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Engagement for emotional prototypicality is shaped by word frequency in reading: evidence from eye movements

Published online by Cambridge University Press:  23 October 2025

Tongwen Hu Open the ORCID record for Tongwen Hu [Opens in a new window] ,
Xinying Yuan ,
Linlin Zhang ,
Yuru Cheng  and
Jingxin Wang
Tongwen Hu
Affiliation:
College of Education, Anqing Normal University, Anqing, China Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China
Xinying Yuan
Affiliation:
Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China
Linlin Zhang
Affiliation:
Business School, Northeast Normal University, Changchun, China
Yuru Cheng
Affiliation:
Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China
Jingxin Wang*
Affiliation:
Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China
*
Corresponding author: Jingxin Wang; Email: wjxpsy@126.com
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Abstract

Emotional prototypicality (EmoPro) refers to the degree of emotional representativeness of a word and influences emotional content extraction at the lexical level. However, its effect on more complex semantic structures, such as sentences, remains unclear. This study employed eye-tracking to examine the EmoPro effect during Chinese sentence reading. EmoPro (high vs. low) was manipulated in two experiments, with sentences containing either a positive or negative valence target word. The lexical frequency of these target words was also manipulated to assess its influence on emotional semantics activation during reading. The results show that high EmoPro words consistently evoke greater engagement during both early and late word processing, demonstrating a significant advantage in emotional information retrieval. Word frequency influenced this processing advantage differently for words with a positive or negative valence. For positive valence, high-frequency words facilitated emotional extraction for high EmoPro words; for negative valence, low-frequency words enhanced their salience, leading to faster emotional retrieval. These findings provide the first evidence that EmoPro significantly impacts the processing of words in natural reading. The findings also highlight a complex interplay between affective and linguistic information in emotional semantics embodiment, with word frequency playing a pivotal role in shaping its depth during reading.

Keywords

Emotional prototypicalityEmotional wordsNatural readingWord frequency

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Original Article
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Applied Psycholinguistics , Volume 46 , 2025 , e36
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
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© The Author(s), 2025. Published by Cambridge University Press

Introduction

Emotions are a fundamental part of human nature, influencing both physiological and psychological processes (Pekrun et al., Reference Pekrun, Marsh, Elliot, Stockinger, Perry, Vogl, Goetz, van Tilburg, Lüdtke and Vispoel2023) and playing a key role in cognitive functions such as attention (Sharif & Mahmood, Reference Sharif and Mahmood2023). Emotionally salient stimuli, typically marked by high arousal and either positive or negative valence, tend to capture attention and be processed with higher priority, resulting in faster and more accurate recognition (Rouhani et al., Reference Rouhani, Niv, Frank and Schwabe2023). Research on emotionally salient stimuli has focused on visual stimuli, such as pictures (Zsidó et al., Reference Zsidó, Bali, Kocsor and Hout2023), facial expressions (Schindler & Bublatzky, Reference Schindler and Bublatzky2020), and emojis (Kaye et al., Reference Kaye, Rocabado, Rodriguez-Cuadrado, Jones, Malone, Wall and Dunabeitia2023). In contrast, much less attention has been given to how emotions are perceived and interpreted through language, especially in the absence of salient perceptual cues (Hinojosa et al., Reference Hinojosa, Herbert and Kissler2023; Speed & Brysbaert, Reference Speed and Brysbaert2023). Investigating emotional processing during language comprehension is nevertheless important, as written language can convey complex emotional information without visual or sensory cues and plays a vital role in socialization (Snefjella et al., Reference Snefjella, Lana and Kuperman2020). Accordingly, the present study aimed to uncover how emotions conveyed through language are processed.

As words play a central role in language comprehension (Rayner, Reference Rayner2009), research has focused on understanding the influence of emotion on cognitive processes in word recognition (Ferré et al., Reference Ferré, Guasch, Stadthagen-González, Hinojosa, Fraga, Marín and Pérez-Sánchez2024; Hinojosa et al., Reference Hinojosa, Moreno and Ferré2020). The emotional content of words is typically characterized in terms of arousal and valence, where words might be high or low arousal and have either a positive, or negative valence (Scott et al., Reference Scott, O’Donnell, Leuthold and Sereno2009). Research has shown that these emotional word characteristics can influence processes of word recognition across a range of tasks (Hinojosa et al., Reference Hinojosa, Herbert and Kissler2023). In lexical decision tasks, positive and negative valence words are recognized more quickly and accurately than neutral words (Vinson et al., Reference Vinson, Ponari and Vigliocco2014), suggesting that the lexical representation of emotional words is swiftly activated and readily available. Similarly, in masked priming paradigms, emotional word primes typically produce larger semantic priming effects than neutral word primes (Spruyt et al., Reference Spruyt, Hermans, Houwer and Eelen2002), revealing that the emotional content of words can influence their sub-threshold processing (Gaillard et al., Reference Gaillard, Del Cul, Naccache, Vinckier, Cohen and Dehaene2006). Other studies show an influence of emotional content in implicit semantic processing tasks (which do not require participants to make overt semantic judgments). Studies using the dot probe (Sutton & Altarriba, Reference Sutton and Altarriba2011) or the Stroop tasks (Siakaluk et al., Reference Siakaluk, Knol and Pexman2014) typically manipulate the valence of a cue word, requiring participants to indicate either the location (dot probe) or the color (Stroop) of the stimulus. Results from these tasks show that participants are better at judging the location of emotional words compared to neutral words (Sutton & Altarriba, Reference Sutton and Altarriba2011), but poorer at identifying the color of emotional words compared to neutral words (Siakaluk et al., Reference Siakaluk, Knol and Pexman2014). These findings are explained in terms of emotional words having a greater influence on attention allocation, by more strongly capturing a reader’s attention. Electrophysiological studies additionally reveal a neural basis for this attentional bias by revealing larger effects for emotional compared to neutral words in event-related potentials (ERPs), with differential effects observed in the Early posterior negativity (EPN) (250–300 ms, Schindler & Kissler, Reference Schindler and Kissler2016) and Late positive complex (LPC) (500–700 ms, Kaltwasser et al., Reference Kaltwasser, Ries, Sommer, Knight and Willems2013). Overall, these effects suggest that cognitive engagement with emotional words, even in the absence of explicit emotional processing demands, activates their emotional content, captures attention, and plays a central role in lexical access.

There is nevertheless disagreement over the nature of the effects. Some studies suggest a positive valence bias, such that participants exhibit a larger word recognition advantage for positive compared to neutral words than for negative compared to neutral words (e.g., Hinojosa et al., Reference Hinojosa, Mercado, Albert, Barjola, Peláez, Villalba-García and Carretié2015; Kuperman et al., Reference Kuperman, Estes, Brysbaert and Warriner2014; Scott et al., Reference Scott, O’Donnell, Leuthold and Sereno2009). Others show a negative valence bias, with a larger word recognition advantage for negative compared to neutral words than for positive compared to neutral words (e.g., Espuny et al., Reference Espuny, Jiménez-Ortega, Casado, Fondevila, Muñoz, Hernández-Gutiérrez and Martín-Loeches2018; Sutton & Altarriba, Reference Sutton and Altarriba2011). Yet other studies suggest that emotional words, whether positive or negative, have general processing advantages over neutral words (e.g., Ferré et al., Reference Ferré, Fraga, Comesaña and Sánchez-Casas2015; Kousta et al., Reference Kousta, Vinson and Vigliocco2009). Notably, not all emotional words rapidly activate emotional content during lexical processing (Haro et al., Reference Haro, Calvillo, Poch, Hinojosa and Ferré2023). Research has shown substantial variability among emotional words in their capacity to trigger emotional activation, with some being significantly more effective than others (Zhang et al., Reference Zhang, Wu, Meng and Yuan2017). These differences have often been overlooked in previous studies, contributing to inconsistent findings regarding valence effects (Betancourt et al., Reference Betancourt, Guasch and Ferré2023). As such, it is essential to clarify how variability in emotional content activation for words influences lexical processing (Wu & Zhang, Reference Wu and Zhang2020).

This has led some researchers to classify emotional words in terms of how strongly their emotional content influences processes of word recognition. A key distinction concerns the extent to which words explicitly represent emotional meanings versus evoke emotional experiences (Pavlenko, Reference Pavlenko2008). Emotion-label words, such as “happy” and “sad,” directly denote emotional states. In contrast, emotion-laden words, such as “failure” and “death,” evoke emotional responses through associative meanings, despite not explicitly referring to emotions. Although both types convey emotional content, it is argued that they differ in their lexical processing due to the distinct mechanisms through which emotional meaning is activated during lexical access (Wu & Zhang, Reference Wu and Zhang2020). For instance, in semantic priming tasks (Kazanas & Altarriba, Reference Kazanas and Altarriba2015, Reference Kazanas and Altarriba2016) and emotion classification tasks (Gu & Chen, Reference Gu and Chen2024), emotion-label words have been shown to activate emotional semantics more rapidly than emotion-laden words. This results in stronger priming effects, faster emotional valence judgments, and more intense emotional responses. These findings suggest that emotion-label words have a distinct advantage in semantic memory accessibility, enabling them to evoke emotional experiences more efficiently. This distinction has been consistently observed across multiple languages, including English (Knickerbocker et al., Reference Knickerbocker, Johnson and Altarriba2015), Chinese (Zhang et al., Reference Zhang, Wu, Yuan and Meng2019), and Spanish (Betancourt et al., Reference Betancourt, Guasch and Ferré2024), and across tasks, such as the Simon task (Altarriba & Basnight-Brown, Reference Altarriba and Basnight-Brown2011) and the Flanker task (Zhang et al., Reference Zhang, Wu, Yuan and Meng2019). Despite these findings, there is no clear consensus on the differentiation between the two word types. Some researchers argue that emotion-label words trigger stronger emotional activation during lexical processing (Zhang et al., Reference Zhang, Wu, Meng and Yuan2017), while others find the opposite (Liu et al., Reference Liu, Fan, Tian, Li and Feng2023).

A potential reason for this inconsistency is the lack of an objective and reliable standard for classifying emotional word types, as most researchers rely on subjective judgments (Hinojosa et al., Reference Hinojosa, Moreno and Ferré2020). To address this limitation, researchers have begun selecting emotional words based on their degree of typicality in representing specific emotions, defined as emotional prototypicality (EmoPro) (Pérez-Sánchez et al., Reference Pérez-Sánchez, Stadthagen-Gonzalez, Guasch, Hinojosa, Fraga, Marín and Ferré2021). This approach, inspired by Rosch’s (Reference Rosch, Rosch and Lloyd1978) work and grounded in prototype theory (Russell, Reference Russell1991), proposes that emotion concepts, similar to other “fuzzy” categories like “bird,” are organized in a prototypical structure and vary in how strongly they represent a specific emotion. This implies that “some words are more representative of the semantic category of emotions than others, and therefore convey a particular emotional meaning more strongly” (Ferré et al., Reference Ferré, Guasch, Stadthagen-González, Hinojosa, Fraga, Marín and Pérez-Sánchez2024, p. 745). For instance, “sadness” is considered a more prototypical emotion word than “failure.”

The prototypicality approach, widely employed in emotion research over the past few decades (e.g., Galati et al., Reference Galati, Sini, Tinti and Testa2008; Shaver et al., Reference Shaver, Schwartz, Kirson and O’Connor1987), involves asking participants to rate the extent to which candidate words refer to an emotion. This method has been applied across various languages, including French (Niedenthal et al., Reference Niedenthal, Auxiette, Nugier, Dalle, Bonin and Fayol2004), Spanish (Pérez-Sánchez et al., Reference Pérez-Sánchez, Stadthagen-Gonzalez, Guasch, Hinojosa, Fraga, Marín and Ferré2021), and more recently in Chinese (Wu, Reference Wu2023). This approach has encouraged researchers to examine how EmoPro influences language processing. Lexical decision studies in Spanish (Haro et al., Reference Haro, Calvillo, Poch, Hinojosa and Ferré2023) and Chinese (Wu et al., Reference Wu, Wu and Gao2024) show that words with high EmoPro facilitate more efficient visual word recognition by enhancing access to emotional content. Such findings suggest that highly prototypical emotion words may have stronger semantic ties to emotional information, making them more effective in activating the emotional meanings they convey.

According to the Embodied Theory of Semantic Representation (Vigliocco et al., Reference Vigliocco, Meteyard, Andrews and Kousta2009), the advantage of high EmoPro words in retrieving emotional information likely arises from their semantic representations, which develop in tandem with affective experiences during socialization and emotional interactions. This process integrates affective experiential information (e.g., feelings, interoception; Ferré et al., Reference Ferré, Guasch, Stadthagen-González, Hinojosa, Fraga, Marín and Pérez-Sánchez2024) into word meanings (Tang, Fu, et al., Reference Tang, Fu, Wang, Liu, Zang and Kärkkäinen2023). As a result, activating the meaning of a high EmoPro word simultaneously triggers its associated emotional experiences. In contrast, low EmoPro words have semantic representations more akin to general vocabulary, with an initial emphasis on conceptual understanding during learning. Their affective experiential associations emerge more gradually as semantic knowledge becomes more refined (Tang, Fu, et al., Reference Tang, Fu, Wang, Liu, Zang and Kärkkäinen2023), meaning that emotional activation typically follows full semantic processing. Current evidence for the processing advantage of high EmoPro words primarily comes from lexical decision tasks (e.g., Hao et al., 2023; Wu et al., Reference Wu, Wu and Gao2024). In these tasks, words are presented individually for recognition, and so are not encountered within a normal language context or read normally as part of a sentence. As a result, such tasks may provide an incomplete picture of how emotional language is processed in real-world contexts and when reading text naturally (Alexander & Buzzell, Reference Alexander and Buzzell2024). Extrapolating findings from isolated-word studies to natural language processing poses significant challenges (Alexander & Buzzell, Reference Alexander and Buzzell2024). In everyday contexts, emotional information is typically conveyed through sentences that embed emotional words within broader linguistic structures (Scott et al., Reference Scott, O’Donnell and Sereno2012). Accordingly, it is also important to investigate EmoPro under more naturalistic reading conditions, to move fully understand its influence on everyday language processing. To our knowledge, no research has yet explored EmoPro effects in natural reading contexts. Eye-tracking offers a powerful tool for investigating word recognition processes in natural reading, as it allows for the continuous recording of eye movements under relatively normal reading conditions that preserve natural reading behaviors. This approach has been shown to yield valuable insights into the cognitive mechanisms underlying sentence comprehension and provide an ecologically valid method for investigating how emotion influences language processing (Scott et al., Reference Scott, O’Donnell and Sereno2012). Accordingly, the present research used eye-tracking methodology to investigate the effects of EmoPro on the processing of words during natural reading, aiming to generate more realistic and nuanced evidence of a processing advantage for high EmoPro words.

In addition to this concern, the embodied theory (Vigliocco et al., Reference Vigliocco, Meteyard, Andrews and Kousta2009) suggests that the semantic activation of emotional words depends not only on affective experiences but also on the linguistic experiences associated with words (Villani et al., Reference Villani, Lugli, Liuzza, Nicoletti and Borghi2021). Thus, linguistic information, such as word frequency, age of acquisition, and semantic diversity, will also influence the processing of emotional semantics (Sheikh & Titone, Reference Sheikh and Titone2013; Diveica et al., Reference Diveica, Muraki, Binney and Pexman2024). These linguistic factors serve as a bridge between lexical form and semantic representation in the reader’s mental lexicon, supporting both word recognition and semantic retrieval (Chapman & Martin, Reference Chapman and Martin2022). Among them, word frequency, defined as the rate at which a word appears in a given corpus, plays a critical role in lexical access during natural reading (Mei et al., Reference Mei, Chen, Xia, Yang and Liu2025). Moreover, studies show that word frequency can limit the influence of other experiential information associated with words. For example, the effect of action-related experiential information, where word meanings activate the sensorimotor system (e.g., “grasp” activates motor-related brain regions), is observed only with words having a low frequency rather than words with high frequency (Sheikh & Titone, Reference Sheikh and Titone2013). Word frequency also appears to moderate access to emotional semantics, permitting the extraction of emotional content only for lower frequency (Kuperman et al., Reference Kuperman, Estes, Brysbaert and Warriner2014). This suggests that emotional meaning is shaped not just by experience but also by linguistic factors like word frequency. To fully understand how EmoPro influences language processing, it will be important to consider both types of information, the reader’s emotional experience and their linguistic knowledge. Accordingly, building on previous research examining the role of EmoPro in natural reading, this study investigates how word frequency affects the ability of high EmoPro words to facilitate emotional meaning retrieval during sentence processing.

In summary, the present uses eye-tracking methods to overcome limitations of previous research, which predominantly relied on isolated word presentations. By embedding emotional words with varying degrees of typicality in representing specific emotions within sentence contexts, we examined how such differences affected natural reading processes. In addition, the study manipulated word frequency to explore its role in moderating emotional information retrieval at different levels of EmoPro during sentence reading. Given that valence—a critical emotional attributes—significantly influences lexical processing, and to avoid potential confounding effects from mixed-valence stimuli (Kazanas & Altarriba, Reference Kazanas and Altarriba2015), our study adopted an approach consistent with prior research (Knickerbocker et al., Reference Knickerbocker, Johnson and Altarriba2015; Tang & Ding, Reference Tang and Ding2024). Specifically, it examined these questions separately under positive and negative valence conditions to provide a clearer perspective on how semantic representation information varies across valence. Our study, therefore, consisted of two experiments: one focusing on positive valence (Experiment 1) and the other on negative valence (Experiment 2). Both adopted a 2 (EmoPro: high vs. low) × 2 (Word Frequency: high vs. low) within-subjects design and were conducted in Chinese. Importantly, the selection of materials and control of lexical attributes were conducted separately for each experiment. This ensured that the attributes of target words were independently tailored to positive and negative valences, rather than being combined into a single experiment with uniformly controlled words. The specific hypotheses for each experiment are detailed below.

Experiment 1

Experiment 1 examined how EmoPro and word frequency influenced the processing of specific target words in sentences during reading. Each participant’s eye movements were recorded as they read a series of trials comprising an isolated sentence presentation, which was followed by a simple comprehension question (unrelated to the emotion of the target word) on a proportion of trials.

Drawing from prior studies, we hypothesized that EmoPro would exert a significant influence in natural reading contexts, with high EmoPro words maintaining their advantage in emotional content retrieval. According to the model of motivated attention and affective states, emotional stimuli are highly relevant to survival and self-defense, making them central to attention allocation and facilitating more comprehensive cognitive processing (Lang et al., Reference Lang, Bradley and Cuthbert1990). High-prototypical emotional words, characterized by the simultaneous activation of emotional information embedded in their semantics, are hypothesized to generate faster attentional biases (Altarriba & Basnight-Brown, Reference Altarriba and Basnight-Brown2011) due to this concurrent activation (Haro et al., Reference Haro, Calvillo, Poch, Hinojosa and Ferré2023; Wu et al., Reference Wu, Wu and Gao2024). Once this emotional information captures attention, it is likely to maintain attention for an extended period before disengagement occurs (Siakaluk et al., Reference Siakaluk, Knol and Pexman2014). Consequently, we hypothesize that high EmoPro words will result in prolonged fixation times while reading.

Predictions regarding the influence of word frequency were based on the Semantic Network Model (Collins & Loftus, Reference Collins and Loftus1975). According to this model, word semantic representations are stored in a hierarchically organized network, with prototype concepts located at higher levels. The closer a word’s semantic distance is to a prototype concept, the faster its associated semantic memory is activated (Kumar et al., Reference Kumar, Steyvers and Balota2022). This activation is also influenced by the strength of connections, determined by word familiarity (Collins & Loftus, Reference Collins and Loftus1975). Word frequency reflects the familiarity of a word to readers, with higher frequency indicating greater familiarity (Rayner, Reference Rayner2009). Consequently, frequent word occurrences can strengthen connections to prototype concepts, making their activation more automatic (Collins & Loftus, Reference Collins and Loftus1975). Positive words are stored with higher density and shorter semantic distances in the network (Unkelbach et al., Reference Unkelbach, Fiedler, Bayer, Stegmüller and Danner2008), and their frequent occurrences further enhance the automaticity of semantic activation. High EmoPro words are inherently closer semantically to emotional prototype concepts than low EmoPro words (Ferré et al., Reference Ferré, Guasch, Stadthagen-González, Hinojosa, Fraga, Marín and Pérez-Sánchez2024). Frequent occurrences of positive EmoPro words offer a stronger advantage in activating emotional semantics. This facilitates the efficient extraction of emotional information from positive high EmoPro words, capturing and sustaining readers’ attention. Therefore, we hypothesized that the activation advantage of positive high EmoPro words would be more pronounced at high frequencies, leading to greater attention and longer fixation durations.

Participants

The sample comprised 40 undergraduates, 31 of whom were female, with ages ranging from 18 to 25 years (M age = 20.77 years, SD = 2.01), based on sample sizes from previous EmoPro studies (Wu et al., Reference Wu, Wu and Gao2024). Participants were right-handed, native Chinese speakers with normal or corrected vision. They had no history of reading disorders. Statistical power was evaluated with the Mixedpower package (Kumle et al., Reference Kumle, Võ and Draschkow2021) in R (R Development Core Team, 2016), running 1000 simulations based on the gaze duration effects of word frequency as described by Scott et al. (Reference Scott, O’Donnell and Sereno2012) in natural reading. The analysis confirmed that the current sample size was sufficient to detect similar effects, achieving statistical power greater than 0.9 at α = 0.05.

Materials and apparatus

Following the EmoPro rating approach used by Haro et al. (Reference Haro, Calvillo, Poch, Hinojosa and Ferré2023) and Wu et al. (Reference Wu, Wu and Gao2024), this study selected 513 potential two-character emotional words from the Cai and Brysbaert (Reference Cai and Brysbaert2010) corpus. The selected words were divided into five sets, and participants rated each word on a 5-point scale to indicate the extent to which it referred to an emotion (1 = “This word does not refer to an emotion,” 5 = “This word clearly refers to an emotion”). For the rating task, 338 participants were recruited and randomly assigned to one of the five sets. Each set was rated by at least 50 participants, ensuring comprehensive coverage across all word sets. To assess the validity of our ratings, we conducted correlation analyses between our ratings and those in the EmoPro Chinese word databases developed by Wu (Reference Wu2023) and Zheng et al. (Reference Zheng, Zhang, Guo, Guasch and Ferré2023). The results showed a strong positive correlation between the ratings (Wu’s database: r (208) = 0.91, p < 0.001; Zheng et al.’s database: r (155) = 0.81, p < 0.001). We also calculated intraclass correlation coefficients (ICCs) to assess the inter-rater reliability of the ratings for all word sets, following the procedure outlined by Pérez-Sánchez et al. (Reference Pérez-Sánchez, Stadthagen-Gonzalez, Guasch, Hinojosa, Fraga, Marín and Ferré2021). The average ICC (2, k) across all versions was 0.97 (SD = 0.01, coefficient of variation = 0.6%, range = 0.97–0.98), demonstrating high inter-rater reliability. These results show that the ratings are reliable and can be used as the target word database for our study. This database is available at https://osf.io/jr7zm/.

Based on the number of materials used by Wu et al. (Reference Wu, Wu and Gao2024), 80 two-character positive target words were selected, controlling for various lexical properties: pleasantness (F(3, 76) = 1.65, p = 0.19), arousal (F(3, 76) = 1.52, p = 0.22), abstractness (F(3, 76) = 0.74, p = 0.53), number of strokes (F(3, 76) = 0.30, p = 0.83), word family size (F(3, 76) = 0.87, p = 0.46), and age of acquisition (F(3, 76) = 0.68, p = 0.57). The final set included 40 high-prototypical emotion words (Mean emotional prototypicality > 3) and 40 low-prototypical emotion words (Mean emotional prototypicality < 3). These 80 target words were further categorized by frequency: 40 high-frequency words (Mean per million frequency > 15, Mean log frequency > 2) and 40 low-frequency words (Mean per million frequency < 10, Mean log frequency < 2). Pleasantness, arousal, and abstractness were rated by 27 non-reading experiment participants on a 9-point scale (1 = unpleasant, calm, concrete; 9 = pleasant, excited, abstract). Stroke count and word frequency data were taken from the Cai and Brysbaert (Reference Cai and Brysbaert2010) corpus, while word family size (the number of words containing a given character) was obtained from the Chinese Lexical Database (CLD; Sun et al., Reference Sun, Hendrix, Ma and Baayen2018). Age of acquisition information was sourced from the corpus of Xu et al. (Reference Xu, Li and Guo2021). The mean values of these variables for the target words are presented in Table 1. High- and low-EmoPro words showed significant differences in emotional prototypicality (Mean high EmoPro = 3.56 (SD = 0.55), Mean low EmoPro = 2.38 (SD = 0.40), t(78) = 10.78, p < 0.001), but no significant differences in word frequency (per million frequency for Mean high EmoPro = 12.52 (SD = 18.42), per million frequency for Mean low EmoPro = 14.10 (SD = 19.04), t (78) = 0.38, p = 0.71; log frequency for Mean high EmoPro = 2.10 (SD = 0.78), log frequency for Mean low EmoPro = 2.26 (SD = 0.72), t(78) = 1.00, p = 0.32). In contrast, high- and low-frequency words exhibited significant differences in frequency (per million frequency: Mean high frequency = 18.71 (SD = 21.34), Mean low frequency = 7.91 (SD = 13.70), t(78) = 2.69, p = 0.009; log frequency: Mean high frequency = 2.51 (SD = 0.55), Mean low frequency = 1.85 (SD = 0.79), t(78) = 4.31, p < 0.001), but no significant differences in emotional prototypicality (EmoPro for Mean high frequency = 2.97 (SD = 0.74), EmoPro for Mean low frequency = 2.96 (SD = 0.78), t(78) = 0.09, p = 0.93).

Table 1.

Means (Standard deviations) for positive target word properties

Note. HF = high frequency. LF = low frequency. EmoPro = emotional prototypicality. HEP = high emotional prototypicality. LEP = low emotional prototypicality. These abbreviations remain consistent in following tables.

Experimental sentences were created by embedding positive high- and low-EmoPro words with high and low frequency, resulting in 80 distinct sentence structures. We also used the Mixedpower package (Kumle et al., Reference Kumle, Võ and Draschkow2021) to run 1000 simulations to assess whether the current number of materials has sufficient statistical power to detect the gaze duration moderation effects of word frequency, as described by Scott et al. (Reference Scott, O’Donnell and Sereno2012) in natural reading. The simulation results indicated that the statistical power at α = 0.05 exceeded 0.95 for the current size of experimental sentences.

Target words appeared centrally within the sentences, avoiding the first or last three positions (see Table 2 for examples). Sentence length ranged from 21 to 33 characters, with an average of 25 characters (SD = 3.49). Sentence contexts were designed to be non-predictive to minimize top-down processing, validated by a cloze test with 15 participants. The test showed low predictability for target words (Mean predictability = 0.01%, SD = 4%), with no significant differences across conditions (F < 2.50, p > 0.10). In addition, 40 participants, not involved in the cloze test or reading experiment, rated the sentences on a 1–5 scale for understandability and plausibility. The sentences were rated as highly understandable (Mean understandability = 4.12, SD = 0.33) by 20 participants and highly plausible (Mean plausibility = 4.08, SD = 0.26) by another 20 participants, with no significant differences across conditions (Fs < 2.75, ps > 0.10). The mean values of understandability, plausibility, and predictability for each condition are presented in Table 1.

Table 2.

Examples of experimental sentences for positive target words

Note. Target words presented in bold and italics above, but were not bold and non-italicized during experimental sessions.

The experimental sentences were organized into four sets using a Latin square design. Each set contained 80 experimental sentences, 40 fillers, and 10 practice sentences, totaling 130 sentences. Each participant read one list, with each target word and sentence appearing only once. The sentences were presented in a random sequence during the experiment, with practice sentences appearing before the experimental and filler sentences.

Eye movements from the right eye were captured using the SR Eyelink 1000 Plus tracker, sampling data at 1000 Hz during binocular viewing. The sentences were displayed in Song 32-point font (43 × 43 pixels) on a 24-inch DELL display (1920 × 1080 pixels; 120 Hz refresh rate). Participants sat 70 cm away from the screen, with their heads supported by a chin rest to minimize movement. The visual angle of each character was approximately 0.97°, typical for reading.

Procedure

The experiment was approved by the Ethics Committee of Tianjin Normal University (EC2024032508) and conducted in accordance with the principles of the Declaration of Helsinki. Participants completed the experiment individually, reading the sentences silently at their own pace to ensure comprehension. A three-point horizontal calibration was performed at the beginning, aligning with the sentence line to achieve spatial accuracy of 0.35° or better for all participants. Each trial began with the presentation of a fixation cross (“+”) at the leftmost position of the sentence. The sentence appeared when participants fixated on this spot. After reading each sentence, participants pressed a response key. Approximately 30% of sentences were followed by yes/no questions, balanced between “yes” and “no” responses. Participants’ average accuracy was about 94% (SD accuracy = 9%). The entire experiment lasted about 20 minutes for each participant, with a 3-minute break midway through. Participants received ¥20 as compensation upon completion of the experiment.

Result

Consistent with previous research (Scott et al., Reference Scott, O’Donnell and Sereno2012), we calculated eye movement metrics for the areas of interest (AOI) containing the target word. The key metrics included first fixation duration (FFD), single fixation duration (SFD), gaze duration (GD), and total fixation time (TT). FFD refers to the duration of the first fixation in an AOI, SFD refers to the duration of a single fixation in the AOI, GD refers to the total time spent within the AOI before the gaze moves elsewhere, and TT refers to the cumulative fixation duration within an AOI (Rayner, Reference Rayner2009). FFD, SFD, and GD reflect early processing differences during lexical access, with FFD and SFD serving as the earliest indicators of lexical processing (Rayner, Reference Rayner2009). In contrast, TT reflects the overall process of semantic integration, encompassing both lexical and post-lexical processing (Rayner, Reference Rayner2009).

Standard procedures (Knickerbocker et al., Reference Knickerbocker, Johnson and Altarriba2015) were followed, excluding fixations shorter than 80 ms and longer than 1000 ms. Trials were also excluded based on the following criteria: (1) three or fewer fixations for the sentence (0.2%); (2) loss of tracking due to coughing, head movements, or blinks within the AOI (0.1%); and (3) fixations exceeding 2.5 standard deviations from the mean within the AOI (2%). The remaining data were analyzed using a linear mixed-effects model, using the lme4 package in R (Kuznetsova et al., Reference Kuznetsova, Brockhoff and Christensen2017). EmoPro, word frequency, and their interaction were treated as fixed effects, with participants and sentences as crossed random effects. The analysis began with a maximal random model, including random slopes for all variables for both participants and sentences. If the model did not converge, the model structure was then simplified, starting with removing correlations for sentences, then excluding random slopes. The final random effects structure, established after successful model convergence, is detailed in the statistical effects table (see below). Log-transformed fixation-time effects are reported (Sheikh & Titone, Reference Sheikh and Titone2013). Mean fixation times for target words across all conditions are presented in Table 3, and statistical effects are summarized in Table 4.

Table 3.

Means (Standard Deviations) for eye-movement measures for positive target words

Note. FFD = first fixation duration; SFD = single fixation duration; GD = gaze duration; TT = total fixation time. These abbreviations remain consistent in following tables.

Table 4.

Results of linear mixed effects models for positive target words

Note. *p<0.05. ** p<0.01. *** p<0.001. Freq = word frequency. This abbreviation remains consistent in following tables.

The results revealed significant main effects of EmoPro on FFD (b = −0.04, SE = 0.01, t = −2.69, p = 0.007), SFD (b = −0.04, SE = 0.01, t = −2.92, p = 0.004), GD (b = −0.04, SE = 0.02, t = −2.51, p = 0.01), and TT (b = −0.07, SE = 0.02, t = −3.65, p<0.001). High EmoPro elicited longer fixations, indicating they captured more attention compared to low EmoPro. A significant interaction between word frequency and EmoPro was found for FFD (b = 0.06, SE = 0.03, t = 2.01, p = 0.04) and SFD (b = 0.06, SE = 0.03, t = 2.09, p = 0.04). This suggests that word frequency moderated the advantage of high EmoPro in extracting emotional information during the initial stages of processing. To further assess the interaction between word frequency and EmoPro, we conducted post hoc comparisons employing a Bonferroni correction using the emmeans function (Lenth et al., Reference Lenth, Buerkner, Herve, Love, Miguez, Riebl and Singmann2022). Post hoc comparisons revealed the differences between high and low EmoPro appeared only under high-frequency (FFD: Mean (high EmoPro) = 225 (SE = 5.31), Mean (low EmoPro) = 211 (SE = 5.32), contrast estimate = 0.06, SE = 0.02, t = 3.31, p<0.001; SFD: Mean (high EmoPro) = 225 (SE = 5.35), Mean (low EmoPro) = 210 (SE = 5.36), contrast estimate = 0.07, SE = 0.02, t = 3.52, p<0.001), as shown in Figure 1. No significant differences were found in low-frequency (FFD: Mean (high EmoPro) = 228 (SE = 5.30), Mean (low EmoPro) = 225 (SE = 5.31), contrast estimate = 0.01, SE = 0.02, t = 0.48, p = 0.63; SFD: Mean (high EmoPro) = 227 (SE = 5.34), Mean (low EmoPro) = 224 (SE = 5.33), contrast estimate = 0.01, SE = 0.02, t = 0.59, p = 0.55).

Figure 1.

The interaction between EmoPro and word frequency in FFD (with standard errors bars) for positive target words.

Note. The interaction patterns for FFD and SFD were consistent, so only the interaction plot for FFD is presented.

Moreover, the main effect of word frequency was significant for both early and late measures (FFD: b = 0.04, SE = 0.01, t = 2.72, p = 0.007; SFD: b = 0.03, SE = 0.01, t = 2.37, p = 0.02; GD: b = 0.04, SE = 0.02, t = 2.33, p = 0.02; TT: b = 0.04, SE = 0.02, t = 2.14, p = 0.03). High-frequency words elicited shorter fixations, reflecting faster lexical access compared to low-frequency words.

Discussion

Experiment 1 investigated whether EmoPro affects the retrieval of emotional information during Chinese natural reading, with words embedded in sentences under positive valence. Furthermore, it examined how word frequency, as a key form of linguistic information, moderates the retrieval advantage of emotional information associated with high-prototypical emotion words in natural language contexts.

Findings from Experiment 1 indicate that under positive valence, the degree of emotional semantic representation significantly affected reading processes. Consistent with previous research (Haro et al., Reference Haro, Calvillo, Poch, Hinojosa and Ferré2023; Wu et al., Reference Wu, Wu and Gao2024), high EmoPro words facilitated more efficient retrieval of emotional information from the lexicon. This advantage was evident in both early (e.g., FFD, SFD, GD) and later (e.g., TT) stages of lexical processing: high EmoPro words enabled faster access to emotional semantics, drawing and maintaining readers’ attention. Sustained attentional engagement resulted in longer fixation durations for high-prototypical emotion words during reading. In addition, the interaction between EmoPro and word frequency in the earliest measures (FFD, SFD) suggested that under positive valence, word frequency affected the retrieval of affective experiential information only during the initial stages of lexical access. In the initial stages of lexical access, the emotional semantics advantage of high EmoPro words appeared exclusively at high frequency, as frequent word occurrence enhances the automatic activation of emotional semantics. However, once lexical meaning was fully accessed (GD) and integrated during the late processing stage (TT), word frequency no longer had an impact. This suggests that the influence of word frequency on the emotional semantics of positive words is relatively weak during lexical access (Scott et al., Reference Scott, O’Donnell and Sereno2012), affecting only the initial stages of word processing.

Experiment 2

Experiment 2 further examined the effect of EmoPro under negative valence during reading and how word frequency moderates this effect. The experimental design followed the same procedure as in Experiment 1.

Regarding the impact of EmoPro in natural reading under negative valence, we anticipated results similar to Experiment 1, showing an emotional semantics advantage for high EmoPro words. We expected negative high EmoPro words to access emotional semantics more quickly, capture readers’ attention, and lead to longer fixation duration. However, we expected a different moderating effect of word frequency under negative valence compared to Experiment 1. Negative words are characterized by lower storage density and greater semantic distance within semantic memory (Unkelbach et al., Reference Unkelbach, Fiedler, Bayer, Stegmüller and Danner2008). Although frequent occurrences of negative words can strengthen their connection to semantics, this effect is constrained by their semantic distance. High word frequency primarily strengthens semantic activation when semantic distance is short (Collins & Loftus, Reference Collins and Loftus1975). Conversely, negative stimuli possess a distinct advantage due to heightened alertness, which is vital for survival and significantly influences attention (Sakaki et al., Reference Sakaki, Gorlick and Mather2011). Their heightened survival value makes negative information more attention-grabbing and engaging. However, the attentional advantage of negative stimuli varies with their frequency of occurrence. When negative words are frequent, their salience diminishes, reducing their ability to capture attention and sustain engagement. In contrast, infrequent negative words, due to their lower familiarity, are more likely to elicit alertness and engagement (Scott et al., Reference Scott, O’Donnell and Sereno2012). Thus, the impact of salience on attention to negative words is more pronounced at low frequencies (Scott et al., Reference Scott, O’Donnell and Sereno2012). The emotional semantic advantage of negative high EmoPro words arises from their faster extraction of emotional information during lexical access. When these words are frequent, the salience of their emotional information may decrease, leading to a reduced semantic advantage. In contrast, when these words are infrequent, their emotional information remains salient, making them more engaging and capturing readers’ attention more effectively. Therefore, we hypothesized that the emotional semantics advantage of negative high EmoPro words would be more pronounced at low frequencies. Specifically, we expected low-frequency negative high EmoPro words to exhibit heightened alertness in emotional meanings, capturing readers’ attention and leading to longer fixation duration.

Participants

To minimize individual differences in emotional semantics processing (Haro et al., Reference Haro, Hinojosa and Ferré2024), the same sample from Experiment 1 was used. To reduce fatigue from continuous participation, Experiment 2 was conducted one week after Experiment 1.

Materials and apparatus

Consistent with Experiment 1, after controlling for the number of strokes (F(3, 76) = 0.55，p = 0.65), word family size (F(3, 76) = 1.09，p = 0.36), abstractness (F(3, 76) = 0.70，p = 0.56), pleasantness (F(3, 76) = 0.27，p = 0.85), arousal (F(3, 76) = 0.29，p = 0.83), and age of acquisition (F(3, 76) = 1.95，p = 0.13), 80 two-character target words were selected from the word database used in Experiment 1. The mean values for these variables are presented in Table 5. High- and low-EmoPro words showed significant differences in emotional prototypicality (Mean high EmoPro = 4.01 (SD = 0.37), Mean low EmoPro = 2.54 (SD = 0.56), t(78) = 13.84, p < 0.001), but no significant differences in word frequency (per million frequency for Mean high EmoPro = 13.39 (SD = 34.14), per million frequency for Mean low EmoPro = 11.32 (SD = 18.89), t(78) = 0.34, p = 0.74; log frequency for Mean high EmoPro = 2.00 (SD = 0.77), log frequency for Mean low EmoPro = 2.19 (SD = 0.59), t(78) = 1.23, p = 0.22). In contrast, high- and low-frequency words exhibited significant differences in frequency (per million frequency: Mean high frequency = 21.05 (SD = 36.74), Mean low frequency = 3.66 (SD = 4.44), t(78) = 2.94, p = 0.004; log frequency: Mean high frequency = 2.42 (SD = 0.62), Mean low frequency = 1.77 (SD = 0.60), t(78) = 4.82, p < 0.001), but no significant differences in emotional prototypicality (EmoPro for Mean high frequency = 3.22 (SD = 0.93), EmoPro for Mean low frequency = 3.33 (SD = 0.83), t(78) = 0.54, p = 0.59).

Table 5.

Means (Standard deviations) for negative target word properties

In Experiment 2, we followed the design of Experiment 1, resulting in 80 unique sentence frames. Sentence lengths ranged from 18 to 34 characters, with a mean of 28 characters (SD = 3.78). Examples of experimental sentences are presented in Table 6. No significant differences in predictability, understandability, and plausibility were found across conditions (Fs < 2.20, ps > 0.10). The mean values of understandability, plausibility, and predictability for each condition are presented in Table 5.

Table 6.

Examples of experimental sentences for negative target words

Note. Target words presented in bold and italics above, but were not bold and non-italicized during experimental sessions.

The experimental sentences were organized into four lists, similar to Experiment 1.

Procedure

Same as Experiment 1. Participants achieved an average accuracy of approximately 92% (SD accuracy = 7%).

Result

Standard eye movement measurements and analysis methods were used, consistent with those in Experiment 1. Specifically, (1) trials with fewer than three fixations (0.1%); (2) loss of tracking due to coughing, head movements, or blinks within the AOI (0.1%); and (3) fixations deviating by more than 2.5 standard deviations from the mean within the AOI (2%). The mean fixation times for target words across all conditions are presented in Table 7, and statistical effects are summarized in Table 8.

Table 7.

Means (Standard Deviations) for eye-movement measures for negative target words

Table 8.

Results of linear mixed effects models for negative target words

The main effect of EmoPro was significant for FFD (b = −0.04, SE = 0.01, t = −2.75, p = 0.006), SFD (b = −0.04, SE = 0.02, t = −2.30, p = 0.03), GD (b = −0.04, SE = 0.02, t = −2.34, p = 0.02), and TT (b = −0.06, SE = 0.02, t = −2.36, p = 0.02). As in Experiment 1, longer fixations were observed for high EmoPro words, indicating that they captured more attention during both early and late stages. The interaction between word frequency and EmoPro was significant for early measures (FFD: b = −0.07, SE = 0.03, t = −2.52, p = 0.01; SFD: b = −0.09, SE = 0.03, t = −3.27, p = 0.001; GD: b = −0.11, SE = 0.03, t = −3.47, p < 0.001) as well as the late measure (TT: b = −0.16, SE = 0.05, t = −3.46, p = 0.001). This suggests that, under negative valence, word frequency modulated the emotional semantic advantage of high EmoPro words during both early and late stages. In line with Experiment 1, we performed post hoc comparisons with the Bonferroni correction using the emmeans function (Lenth et al., Reference Lenth, Buerkner, Herve, Love, Miguez, Riebl and Singmann2022) to further assess the significant interaction between word frequency and EmoPro under negative valence. The contrast result revealed the differences between high and low EmoPro words appeared only for low-frequency words (FFD: Mean (high EmoPro) = 235 (SE = 5.35), Mean (low EmoPro) = 218 (SE = 5.42), contrast estimate = 0.07, SE = 0.02, t = 3.51, p = 0.001; SFD: Mean (high EmoPro) = 236 (SE = 6.08), Mean (low EmoPro) = 216 (SE = 5.05), contrast estimate = 0.09, SE = 0.02, t = 3.87，p < 0.001; GD: Mean (high EmoPro) = 254 (SE = 6.39), Mean (low EmoPro) = 231 (SE = 6.47), contrast estimate = 0.09, SE = 0.02, t = 3.93, p < 0.001; TT: Mean (high EmoPro) = 349 (SE = 13.6), Mean (low EmoPro) = 300 (SE = 13.7), contrast estimate = 0.13, SE = 0.03, t = 3.85, p < 0.001), as shown in Figure 2. These differences were not significant for high-frequency words (FFD: Mean (high EmoPro) = 216 (SE = 5.38), Mean (low EmoPro) = 216 (SE = 5.39), contrast estimate = 0.003, SE = 0.02, t = 0.16, p = 0.87; SFD: Mean (high EmoPro) = 213 (SE = 5.75), Mean (low EmoPro) = 215 (SE = 5.17), contrast estimate = − 0.01, SE = 0.02, t = 0.37, p = 0.71; GD: Mean (high EmoPro) = 229 (SE = 6.70), Mean (low EmoPro) = 233 (SE = 6.69), contrast estimate = 0.02, SE = 0.02, t = 0.80, p = 0.42; TT: Mean (high EmoPro) = 308 (SE = 13), Mean (low EmoPro) = 308 (SE = 13), contrast estimate = −0.02, SE = 0.03, t = 0.76, p = 0.45). Consistent with Experiment 1, the main effect of word frequency was significant for both early (FFD: b = 0.05, SE = 0.02, t = 3.03, p = 0.004; SFD: b = 0.05, SE = 0.02, t = 3.22, p = 0.002; GD: b = 0.05, SE = 0.02, t = 2.85, p = 0.007) and late (TT, b = 0.07, SE = 0.02, t = 2.51, p = 0.02) measures. High-frequency words elicited shorter fixations than low-frequency words.

Figure 2.

The interaction between EmoPro and word frequency in GD (with standard errors bars) for negative target words.

Note. The interaction patterns for FFD, SFD, GD and TT were consistent, so only the interaction plot for GD is presented.

Discussion

Experiment 2 further examined the effect of EmoPro on natural reading under negative valence. It also explored how word frequency affects this effect to provide a more comprehensive understanding of language processing.

The results demonstrated that under negative valence, the influence of EmoPro on reading processes remained robust. Consistent with Experiment 1 and prior research (e.g., Haro et al., Reference Haro, Calvillo, Poch, Hinojosa and Ferré2023; Wu et al., Reference Wu, Wu and Gao2024), high EmoPro words demonstrated a significant semantic advantage in extracting emotional content, effectively capturing and sustaining readers’ attention. This was evidenced by prolonged fixation durations during both early processing stages (FFD, SFD, and GD) and the later stage of semantic integration (TT). A key difference from Experiment 1 was found in the moderating role of word frequency. In addition to its moderating effects observed during the initial stages of lexical processing, word frequency showed a sustained influence that extended into the later stage of semantic integration (i.e., from FFD to TT), highlighting its broader and more profound impact under negative valence, spanning multiple processing stages. Further analysis revealed that the emotional semantics advantage of high-prototypical emotional words during natural reading was confined to low-frequency words. This finding aligns with prior research (Scott et al., Reference Scott, O’Donnell and Sereno2012), which suggests that frequently encountered words reduce the salience of their emotional information, whereas less frequently encountered negative words exhibit heightened alerting salience, rendering them more effective at capturing attention.

General discussion

To investigate the impact of EmoPro on natural language processing, words differing in their emotional semantic representativeness were embedded within Chinese sentence contexts. Processing patterns were recorded using eye-tracking methods. Word frequency was manipulated to explore how linguistic information modulates these effects. These questions were explored under positive valence (Experiment 1) and negative valence (Experiment 2).

The results revealed significant semantic advantages for high EmoPro words, effects of word frequency, and their interactions in eye movement measures. This suggests that during natural reading, differences in emotional semantic activation are evident, with high EmoPro words facilitating faster activation of emotional semantics and earlier extraction of emotional information across both early and late stages. This resulted in prolonged fixations and heightened engagement, revealing an advantage of high EmoPro words in activating and retrieving emotional information during natural language processing. The semantic advantage of high EmoPro words was moderated by word frequency, with distinct patterns emerging across valence. Specifically, under positive valence, the moderating effect of word frequency was confined to the initial stages of lexical access, where high EmoPro words showed an emotional semantics advantage at high frequency. In contrast, under negative valence, the effect of word frequency spanned from the initial stages of lexical access to the later semantic integration stage, with high EmoPro words demonstrating an emotional semantic advantage at low frequency. These findings underscore the complex interplay of multiple sources of semantic information in the processing of emotional content in natural language and highlight the critical role of linguistic information in shaping the emotional content retrieval during language comprehension.

The influence of EmoPro on reading

The degree of emotional semantic representativeness for emotional words (i.e., EmoPro) influences the retrieval of emotional content during lexical access (Ferré et al., Reference Ferré, Guasch, Stadthagen-González, Hinojosa, Fraga, Marín and Pérez-Sánchez2024), with highly prototypical emotional words facilitating rapid emotional content extraction. However, current evidence supporting this advantage for high EmoPro words primarily comes from lexical decision tasks (e.g., Hao et al., 2023; Wu et al., Reference Wu, Wu and Gao2024), where words are presented in isolation. This manipulation includes a decision-making component (e.g., deciding whether the presented string is a word, Hao et al., 2023, Wu et al., Reference Wu, Wu and Gao2024 Experiment 1; or determining the word’s valence, Wu et al., Reference Wu, Wu and Gao2024 Experiment 2), reducing the construct validity of this variable as an index of online cognitive processes in word recognition (Kuperman et al., Reference Kuperman, Drieghe, Keuleers and Brysbaert2013), and excludes context information and parafoveal previewing of upcoming content, both of which significantly impact the time course of visual word processing (Alexander & Buzzell, Reference Alexander and Buzzell2024). Consequently, it remains unclear whether the advantage of EmoPro in lexical processing in this task can be generalized to natural language processing, especially when readers can preview upcoming content through parafoveal vision. In natural language processing, emotional information is typically conveyed by embedding emotion words within sentences (Scott et al., Reference Scott, O’Donnell and Sereno2012). Therefore, to examine the impact of EmoPro in natural language, a naturalistic task is essential.

Based on this, the present study employed a natural reading task in which participants silently read and comprehend sentences containing emotional words with varying levels of EmoPro while their eye movements were recorded. This task not only ensures natural language processing patterns but also reveals the online semantic processing of emotional words within sentences, allowing for the observation and analysis of visual attention allocation during sentence processing. In addition, by incorporating eye-tracking methodology, this task captures the dynamic process of sentence processing, during which lexical meaning is rapidly activated and integrated into the context, providing ecologically valid evidence of EmoPro’s impact.

The results obtained using this task were consistent with prior research (Haro et al., Reference Haro, Calvillo, Poch, Hinojosa and Ferré2023; Wu et al., Reference Wu, Wu and Gao2024), which indicated that high-prototypical emotional words are more effective in activating emotional semantics. The findings also align with prior research on emotional word types (Gu & Chen, Reference Gu and Chen2024), which demonstrated that when emotional words directly represent emotional semantics, emotional information exerts a rapid and enduring influence during lexical processing. This effect persists throughout reading, continually capturing and sustaining readers’ attention, leading to longer fixations. Beyond corroborating findings on the semantic advantage of high EmoPro words at the isolated word level (Haro et al., Reference Haro, Calvillo, Poch, Hinojosa and Ferré2023; Wu et al., Reference Wu, Wu and Gao2024; Zheng et al., Reference Zheng, Zhang, Guo, Guasch and Ferré2023), this study used eye-tracking methods to reveal fine-grained emotional semantics processing in natural language contexts, demonstrating its impact on attention allocation during reading. Readers exhibited heightened sensitivity to the emotional semantics of high EmoPro words, devoting more attention to them. This finding represents a significant advancement, providing the first direct evidence of emotional semantic differences in natural language processing with enhanced ecological validity. Furthermore, it supports prior evidence on the relationship between high EmoPro words and affective experiences (Ferré et al., Reference Ferré, Guasch, Stadthagen-González, Hinojosa, Fraga, Marín and Pérez-Sánchez2024), showing that these words facilitate more efficient access to emotional information during sentence processing.

The finding that high EmoPro words demonstrate a semantic advantage during the initial stages of processing highlights the critical role of affective experiential information in facilitating semantic activation and retrieval. This finding aligns with the embodied theory of semantic representation (Vigliocco et al., Reference Vigliocco, Meteyard, Andrews and Kousta2009), which suggests that when word meaning acquisition aligns with affective development, affective experiences establish robust semantic associations with word meanings in the mental lexicon. As a result, these experiences can be rapidly activated during subsequent lexical access (Tang, Fu, et al., Reference Tang, Fu, Wang, Liu, Zang and Kärkkäinen2023). The present findings from natural reading extend the affective experience embedded in words to more complex semantic units (i.e., sentences), suggesting that deep affective connections underpin emotional embodiment by influencing emotions activated across various semantic levels. It further emphasizes the importance of a strong link between lexical meaning and deep affective experience, a relationship essential for processing emotional semantics. The early semantic advantage of high EmoPro words also suggests that rapidly extracted emotional information impacts attention allocation even during the initial stages of visual recognition, effectively capturing cognitive processing resources (Citron, Reference Citron2012). These results provide strong empirical evidence for the emotional processing advantage, demonstrating that emotional information affects cognitive responses more rapidly and intensely. This highlights the prioritized and dominant role of emotional information within the cognitive system.

This study further demonstrated that the semantic advantage of high EmoPro words in sentence processing spans from the initial stages of lexical access to the later stages of semantic integration. High EmoPro words consistently capture and sustain readers’ attention, making disengagement difficult. This finding supports the model of motivated attention and affective states, which suggests that individuals experience delayed attentional disengagement for emotionally salient information (Zsidó et al., Reference Zsidó, Bali, Kocsor and Hout2023). Emotionally significant stimuli, due to their ecological relevance, rapidly attract and sustain attention, hindering disengagement (Pratto & John, Reference Pratto and John1991). These results highlight the ecological significance of high EmoPro words, emphasizing their stronger attentional bias toward emotional information. High EmoPro words are typically acquired in early childhood, with their affective experiential information emerging through social interactions. In contrast, low EmoPro words are generally learned later, with affective information emerging only after word meanings are refined (Ferré et al., Reference Ferré, Guasch, Stadthagen-González, Hinojosa, Fraga, Marín and Pérez-Sánchez2024). Consequently, high EmoPro words carry more ecologically salient emotional information, eliciting greater attentional engagement than low EmoPro words. This finding aligns with research on emotional images and faces, which demonstrates that stimuli with prominent emotional content are more likely to capture attention and induce an attentional bias (Zsidó et al., Reference Zsidó, Bali, Kocsor and Hout2023). The present finding highlights the cross-modal consistency of the emotional processing advantage, reflecting its robustness across different sensory modalities. Whether presented as visual (e.g., images or faces; Schindler & Bublatzky, Reference Schindler and Bublatzky2020; Zsidó et al., Reference Zsidó, Bali, Kocsor and Hout2023), auditory (e.g., emotional tone; Tang, Fan, et al., Reference Tang, Fu, Wang, Liu, Zang and Kärkkäinen2023), or semantic cues (e.g., the degree to which words represent emotional semantics), emotionally salient information consistently induces attentional bias across modalities.

The moderation of word frequency on EmoPro’s effect during reading

This study revealed that high-frequency words elicited shorter fixation durations and were accessed more rapidly during sentence reading compared to low-frequency words. This finding aligns with previous research (Mei et al., Reference Mei, Chen, Xia, Yang and Liu2025), which suggests that repeated exposure to a word reduces its activation threshold, facilitating faster access to its meaning (Adelman et al., Reference Adelman, Brown and Quesada2006). Moreover, the findings indicate that semantic activation is inherently multidimensional, encompassing both affective information and frequency-based distributional properties of words. The activation of frequency information facilitates the integration of words into sentence contexts during reading (Caldwell-Harris, Reference Caldwell-Harris2021). This highlights the importance of word frequency as a form of linguistic information that modulates the activation threshold, emphasizing its critical role in the processing of emotional information within natural language contexts.

This study revealed that under positive valence, word frequency influenced the semantic advantage of high EmoPro words, which was limited to the initial stages of lexical processing. This indicates that the emotional semantic advantage of high EmoPro words in the earliest phase of lexical recognition depends on linguistic information for activation. This effect is closely associated with the intrinsic semantic advantage of positive words. For positive words, characterized by high storage density and shorter activation distances in semantic memory (Unkelbach et al., Reference Unkelbach, Fiedler, Bayer, Stegmüller and Danner2008), linguistic information serves a more prominent role in semantic processing. When positive words appear frequently, the connection between emotional meanings and lexical concepts is reinforced, making the emotional semantic advantage of positive high EmoPro words more pronounced at high frequencies. Due to the semantic advantage of positive words and their rapid accessibility without requiring sustained deep processing (Unkelbach et al., Reference Unkelbach, Fiedler, Bayer, Stegmüller and Danner2008), the influence of word frequency was transient, manifesting only in the earliest stages of lexical processing. This supports the view that positive valence derives a greater advantage from semantic processing (Snefjella & Kuperman, Reference Snefjella and Kuperman2016) and that linguistic information enhances emotional embodiment, facilitating more automatic semantic activation (Borghi & Barsalou, Reference Borghi and Barsalou2021).

In contrast, the moderating effect of word frequency on the EmoPro advantage under negative valence showed a distinct pattern. The moderating effect of word frequency not only failed to enhance the emotional semantic advantage of high EmoPro words but also imposed a sustained constraint across early to late processing stages. Specifically, the semantic advantage of high EmoPro words was observed only under low-frequency conditions. This finding aligns with prior research on the emotional advantage of negative words in low-frequency contexts (Kuperman et al., Reference Kuperman, Estes, Brysbaert and Warriner2014; Scott et al., Reference Scott, O’Donnell and Sereno2012), suggesting that for negative words, abundant linguistic information diminishes the salience of their emotional content. Negative words, characterized by low storage density and distant activation distances in semantic memory, are dispersed throughout the network (Unkelbach et al., Reference Unkelbach, Fiedler, Bayer, Stegmüller and Danner2008). Thus, the contribution of word frequency to strengthening connections for negative words is constrained by their semantic distance within the network, making the influence of high frequency on their emotional semantics relatively limited. Instead, the processing advantage of negative words stems from their association with survival alertness (Sakaki et al., Reference Sakaki, Gorlick and Mather2011). The alertness conveyed by negative information is highly salient for survival, increasing its likelihood of capturing attention (Pratto & John, Reference Pratto and John1991). When negative words appear frequently, their salience diminishes; however, infrequent occurrences enhance the information’s salience and increase its likelihood of capturing attention (Scott et al., Reference Scott, O’Donnell and Sereno2012). Therefore, the emotional semantic advantage of negative high EmoPro words appears at low frequency. This highlights a crucial aspect of emotional embodiment under negative valence, involving the cognitive simulation and integration of survival instincts. In addition, negative words require deep processing to access their meanings accurately (Unkelbach et al., Reference Unkelbach, Fiedler, Bayer, Stegmüller and Danner2008). They are more susceptible to other lexical information (Kuperman et al., Reference Kuperman, Estes, Brysbaert and Warriner2014), causing the impact of word frequency on negative words to persist across all processing stages.

Overall, the effect of word frequency on processing emotional semantics activation differences across valences supports distinct cognitive processing patterns of positive and negative valences (Sakaki et al., Reference Sakaki, Gorlick and Mather2011). Specifically, positive valence is linked to a more flexible semantic processing advantage, while negative valence is associated with a more essential evolutionary advantage (Sakaki et al., Reference Sakaki, Gorlick and Mather2011). This highlights that word frequency not only influences the storage and retrieval of words in memory but also modulates the extent of emotional embodiment based on valence: more direct and automatic for positive emotions, and more profound and integrative for negative ones. Furthermore, this effect emphasizes that emotional embodiment in natural language processing is not merely a mapping of lexical and emotional meanings but rather entails the dynamic integration of multiple semantic information sources.

Limitations

This study has several limitations. First, task characteristics can affect the effects of lexical factors in lexical processing (Kuperman et al., Reference Kuperman, Drieghe, Keuleers and Brysbaert2013). This study used only a natural reading task to examine EmoPro’s impact on natural language processing, without comparing the effects of different tasks on sentence processing. Recent studies have shown that emotional content is more easily accessed during vocalization tasks (e.g., oral reading) (Mason et al., Reference Mason, Hameau and Nickels2025), which require precise semantic correspondence or phonological transcoding between orthographic analysis and phonological code identification, compared to visual word recognition tasks (e.g., lexical decision or silent reading) (Zoccolotti et al., Reference Zoccolotti, De Luca, Di Filippo, Marinelli and Spinelli2018). Crucially, the initial phoneme of a word plays a key role in this task (Kuperman et al., Reference Kuperman, Drieghe, Keuleers and Brysbaert2013). This suggests that if using a naturalistic oral reading task in Chinese sentence processing, EmoPro might be more sensitive to the semantic radicals of the characters or the semantic transparency of initial characters. Future research could further compare the impact of EmoPro in silent and oral reading tasks.

Then, the difference in raw word frequency (per million) between high- and low-frequency groups was limited due to material constraints. To address this issue, log-transformed frequencies were used to ensure the validity of the manipulation. Previous research suggests that the effects of the experimental manipulation persist even when the frequency range is expanded (Cheng et al., Reference Cheng and Xie2021). Future studies could further explore this by incorporating higher-frequency words. In addition, the sentence construction requirements of the eye-tracking experiments necessitated separate material controls for positive and negative valence. As a result, lexical attributes were not fully matched across the two experiments, limiting the potential for direct comparative analysis. Future studies could address this limitation by incorporating valence as a variable, allowing for more comprehensive and integrative findings. Moreover, to reduce potential effects of experimental fatigue and possible carry-over effects related to valence-based processing, the two experiments were conducted one week apart, with the positive valence experiment conducted first, following prior research (Knickerbocker et al., Reference Knickerbocker, Johnson and Altarriba2015; Tang & Ding, Reference Tang and Ding2024). Future studies could counterbalance the order of positive and negative valence experiments.

Third, although the sample size and number of sentences in this study were based on prior research (Wu et al., Reference Wu, Wu and Gao2024), they were still relatively limited, particularly with only 20 sentences per condition. A post hoc power analysis was conducted using the Mixedpower package (Kumle et al., Reference Kumle, Võ and Draschkow2021) in R with 1000 simulations based on the current sample of 40 participants and 80 sentences. The analysis revealed that the EmoPro effects on sentence integration processing, as examined in this study, could be replicated in over 80% of cases at the α = 0.05 level (positive valence: 95% for 40 participants, 94% for 80 sentences; negative valence: 83% for 40 participants, 81% for 80 sentences). To enhance the robustness and generalizability of these findings, future research could increase both the sample size and the number of experimental sentences.

Last, this study’s sample was gender-imbalanced, with a significantly higher number of female participants than male participants. Consequently, the external validity of the findings regarding the male population may be restricted. Future studies should aim to balance the gender distribution.

Conclusion

This study, using eye-tracking methods, offered the first evidence from natural language processing that the degree of emotional semantics representation significantly shapes the perception of emotion. High EmoPro words showed a pronounced semantic advantage, enabling readers to retrieve emotional content more efficiently, sustain deeper attentional engagement, and experience greater difficulty disengaging. These findings highlight the ecological relevance of high EmoPro words, supporting both the embodied theory of semantic representation and the model of motivated attention and affective states. Furthermore, the semantic advantage of high EmoPro words was shown to be affected differently by word frequency across valence. This finding suggests that emotion recognition in language is shaped by the interplay between semantic representation and linguistic information. It emphasizes the intricate relationship between affective and linguistic information in emotional semantics embodiment, with word frequency playing a crucial role in shaping the extent of embodiment.

Replication package

The database for this study is available at https://osf.io/jr7zm/.

Acknowledgement

We thank all participants for their invaluable contributions and all reviewers for their constructive suggestions on our manuscript.

Competing interests

The authors declare no conflicts of interest.

Declarations of AI-based tools

No AI-based tools were used in preparing this manuscript.

Funding statement

This research was supported by grants from the National Natural Science Foundation of China (Grant number 32271119).

Appendix, the experimental sentences with target words

Each set of target words was paired with four sentence frames, and a Latin square design ensured that each target word and sentence frame appeared only once in each experimental list (total of four lists), preventing repeated exposure to target words and sentences.

The experimental sentences for target words were arranged across four conditions: low frequency – high EmoPro, low frequency – low EmoPro, high frequency – high EmoPro and high frequency – low EmoPro. Target words are in bold italics .

Experiment 1 (positive valence).

  1. 1. 我们在那个无比 欢畅/热闹/畅快/浪漫 的夜晚里度过了一段难忘的时光。

  2. 2. 我依然能记得那个 欢畅/热闹/畅快/浪漫 的夜晚，那是我们共同创造的美好回忆。

  3. 3. 大家都说那个晚上的聚会真是 欢畅/热闹/畅快/浪漫 ，让人回味无穷。

  4. 4. 我觉得这次相聚可以说是十分 欢畅/热闹/畅快/浪漫 ，让人难以忘记。

  5. 5. 来到这片山谷我看到了一幅 惬意/迷人/惊喜/壮观 的画面，让人心旷神怡。

  6. 6. 旋律响起，歌词中那些 惬意/迷人/惊喜/壮观 的场景便在脑海中纷纷浮现。

  7. 7. 这个场景让我想起了旅行中的 惬意/迷人/惊喜/壮观 画面，真是让人怀念。

  8. 8. 很难想象这竟是一个如此 惬意/迷人/惊喜/壮观 的地方，真让人流连忘返。

  9. 9. 这次演出对我来说是十分 顺心/风光/轻松/出色 ，不枉这段时间的准备。

  10. 10. 这些举措能让项目汇报非常 顺心/风光/轻松/出色 ，这下我也不用担心了。

  11. 11. 这些工作让我的报告很 顺心/风光/轻松/出色 ，现场听众的反响也很高。

  12. 12. 这次的团队合作无比 顺心/风光/轻松/出色 ，也让我很期待下一次的合作。

  13. 13. 在那段非常时期是她的 激励/赏识/欣赏/信赖 支撑着我，让我重新站了起来。

  14. 14. 求学过程中王老师对我的 激励/赏识/欣赏/信赖 促使我不断进步和超越自己。

  15. 15. 在我的印象里她总是 激励/赏识/欣赏/信赖 每个人，所以她非常受欢迎。

  16. 16. 他对待工作的态度和对同事的 激励/赏识/欣赏/信赖 ，让他在公司中建立了良好的声誉。

  17. 17. 网络上的描述为这个 激奋/壮丽/动人/美丽 的故事增添了不少神秘色彩。

  18. 18. 眼前的这一切简直太 激奋/壮丽/动人/美丽 了，所有的努力都变得值得了。

  19. 19. 她的讲述让这一事迹显得如此 激奋/壮丽/动人/美丽 ，让我不得不感叹她的文字功底。

  20. 20. 这则新闻报道讲述了一个无比 激奋/壮丽/动人/美丽 的寓言故事，让人为之动容。

  21. 21. 历经多日她交出的这份答卷如此 亢奋/不凡/如意/辉煌 ，大家都赞叹不止。

  22. 22. 纵使阔别多年他在舞台上还是那么 亢奋/不凡/如意/辉煌 ，不愧是国家艺术大师的功底。

  23. 23. 他这次的巡演效果可谓是十分 亢奋/不凡/如意/辉煌 ，在场的每个人都为之震撼。

  24. 24. 这次产品发布会的直播成绩可真是无比 亢奋/不凡/如意/辉煌 ，大家都十分满意。

  25. 25. 我第一次见到他，就被他那份 崇拜/孝心/爱心/温暖 所吸引，打心底里敬佩。

  26. 26. 他们经常对我说：我对他们的 崇拜/孝心/爱心/温暖 让他们无比欣慰。

  27. 27. 他总是以行为在表达他的 崇拜/孝心/爱心/温暖 ，而且始终坚持着。

  28. 28. 这幅作品映射了大众内心中的 崇拜/孝心/爱心/温暖 ，不愧是震撼人心的艺术作品。

  29. 29. 在那个夏日是我们之间的 逗乐/亲密/尊重/诚意 让我两存同去异，并且迅速升温。

  30. 30. 即使是正式场合依然能感受同事们之间的 逗乐/亲密/尊重/诚意 ，这大概就是企业文化吧。

  31. 31. 往往双方之间的 逗乐/亲密/尊重/诚意 尤为重要，这可以升华双方的关系。

  32. 32. 我和她互相间的 逗乐/亲密/尊重/诚意 让我们的关系十分稳定。

  33. 33. 这次看到我在学校比赛中的 骄傲/卓越/自豪/灿烂 成绩，父母也都十分开心。

  34. 34. 他们都坚定地认为我会拥有一个 骄傲/卓越/自豪/灿烂 的未来，对此我也是深信不疑的。

  35. 35. 大家都认为我未来的成绩会很 骄傲/卓越/自豪/灿烂 ，这让我备受鼓舞和更自信。

  36. 36. 大家没有预料到我能取得如此 骄傲/卓越/自豪/灿烂 的成绩，改变了对我的看法。

  37. 37. 在周围人的衬托下，他身上那份 安宁/富贵/激情/勤奋 十分凸显，不得不让人多注意几眼。

  38. 38. 看了宣传片后我觉得那应该是个 安宁/富贵/激情/勤奋 之地，不由得在我心中神秘了起来。

  39. 39. 我对于他的评价，想到两个字- 安宁/富贵/激情/勤奋 ，最能凸显他的特质。

  40. 40. 爷爷总是对我回忆起他过往的 安宁/富贵/激情/勤奋 日子，让我很是好奇和向往。

  41. 41. 这封来信中，随处可见他用 赞扬/豪放/真挚/真诚 的文字在抒发他内心深处的情谊。

  42. 42. 今早看到这份来信中 赞扬/豪放/真挚/真诚 的文字，我内心中的一股热潮不停翻涌。

  43. 43. 我想恐怕没人会对这样一段 赞扬/豪放/真挚/真诚 的文字会无动于衷。

  44. 44. 在他的作品之中，可以时常看到他 赞扬/豪放/真挚/真诚 的表达，不得不说文字功底之深。

  45. 45. 不管走了多久我都会 敬重/孝顺/依恋/报答 尊师，因为他教会了我许多人生的智慧。

  46. 46. 不管作为学生还是子女，我都会 敬重/孝顺/依恋/报答 你，不会忘了你们对我的付出。

  47. 47. 虽行将致远，而我会一直 敬重/孝顺/依恋/报答 恩师，因为他是点亮我人生路途的一盏明灯。

  48. 48. 无论身在何处我内心永远会 敬重/孝顺/依恋/报答 这份师恩，他的教诲让我受益终生。

  49. 49. 在他们关系中是彼此的 爱惜/谦逊/欢喜/友好 让他们携手走过了那么长一段岁月。

  50. 50. 一段关系是靠双方彼此 爱惜/谦逊/欢喜/友好 ，而不是单方面付出与讨好。

  51. 51. 或许是两个人彼此的 爱惜/谦逊/欢喜/友好 才能让双方关系走的更长远。

  52. 52. 在品读他们两的关系后，我发现双方的 爱惜/谦逊/欢喜/友好 是维持一段关系的关键因素。

  53. 53. 是他让我学会怎样 恭贺/亲近/珍爱/奖励 他人才能让别人感觉更好。

  54. 54. 我还记得他告诉我如何 恭贺/亲近/珍爱/奖励 他人的一些有用建议。

  55. 55. 现在这种结果让大家更想 恭贺/亲近/珍爱/奖励 他了并且想问问他当时的情况。

  56. 56. 当时我应该对她表达 恭贺/亲近/珍爱/奖励 让她知道我的态度。

  57. 57. 无人不对她所表现出的 愉悦/优秀/热情/才华 表示感叹，都想与她深交。

  58. 58. 在赛场上他展现的不仅是自身的 愉悦/优秀/热情/才华 ，更是一种对挑战的勇敢面对。

  59. 59. 他的技艺不仅让人见识了他当下的 愉悦/优秀/热情/才华 ，更是展现了音乐的魅力。

  60. 60. 这幅画不仅尽显她在艺术上的 愉悦/优秀/热情/才华 ，还让人们看到了她对生活的感悟。

  61. 61. 他的这番讲话让人 安心/强大/着迷/进步 ，这或许就是领导者的魄力吧。

  62. 62. 领袖人物的讲话使得我们更加 安心/强大/着迷/进步 ，这就是灵魂人物的体现。

  63. 63. 团队领袖的发言让我们每个人都更 安心/强大/着迷/进步 ，这大概就是团队的凝聚力所在。

  64. 64. 每次导师的说话都让我们更 安心/强大/着迷/进步 ，这应该就是灵魂导师的化身。

  65. 65. 这里的夜景创造出一种无比 欢愉/壮美/欢快/甜美 的视觉享受，吸引了众多摄影爱好者。

  66. 66. 墙壁上的画作描绘出了一种 欢愉/壮美/欢快/甜美 的山水意境，可是让人无限遐想。

  67. 67. 我被他字里行间所流露的 欢愉/壮美/欢快/甜美 意境深深吸引。

  68. 68. 街头巷尾所展现的 欢愉/壮美/欢快/甜美 风采让我仿佛穿越到小说中的世界里。

  69. 69. 即使现在他的技艺还略显稚嫩，但这样 舒心/精湛/真心/用功 的作品很难不让人心动。

  70. 70. 他竟能创作出如此 舒心/精湛/真心/用功 的作品，可见其潜力多大。

  71. 71. 他在艺术创作上是多么得 舒心/精湛/真心/用功 ，足以看出他的功力之深。

  72. 72. 每天她都是如此 舒心/精湛/真心/用功 地安排生活，保持自己对生活的热情。

  73. 73. 故地重游，过去那些 欢欣/珍贵/痛快/亲热 的回忆也在此刻再次回温。

  74. 74. 再次回到故地，过往那些 欢欣/珍贵/痛快/亲热 的记忆再次被激活。

  75. 75. 此时此景多想把我两这些 欢欣/珍贵/痛快/亲热 的记忆给牢牢封印在心里。

  76. 76. 风吹云涌的当下是多么 欢欣/珍贵/痛快/亲热 的瞬间，我更要好好享受每一刻。

  77. 77. 这个小园里的一切都显得那么 舒畅/聪慧/快活/平安 ，它巧妙地隔离了外界的嘈杂与不安。

  78. 78. 这里的每一个细节都流露出一种 舒畅/聪慧/快活/平安 的气质，让人置身于一片宁静的净土。

  79. 79. 宁静的小镇随处都流露出 舒畅/聪慧/快活/平安 的气息，巧妙地让人沉浸在一片安定中。

  80. 80. 花园中花类设计无不显示出其 舒畅/聪慧/快活/平安 的气质，巧妙地让人进入心中的宁静。

Experiment 2 (negative valence).

  1. 1. 这家族曾被誉为豪门，但在遭受 悲苦/极端/消沉/危急 的困境后名誉一落千丈。

  2. 2. 即使是时过境迁，那个 悲苦/极端/消沉/危急 的夜晚依然让往后的日子都是一种煎熬。

  3. 3. 这部小说直观地揭示了 悲苦/极端/消沉/危急 的社会，真实到让人震撼。

  4. 4. 现在每个人都陷入了一种 悲苦/极端/消沉/危急 的状态，不得不面对困境和不安。

  5. 5. 这部电影通过人物上的 焦躁/虚伪/暴躁/变态 性格，展现了现实的残酷。

  6. 6. 他的笑容背后隐藏着一颗 焦躁/虚伪/暴躁/变态 的内心，让人同情又无法理解。

  7. 7. 他的性格变得也越来越 焦躁/虚伪/暴躁/变态 ，让周围的人都感到难以适从。

  8. 8. 我算是见识了他那 焦躁/虚伪/暴躁/变态 的性格，让我绝对不想见他第二次。

  9. 9. 他的画作内容总是充斥着 哀愁/阴暗/痛苦/罪恶 ，毫无艺术魅力。

  10. 10. 他的眼神里充满了 哀愁/阴暗/痛苦/罪恶 ，让人感觉十分沉重。

  11. 11. 她背后有一种不为人知的 哀愁/阴暗/痛苦/罪恶 正悄悄地侵蚀着她。

  12. 12. 主人公是如何在这样一个 哀愁/阴暗/痛苦/罪恶 的家庭中挣扎，人性的脆弱被展现得淋漓尽致。

  13. 13. 我忘不了在那段非常时期，是她的 忿怒/多疑/厌倦/怨言 击溃了我且无法承受这一切。

  14. 14. 是那位我信任的李老师的 忿怒/多疑/厌倦/怨言 击垮了我，让我对自己的能力再无任何信心。

  15. 15. 在我看来是她对朋友的 忿怒/多疑/厌倦/怨言 深深地伤害了两人之间的情谊。

  16. 16. 他对待工作的态度和对同事的 忿怒/多疑/厌倦/怨言 ，让他在公司中失去了信誉。

  17. 17. 人生的尽头落得如此 惋惜/寒酸/失意/恶果 ，也不过是他咎由自取。

  18. 18. 落魄背影的他落得如今这般 惋惜/寒酸/失意/恶果 ，终是他恣意妄为的下场。

  19. 19. 曾经指点江山的他如今竟落得如此 惋惜/寒酸/失意/恶果 ，可真是风水轮流转！

  20. 20. 曾经的英勇战士落得如此 惋惜/寒酸/失意/恶果 ，在他身上的一切荣誉和尊严都已成为过去。

  21. 21. 面对他这种日积月累所形成的 惊恐/野蛮/惊慌/恶霸 心态，我是有苦说不出也无力应对。

  22. 22. 看到大街上人群里的 惊恐/野蛮/惊慌/恶霸 ，让我不得不害怕和担忧。

  23. 23. 我再也不愿遭受他那样的 惊恐/野蛮/惊慌/恶霸 ，对我来说是十分折磨。

  24. 24. 劫匪四起的年代，人们面临着各种 惊恐/野蛮/惊慌/恶霸 ，不得不奋力挣扎生存。

  25. 25. 在这紧要关头人们的四周遍地 哀伤/危难/忧愁/谣言 ，一时间人心惶惶，不知所措。

  26. 26. 如今有关生死存亡的 哀伤/危难/忧愁/谣言 一时间漫天遍野，民众们就如惊弓之鸟般生存着。

  27. 27. 人们每天都面临数不尽的 哀伤/危难/忧愁/谣言 ，每一日都是惶惶不安。

  28. 28. 疫情肆虐之下，网络上所渲染的 哀伤/危难/忧愁/谣言 让广大群众心神不安。

  29. 29. 在那个冰冷的夜晚，他的行为就如 憎恨/禽兽/仇恨/恶魔 一样让人不寒而栗。

  30. 30. 在那场激烈的辩论中对方的言辞就像 憎恨/禽兽/仇恨/恶魔 一样，充满了攻击性和侮辱。

  31. 31. 她在这段感情里的所作所为就如 憎恨/禽兽/仇恨/恶魔 一般，牢牢扎根在我脑海里。

  32. 32. 在那次失利的比赛后他的眼神犹如 憎恨/禽兽/仇恨/恶魔 一样，行为超出了正常理智。

  33. 33. 他的解释深刻揭示了他内心的 愤慨/狂妄/失落/质疑 ，透露出更深层次的意图。

  34. 34. 他的每一个举动都流露出他心中的 愤慨/狂妄/失落/质疑 ，但好像无人关注他的表现。

  35. 35. 今日这番露面才让他心中的 愤慨/狂妄/失落/质疑 尽显无疑，让大家了解了一个真实的他。

  36. 36. 她的言论完全揭示了她内心的 愤慨/狂妄/失落/质疑 ，让大家对她有了更深入的认识。

  37. 37. 这次比赛的结果让他 伤感/嚣张/伤心/取笑 的是自己势在必得的决心。

  38. 38. 如今能够让他 伤感/嚣张/伤心/取笑 的是自己的这份成绩。

  39. 39. 在比赛中能让她 伤感/嚣张/伤心/取笑 的是自己的实力。

  40. 40. 在公司这些年能让他 伤感/嚣张/伤心/取笑 的是自己的资历。

  41. 41. 在背后突然有人对我 愤然/凶狠/急躁/勒索 ，让我完全措手不及。

  42. 42. 在谈判中竟然有人对我 愤然/凶狠/急躁/勒索 ，让整个场面陷入了沉默。

  43. 43. 那天晚上突然有人在背后对我 愤然/凶狠/急躁/勒索 ，让我一时间大脑空白。

  44. 44. 在我上台发言时竟然有人对我 愤然/凶狠/急躁/勒索 ，这让我感到非常慌乱。

  45. 45. 那天突然听到关于好友 沉痛/病危/悲痛/犯罪 的消息时，我整个人都陷入了错愕中。

  46. 46. 我们这一代正处于这样一个 沉痛/病危/悲痛/犯罪 的现实社会中，又怎能独善其身呢？

  47. 47. 微信群中关于好友的 沉痛/病危/悲痛/犯罪 消息让我十分错乱。

  48. 48. 艺术家通过作品展示了 沉痛/病危/悲痛/犯罪 的社会现实，强烈引起了我的共鸣。

  49. 49. 她在街角被当作丑小鸭一样任人 憎恶/凌辱/轻视/玩弄 ，那份孤独无助谁又能听得见。

  50. 50. 此刻他站在舞台上感觉像个小丑被人 憎恶/凌辱/轻视/玩弄 ，仿佛失去了自我。

  51. 51. 我不能忍受自己像个小丑一样被人 憎恶/凌辱/轻视/玩弄 ，我要坚决地去维护自己。

  52. 52. 在这段感情里她像个小丑一样被他 憎恶/凌辱/轻视/玩弄 ，可见是多么不平等的感情。

  53. 53. 他这般作为无非是因为自己 惧怕/侵犯/害怕/抛弃 了权贵，不能再坚持自己的选择了。

  54. 54. 他的退缩可能是因为 惧怕/侵犯/害怕/抛弃 了强权，失去了自己的信念和方向。

  55. 55. 他的沉默无非是因为 惧怕/侵犯/害怕/抛弃 了皇权，无法再为理想而战斗。

  56. 56. 他的妥协无非是因为 惧怕/侵犯/害怕/抛弃 了商业巨头，中断了自己的创作。

  57. 57. 他的种种不法行为让被 震怒/骚扰/恼怒/抄袭 的芳芳决定要有所行动。

  58. 58. 那些无休止的谣言使得被 震怒/骚扰/恼怒/抄袭 的晓华决定要站出来澄清。

  59. 59. 那些不正当行为导致作家时常被 震怒/骚扰/恼怒/抄袭 ，有必要采取法律手段了。

  60. 60. 他的作为让文艺人员不断被 震怒/骚扰/恼怒/抄袭 ，大家纷纷都对他进行了起诉。

  61. 61. 他谈话过程中的举动让我感到十分 烦心/蛮横/内疚/可耻 ，这让我对他有所改观。

  62. 62. 她突然提到的事情让我觉得非常 烦心/蛮横/内疚/可耻 ，只想快点离开。

  63. 63. 那家伙的言论让在场的人都觉得非常 烦心/蛮横/内疚/可耻 ，气氛一度变得尴尬。

  64. 64. 他提到的一些事情让我觉得有些 烦心/蛮横/内疚/可耻 ，只想沉默不予回应。

  65. 65. 一个人如果变得真正 沉闷/低劣/消极/狡猾 时，行为也会陷入以自我为中心。

  66. 66. 在很长一段时间里她的 沉闷/低劣/消极/狡猾 行为总是让大家无言以对。

  67. 67. 这种事情只会让他那 沉闷/低劣/消极/狡猾 的人生愈来愈糜烂。

  68. 68. 她的童年接触的都是一些很 沉闷/低劣/消极/狡猾 的伙伴，所以如今这样就不足为奇了。

  69. 69. 这次旅行的经历对他们来说非常 疲乏/惨重/难受/恶劣 ，体力和心理都消耗严重。

  70. 70. 艰苦训练的经历对瘦弱的她来说是非常 疲乏/惨重/难受/恶劣 的，心灵时常被受打击。

  71. 71. 再次的变故对他来说是非常 疲乏/惨重/难受/恶劣 了，让他再一次地陷入了无助中。

  72. 72. 再次出现的紧迫任务对他来说是十分 疲乏/惨重/难受/恶劣 了，让人更加对他的状况有所担忧。

  73. 73. 看她那满脸笑容的脸上透露出些许 惶恐/虚假/悲伤/虚荣 ，就让人不禁唏嘘。

  74. 74. 他演讲时的语调夹杂着一些 惶恐/虚假/悲伤/虚荣 ，让人听了感觉不舒服。

  75. 75. 他那看似冷静的眼神中流露出一丝 惶恐/虚假/悲伤/虚荣 ，让人不由得深思他的真实内心。

  76. 76. 她眼神中隐约透露出丝丝 惶恐/虚假/悲伤/虚荣 ，让人对她的信念产生了疑问。

  77. 77. 在他身上总会有一些 疑虑/冒失/茫然/恶意 ，因而周围无人愿意与他合作。

  78. 78. 这次这个计划就是由于他的 疑虑/冒失/茫然/恶意 ，险些失败，大家都对他非常失望。

  79. 79. 谁又能预料这个项目因为他的 疑虑/冒失/茫然/恶意 ，就让整个团队陷入了困境。

  80. 80. 要不是因为他在赛场上的 疑虑/冒失/茫然/恶意 ，这次比赛就不会败北。

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Table 1. Means (Standard deviations) for positive target word properties

Figure 1

Table 2. Examples of experimental sentences for positive target words

Figure 2

Table 3. Means (Standard Deviations) for eye-movement measures for positive target words

Figure 3

Table 4. Results of linear mixed effects models for positive target words

Figure 4

Figure 1. The interaction between EmoPro and word frequency in FFD (with standard errors bars) for positive target words.Note. The interaction patterns for FFD and SFD were consistent, so only the interaction plot for FFD is presented.

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Table 5. Means (Standard deviations) for negative target word properties

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Table 6. Examples of experimental sentences for negative target words

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Table 7. Means (Standard Deviations) for eye-movement measures for negative target words

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Table 8. Results of linear mixed effects models for negative target words

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Figure 2. The interaction between EmoPro and word frequency in GD (with standard errors bars) for negative target words.Note. The interaction patterns for FFD, SFD, GD and TT were consistent, so only the interaction plot for GD is presented.