Álvaro Cabana

When participants are engaged with auditory narratives, physiological and neural signals exhibit temporal correlations between subjects. The intersubject correlation (ISC) increases when attention is directed to the stories, suggesting that shared neural and bodily dynamics arise from a similar processing of the narratives. In this study, we investigated whether the ISC of the EEG elicited by auditory narratives is driven by low-level acoustic (envelope, spectrogram) and/or higher-level linguistic information (word onset, word surprisal) in two groups of healthy participants during passive, attentive and distracted listening. We use temporal response functions (TRFs) for acoustic, and linguistic features to assess the contribution of each feature to the ISC, measured using correlated component analysis (CorrCA). TRFs derived for acoustic features explained a larger fraction of variance in the EEG than linguistic features and were the main contributors to the ISC. The attention-related increase in ISC was driven by all features. Importantly, word surprisal had an effect on ISC only during active story engagement, with timing and scalp distribution consistent with language processing. Notably, the linear responses captured by TRFs only explained a small amount of the overall ISC, suggesting that ISC is largely driven by nonlinear responses to the narratives. Identifying the factors that drive these common neural responses, is clinically relevant for interpreting EEG ISC exhibited in unresponsive patients. We propose that the combined use of ISC and TRFs has the potential to provide meaningful markers of language processing in patients with disorders of consciousness, and we suggest practical recommendations for implementing these approaches in clinical settings.

Background: At-risk mental states (ARMS), involving subclinical attenuated psychotic experiences, occur in the general population with persistence rates above 30%, and may substantially affect quality of life, well-being and global functioning. Cannabis use has been linked to the onset, persistence and intensity of attenuated psychotic symptoms, whereas other evidence identifies trauma as a mediating factor. This study examined the potential effect and degree of interaction of cannabis use patterns in a general population sample with a history of traumatic experiences during childhood and adolescence. Methods: A selective ex post facto cross-sectional survey design was used. Adults residing in Uruguay with no prior psychopathological diagnosis (n = 652, 66.3% women, M = 31.9 years, SD = 8.5) completed a controlled online survey assessing traumatic experiences, negative symptoms, aberrant salience, ideas of reference and cannabis use patterns. Results: Statistically significant correlations confirmed a dual interaction of traumatic experiences, and of the frequency and grams of cannabis used, on attenuated psychotic symptoms. Traumatic experiences explained R2 = .202 of the variance in attenuated psychotic symptomatology, more than the dose (R2 = .14) and frequency (R2 = .048) of cannabis use. Combining the trauma effect with both use-pattern effects yielded a model with better fit and greater explained variance (R2 = .252). Conclusions: Traumatic experiences during childhood and adolescence are positively associated with cannabis use and, to a greater extent, with attenuated psychotic symptoms (ARMS) such as ideas of reference, aberrant salience and negative symptoms, showing greater explanatory power than cannabis use.

Most words in a language are lexically ambiguous and are associated with multiple meanings that vary in their frequency and relatedness. Although ambiguity is a fundamental property of language, there are extensive issues with existing measures of this construct. For instance, dictionary-based classifications and subjective ratings of meaning number and frequency struggle to capture the graded nature of ambiguity and, by proxy, its impact on cognition and performance in experimental tasks. It is also difficult to scale subjective measures to the full lexicon. We introduce a novel, automated framework to measure lexical ambiguity based on word association data from the Small World of Words (SWOW) project. We apply community detection algorithms to association graphs to quantify both the number and distribution of semantic communities for each word. This in turn allows us to derive graded representations of meaning frequency and relatedness. To better understand our new metrics, we compare them to previously published subjective norms, and establish their validity by showing that they predict lexical decision performance in English and Rioplatense Spanish. Furthermore, our results reveal cross-linguistic differences in lexical ambiguity-Spanish is less ambiguous than English overall-which we hypothesize is due to typological differences between the languages. Our validated framework contributes novel insights for computational and psycholinguistic models of semantic processing, and offers a scalable, automated, and language-independent framework for quantifying different facets of lexical ambiguity. We provide all of our code and ambiguity measures for approximately 7,000 words in both languages to facilitate their use by other researchers.

When participants are engaged with auditory narratives, physiological and neural signals exhibit temporal correlations between subjects. The intersubject correlation (ISC) increases when attention is directed to the stories, suggesting that shared neural and bodily dynamics arise from a similar processing of the narratives. Identifying the factors that drive these common responses is clinically relevant for interpreting EEG ISC exhibited in unresponsive patients. In this study, we investigated whether the ISC of the EEG elicited by auditory narratives is driven by low-level acoustic (envelope, spectrogram) and/or higher-level linguistic information (word onset, word surprisal) in two groups of healthy participants during passive, attentive and distracted listening. We use temporal response functions (TRFs) for acoustic, and linguistic features to assess the contribution of each feature to the ISC, measured using correlated component analysis (CorrCA). TRFs derived for acoustic features explained a larger fraction of variance in the EEG than linguistic features and were the main contributors to the ISC. The attention-related increase in ISC was driven by all features. Importantly, word surprisal had an effect on ISC only during active story engagement, with timing and scalp distribution consistent with language processing. Notably, the linear responses captured by TRFs only explained a small amount of the overall ISC, suggesting that ISC is largely driven by nonlinear responses to the narratives. We propose that the combined use of ISC and TRFs has the potential to provide meaningful markers of language processing in patients with disorders of consciousness, and we suggest practical recommendations for their implementation.

Reading, unlike spoken language, requires explicit instruction and sustained practice throughout life. Empirical evidence shows that most new words learned by older children and adults are acquired through reading. The Author Recognition Test (ART) is a widely used psychometric tool designed to assess print exposure indirectly. Although the ART has been adapted to various languages, no robust version existed for Spanish speakers in the Rioplatense region of Uruguay and Argentina. This article presents the development, adaptation, and validation of a Rioplatense Spanish version of the ART (ART-RP), based on data from two large-scale online studies.In Study 1, 2347 participants completed the ART-RP along with a vocabulary test and a reading habits questionnaire. Exploratory factor analysis and item response theory (IRT) models were used to analyze test structure and psychometric properties. In Study 2, an independent sample of 257 participants completed the ART-RP along with two reading tasks: a lexical decision task and a self-paced reading task. ART-RP scores correlated significantly with vocabulary scores, self-reported reading habits, and response latencies in both tasks.Despite some limitations in test informativeness at high ability levels, the results support the validity and reliability of the ART-RP as a measure of lifetime print exposure in Spanish-speaking populations. The test, including source code and item lists, is openly available for use in research and educational contexts.

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. Although previous studies provide insight into the cognitive underpinnings of semantic representations, they have suffered from small sample sizes and a lack of linguistic and cultural diversity. In this Registered Report, we measured the size and the variability of the semantic priming effect across 19 languages (n = 25,163 participants analysed) by creating the largest available database of semantic priming values using an adaptive sampling procedure. We found evidence for semantic priming in terms of differences in response latencies between related word-pair conditions and unrelated word-pair conditions. Model comparisons showed that the inclusion of a random intercept for language improved model fit, providing support for variability in semantic priming across languages. This study highlights the robustness and variability of semantic priming across languages and provides a rich, linguistically diverse dataset for further analysis. The Stage 1 protocol for this Registered Report was accepted in principle on 15 July 2022. The protocol, as accepted by the journal, can be found at https://osf.io/u5bp6 (registration) or https://osf.io/q4fjy (preprint version 6, 31 May 2022).

In anurans, acoustic communication is a crucial reproductive behavior shaped by individual traits, social interactions, and environmental cues. External factors such as temperature, rainfall, and photoperiod can elicit physiological responses that drive behavioral rhythms at individual and population scales. While temperature and rainfall effects on anuran calling activity are well-established, photoperiodic influences remain comparatively understudied, particularly within southern subtropical assemblages. We assessed hourly calling activity of Boana pulchella in Uruguay over a 12-month period using Passive Acoustic Monitoring (PAM) and validated semi-automated data processing methods to examine environmental drivers in a subtropical permanent pond. Calling behavior increased during the warmer months, showing a clear seasonal pattern. Peak activity timing remained consistent across seasons, but the temporal window of vocalizations expanded during winter (long nights) and contracted during summer (long days), reflecting photoperiodic variation. Linear regression analyses showed significant effects of photoperiod, temperature and their interaction on calling activity, while rainfall and atmospheric pressure showed no effect. These findings underscore the regulatory role of photoperiod in shaping reproductive acoustic behavior and highlight the need to further explore its physiological and adaptive significance, especially within underrepresented subtropical assemblages.

In recent years, it has been emphasized the importance of studying cognitive processes in mental health using paradigms that allow to track the dynamics of the stream of thought. In this study, we investigated how rumination, depression, and anxiety symptoms relate to the content and dynamics of self-generated thought using the Free Association Semantic Task (FAST). Ninety-one participants completed the FAST task online, generating chains of concepts from seed words and rating each for valence and self-reference. Self-report measures of rumination, depression and anxiety symptoms were collected. We found that higher levels of these symptoms predicted the generation of more negatively valenced concepts and greater self-reference for negative content. Markov chain analysis revealed that individuals with higher rumination and symptomatology were more likely to transition from positive to negative states and to persist in negative states. Using natural language processing, we found that participants with elevated rumination, depression and anxiety symptoms produced concepts that were semantically closer to core beliefs characteristic of these disorders, as proposed by cognitive theories. These findings support the view that rumination is a rigid and sticky form of cognition, constrained to negative affective states, and highlight the utility of combining computational approaches with free association paradigms in the study of psychopathology.

Most words in a language are lexically ambiguous and are associated with multiple meanings that vary in their frequency and relatedness. Although ambiguity is a fundamental property of language, there are extensive issues with existing measures of this construct. For instance, dictionary-based classifications and subjective ratings of meaning number and frequency struggle to capture the graded nature of ambiguity and, by proxy, its impact on cognition and performance in experimental tasks. It is also difficult to scale subjective measures to the full lexicon. We introduce a novel, automated framework to measure lexical ambiguity based on word association data from the Small World of Words (SWOW) project. We apply community detection algorithms to association graphs to quantify both the number and distribution of semantic communities for each word. This in turn allows us to derive graded representations of meaning frequency and relatedness. To better understand our new metrics, we compare them to previously published subjective norms, and establish their validity by showing that they predict lexical decision performance in English and Rioplatense Spanish. Furthermore, our results reveal cross-linguistic differences in lexical ambiguity—Spanish is less ambiguous than English overall—which we hypothesize is due to typological differences between the languages. Our validated framework contributes novel insights for computational and psycholinguistic models of semantic processing, and offers a scalable, automated, and language-independent framework for quantifying different facets of lexical ambiguity. We provide all of our code and ambiguity measures for approximately 7000 words in both languages to facilitate their use by other researchers.