The secondary exploratory analysis used data originally collected to develop a conversation-based depression screening tool for community staff. Participants were 116 adults recruited from a single Hong Kong nonprofit community center between August 2018 and July 2019. Eligibility required at least 14 hours of unpaid care per week for one or more family members and the ability to communicate verbally in Cantonese. People whom registered social workers judged unable to complete the interview or instruments because of severe psychiatric disorders or cognitive impairment were excluded.

Depressive symptom severity was measured with the Chinese version of the Beck Depression Inventory II, a 21-item self-report scale ranging from 0 to 63. A score of 20 was used to divide participants into groups: 84 scored below 20 and were classified as having low depressive symptoms, while 32 scored 20 or above and were classified as having high depressive symptoms. The classification therefore concerned elevated depressive symptoms rather than formally diagnosed depression, which was not ascertained through a clinical interview.

All participants answered 12 open-ended questions about family circumstances and family resilience. Their recorded responses were transcribed with automatic speech recognition, normalized into Traditional Chinese and tokenized with Cantonese-language tooling. Words found in the Chinese Valence-Arousal Words dictionary were assigned baseline valence and arousal ratings. The researchers also modeled the effects of negation and gradable adverbs, then calculated distributional, temporal, frequency-domain and synchrony features from the resulting sequences.

Participants in the high-symptom group generally used words with more negative valence and lower arousal, but differences between the groups in mean valence and mean arousal were not statistically significant. Mean valence differed by a t value of −1.55, p = 0.124 and 95% confidence interval of −0.165 to 0.020, while mean arousal differed by a t value of −0.71, p = 0.482 and 95% confidence interval of −0.103 to 0.049. The study also reported that the high-symptom group had more irregular valence patterns and more high-arousal spikes, with statistically significant group differences and medium effect sizes for both measures.

Nine machine-learning classifiers were evaluated under repeated five-fold cross-validation with 100 independent repetitions. The validation procedure selected classification cutoffs by minimizing an expected-cost function that penalized false negatives more heavily than false positives. Across the models, sensitivity was generally higher than specificity. For classifiers using combined valence and arousal predictors, QDA produced a sensitivity of 0.725, specificity of 0.563 and AUC of 0.700, while k-nearest neighbors produced a sensitivity of 0.746 and specificity of 0.509.

Valence-only and arousal-only configurations produced different trade-offs. QDA based on valence alone had sensitivity of 0.763 and specificity of 0.521, while linear discriminant analysis based on arousal had sensitivity of 0.734 and specificity of 0.323. The valence-only QDA configuration also had a lower expected cost of 1.416 than the combined QDA configuration, which had an expected cost of 1.528, reflecting the greater penalty applied to missed high-symptom cases. The authors therefore characterized the results as evidence that emotional-word features can provide discriminatory information, rather than as validation of a screening tool.

Several limitations constrain the findings. The sample was small, specific and drawn from one organization, and the 84-to-32 class imbalance could affect reported performance metrics. Symptom groups were based on self-report rather than clinical diagnosis. Automatic transcription may have introduced errors, and the analysis did not include acoustic features such as tone, pitch or speech rate. The researchers also did not systematically assess whether participants found the family-resilience questions less stigmatizing or intrusive, and the study did not conduct external validation in other populations or settings.

Overall, the study provides exploratory evidence that valence and arousal patterns in caregiver speech can carry information associated with depressive symptom severity. The most favorable combined model found 72.5% of high-symptom participants and correctly identified 56.3% of those with lower symptom scores under cross-validation. The authors concluded that further refinement and validation in larger, more diverse and clinically characterized samples are needed before the method could be considered for practical use, including possible integration with other linguistic and acoustic features.

Subject of Research: Exploratory speech sentiment analysis of depressive symptoms in Hong Kong family caregivers

Article Title: Using valence and arousal scoring in speech to detect depressive symptoms: A study on family caregivers

Article References: So, M. K. P., Tsang, J. T. Y., Chan, L. S. H., Chan, J. N. L., Tiwari, A., & Chu, A. M. Y. (2026). Using valence and arousal scoring in speech to detect depressive symptoms: A study on family caregivers. PLOS One, 21(10), e0359556. https://doi.org/10.1371/journal.pone.0359556

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0359556

Keywords: depressive symptoms, speech analytics, family caregivers, dimensional sentiment, valence, arousal, machine learning, cross-validation, Hong Kong, scoring, speech, detect

The research, published in the journal Behavior Research Methods, was conducted by Celia Martínez-Tomás of the Universidad Complutense de Madrid and colleagues, including Marc Guasch and Pilar Ferré of the Universitat Rovira i Virgili. It belongs to a class of experiments known as megastudies: instead of testing a few dozen carefully chosen items on a small group of volunteers, megastudies throw thousands of stimuli at huge samples of participants and let statistical models tease apart the factors that shape performance. The sheer scale makes it possible to detect effects that smaller experiments might miss, and to examine how different properties of words—frequency, emotional tone, structure—interact with one another in a single dataset.

In this case, the scale was extraordinary. A total of 1,025 native Spanish speakers made lexical decisions on 1,200 real experimental words and 4,800 experimental pseudowords, along with 1,200 filler words and 1,200 filler pseudowords. The pseudowords were not random gibberish. They were systematically constructed from emotional and neutral base words, varying in their morphological structure: some contained both a real root and a real suffix, some contained just one real morphological component, and some contained neither. Every participant thus faced letter strings that ranged from fully word-like assemblies to wholly invented forms, allowing the researchers to ask precisely how much each ingredient of wordiness influences the decision to accept or reject an item.

The results, analyzed with linear and generalized linear mixed-effects models—statistical techniques that simultaneously account for variability across participants and across items—revealed a clear hierarchy. Pseudowords containing both a real root and a real suffix were the hardest to reject: they produced the slowest response times and the highest error rates. Pseudowords with only one real morphological component, such as a real root paired with a non-suffix or a non-root paired with a real suffix, fell in between. And pseudowords lacking any identifiable morphological parts were rejected fastest and most accurately. In other words, the more the fake word resembled a genuine morphological assembly, the more the brain hesitated before declaring it a fake.

This pattern fits with decades of work on morphological processing. Skilled readers do not simply match letter strings against a mental dictionary; they also decompose words into their component parts. Classic experiments have shown that readers segment words along morpho-orthographic lines almost automatically, even when the segmentation leads to odd results—famously, the string “brother” surfacing inside “brothel.” When a pseudoword contains a real stem and a real affix, that decomposition machinery fires anyway, activating traces of real words and creating a lingering sense of familiarity. The new megastudy extends this logic to Spanish and demonstrates, with unprecedented statistical power, that morphological composition is a major determinant of how confidently we reject nonwords.

But the emotional findings are what make the study stand out. The researchers found that pseudowords with higher emotional valence—whether strongly positive or strongly negative, as rated for their base words—and higher arousal, meaning greater emotional intensity, elicited more errors and slower rejection times. Emotionally charged fake words, in short, were more likely to be mistaken for real ones. This suggests that affective information is not a bonus feature that only activates once a word has been recognized; it can be accessed even in the complete absence of a lexical representation. The brain appears to extract emotional meaning from strings that it has never seen before and that, by definition, have no stored meaning at all.

How can a meaningless string carry emotional weight? The authors propose two complementary routes. The first is via activation of existing representations: a pseudoword built from the root of “death” or “love” partially activates those real words and, through them, their emotional associations. The second route is more radical and touches on a lively debate in psycholinguistics: systematic links between certain sublexical features—particular sounds, letter sequences, articulatory gestures—and emotional meaning. Research on affective iconicity has shown, for example, that words involving the lips versus the tongue can differ systematically in pleasantness, and that the sound of a word alone can evoke affective brain responses. Previous studies have even found that people can learn emotional connotations for pseudowords through conditioning, and that statistical regularities between phonological form and valence exist in the lexicons of English, Spanish, and German.

Crucially, the new study found that the emotional effects interacted with morphological structure, indicating that affective cues and sublexical, structural cues do not operate in isolation but contribute jointly to pseudoword processing. A fake word’s emotional pull depends partly on how word-like its architecture is. This joint contribution complicates simple models of lexical decision, in which a noisy evidence-accumulation process compares the input against stored word knowledge. It suggests that multiple parallel streams of information—orthographic, morphological, and affective—all feed into the decision, and that emotion can bias the process even when the lexical stream comes up empty.

The findings also speak to a broader theoretical question: where does emotional meaning come from in the first place? One influential view holds that emotional content is grounded in embodied experience—words for disgust, say, inherit their affect from bodily reactions. The pseudoword results add a twist: if emotional information can be gleaned from form alone, then the mapping between sound, structure, and feeling may be more systematic and more deeply woven into language than previously assumed. For models of word recognition, this means that affective dimensions such as valence and arousal cannot be treated as late-arriving semantic add-ons; they may need to be built into the earliest stages of processing.

Beyond its theoretical implications, the study delivers a substantial practical resource. The researchers have released an open database, hosted on the Open Science Framework, containing reaction time and accuracy data for 2,400 Spanish words and 6,000 Spanish pseudowords that vary in emotional and morphological composition. Materials and analysis code are also publicly available, although the study itself was not preregistered. For researchers designing experiments on emotional language, the database offers a way to select stimuli with known processing profiles; for modelers, it provides a rich benchmark against which computational accounts of lexical decision can be tested. The work was approved by the Psychological Ethics Committee of the Universidad Complutense of Madrid and funded by Spanish and European research grants.

There are, of course, limits to what any single dataset can settle. The study was conducted in Spanish with native speakers, and morphological structure is far richer in Spanish than in some other languages, so cross-linguistic replication will be needed to establish generality. The authors also note that the emotional properties of the pseudowords were inherited from their base words and from sublexical statistics, rather than being directly felt by participants—future work combining lexical decisions with explicit ratings, electrophysiology, or learning paradigms could trace exactly when and how affective activation arises. Still, the headline conclusion is striking: the brain’s word detector does not merely check whether a string exists in the mental dictionary. It also listens for echoes of feeling. Even a word that means nothing can, in a very real sense, mean something emotionally—and that hidden emotional life is enough to fool us, if only for a few hundred milliseconds.

Subject of Research: Effects of morphological structure and emotional content on pseudoword recognition in a large-scale Spanish lexical decision megastudy

Article Title: Meaningless, but not emotionless: Evidence from a megastudy of pseudoword lexical decisions

Article References: Martínez-Tomás, C., Guasch, M., Lázaro, M., Baciero, A., Ferré, P., & Hinojosa, J. A. (2026). Meaningless, but not emotionless: Evidence from a megastudy of pseudoword lexical decisions. Behavior Research Methods, 58(11), Article 314. https://doi.org/10.3758/s13428-026-03186-8

Image Credits: AI Generated

DOI: 10.3758/s13428-026-03186-8

Keywords: pseudowords, lexical decision task, megastudy, emotion, valence, arousal, morphology, psycholinguistics, word recognition, affective iconicity, Spanish language, cognitive psychology

Number sense, sometimes called the approximate number system, is the capacity to quickly recognize quantities, estimate magnitudes, and infer relationships between numbers without counting each item one by one. It is what allows you to glance at a parking lot and roughly gauge how many cars are present, or to compare two crowds at a party without tallying heads. Because this ability emerges early in development, is shared with many animal species, and functions quickly and automatically, researchers have traditionally treated it as a foundational perceptual attribute that sits largely outside the reach of top-down cognitive processing, such as attention, expectation, and mood.

Xue and Yao set out to test whether that independence survives contact with positive valence. Their reasoning drew on a well-documented feature of positive affect: pleasant emotional states and rewarding outcomes tend to enhance both the frequency and the intensity of the actions they reinforce. If positive valence changes how we act, the authors asked, might it also change how we perceive, even at the apparently objective level of judging how many dots are on a screen? To answer this, the researchers ran three behavioral experiments, each built around a numerical comparison task in which participants judged the quantities of dot arrays.

The experimental design was elegantly simple. Participants viewed arrays of dots and compared the numerosity they perceived against a reference. Crucially, some of the dot arrays were constructed from positive-associated stimuli, meaning the individual units carried pleasant emotional associations or were linked to monetary reward, while others were built from neutral stimuli. The two types of arrays were presented randomly across trials, allowing the researchers to isolate the effect of valence from any general difficulty or fatigue effects that might accumulate over the course of the session.

The results were consistent across all three experiments. Both positive emotion and monetary reward elicited a measurable underestimation effect in number sense: when the dot arrays were made of positive-valence units, participants systematically judged the quantities to be smaller than they actually were. The same bias did not appear when the arrays were composed of neutral stimuli, whose numerosity judgments remained comparatively accurate. In other words, it was not that participants became generally worse at estimating quantities when they were in a positive state; rather, the positive valence of the stimuli themselves pulled their perceptual estimates downward in a specific and reproducible way.

This dissociation matters because it rules out several mundane explanations. If positive mood simply made participants careless or less motivated, the error should have appeared across all stimulus types, including neutral ones. Instead, the underestimation was tied to the valence of the material being judged, which points to a perceptual mechanism rather than a general performance decrement. The authors describe this as evidence for a perceptual underestimation bias in number sense associated with positive-valence stimuli, a bias that operates below the level of deliberate judgment and shapes the raw estimate before any conscious decision is made.

To explain the effect, the researchers turn to predictive processing theory, an influential framework in modern neuroscience that recasts perception as a process of prediction rather than passive reception. According to this view, the brain constantly generates hypotheses about the causes of its sensory input and uses incoming signals mainly to correct those hypotheses. Perception, on this account, is a controlled hallucination constrained by data, and the priors, or expectations, that feed the predictive machinery can be shaped by emotion, motivation, and reward. Positive-valence stimuli, the theory suggests, may activate priors associated with abundance, safety, or reward, and those priors could tilt the interpretation of ambiguous perceptual evidence toward smaller quantities.

The predictive processing account also helps explain why the bias is directional rather than random. Under this framework, affective states are not noise injected into perception; they are signals that adjust the weighting of evidence and the precision assigned to prediction errors. A rewarding or pleasant stimulus may lead the brain to allocate processing resources differently, effectively changing the gain of the neural populations that encode numerosity. If positive valence reduces the effective resolution or salience of the quantity signal, the perceptual system would settle on a lower estimate, producing exactly the kind of systematic underestimation that Xue and Yao observed in their dot-comparison tasks.

The study’s implications extend beyond the laboratory bench. Numerical estimation underlies a surprising range of everyday and high-stakes judgments, from assessing crowd sizes and inventory quantities to evaluating financial figures and risk statistics. If positive emotional contexts and reward cues reliably shrink our sense of quantity, then environments saturated with pleasant imagery or monetary incentives, such as advertising, gambling interfaces, and marketing displays, may be quietly distorting the numerical judgments people make within them. A shopper estimating how many items constitute a good deal, or an investor gauging figures presented alongside rewarding imagery, could be operating with a systematically biased perceptual baseline without any awareness of the distortion.

As with any behavioral study, the findings invite further questions. The experiments demonstrate the bias for dot-array comparison, and future work will need to determine how far it generalizes to other magnitude domains, such as time, size, and distance, which are known to share neural resources with numerosity. It also remains to be tested whether negative-valence stimuli produce a compensating overestimation, which would clarify whether the effect reflects a general valence-driven scaling of magnitude or a specific property of positive affect. What is already clear, however, is that the number sense is not the emotion-proof perceptual module it was long assumed to be. The study, funded by the Social Science Foundation of Jiangsu Province and approved by the ethics committee of Suzhou University of Science and Technology, adds a striking data point to a growing body of research showing that even our most basic perceptions are colored, and in this case literally diminished, by the emotional value of what we perceive. The next time a pleasant scene makes a crowd look sparse, it may not be your eyes that are deceived, but the ancient quantity sense quietly bending to your good mood.

Subject of Research: The influence of positive-valence stimuli on the approximate number system and numerical estimation bias

Article Title: The underestimation bias of the number sense in positive-valence stimuli

Article References: Xue, X., & Yao, Y. (2026). The underestimation bias of the number sense in positive-valence stimuli. BMC Psychology. https://doi.org/10.1186/s40359-026-05747-0

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05747-0

Keywords: number sense, numerical cognition, approximate number system, positive emotion, monetary reward, predictive processing theory, perception, underestimation bias, valence, cognitive psychology, visual perception, BMC Psychology

The research, published in the journal Behavior Research Methods by Beatriz Bermúdez-Margaretto of the University of Salamanca and colleagues, collected valence and arousal ratings for more than 2,000 English words from over 600 Spanish-speaking college students learning English as a foreign language, or EFL. Valence captures how pleasant or unpleasant a word feels, ranging from deeply negative to strongly positive, while arousal measures the intensity of the emotional reaction it provokes, from calming to invigorating. These two dimensions form the backbone of the circumplex model of affect, one of the most influential frameworks in emotion research, and they have been systematically catalogued for English native speakers since the landmark Affective Norms for English Words dataset created by Margaret Bradley and Peter Lang in 1999.

What has been missing, the authors argue, is an equivalent resource for the enormous population of learners whose exposure to a second language is confined to formal, instructional settings. Previous research has consistently shown that bilinguals process emotional language differently from monolinguals, and that the gap between a first and second language can be striking. Physiological studies have found reduced skin-conductance responses to emotional phrases in a second language, eye-tracking experiments reveal muted pupillary reactions to affective words, and surveys of multilinguals famously report that swearwords feel blunted and clinical when spoken in a tongue learned late. Yet most existing second-language norms have been collected from immersed bilinguals, people living and studying inside the language community, whose daily experiences knit emotional associations into the new lexicon in ways that classroom learners rarely replicate.

To fill that gap, the Spanish research team recruited a large, homogeneous and representative sample of university students whose English instruction had taken place almost entirely in classrooms. Participants rated each word on nine-point scales for valence and arousal, following procedures similar to those used in earlier norming projects. The researchers also gathered proficiency data using the LexTALE lexical test and detailed language-history information through the Language Experience and Proficiency Questionnaire, allowing them to characterize a population that is statistically unusual in the bilingualism literature: late learners, embedded in a Spanish-dominant society, whose contact with English is bounded by lessons, textbooks and sporadic media consumption.

The psychometric quality of the resulting database proved robust. Reliability analyses and checks against external validity benchmarks indicated that both the valence and arousal dimensions behaved as established affective measures should, with ratings for known positive and negative words falling where theory predicts. The team then performed the analysis that gives the dataset its scientific punch: direct comparisons between the new EFL norms, existing ratings from immersed learners of English as an additional language, and native English speaker norms drawn from the massive Warriner, Kuperman and Brysbaert database of more than 13,000 English lemmas. Those comparisons revealed systematic patterns rather than random noise, indicating that the learning environment itself leaves measurable fingerprints on how emotional content is represented in the mental lexicon.

Two findings stand out. First, the discrepancy between native speaker ratings and EFL learner ratings grew systematically as a function of affective, lexical and sensory experience factors. In other words, the emotional dimensions of a word were not distorted uniformly; the divergence tracked properties such as how strongly the word engages emotion, how familiar and frequent it is, how early in life it is typically acquired, and how much it appeals to the senses. This pattern is consistent with grounded and embodied accounts of cognition, which hold that word meanings are scaffolded on sensory and motor experience. A classroom learner simply accumulates fewer embodied encounters with an English word than a native speaker does, and the deficit appears to scale with exactly those experience-dependent factors.

Second, and perhaps most strikingly, the gap was particularly pronounced in the arousal dimension. While valence judgments, the basic pleasantness of a word, remained relatively well aligned between learners and native speakers, the felt intensity of emotionally charged words was substantially attenuated in the instructional-setting sample. This points to emotional intensity as a critical ingredient in the acquisition and use of emotional language. Words learned through arousing, personally resonant experiences appear to acquire a physiological charge that classroom exposure struggles to reproduce. The finding dovetails with a growing body of evidence that the second language functions as a kind of emotional dampener, but it refines the picture by suggesting that what is dampened is chiefly arousal, the bodily component of emotion, rather than the evaluative component.

The methodological care underlying these conclusions deserves emphasis. The authors note that earlier comparable datasets suffered from subtle scale-direction confusions, and they deliberately standardized their procedures to avoid such artifacts. They also documented which words failed to gather sufficient valid observations, showing that rarer, later-acquired words were more likely to be reported as unknown by participants, an honest accounting of the vocabulary limits of an instructional population. All individual and averaged ratings, together with the study materials, have been released through the Open Science Framework, making the resource immediately usable by other researchers. Analyses were conducted in the open-source statistical package JASP, and the study, while not preregistered, was approved by the Human Research Ethics Committee of the University of Salamanca.

Why does this matter beyond the laboratory? Affective norms are the raw material of experimental psychology. Researchers designing studies of memory, attention, decision-making or reading rely on them to select stimuli, and using native speaker norms with learner populations risks systematic error, since a word the experimenter believes is arousing may register as flat to the participants. The new database gives scientists studying EFL populations a calibrated instrument for the first time at this scale, joining a growing international effort that has produced affective norms for Chinese, Dutch, German as a second language, Spanish and many other linguistic contexts. For educators, the implications are more practical still. Previous work by members of the same team found that EFL textbooks are strikingly poor in emotionally charged vocabulary, and the arousal gap documented here suggests a concrete mechanism by which that poverty limits learning. Teaching strategies that deliberately weave emotional intensity into vocabulary instruction, through personal narratives, vivid contexts and embodied activities, may help classroom learners close the gap that immersion closes naturally.

The study also resonates with a broader scientific debate about the relationship between language and emotion. Whether emotions are biologically basic categories, as classical theories propose, or constructed from linguistic and cultural ingredients, as psychological constructionist accounts argue, the new data show empirically that the linguistic environment in which a word is acquired shapes its emotional profile. Emotion semantics have been shown to exhibit both universal structure and cultural variation, and the Salamanca findings add an environmental variable to that equation: not just which language you speak, but where and how you learned it. For the world’s classroom-trained multilinguals, who vastly outnumber immersed bilinguals, the message of this research is both humbling and empowering. Their foreign-language words do carry less emotional voltage than their mother-tongue equivalents, and that asymmetry influences memory, moral judgment and everyday communication. But the asymmetry is measurable, tractable and, the authors suggest, addressable, provided that researchers and teachers design language experiences with the emotional intensity that the human lexicon seems to require.

Subject of Research: Affective norms for emotional word processing in college learners of English as a foreign language

Article Title: Emotion in English as a foreign language: Affective ratings for over 2,000 words by college learners

Article References: Bermúdez-Margaretto, B., Pérez-García, E., Fernandez, A., & Sánchez, M. J. (2026). Emotion in English as a foreign language: Affective ratings for over 2,000 words by college learners. Behavior Research Methods, 58(10), Article 291. https://doi.org/10.3758/s13428-026-03159-x

Image Credits: AI Generated

DOI: 10.3758/s13428-026-03159-x

Keywords: affective norms, valence, arousal, English as a foreign language, bilingualism, emotional word processing, second language acquisition, psycholinguistics, language learning, classroom learners, Emotion, English