[Pilot] How Our Brains Predict and Shape Everyday Life

This is one of our early episodes. We shared a mic and the audio is a bit raw, so feel free to check out our latest episodes for a more polished experience.

Welcome to the first episode of Science Savvy with Carmen. In this episode, I explore how our brains work as prediction machines to help us make sense of the world around us. With my background in pharmacology and biomedical engineering, I break down the science behind how the brain constantly anticipates and adapts to everyday experiences.

This episode dives into how your brain predicts everything from the next note in a song to the social signals in a conversation. I unpack key theories in neuroscience and explain how the brain’s ability to make sense of uncertainty shapes your emotions, perceptions, and actions. If you’ve ever wondered how your brain seems to be one step ahead, this episode offers a practical and research-backed look at why prediction is at the core of everything we do.

Science Savvy is about understanding the hidden systems that guide your thoughts, your feelings, and your health. If you're curious about how your brain works and how that knowledge can empower your everyday life, you're in the right place.

Further reading and references:

Barrett, L. F. (2017). The theory of constructed emotion: An active inference account of interoception and categorization. Social Cognitive and Affective Neuroscience, 12(1), 1-23. https://doi.org/10.1093/scan/nsw154 Friston, K. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1456), 815-836. https://doi.org/10.1098/rstb.2005.1622 Barbas, H. (2015). Generalization of the prefrontal cortex in primates: Principles and prediction models. Progress in Brain Research, 219, 27-47. https://doi.org/10.1016/bs.pbr.2015.03.001 Kilford, E. J., Garrett, E., & Blakemore, S. J. (2017). The development of social cognition in adolescence: An integrated perspective. Neuroscience & Biobehavioral Reviews, 70, 106-120. https://doi.org/10.1016/j.neubiorev.2016.08.016 Redgrave, P., & Gurney, K. (2006). The short-latency dopamine signal: A role in discovering novel actions? Nature Reviews Neuroscience, 7(12), 967-975. https://doi.org/10.1038/nrn2022 Schultz, W. (2016). Dopamine reward prediction error coding. Dialogues in Clinical Neuroscience, 18(1), 23-32. https://doi.org/10.31887/DCNS.2016.18.1/wschultz Ito, M. (2008). Control of mental activities by internal models in the cerebellum. Nature Reviews Neuroscience, 9(4), 304-313. https://doi.org/10.1038/nrn2332 Buckner, R. L. (2010). The role of the hippocampus in prediction and imagination. Annual Review of Psychology, 61, 27-48. https://doi.org/10.1146/annurev.psych.60.110707.163508 Schapiro, A. C., Turk-Browne, N. B., Botvinick, M. M., & Norman, K. A. (2017). Complementary learning systems within the hippocampus: A neural network modeling approach to memory consolidation. Hippocampus, 27(3), 244-256. https://doi.org/10.1002/hipo.22675 Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79-87. https://doi.org/10.1038/4580 Morris, R. G. (2006). Elements of a neurobiological theory of the hippocampus: The role of synaptic plasticity, synaptic tagging, and schemas. The European Journal of Neuroscience, 23(11), 2829-2846. https://doi.org/10.1111/j.1460-9568.2006.04888.x Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003). Discrete coding of reward probability and uncertainty by dopamine neurons. Science, 299(5614), 1898-1902. https://doi.org/10.1126/science.1077349 Behrens, T. E., Hunt, L. T., Woolrich, M. W., & Rushworth, M. F. S. (2008). Associative learning of social value. Nature, 456(7219), 245-249. https://doi.org/10.1038/nature07538 Powers, A. R., Mathys, C., & Corlett, P. R. (2017). Pavlovian conditioning–induced hallucinations result from overweighting of perceptual priors. Science, 357(6351), 596-600. https://doi.org/10.1126/science.aan3458 Pellicano, E., & Burr, D. (2012). When the world becomes ‘too real’: A Bayesian explanation of autistic perceptio