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University of Navarra

Academic institutionnorthamerica · us
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Research library6linked papers
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Selected work

Representative Papers

Artificial Rigidities vs. Biological Noise: A Comparative Analysis of Multisensory Integration in AV-HuBERT and Human Observers

Jan 22, 2026

This study evaluates the biological fidelity of the self-supervised audiovisual speech model AV-HuBERT in modeling human multisensory integration, particularly the McGurk effect. Using a standard McGurk experimental paradigm and statistical analyses, the authors compare AV-HuBERT’s responses to incongruent audiovisual stimuli against behavioral data from 44 human participants. The model exhibits a striking alignment with human perception in auditory dominance rates (32.0% vs. 31.8%) but demonstrates significantly higher phoneme fusion rates (68.0% vs. 47.7%). Moreover, it lacks the variability and diversity of perceptual errors characteristic of human responses. These findings indicate that while AV-HuBERT captures certain aspects of human multisensory speech integration, it remains limited by rigid decision-making mechanisms that fail to replicate the flexibility and stochasticity inherent in human perception.

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Imperfect Language, Artificial Intelligence, and the Human Mind: An Interdisciplinary Approach to Linguistic Errors in Native Spanish Speakers

Nov 03, 2025

This study investigates the capacity of large language models (LLMs) to detect, reproduce, and correct authentic speech errors produced by native Spanish speakers, thereby exposing fundamental limitations in their emulation of human linguistic cognition. Methodologically, it integrates interdisciplinary approaches: (1) constructing a curated, annotated corpus of 500+ naturally occurring Spanish errors; (2) conducting high-temporal-resolution EEG experiments to characterize real-time neural processing dynamics; and (3) systematically evaluating state-of-the-art LLMs—including GPT and Gemini—on error detection, generative generalization, and fault-tolerant reasoning. This work achieves the first tri-level, cross-domain modeling of linguistic errors across cognitive representation, neurophysiological response, and AI behavioral output. Results advance theoretical understanding of Spanish linguistic competence and variation, while providing an empirical foundation and methodological framework for developing cognitively aligned, robust, and error-resilient NLP systems.

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Causal Interventions in Bond Multi-Dealer-to-Client Platforms

Jun 22, 2025

This paper addresses the core challenge in multi-to-multi bond trading platforms where dealers cannot observe counterparties’ quotes and struggle to ensure profitability. We propose the first general analytical framework integrating causal inference with probabilistic graphical models. Methodologically, we distinguish generative versus discriminative modeling approaches, explicitly capture the dynamic impact of RFQ (Request-for-Quote) negotiation mechanisms, identify key pricing drivers via causal interventions, and design prediction evaluation metrics tailored for optimal pricing. Our contribution lies in the first incorporation of structured causal modeling into electronic RFQ decision-making—overcoming the limitations of traditional black-box predictive models. Empirical results demonstrate significant improvements in price prediction accuracy and revenue estimation reliability, thereby enhancing dealers’ pricing capability and profitability under information asymmetry.

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Recent publications

Latest Papers

Artificial Rigidities vs. Biological Noise: A Comparative Analysis of Multisensory Integration in AV-HuBERT and Human Observers

Jan 22, 2026

This study evaluates the biological fidelity of the self-supervised audiovisual speech model AV-HuBERT in modeling human multisensory integration, particularly the McGurk effect. Using a standard McGurk experimental paradigm and statistical analyses, the authors compare AV-HuBERT’s responses to incongruent audiovisual stimuli against behavioral data from 44 human participants. The model exhibits a striking alignment with human perception in auditory dominance rates (32.0% vs. 31.8%) but demonstrates significantly higher phoneme fusion rates (68.0% vs. 47.7%). Moreover, it lacks the variability and diversity of perceptual errors characteristic of human responses. These findings indicate that while AV-HuBERT captures certain aspects of human multisensory speech integration, it remains limited by rigid decision-making mechanisms that fail to replicate the flexibility and stochasticity inherent in human perception.

0 citationsRead paper

Imperfect Language, Artificial Intelligence, and the Human Mind: An Interdisciplinary Approach to Linguistic Errors in Native Spanish Speakers

Nov 03, 2025

This study investigates the capacity of large language models (LLMs) to detect, reproduce, and correct authentic speech errors produced by native Spanish speakers, thereby exposing fundamental limitations in their emulation of human linguistic cognition. Methodologically, it integrates interdisciplinary approaches: (1) constructing a curated, annotated corpus of 500+ naturally occurring Spanish errors; (2) conducting high-temporal-resolution EEG experiments to characterize real-time neural processing dynamics; and (3) systematically evaluating state-of-the-art LLMs—including GPT and Gemini—on error detection, generative generalization, and fault-tolerant reasoning. This work achieves the first tri-level, cross-domain modeling of linguistic errors across cognitive representation, neurophysiological response, and AI behavioral output. Results advance theoretical understanding of Spanish linguistic competence and variation, while providing an empirical foundation and methodological framework for developing cognitively aligned, robust, and error-resilient NLP systems.

0 citationsRead paper

Causal Interventions in Bond Multi-Dealer-to-Client Platforms

Jun 22, 2025

This paper addresses the core challenge in multi-to-multi bond trading platforms where dealers cannot observe counterparties’ quotes and struggle to ensure profitability. We propose the first general analytical framework integrating causal inference with probabilistic graphical models. Methodologically, we distinguish generative versus discriminative modeling approaches, explicitly capture the dynamic impact of RFQ (Request-for-Quote) negotiation mechanisms, identify key pricing drivers via causal interventions, and design prediction evaluation metrics tailored for optimal pricing. Our contribution lies in the first incorporation of structured causal modeling into electronic RFQ decision-making—overcoming the limitations of traditional black-box predictive models. Empirical results demonstrate significant improvements in price prediction accuracy and revenue estimation reliability, thereby enhancing dealers’ pricing capability and profitability under information asymmetry.

0 citationsRead paper