Science Savvy

Periods - Why Can’t Mother Nature Just Text Me I'm Not Pregnant?

In this episode of Science Savvy, we tackle one of the most common yet least understood experiences in women’s health: why do we get periods. From evolutionary theories to hormonal rollercoasters, I explore the biology and history behind menstruation and the science that underlies symptoms like bloating, mood swings, and acne. With my background in pharmacology and biomedical engineering, I break down why periods exist in the first place and what they can reveal about your health.
We look at the theories around menstruation as a defense mechanism, the evolution of concealed ovulation, and how different phases of the cycle impact your brain, energy levels, and even your creativity. Whether you’re curious about how your body works or want to better align your lifestyle with your cycle, this episode offers practical insights grounded in biology and evolutionary science.
Science Savvy is here to help you understand your body and brain through a scientific lens. If you’re ready to work with your cycle instead of against it, this episode is for you.
Further reading and references:
Profet, M. (1993). Menstruation as a defense against pathogens transported by sperm. The Quarterly Review of Biology, 68(3), 335-386.Strassmann, B. I. (1996). The evolution of endometrial cycles and menstruation. The Quarterly Review of Biology, 71(2), 181-220.Pawlowski, B. (1999). Loss of oestrus and concealed ovulation in human evolution: The case against the sexual-selection hypothesis. Current Anthropology, 40(3), 257-275.Emera, D., Romero, R., & Wagner, G. (2012). The evolution of menstruation: A new model for genetic assimilation. BioEssays, 34(1), 26-35.Hillard, P. J. A., & Speroff, L. (2019). Clinical Gynecologic Endocrinology and Infertility. Wolters Kluwer Health.Miller, G., Tybur, J. M., & Jordan, B. D. (2007). Ovulatory cycle effects on tip earnings by lap dancers: Economic evidence for human estrus. Evolution and Human Behavior, 28(6), 375-381.Haselton, M. G., & Gildersleeve, K. (2011). Can men detect ovulation. Current Directions in Psychological Science, 20(2), 87-92.Johnson, S., Marriott, L., & Zinaman, M. (2018). Accuracy of an online fertility tracker. Journal of Women's Health, 27(4), 435-442.Wilcox, A. J., Weinberg, C. R., & Baird, D. D. (1995). Timing of sexual intercourse in relation to ovulation. The New England Journal of Medicine, 333(23), 1517-1521.Yang, Z., & Schank, J. C. (2006). Women do not synchronize their menstrual cycles. Human Nature, 17(4), 433-447.Frank-Herrmann, P., et al. (2007). The effectiveness of a fertility awareness-based method to avoid pregnancy in relation to a couple's sexual behavior during the fertile time. Human Reproduction, 22(5), 1310-1319.Berglund Scherwitzl, E., et al. (2017). Fertility awareness-based mobile application for contraception. The European Journal of Contraception & Reproductive Health Care, 22(5), 365-373.

[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/nsw154Friston, 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.2005.1622Barbas, 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.2015.03.001Kilford, 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.2016.08.016Redgrave, 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/nrn2022Schultz, W. (2016). Dopamine reward prediction error coding. Dialogues in Clinical Neuroscience, 18(1), 23-32. https://doi.org/10.31887/DCNS.2016.18.1/wschultzIto, M. (2008). Control of mental activities by internal models in the cerebellum. Nature Reviews Neuroscience, 9(4), 304-313. https://doi.org/10.1038/nrn2332Buckner, 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.60.110707.163508Schapiro, 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.22675Rao, 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/10.1038/4580Morris, 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.xFiorillo, 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.1077349Behrens, 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/nature07538Powers, 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.aan3458Pellicano, 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.2012.08.009Friston, 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.1002327Griffiths, 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.12142Wang, X.-J., & Krystal, J. H. (2014). Computational psychiatry. Neuron, 84(3), 638-654. https://doi.org/10.1016/j.neuron.2014.10.018Clark, 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/S0140525X12000477Ma, 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