word (homograph) in the sentence. López & Leekam [36] found that, even though children with ASD performed worse than controls, they were more successful with homographs occurring later in the sentence (middle or end), suggesting sensitivity to context also in that group.

A study by Brock, Norbury, Einav & Nation [40] showed that participants with autism display a tendency similar to controls to use predictions based on the meaning of the lexical verb, as reflected in an increase in looks to the picture matching the object of the verb. At the same time both groups were less likely to be distracted by a phonological competitor of the object word. This tendency, however, was mediated by language status in the autism group. In that study context was restricted to the level of the sentence, with a focus on the information encoded in the lexical verb and the ability to use that information to successfully orient to the possible object. Also, Frith [41] argues that individuals with autism need a concrete context to be able to understand words and expressions, and Saldaña & Frith [42] document that children with autism have a normal reduction in reading times for expressions which are congruent with previous events. Given this evidence, we expect that context will not place any additional burden on the interpretation of the stimuli used in the current design.

Idiomatic expressions vary in degree of transparency/decomposability, and it seems clear that the more transparent the expression, the easier it is to understand, and conversely, the more opaque, the more difficult. A number of studies [43–44] demonstrate that the lexical makeup of idioms is a matter of degree and largely depends on the idiom’s compositional properties, and that idioms are accessed differentially according to their degree of syntactic frozenness. These authors thus claim that idioms do not form a unique class of linguistic items, but rather share properties with “more” literal language. Following this tradition, and with Titone & Connine [7], we expect that comprehension difficulty will be a direct function of the transparency/decomposability of the idiom [31, 33].

It can be assumed that the ability to understand idioms relies on the competence to process these expressions beyond the literal interpretation of individual words. This means that understanding idiomatic expressions demands making inferences, as well as integrating contextual information from both verbal and non-verbal sources. In our daily life, our knowledge about the world around us is based on the semantic and conceptual information gathered through our senses, implying that we perceive the world through different modalities [45]. Consequently, it seems logical to expect that some of the difficulties that individuals with autism demonstrate in figurative language may be due to a more generic deficit in multimodal integration. Indeed, problems with multi-sensory information have been documented in autism, and specifically at speech-specific multisensory integration [46]. Irwin, Tornatore, Brancazio & Whalen suggest that there are fundamental differences in audio-visual integration in children and adolescents with autism in comparison to typical peers [47]. Furthemore, individuals with autism have been shown to present with a reduced McGurk effect in the processing of audio-visual speech [48]. It has also been suggested that the neurodevelopmental abnormalities in autism may be due to poor information integration across brain regions [49]. Van Der Smagt, Engeland & Kemner [50] further suggest that any problems arising from integrating auditory and visual information must stem from higher processing stages, as is the case in processing complex linguistic material, and not low-level integration processes (e.g., at the level of individual sound/visual stimulus).

Participants

Two age groups of HFA (n = 45) and typically developing (TD) controls (n = 39) were recruited, all native speakers of Spanish. The first age group included children in the age range ten to twelve years. The second age group included young adults from sixteen to twenty-two years old (see Table 1 for the participants’ descriptive and demographic data). Participants and their legal guardians (usually the parents) provided written informed consent for entry into the study according of the declaration of Helsinki principles. The study was approved by the Andalusian Biomedical Research Ethics Committee. The diagnosis of HFA was confirmed with the Autism Diagnostic Observation Schedule (ADOS) [51] (Children, = 10,8; Young adults, = 12,3).

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Table 1. Mean (and standard deviation) of background measures for each age and group.

HFA (high functioning autism), FSIQ (intelligence quotient, Full Scale score), VCI (verbal comprehension index), RG (receptive grammar scores), RC (reading comprehension scores). Note: p < .05 for scores with the same superscript.

https://doi.org/10.1371/journal.pone.0168571.t001

The individuals with HFA and their typically developing peers were matched on age, gender and the verbal comprehension based on the Wechsler scale (WISC-IV [52] or WAIS-IV [53] were used, depending on the chronological age of the participant). The Verbal Comprehension Index (VCI) was used as an overall measure of verbal reasoning and includes measures of semantic/conceptual reasoning (similarities and vocabulary), and verbal comprehension. The descriptive statistics are given in Table 1. The matching of the groups was based on the Wilcoxon Test and the smallest p value was p = .222 suggesting that there were no significant differences observed between the groups (See Table 1). We also considered reading comprehension scores and receptive grammar (based on CEG, which is the Spanish adaptation of TROG [54]) as background measures to ensure our participants display scores within the norm (see Table 1). No differences were observed between participants for reading comprehension. However, despite all our participants being within the norm for receptive grammar, differences were observed between the two groups of young adult participants. For this reason, we included receptive grammar as a co-variate in the analyses of the data.

Apparatus and stimuli

In a pilot study with 50 adults, all native speakers of Spanish, we determined the degree of familiarity and frequency of the figurative expressions to be used in the main study. We selected 124 idioms and instructive expressions and 20 metaphors, for which participants in the pilot study had to determine: 1. “Do you know this expression?” 2. “If yes, do you know what it means?” 3. “Do you use this expression yourself?”. Participants were asked to answer truthfully, and to take into account that there were no wrong answers. We established ratings for each expression by assigning numerical values to the answers (1 for yes and 0 for no) and averaging the scores from the 3 questions. The idiomatic expressions which received a rating of over 0.80 were selected for the study and classified into three main categories: a/ biological idioms; b/ cultural idioms; and c/ instructive expressions, as used in Vulchanova et al. [8]. For the first two categories they followed the typology adopted in Penttilä, Nenonen & Niemi [55], in turn inspired by Searle’s idea of deep background (the human biological nature) vs. local background (local cultural practices), to account for the grounding of language in human experience and practice. The stimuli in these two categories were selected by 2 independent expert linguist raters (91% interrater agreement). Examples of biologically-based idioms are e.g., estar con el agua hasta el cuello (to be with water up to the neck); or venir como anillo al dedo (to come as ring to the finger), and of culturally-based idioms, e.g estar como una cabra (to be like a goat). The difference between the two categories is that, while biological idioms are derived from human (bodily) experience and interaction with the environment, and as such, tend to be more transparent and available to compositional parsing, cultural idioms are more idiosyncratic, tend to vary from one culture to another, and are, in general, less transparent. Our third category were instructive expressions (i.e., proverbs), whose meaning can be computed on-line, by drawing inferences on the basis of their individual constituent phrases (e.g., Las mentiras tienen las patas cortas—Lies have short legs—). The inclusion of this category was dictated by the need to compare such expressions, whose interpretation may be more directly available in appropriate context, to the comprehension of idioms. Finally, novel metaphors were included as a category that may be assumed to be less frozen/fixed, and as such more readily interpretable. From the pilot study, we selected for inclusion in the novel metaphors category those metaphors which earned a rating below .20 on the familiarity and frequency scale (e.g.: Estas flotando en el aire—You are floating in the air).

The study was designed as a sentence-picture matching task (38 visual—presented as text—and 38 auditory expressions (a total of 76 expressions—20 biological idioms/20 cultural idioms/16 instructive expressions/20 metaphors) randomized and counterbalanced between groups and participants). Each expression was linked to four possible images: One reflecting the figurative target meaning of the expression, a second image reflecting the literal meaning, a third image corresponding to a figurative meaning, but not the target one, and finally, a distractor as forth image (Cf. Fig 1). Each expression was presented in a short appropriate context (See S1 Appendix for examples). Thus, the study design included both verbal (presented as text/discourse) and visual (pictures) context.

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Fig 1. Images for the idiom “estar con el agua hasta el cuello!” (lit.: be with water up to the neck/fig.: drown in work)

https://doi.org/10.1371/journal.pone.0168571.g001

Matlab (R2014b) (matrix laboratory) was used as a multiparadigm numerical programming language to design, build and run the experiment.

Procedure

In this experiment, we used visual stimuli (images and sentences) that reflected literal meanings and idiomatic meanings. First, a fixation point (+) appeared for 500ms. Participants then saw on the screen the idiom or metaphor. Depending on the experimental block, the participant heard the expression in context via loud-speakers or read it in context in a short text presented on the computer screen orthographically. Thus, the condition in which stimuli were presented auditorily presented a cross-modal environment whereby participants had to select a visual target which matched a figurative expression they had heard via the loud-speakers. In the auditory experimental block, first the figurative expression appeared on the computer screen followed by a blank screen, while the expression in appropriate context were being presented auditorily. In the text (orthographic) condition both stimulus (expression and context) and target picture were all in the same (visual) modality. First the expression appeared on the screen, and was then followed by the context. Then, in both experimental conditions a fixation point (+) reappeared in the middle of the screen followed by four images in each corner, each one reflecting the figurative meaning (target) meaning, the literal meaning, figurative, but not target, meaning, and a distractor image. The position of presenting the images was counterbalanced between participants and between the modality of presenting the context and the order of the expressions. The participant’s task was to click on the stimulus he/ she considered most appropriate for the meaning of the expression. Accuracy and reaction times were collected to determine the ease of processing.

Reaction time analyses

Reaction times were analysed using Log(RT) (ms) as a measure rather than reaction times to correct for right skewedness of the reaction time data. Only correct responses are included in the analyses. We also excluded extremely fast or slow responses. We excluded two trials with reaction times longer than 18s and we calculated the Z value of Speed (Log(RT) (ms)) for each participant (Z_p) and for each item per age and group (Z_i). If the sum of the squares of the Z values was smaller or equal than 8 (Z_p²+Z_i² ≤ 8), the trial was included, and values bigger than 8 were excluded.

An overall linear mixed model analysis on Log (RT) (ms) performance has been done with R, which provided us with the best fitting model by step-wise backward selection based on the Akaike Information Criterion (See S2 Appendix). We were left with a model including a three-way interaction of group, modality and type of expression, a two-way interaction of age and type of expression, and all lower effects. Also included in the model were the by-subject random intercepts and slopes for modality and by-item random intercepts and slopes for Age and Group.

The overall model reveals that there are differences in reaction latency for Age (children/young adults) (F (1, 82.64) = 20.38, p < .001) (Satterthwaite approximation for degrees of freedom) and Group (control/HFA) (F (1, 86.85) = 10.64, p = .001), with slower responses by children and individuals with autism (see Fig 2). Furthermore, an interaction between age and type of expression was found (F (3, 66.11) = 2.903, p = .041) (see Fig 3). A multiple comparison with Tukey contrasts showed that the interaction is due to differences between different types of expressions in children, but not adults. Here, a significant difference was observed between novel metaphors and instructive expressions (p = .0014). In addition, a marginally significant difference between cultural idioms and novel metaphors was observed (p = .07) showing that instructive expressions were the most demanding and difficult to process for the children.

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Fig 2. Average reaction times (ms) for each age (children/young adults) and group (control/HFA).

Error bars denote one standard error of the mean.

https://doi.org/10.1371/journal.pone.0168571.g002

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Fig 3. Average reaction times (RTs) for each age (children/young adults) and group (control/HFA) for each type of expression.

Error bars denote one standard error of the mean.

https://doi.org/10.1371/journal.pone.0168571.g003

As we have pointed out, a three-way interaction between type of expression, group and modality was observed (p < .001) (see Fig 4A and 4B). In order to establish the origin of the three-way interaction, additional multiple comparisons with Tukey contrasts were run. For the HFA group in the auditory modality, no significant differences were observed between different types of expressions. However, for the visual modality, an almost significant difference was observed between instructive expressions and novel metaphors (p = .0501), with novel metaphors processed faster than instructive expressions. For the control group in the auditory modality, significant differences were observed between instructive expressions and biological idioms (p = .013), with slower responses in the instructive expressions category, and between novel metaphors and cultural idioms (p = .033), with faster responses in the novel metaphor category, and between novel metaphors and instructive expressions (p < .001), with faster responses for novel metaphors. No significant differences were observed in the visual modality in the control group between different types of expressions. These results suggest a different interaction pattern between participants with autism and controls. While for controls there is differential processing depending on type of expression in the oral language modality, with more transparent expressions processed faster, this pattern is absent in participants with autism.

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Fig 4.

4a, 4b. Average reaction times (ms) group (control/HFA) type of expression and modality. Error bars denote one standard error of the mean.

https://doi.org/10.1371/journal.pone.0168571.g004

Moreover, we explored the differences between groups and modalities for each type of expression. For biological idioms, while the control group was faster in the auditory modality, the HFA group was faster in the visual modality (p = .026), but always significantly slower than controls (p = .001). For cultural idioms, the HFA group was significantly slower than controls (p = .001). In addition, no modality differences were observed in the HFA group for this type of expression, while the control group were faster in the visual modality (p = .02). For instructive expressions, the control group was significantly slower in the auditory modality (p = .01), while the HFA group showed no modality preference in this category and was slower in comparison to the control group overall in this type of expression (p = .015). Finally, for the novel metaphor category, again the HFA group was much slower overall (p < .001), and showed no modality differences, while the control group showed faster responses in the auditory modality (p = .01). To sum up those results, with increase of expression non-transparency, the advantage of modality changes only for controls. Thus, for controls transparent expressions are processed faster in the oral modality and less transparent expressions are processed faster in the orthographic modality. No such trend is observed for the participants with autism. These results suggest a different processing pattern in autism.

Comparison between figurative target responses and non-target responses

An overall generalized linear mixed model analysis on ACC (mean errors) performance has been done with R, which provided us with the best fitting model by step-wise backward selection based on the Akaike Information Criterion. We were left with a model including a two-way interaction of age and group and main effects. Also included in the model were the by-subject random intercepts and slopes for modality and by-item random intercepts and slopes for Age and Group.

The overall model thus reveals differences in accuracy in Age (children/young adults) (χ² (1, 23) = 5.73, p = .0016) and Group (control/HFA) (χ² (1, 23) = 11.21, p < .001), with more errors by children and individuals with autism (see Fig 5). In addition, a main effect of type of expression was observed as well (χ² (1, 21) = 8.49, p = .036). Multiple Comparisons of Means with Tukey contrasts (See S3 Appendix) revealed that this effect was due to marginally significant differences between instructive expressions and biological idioms (p = .07), and between instructive expressions and novel metaphors (p = .06), with more errors in the instructive expressions category.

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Fig 5. Average response accuracy (mean errors) for each age (children/young adults) and group (control/HFA).

Error bars denote one standard error of the mean.

https://doi.org/10.1371/journal.pone.0168571.g005

Moreover, a two-way interaction between age and group was observed (χ² (1, 23) = 4.98, p = .025). Multiple Comparisons of Means with Tukey contrasts (See S3 Appendix) revealed that the interaction was due mainly to the differences between young adults (HFA and control) (p < .001), and between the two control groups (children and young adults) (p = .01). No differences were observed between the two HFA groups (p = 0.99) or children (HFA and control) (p = 0.74). These results suggest that participants with autism perform at the same level regardless of age.

Comparison between figurative target responses and literal responses

In order to establish responses to target figurative meaning contra literal interpretations, we re-ran the model, while excluding non-target figurative, and distractor responses (See S4 Appendix). In this model, Target responses were scored with 1 and literal responses were scored with 2. The model includes a significant three-way interaction between modality, age and group (control/HFA), and all lower interactions and main effects. Also included in the model were the by-subject random intercepts and slopes for modality and by-item random intercepts and slopes for Age and Group.

This model reveals a main effect of group (control/HFA) ((χ² (1, 26) = 5.22, p = .022), with more literal responses by children and young adults with autism and a marginally significant difference in accuracy between Age (children/young adults) ((χ² (1, 26) = 3.51, p = .06) (see Fig 6). In addition, a main effect of type of expression was observed (χ² (1, 24) = 10.37, p = .015). Multiple Comparisons of Means with Tukey contrasts (See S4 Appendix) revealed that this effect was due to a difference between instructive expressions and novel metaphors (p = .02), with more literal responses in the instructive expressions category, and to a marginally significant difference between cultural idioms and novel metaphors, with more literal responses in the cultural idioms category. These results suggest that less transparent expressions are likely to trigger more literal responses.

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Fig 6. Average literality for each age (children/young adults) and group (control/HFA).

Target responses were scored with 1 and literal responses were scored with 2. Higher means indicate more literal responses. Error bars denote one standard error of the mean.

https://doi.org/10.1371/journal.pone.0168571.g006

Furthermore, a two-way interaction between age and group was observed (χ² (1, 26) = 4.89, p = .02). We ran multiple comparisons of means with Tukey contrasts which revealed that this interaction was due to a significant difference between control young adults and HFA young adults (p = .015), with the young adults with autism giving more literal responses than their respective typically developing peers. Also, the difference between both ages of control participants was significant (p = .03), where the young adults detected the figurative target image more accurately. No differences were observed between the two HFA groups (p = 0.99) or children (HFA and control) (p = 0.99). These results suggest that children and participants with autism are more likely to provide literal responses.

A three-way interaction between age, group and modality was observed (χ² (1, 26) = 3.97, p = 0.046) (see Fig 7). Again, a multiple comparison of means with Tukey contrasts showed no significant effects in the visual modality in any of the age groups, but clear differences in the auditory modality between the control young adults and the young adults with autism (p = 0.001), where the older groups of participants had inverted patters of responses. The young adults with HFA were less literal in the visual modality than the auditory modality, while the control young adults were better in the auditory modality, suggesting a differential pattern in response. Moreover, a marginally significant difference was observed between the control children and the control young adults (p = .055), where the children did not show modality differences, but the older group was better in the auditory modality.

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Fig 7. Average literality for each age (children/young adults) and group (control/HFA) and modality (visual/auditory).

Target responses were scored with 1 and literal responses were scored with 2. Higher means indicate more literal responses. Error bars denote one standard error of the mean.

https://doi.org/10.1371/journal.pone.0168571.g007

Comparison between figurative target responses and figurative non-target responses

The final part of the analyses compared figurative target responses and figurative non-target ones. Therefore, we ran the model again without responses to the literal image and responses to the distractor (See S5 Appendix). From the overall model it is clear again, that there are differences in accuracy between groups (control/HFA) (χ² (1, 11) = 6.49, p = .01), with more errors by individuals with autism. Additionally, a main effect of age (children/young adults) was found as well (χ² (1, 11) = 12.92, p < .001 and (see Fig 8), with more errors in the two younger groups. These results suggest that figurative language comprehension is not yet stabilized and still developing in the child participants. No other significant effects were found in this comparison. Despite this, figurative non-target responses only appeared in very few instances (4.5% of the responses in comparison with 81.8% for figurative target ones). Consequently, the comparison between figurative target and figurative non-target responses shows a very small error range.

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Fig 8. Average accuracy in figurative meanings for each age (children/young adults) and group (control/HFA) and modality (visual/auditory).

Target responses were scored with 1 and literal responses were scored with 0. Higher means indicate more accurate responses. Error bars denote one standard error of the mean.

https://doi.org/10.1371/journal.pone.0168571.g008

General linear model—Receptive grammar as a covariate

We were also interested in the extent to which the difference between the two young adult groups on receptive grammar influenced these groups’ performance on the tasks. Repeated measures ANOVA were run in SPSS [59], where we considered age and group as between subject factors, modality and type of expression as within subject variables and receptive grammar as a covariate. Bonferroni confidence interval adjustment was considered and observed power was calculated in this analysis (See S6 Appendix). We followed the same procedure for all the previously considered dependent variables (reaction time, overall accuracy, scores for target and literal responses, and scores for target and figurative non target responses).

Receptive grammar as a covariate did not affect in any way any of the models and none of the main effects or interactions changed with the inclusion of receptive grammar scores as a covariate in the regression model. We report the power effects analyses in S7 Appendix.