Codex Mentis: Science and technology to study cognition

Pablo Bernabeu

Codex Mentis, produced by Dr Pablo Bernabeu, offers an exploration into cognitive science and its technologies with the assistance of advanced artificial intelligence. This podcast delves deep into how we think, perceive and interact with the world, dissecting both the profound mysteries of the human mind and the cutting-edge science and technology that illuminate its inner workings. Each episode presents a fascinating journey through diverse aspects of cognition. Beyond the theoretical, Codex Mentis demystifies the methodologies driving cognitive research. Contact: pcbernabeu@gmail.com

Episodes

  1. Modality switch effects: The brain friction of switching senses

    3H AGO

    Modality switch effects: The brain friction of switching senses

    🪄 Created using NotebookLM, with all the benefits and blind spots of human editing. This episode explores whether the human mind functions as an abstract symbol processor or a physical simulator deeply rooted in bodily experience. We delve into the 'modality switch effect', a phenomenon where shifting from one sensory modality to another, such as from sound to sight, incurs a measurable cognitive penalty. Foundational research initially suggested that people are consistently slower when verifying properties of concepts across different senses, suggesting the brain must physically reconfigure its neural resources to understand language. However, later studies proposed that our brains might be efficient rather than thorough, often relying on 'quick and fuzzy' linguistic shortcuts before booting up heavy sensory simulations. New evidence from event-related potential studies shows that this sensory activation occurs as early as 160 milliseconds after seeing a word, reinforcing the idea that grounding is a fundamental part of accessing meaning. We also discuss findings that demonstrate how even second languages, typically learned in abstract classroom settings, recruit the body's native sensory systems. Furthermore, the latest research indicates that these perceptual simulations are so automatic they activate even during 'shallow' tasks where participants are not explicitly trying to process word meaning. Finally, we consider what this means for a world increasingly dominated by flat screens and artificial intelligence, questioning if a lack of physical interaction might lead to a shallowing of human thought. References (in order of appearance) Pecher, D., Zeelenberg, R., & Barsalou, L. W. (2003). Verifying different-modality properties for concepts produces switching costs. Psychological Science, 14(2), 119–124. https://doi.org/10.1111/1467-9280.t01-1-01429 Louwerse, M., & Connell, L. (2011). A taste of words: Linguistic context and perceptual simulation predict the modality of words. Cognitive Science, 35(2), 381–398. https://doi.org/10.1111/j.1551-6709.2010.01157.x Collins, J., Pecher, D., Zeelenberg, R., & Coulson, S. (2011). Modality switching in a property verification task: An ERP study of what happens when candles flicker after high heels click. Frontiers in Psychology, 2, Article 10. https://doi.org/10.3389/fpsyg.2011.00010 Hald, L. A., Marshall, J.-A., Janssen, D. P., & Garnham, A. (2011). Switching modalities in a sentence verification task: ERP evidence for embodied language processing. Frontiers in Psychology, 2, Article 45. https://doi.org/10.3389/fpsyg.2011.00045 Bernabeu, P., Willems, R. M., & Louwerse, M. M. (2017). Modality switch effects emerge early and increase throughout conceptual processing: Evidence from ERPs. Proceedings of the 39th Annual Conference of the Cognitive Science Society. https://doi.org/10.31234/osf.io/a5pcz Platonova, O., & Miklashevsky, A. (2025). Warm and fuzzy: Perceptual semantics can be activated even during shallow lexical processing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 51(9), 1471–1496. https://dx.doi.org/10.1037/xlm0001429 Wentura, D., Shi, E., & Degner, J. (2024). Examining modal and amodal language processing in proficient bilinguals: Evidence from the modality-switch paradigm. Frontiers in Human Neuroscience, 18, Article 1426093. https://doi.org/10.3389/fnhum.2024.1426093

    30 min
  2. The dead salmon problem: Multiple tests, minimality and data-driven alternatives

    JAN 30

    The dead salmon problem: Multiple tests, minimality and data-driven alternatives

    🪄 Created using NotebookLM, with all the benefits and blind spots of human editing. In 2009, a deceased Atlantic salmon was placed inside a functional magnetic resonance imaging scanner to test its calibration parameters. Although the subject was undeniably dead, the standard statistical software produced results suggesting the fish was actively contemplating human emotions. This bizarre outcome highlights a systemic fragility in modern science known as the multiple tests trap, where conducting thousands of tests without adjustment guarantees that random noise will eventually look like a discovery. Just as flipping a coin enough times will inevitably produce a streak of ten heads, asking too many questions of a large dataset ensures that a researcher will find significant results purely by luck. Escaping this trap requires rigorous pre-planning and methodological self-restraint to avoid the statistical cheating known as hypothesising after the results are known. While the classical Bonferroni correction acts as a 'sledgehammer' by dividing the significance threshold by the total number of tests, more sensitive sequential procedures like the Holm-Bonferroni method offer a more refined approach. Modern researchers often prefer sophisticated data-driven strategies such as permutation testing, which shuffles experimental labels thousands of times to build a custom noise map specific to the dataset rather than relying on broad theoretical assumptions. Choosing between the precise spatial localisation of maximum t-statistic testing and the sensitive yet fuzzy cluster-based methods reveals that statistical truth is often a philosophical judgement call. Ultimately, the decision of how to define a family of tests depends on the logical structure of a scientific claim and the intent of the investigator. By embracing the principle of test minimality, researchers can move beyond mere p-value adjustments and toward a more robust, transparent and honest scientific practice. References Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x Bennett, C. M., Miller, M. B., & Wolford, G. L. (2009). Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: An argument for multiple comparisons correction. Neuroimage, 47(Suppl 1), S125. https://doi.org/10.1016/S1053-8119(09)71202-9 Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7–29. https://doi.org/10.1177/0956797613504966 Frane, A. V. (2021). Experiment-wise type I error control: a focus on 2× 2 designs. Advances in Methods and Practices in Psychological Science, 4(1), 2515245920985137. https://doi.org/10.1177/2515245920985137 García-Pérez, M. A. (2023). Use and misuse of corrections for multiple testing. Methods in Psychology, 8, 100120. https://doi.org/10.1016/j.metip.2023.100120 Groppe, D. M., Urbach, T. P., & Kutas, M. (2011). Mass univariate analysis of event‐related brain potentials/fields I: A critical tutorial review. Psychophysiology, 48(12), 1711-1725. http://doi.org/10.1111/j.1469-8986.2011.01273.x Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70. https://www.jstor.org/stable/4615733 Rubin, M. (2021). When to adjust alpha during multiple testing: A consideration of disjunction, conjunction, and individual testing. Synthese, 199(3-4), 10969–11000. https://doi.org/10.1007/s11229-021-03276-4

    41 min
  3. The digital parrot or the universal machine? Debating the mind in the model

    JAN 15

    The digital parrot or the universal machine? Debating the mind in the model

    🪄 Created using NotebookLM, with all the benefits and blind spots of human editing. Can a machine that writes Shakespearean sonnets about traffic jams actually help us understand the human soul? In this episode of Codex Mentis, we dive into a 'potential bomb' thrown into the heart of cognitive science: the rise of Large Language Models (LLMs) and their challenge to how we think humans learn to speak. For fifty years, the 'nativist' view, championed by Noam Chomsky, argued that children are born with a 'built-in grammar' because the speech they hear is too messy and 'impoverished' to learn from scratch—a concept known as the Poverty of Stimulus. However, new research suggests LLMs provide an 'existence proof' that complex grammar can indeed be mastered through pure statistical patterns alone, potentially refuting half a century of linguistic theory. But are these models truly 'brain-like,' or are we looking at a 'Cessna vs. Bird' problem? While both an aircraft and a bird achieve flight, their internal mechanisms are worlds apart. We explore the rigorous 'Four Questions' framework from ethologist Niklas Tinbergen to see where the comparison between silicon and synapse breaks down—from the lack of biological evolution to the 'unimodal' nature of text-only training. We also tackle the 'Grounding Problem' and the 'Spanish Dictionary' thought experiment: can a model truly 'understand' a sunset if it has only ever read descriptions of one? We discuss the fascinating dissociation between formal linguistic competence (grammar) and functional competence (thought), and why the model’s greatest failures—like its inability to handle unwritten sign languages or pass the BabyLM Challenge—might be its most important scientific gifts. Join us as we determine if LLMs are a new theory of the mind or simply the sharpest tool cognitive science has ever been handed. References (in order of appearance) Chomsky, N. (1980). Rules and representations. MIT Press. https://doi.org/10.1017/S0140525X00001515 Contreras Kallens, P., Kristensen-McLachlan, R. D., & Christiansen, M. H. (2023). Large language models demonstrate the potential of statistical learning in language. Cognitive Science, 47(3), e13256. https://doi.org/10.1111/cogs.13256 Piantadosi, S. T. (2024). Modern language models refute Chomsky’s approach to language. In E. Gibson & M. Poliak (Eds.), From fieldwork to linguistic theory: A tribute to Dan Everett (pp. 353–414). Language Science Press. https://doi.org/10.5281/zenodo.12665933 Cuskley, C., Woods, R., & Flaherty, M. (2024). The limitations of large language models for understanding human language and cognition. Open Mind: Discoveries in Cognitive Science, 8, 1058–1083. https://doi.org/10.1162/opmi_a_00160 Tinbergen, N. (1963). On aims and methods of ethology. Zeitschrift für Tierpsychologie, 20(4), 410–433. https://doi.org/10.1111/j.1439-0310.1963.tb01161.x Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2021). The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences, 118(45), e2105646118. https://doi.org/10.1073/pnas.2105646118 Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A., Aubrey, B., Nastase, S. A., Feder, A., Emanuel, D., Cohen, A., Jansen, A., Gazula, H., Choe, G., Rao, A., Kim, C., Casto, C., Fanda, L., Doyle, W., Friedman, D. … Hasson, U. (2022). Shared computational principles for language processing in humans and deep language models. Nature Neuroscience, 25, 369–380. https://psycnet.apa.org/doi/10.1038/s41593-022-01026-4 Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 5185–5198). https://doi.org/10.18653/v1/2020.acl-main.463 Further references available at https://youtu.be/7lOVAkCk-sc

    37 min
  4. Beyond the cloud: Reclaiming data sovereignty in speech transcription

    JAN 5

    Beyond the cloud: Reclaiming data sovereignty in speech transcription

    🪄 Created using NotebookLM, with all the benefits and blind spots of human editing. In this episode of Codex Mentis, we explore the critical intersection of generative AI and research methodology, focusing on a production-ready, open-source workflow for secure speech transcription developed by Dr Pablo Bernabeu. While OpenAI’s Whisper models have set a new gold standard for speech-to-text accuracy, relying on consumer-grade cloud interfaces like ChatGPT or Google Gemini often proves incompatible with the rigorous demands of academic and clinical research. We dissect the three primary limitations of these cloud-based tools—restrictive file size caps, a lack of methodological reproducibility, and the significant privacy and GDPR risks inherent in transmitting sensitive human data to third-party servers. The discussion highlights a sophisticated alternative that leverages high-performance computing environments to achieve complete data sovereignty by running transcription entirely offline within a secure institutional perimeter. We break down the engineering behind this transition, including the use of SLURM job scheduling for unlimited scalability across GPU nodes and the implementation of advanced quality controls to fix common AI hallucinations such as spurious repetitions and accidental language switching. Furthermore, we examine the system's intelligent, multi-tiered approach to personal name masking and speaker diarisation, which ensures participant anonymity and structured dialogue without compromising the semantic integrity of the research data. This episode provides a comprehensive look at how researchers can balance the power of modern AI with the non-negotiable requirements of ethical compliance and long-term scientific sustainability. Sources and related content can be consulted at https://pablobernabeu.github.io/2025/speech-transcription-python

    32 min

  5. 11/11/2025

    The modular mini-grammar: Building testable and reproducible artificial languages using FAIR principles

    🪄 Created using NotebookLM, with all the benefits and blind spots of human editing. In the high-stakes world of scientific inquiry, methods and findings are inextricable. Yet, issues of reproducibility remain a challenge, especially in experimental linguistics and cognitive science. As the old adage goes, "To err is human", but when creating research materials, adhering to best practices can significantly reduce mistakes and enhance long-term efficiency. In this episode of Codex Mentis, we explore the crucial application of the FAIR Guiding Principles—making materials Findable, Accessible, Interoperable, and Reusable—to the creation of stimuli and experimental workflows. Drawing on research presented by Bernabeu and colleagues, we delve into a complex study on multilingualism using artificial languages, designed specifically to ensure the materials are reproducible, testable, modifiable, and expandable. Unlike many previous artificial language studies that showed low to medium accessibility, this methodology emphasizes high standards for scientific data management. What you will learn: • The Power of Open Source: We discuss the importance of using free, script-based, open-source software, such as R and OpenSesame, to augment the credibility and reliability of research. • Modular Frameworks: Discover how creating a modular workflow based on minimal components in R facilitates the expansion of materials to new languages or within the same language set. • Rigour and Reproducibility: We examine crucial testing steps exerted throughout the preparation workflow—including checking if all experimental elements appear equally often—to prevent blatant disparities and spurious effects. • Detailed Experimentation: Hear how custom Python code within OpenSesame was implemented to manage complex procedures across multiple sessions, including assigning participant-specific parameters (like mini-language or resting-state order). • Measuring the Brain: We look at the technical challenge of accurately time-locking electroencephalographic (EEG) measurements. The episode details the custom Python script used in OpenSesame to send triggers to the serial port, enabling precise Event-Related Potential (ERP) recording. • Generous Documentation: Why detailed documentation, using formats like README.txt that are universally accessible, is essential for allowing collaborators and future researchers (or even your future self) to understand, reproduce, and reuse the materials. Adhering to FAIR standards ensures that the investment in research materials facilitates researchers' work beyond the shortest term, contributing to the best use of resources and increasing scientific reliability. Sources and related content can be consulted at ⁠https://pablobernabeu.github.io/presentation/making-research-materials-findable-accessible-interoperable-reusable-fair

    36 min

  6. 10/23/2025

    Third language learning and morphosyntactic transfer

    🪄 Created using NotebookLM, with all the benefits and blind spots of human editing. In this episode of Codex Mentis, we unpack why learning a third language is not simply ‘second language learning, but easier’. Third Language Acquisition (L3) forces the brain to juggle two existing linguistic systems, creating a ‘two-blueprint’ problem where prior knowledge can help or hinder in unexpected ways. We explore cross-linguistic influence, including evidence that learners may borrow rules from the ‘wrong’ prior language, and the central idea of cognitive economy: the mind reuses what it already has, even when that reuse carries costs. We then dig into the field’s main disagreement about how the brain chooses what to transfer: does it copy the overall grammar of the most similar known language, or negotiate feature by feature? To test this, researchers build tightly controlled artificial and semi-artificial languages that sidestep vocabulary learning and focus on grammar. Finally, we look at EEG findings showing that early stages of learning a new grammar rely more on attention and pattern detection than automatic, native-like processing, and we discuss an ongoing longitudinal study asking whether this deliberate processing can shift over time into the brain’s more implicit, automatic signature for grammar. Sources and related content can be consulted at ⁠https://pablobernabeu.github.io/publication/third-language-longitudinal-data-artificial-language-learning-eeg

    32 min

  7. 09/08/2025

    Behind the curtains: Methods used to investigate conceptual processing

    🪄 Created using NotebookLM, with all the benefits and blind spots of human editing. How do scientists measure a thought? While the great philosophical questions about the nature of meaning have been debated for centuries, the last few decades have seen the development of a sophisticated scientific toolkit designed to turn these abstract queries into concrete, measurable data. In this episode of Codex Mentis, we go behind the curtains of cognitive science to explore the very methods used to investigate how the human brain processes language and constructs meaning. Moving from the 'what' to the 'how', this programme offers a detailed review of the modern psycholinguist's toolkit. The journey begins with the foundational behavioural paradigms that capture cognition in milliseconds. Discover the logic behind the Lexical Decision Task, where a simple button press reveals the speed of word recognition, and the Semantic Priming paradigm, which uses subtle manipulations of time to dissociate the mind's automatic reflexes from its controlled, strategic operations. From there, the discussion delves into the neuro-cognitive instruments that allow us to eavesdrop on the brain at work. Learn how Electroencephalography (EEG) and its famous N400 component provide a precise electrical timestamp for the brain's "sense-making" effort. Explore how Functional Magnetic Resonance Imaging (fMRI) creates detailed maps of the brain's "semantic system," showing us where meaning is processed. And see how Eye-Tracking in the Visual World Paradigm provides a direct, observable trace of the brain making predictions in real time. Finally, the episode demystifies the complex statistical techniques required to analyse this intricate data. We delve into the shift from older statistical methods to modern Linear Mixed-Effects Models, which are designed to handle the inherent variability between people and words. The conversation concludes with a crucial look at the foundations of trustworthy research, examining how scientists determine the sensitivity of their experiments and calculate the required sample sizes to ensure their findings are robust and reproducible. This episode provides a comprehensive guide to the ingenious procedures scientists employ to understand one of the most fundamental aspects of human experience: how we make sense of the world, one word at a time. Sources and related content can be consulted at ⁠https://pablobernabeu.github.io/publication/pablo-bernabeu-2022-phd-thesis

    40 min
  8. The architecture of meaning: Inside the words we use

    07/05/2025

    The architecture of meaning: Inside the words we use

    🪄 Created using NotebookLM, with all the benefits and blind spots of human editing. In this episode of Codex Mentis, we unpack why learning a third language is not simply ‘second language learning, but easier’. Third Language Acquisition (L3) forces the brain to juggle two existing linguistic systems, creating a ‘two-blueprint’ problem where prior knowledge can help or hinder in unexpected ways. We explore cross-linguistic influence, including evidence that learners may borrow rules from the ‘wrong’ prior language, and the central idea of cognitive economy: the mind reuses what it already has, even when that reuse carries costs. We then dig into the field’s main disagreement about how the brain chooses what to transfer: does it copy the overall grammar of the most similar known language, or negotiate feature by feature? To test this, researchers build tightly controlled artificial and semi-artificial languages that sidestep vocabulary learning and focus on grammar. Finally, we look at EEG findings showing that early stages of learning a new grammar rely more on attention and pattern detection than automatic, native-like processing, and we discuss an ongoing longitudinal study asking whether this deliberate processing can shift over time into the brain’s more implicit, automatic signature for grammar. Sources and related content can be consulted at ⁠https://pablobernabeu.github.io/publication/pablo-bernabeu-2022-phd-thesis

    38 min
  • 8 Episodes

About

Codex Mentis, produced by Dr Pablo Bernabeu, offers an exploration into cognitive science and its technologies with the assistance of advanced artificial intelligence. This podcast delves deep into how we think, perceive and interact with the world, dissecting both the profound mysteries of the human mind and the cutting-edge science and technology that illuminate its inner workings. Each episode presents a fascinating journey through diverse aspects of cognition. Beyond the theoretical, Codex Mentis demystifies the methodologies driving cognitive research. Contact: pcbernabeu@gmail.com

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