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Join me, Carmen, on Science Savvy as we dive into the mysteries of consciousness and self-awareness! In this episode, I’ll explore what it really means to be conscious, how self-awareness shapes our identity and self-esteem, and why some of these questions have puzzled scientists and philosophers for centuries. From Descartes' "I think, therefore I am" to modern neuroscience and theories like the "Astonishing Hypothesis," we’ll examine how genetics, brain chemistry, and life experiences impact our sense of self.
Whether you’re curious about the science behind identity or the deeper philosophical questions about why we experience life as we do, this episode has something for you. Let’s get savvy about the brain together!
Further Reading:
Crick, F. (1994). The Astonishing Hypothesis: The Scientific Search for the Soul. Scribner.Chalmers, D. J. (1995). Facing up to the problem of consciousness. Journal of Consciousness Studies, 2(3), 200-219.Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5(42).Koch, C., Massimini, M., Boly, M., & Tononi, G. (2016). Neural correlates of consciousness: progress and problems. Nature Reviews Neuroscience, 17(5), 307-321.Northoff, G., Heinzel, A., de Greck, M., Bermpohl, F., Dobrowolny, H., & Panksepp, J. (2006). Self-referential processing in our brain—A meta-analysis of imaging studies on the self. NeuroImage, 31(1), 440-457.Lieberman, M. D., & Eisenberger, N. I. (2009). Pains and pleasures of social life. Science, 323(5916), 890-891.Panksepp, J. (1998). Affective Neuroscience: The Foundations of Human and Animal Emotions. Oxford University Press. -
In this episode of Science Savvy, we're diving into the science of love—beyond just romance. From the neuroscience that drives attraction to the biology behind long-term bonding, we explore the fascinating mechanisms behind one of humanity’s most powerful emotions. Together with my friend Alejandra, we break down the stages of love—lust, attraction, and attachment—and discuss the roles of hormones like oxytocin, vasopressin, and dopamine in shaping how we connect with others. We also touch on the physical effects of love and how heartbreak can feel like real, physical pain. Whether you’re curious about why you get butterflies or how love helps us thrive, this episode offers both a deep dive into the science and a personal touch.
If you’re interested in learning more, check out the references below for further reading.
Further Reading:
Helen Fisher’s work on the neuroscience of loveResearch on oxytocin and vasopressin related to bonding and attachmentStudies on dopamine and cortisol in romantic relationshipsEvolutionary psychology texts on the biological purpose of loveResearch on the effects of heartbreak on brain activity and emotional regulationReferences:
Sharma, S. R., Gonda, X., Dome, P., & Tarazi, F. I. (2020). What's love got to do with it: Role of oxytocin in trauma, attachment, and resilience. Pharmacology & Therapeutics, 214, 107602. DOI: 10.1016/j.pharmthera.2020.107602Fisher, H., Aron, A., & Brown, L. L. (2005). Romantic love: An fMRI study of a neural mechanism for mate choice. Journal of Comparative Neurology, 493(1), 58-62. DOI: 10.1002/cne.20772Stein, D. J., & Vythilingum, B. (2009). Love and attachment: The psychobiology of social bonding. CNS Spectrums, 14(5), 239-242. DOI: 10.1017/s1092852900025384Acevedo, B. P., Poulin, M. J., Collins, N. L., & Brown, L. L. (2020). After the honeymoon: Neural and genetic correlates of romantic love in newlywed marriages. Frontiers in Psychology, 11, 634. DOI: 10.3389/fpsyg.2020.00634 -
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In this episode of Science Savvy, we tackle the age-old question: why do we even get periods? From evolutionary theories to hormonal rollercoasters, we break down the science behind all that bloating, mood swings, and acne. Why can’t we just get a text saying, "You’re not pregnant"? Join me as we explore why periods are a thing and how understanding your cycle can help you level up your workouts, social life, and creativity. It’s time to work with your body, not against it!
Further reading / 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.
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Join us for lots of giggles and lots of science! In this episode of Science Savvy, I am joined by my bestie of 10 years, Dasha, to dive into the science behind long-term friendships. We explore how your brain syncs up with your closest friends, how oxytocin makes you feel all warm and fuzzy, and why those group chats and weekend hangouts are actually boosting your health and happiness. Find out how your bestie might just be the key to living a longer, healthier life!
Further Reading and References:Dunbar, R. I. M. (2018). Friends: Understanding the Power of Our Most Important Relationships. Little, Brown Spark.Parkinson, C., Kleinbaum, A. M., & Wheatley, T. (2018). "Similar neural responses predict friendship." Nature Communications.Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). "Social relationships and mortality risk: A meta-analytic review." PLoS Medicine.Lieberman, M. D. (2013). Social: Why Our Brains Are Wired to Connect. Crown Publishers.Cohen, S., & Wills, T. A. (1985). "Stress, social support, and the buffering hypothesis." Psychological Bulletin.Lunn, N. (2021). Conversations on Love. Viking.Holt-Lunstad, J. (2018). "Why social relationships are important for physical health: A systems approach to understanding and modifying risk and protection." Annual Review of Psychology.Haslam, C., & Jetten, J. (2014). "Social connectedness and health in older adults." Journal of Aging and Health.Roberts, S. G., & Dunbar, R. I. (2011). "Communication in social networks: Effects of kinship, network size, and emotional closeness." Personal Relationships.Langan, K. A., & Purvis, J. M. (2020). "Long-distance friendship maintenance: An application of expectancy violation theory and the investment model." Current Opinion in Psychology. -
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
