References

Selected reading for my research interests.

Learning & predictive representations

  1. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.

    Rao, R. P. N., & Ballard, D. H. (1999).

    Nature Neuroscience, 2, 79–87.

  2. Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment.

    Berkes, P., Orbán, G., Lengyel, M., & Fiser, J. (2011).

    Science, 331, 83–87.

  3. Expectation in perceptual decision making: neural and computational mechanisms.

    Summerfield, C., & de Lange, F. P. (2014).

    Nature Reviews Neuroscience, 15, 745–756.

  4. A neural substrate of prediction and reward.

    Schultz, W., Dayan, P., & Montague, P. R. (1997).

    Science, 275, 1593–1599.

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Uncertainty & rule updating

  1. An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment.

    Nassar, M. R., Wilson, R. C., Heasly, B., & Gold, J. I. (2010).

    Journal of Neuroscience, 30, 12366–12378.

  2. Context, learning, and extinction.

    Gershman, S. J., Blei, D. M., & Niv, Y. (2010).

    Psychological Review, 117, 197–209.

  3. Uncertainty, neuromodulation, and attention.

    Yu, A. J., & Dayan, P. (2005).

    Neuron, 46, 681–692.

  4. Confidence as Bayesian probability: from neural origins to behavior.

    Meyniel, F., Sigman, M., & Mainen, Z. F. (2015).

    Neuron, 88, 78–92.

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Interoception, affect & self-representation

  1. Neural systems supporting interoceptive awareness.

    Critchley, H. D., Wiens, S., Rotshtein, P., Öhman, A., & Dolan, R. J. (2004).

    Nature Neuroscience, 7, 189–195.

  2. Interoceptive predictions in the brain.

    Barrett, L. F., & Simmons, W. K. (2015).

    Nature Reviews Neuroscience, 16, 419–429.

  3. Being a beast machine: the somatic basis of selfhood.

    Seth, A. K., & Tsakiris, M. (2018).

    Trends in Cognitive Sciences, 22, 969–981.

  4. Self-evaluation of decision-making: a general Bayesian framework for metacognitive computation.

    Fleming, S. M., & Daw, N. D. (2017).

    Psychological Review, 124, 91–114.

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