Science Savvy

[Pilot] How Our Brains Predict and Shape Everyday Life

This is the first pilot episode! Our style has evolved since then—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 navigate everyday life. With my background in pharmacology and biomedical engineering, I aim to demystify the science behind daily experiences—starting with how our brains predict and adapt to the world around us.
Key Topics Covered:
Predictive Coding Model: How your brain uses past experiences to anticipate future events.
Emotion Theories: Discover Lisa Feldman Barrett’s Constructed Emotion Theory and how emotions are predictions, not reactions.
Brain Regions: Learn about the prefrontal cortex, basal ganglia, cerebellum, and how they control your actions.
Mental Health & Brain Predictions: I discuss the role of predictive mechanisms in conditions like schizophrenia, autism, and anxiety.
Gambling & Dopamine: Why uncertainty in gambling triggers dopamine release, leading to addictive behaviors.
Why Listen?
If you’ve ever wondered how your brain is always one step ahead, predicting everything from the next note in a song to social interactions, this episode is for you. I’ll break down complex neuroscience into bite-sized insights that explain how our brains predict and respond to daily challenges.
Whether you're fascinated by brain science, interested in mental health, or curious about how emotions work, this episode offers practical insights and theories to help you understand the brain's powerful role in shaping your life.
 
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