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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. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 1629-1634). Austin, TX: 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

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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

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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