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

Age of acquisition, usually abbreviated AoA, refers to the age at which a person first learns a word in their native language. For half a century, cognitive psychologists have documented that this seemingly simple variable exerts a powerful influence on how the mind processes language. Pictures of objects whose names were learned early in life are named faster, words acquired early are read faster, and in the lexical decision task, where participants judge whether a letter string is a real word, both reaction times and error rates depend on when the word entered a person’s vocabulary. The effect even extends to semantic tasks: words learned early generate more associates in word-association experiments and are categorized more quickly, although interestingly the effect does not appear in picture categorization. Because AoA shapes performance across such a broad range of tasks, researchers treat it as one of the essential psycholinguistic variables, alongside word frequency, length, and concreteness, that must be controlled when designing experiments on reading and lexical processing.

The standard way to measure AoA is simply to ask adults. Participants estimate, in years, the age at which they believe they learned each word, producing what researchers call subjective AoA ratings. Such norms now exist for Chinese, Croatian, Dutch, English, French, German, Icelandic, Italian, Japanese, Portuguese, Spanish, Turkish, and many other languages. Methodological preferences have shifted over time: early studies used coarse rating scales, such as a seven-point scale in which the lowest point covered ages zero to two and the highest covered thirteen and older. More recent work favors asking respondents to report a specific age in years, a technique that avoids artificially restricting the response range and is easier for participants to use. Cross-language comparisons show that the order in which words are learned is remarkably consistent across cultures, with reliability-adjusted correlations between languages reaching as high as .96 between Polish and Slovak.

Until now, Russian researchers have worked with a patchwork of small datasets. Existing norms covered only a few hundred items each: 260 nouns denoting pictured objects, 190 and 375 verbs in separate studies, 696 nouns, 414 verbs, 475 adjectives, and 506 concrete and abstract nouns. Most of these ratings were never validated against children’s actual word knowledge. The new dataset changes that picture dramatically, expanding the lexical coverage of Russian AoA norms by two orders of magnitude and providing, for the first time, a resource comparable in scope to the major English and Dutch norms that each cover roughly 30,000 words.

The scale of the data collection effort was considerable. Approximately 3,000 participants took part, recruited both in person, primarily first-year university students and faculty members, and online through the Yandex.Toloka crowdsourcing platform, where respondents received about $2.50 in compensation. After a battery of quality checks, 2,201 protocols were retained, roughly 73 percent of the total. The respondents, whose mean age was 27.2 years, rated words organized into 103 lists of 300 target words each. Each list also contained ten calibrator words, included to show respondents the possible range of ratings, and thirty control words with previously established ratings, used to verify the validity of each person’s responses. Data collection proceeded in three annual phases between 2022 and 2024, with ratings gathered for 7,500 words in the first year, 16,500 in the second, and 6,900 in the third.

The stimulus set was drawn mainly from a standard frequency dictionary of contemporary Russian, supplemented by words from category norms and other items familiar to modern speakers. The researchers deliberately sampled across grammatical categories: within each list, 134 items were nouns, 76 verbs, 70 adjectives, 17 adverbs, and 3 belonged to other parts of speech, mirroring the distribution in the source dictionary. Words from the same derivational family, such as “cunning” and “craftiness,” were generally kept in different lists to avoid inflating similarity between ratings. The final set ranged from one to 24 letters in length, with words presented in their lemmatized form except where a plural is more common in actual usage.

Ensuring data quality required elaborate screening. Each protocol was checked for autocorrelation, meaning suspiciously smooth sequences of ratings, and for its correlation with the known ratings of the control words. Protocols were excluded if a respondent’s mean rating deviated more than two standard deviations from the list average, if the respondent claimed to have learned every word after age four, or if more than sixty words were marked as unknown. In the online sessions, individual responses with latencies shorter than 800 milliseconds were also discarded, and all data were additionally inspected manually. The result was remarkably consistent: a bootstrap split-half analysis, in which respondents were repeatedly divided into two random halves and their mean ratings correlated, yielded a mean raw correlation of .895 and a Spearman-Brown-corrected reliability of .944, indicating that adults provide highly stable estimates even across thousands of unfamiliar-seeming items.

The most striking aspect of the study is its validation strategy. Rather than relying solely on internal consistency, the researchers tested whether the adult ratings actually predict when children learn words. They constructed multiple-choice vocabulary tests in which students had to select, from five options, the word or phrase best matching a target word, following careful design principles such as ensuring the correct answer was simpler than the target and that distractors were semantically balanced. In the grade-level validation, a 300-item test was administered to students in grades 2, 4, 6, 8, and 10 in schools in the Moscow region and the city of Kursk. A word was considered known at a given grade if at least 75 percent of students answered correctly and students in all higher grades did as well. The correlation between these objective, grade-based estimates and the adult subjective ratings was .805, a remarkably strong correspondence showing that adults’ retrospective judgments closely track the real developmental timeline of word learning.

A second procedure, within-grade validation, examined whether the ratings predict accuracy among children of the same age. Two rounds of testing, eight to ten months apart, involved 700 schoolchildren in total. Across every grade level, the correlations between subjective AoA and the proportion of correct answers were consistently negative: the later a word was estimated to be acquired, the lower the accuracy on its test item, both among second-graders and among eighth-graders. After statistical corrections for measurement error and range restriction, these correlations increased further. The ratings also correlated .698 with objective AoA values obtained by asking children of different ages to name pictured objects, and correlations with the seven previous Russian datasets ranged from .68 to .91, providing converging evidence from every available angle.

The norms also reproduce the expected relationships with other psycholinguistic variables. Later-acquired words tend to be longer, with correlations of about .28 with word length, and less frequent, with correlations between -.38 and -.45 depending on the frequency source. They are also less concrete, less imageable, and less familiar, with the strongest association observed for imageability. Intriguingly, the study also revealed respondent-level patterns: older adults gave slightly later AoA estimates and marked far fewer words as unknown, suggesting that vocabulary knowledge continues to expand across the lifespan and that our memories of word learning may shift with our own age. With the full dataset now freely available on the Open Science Framework, researchers in psycholinguistics, reading development, computational modeling, and language education gain a powerful new tool, and cross-linguistic comparisons of how children build their lexicons become possible for Russian at a scale never before achievable.

Subject of Research: Subjective age-of-acquisition norms for 30,849 Russian words and their validation against schoolchildren's vocabulary knowledge

Article Title: Subjective age of acquisition norms for 30,849 Russian words

Article References: Subjective age of acquisition norms for 30,849 Russian words. (n.d.). https://doi.org/10.3758/s13428-026-03138-2

Image Credits: AI Generated

DOI: 10.3758/s13428-026-03138-2

Keywords: age of acquisition, psycholinguistics, Russian language, lexical norms, vocabulary development, word frequency, lexical processing, language acquisition, Behavior Research Methods, open data, word recognition, cognitive psychology

The English Pseudoword Lexicon Project, or EPLeP, was created by Fabio Marson, Rolando Bonandrini, Iva Šaban, Simona Amenta, Marco Marelli, and colleagues at the University of Milano-Bicocca as a registered report. The team collected lexical decision data for nearly 13,500 non-lexical stimuli and roughly 4,400 real words from 1,416 native English speakers recruited through the Prolific platform, all based in the United States. Across four online experiments, participants judged whether letter strings were words, generating more than 1.4 million individual trials. The sheer scale of the effort places EPLeP alongside the great megastudies of visual word recognition, but with a crucial difference: here, the non-lexical stimuli are the main event rather than an afterthought.

The design of the stimulus sets reflects a sophisticated taxonomy of non-words. The researchers distinguished three categories. Morphologically simple pseudowords are unattested letter strings that obey the graphotactic rules of English but cannot be decomposed into a stem plus an affix, items such as plief or dokstine. Morphologically complex pseudowords are built by combining real stems with real suffixes, yielding items such as plungable or snottant, and respecting the orthographic adjustments that English affixation typically requires. Finally, nonwords are letter strings that violate English graphotactics altogether, such as iprwvca or pghtmyg, making them conspicuously unlike anything in the language. Words in the database were likewise split into morphologically simple and morphologically complex sets, drawn from the MorphoLex database and filtered through frequency corpora including SUBTLEX-UK and SUBTLEX-US.

This careful construction matters because previous megastudies, however large, shared systematic limitations when it came to non-lexical material. None included graphotactically illegal nonwords, morphological complexity of pseudowords was never systematically controlled, and the lack of stimulus control opened the door to list effects, in which an item appears easier or harder to process simply because of what else appears alongside it. EPLeP addresses all three problems by running separate experiments for each pseudoword type, plus a fourth experiment in which all three types were intermixed with an equal number of items, allowing the researchers to compare between-experiment and within-experiment processing of the same stimuli.

The headline finding is that not all non-words are processed alike, and the differences are striking. Morphologically complex pseudowords triggered the deepest level of processing, taking the longest to reject, while graphotactically illegal nonwords were rejected fastest and with the shallowest engagement of lexical machinery. Simple pseudowords fell in between. This graded pattern suggests that the visual word recognition system responds to the degree of linguistic structure in a letter string: the more an item resembles a well-formed English word, complete with a plausible stem and suffix, the more it activates lexical and morphological processes, and the harder it becomes to dismiss as a non-word. The result replicates a long line of evidence showing that even nonexistent strings undergo morphological decomposition, much as real words like goodness are parsed into good and ness.

Perhaps the most consequential discovery concerns how the type of pseudoword in an experiment reshapes the processing of real words. When words appeared in lists containing morphologically complex pseudowords, the classic effects of orthographic neighborhood, length, and frequency on word recognition were amplified. The researchers interpret this as evidence of heightened lexical competition: because complex pseudowords are decomposed into stems and suffixes and can even evoke plausible meanings, they flood the system with lexical and semantic activation, forcing participants to scrutinize words more deeply. Conversely, when words were paired with graphotactically illegal nonwords, these psycholinguistic effects shrank, and in an ancillary analysis the usual advantage for morphologically simple words over complex ones was even reversed. In other words, everything the field knows about word recognition from lexical decision studies is partly a function of the pseudowords used in each study, a conclusion the authors describe as a call for a contextual reframing of the literature.

The dataset also delivered some surprises that complicate standard assumptions. Contrary to expectations, the researchers observed a reversed lexicality effect on response times, with non-lexical items answered faster than words, likely driven by the ceiling-level performance on highly implausible nonwords and by flexible response strategies participants adopt depending on list composition. Reliability analyses showed that response times and accuracy for simple and complex pseudowords reached levels comparable to those reported for words in landmark megastudies such as the French Lexicon Project, but reliability for nonwords was substantially low, apparently because near-perfect performance leaves almost no systematic variance to measure. External validation was successful: response times for the 300 simple pseudowords shared with the British Lexicon Project correlated significantly with the earlier data, confirming that the new measurements are trustworthy.

Methodologically, the project also offers a cautionary tale about measurement. The orthographic Levenshtein distance metric known as OLD20, which quantifies how similar a string is to its twenty closest real-word neighbors, behaved in this dataset much like a proxy for stimulus length, with the two variables highly correlated. The authors advise future users of the resource to interpret OLD20 effects cautiously and to rely on length when analyzing the data. Such transparency about the limits of standard psycholinguistic measures is itself a contribution, and it illustrates the value of megastudy datasets for stress-testing the tools the field takes for granted.

The applications of EPLeP extend well beyond theoretical psycholinguistics. Researchers building computational models of visual word recognition can now evaluate their simulations against a rich behavioral benchmark that systematically varies orthographic legality and morphological structure. Experimentalists can use the database to select stimuli with known performance profiles for priming studies or new lexical decision experiments, and clinicians can draw on it to construct diagnostic tests of pseudoword processing for populations with reading disorders such as pure alexia or dyslexia. The data, along with stimulus generation code and analysis scripts, are freely available on the Open Science Framework, and the registered report format ensured that the analyses were preregistered before data collection began.

The broader message of the project is that pseudowords deserve a central place in the science of reading. Recent work has shown that even strings never encountered before can carry semantic weight, activating meaning-related patterns and producing frequency-like effects, and EPLeP now provides the large-scale evidence base to pursue those questions systematically. Although the findings come from English, a language whose orthographic quirks make it an outlier among the world’s writing systems, the authors argue that the underlying architecture of lexical-semantic processing is likely language-independent, and they call for replications in more transparent orthographies, agglutinative languages, and writing systems such as Mandarin where word boundaries are less clearly defined. For now, the English Pseudoword Lexicon Project stands as a reminder that some of the most informative things a mind ever reads are the words that were never there at all.

Subject of Research: A large-scale lexical decision database for investigating how English pseudowords and nonwords are processed during visual word recognition.

Article Title: The English Pseudoword Lexicon Project (EPLeP): A resource for investigating pseudoword processing

Article References: Marson, F., Bonandrini, R., Šaban, I., Amenta, S., & Marelli, M. (2026). The English Pseudoword Lexicon Project (EPLeP): A resource for investigating pseudoword processing. Behavior Research Methods, 58(11), Article 297. https://doi.org/10.3758/s13428-026-03151-5

Image Credits: AI Generated

DOI: 10.3758/s13428-026-03151-5

Keywords: pseudowords, lexical decision, psycholinguistics, visual word recognition, megastudy, morphology, nonwords, mental lexicon, orthographic neighborhood, Behavior Research Methods, open science, word recognition