[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 perception. Trends in Cognitive Sciences, 16(10), 504-510. https://doi.org/10.1016/j.tics.2012.08.009
Friston, K. J., Shiner, T., FitzGerald, T., Galea, J. M., Adams, R., Brown, H., Dolan, R. J., Moran, R., Stephan, K. E., & Bestmann, S. (2012). Dopamine, affordance, and active inference. PLoS Computational Biology, 8(1), e1002327. https://doi.org/10.1371/journal.pcbi.1002327
Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7(2), 217-229. https://doi.org/10.1111/tops.12142
Wang, X.-J., & Krystal, J. H. (2014). Computational psychiatry. Neuron, 84(3), 638-654. https://doi.org/10.1016/j.neuron.2014.10.018
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181-204. https://doi.org/10.1017/S0140525X12000477
Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience, 9(11), 1432-1438. https://doi.org/10.1038/nn1790