Human single neuron electrophysiology; explore/exploit dilemma; Pavlovian conditioning; art appraisal; reinforcement learning
Aquino, T. G., Cockburn, J., Mamelak, A. N., Rutishauser, U., & O’Doherty, J. P. (2021). Neurons in human pre-supplementary motor area encode key computations for value-based choice. bioRxiv 2021.10.27.466000; doi: https://doi.org/10.1101/2021.10.27.466000
We recorded neurons from the human pre-supplementary motor area (preSMA), ventromedial prefrontal cortex (vmPFC) and dorsal anterior cingulate to probe how integration over decision variables occurs during decision-making. In contrast to the other areas, preSMA neurons not only represented separate pre-decision variables for each choice option, but also encoded an integrated utility signal and, subsequently, the decision itself. Conversely, post-decision related encoding of variables for the chosen option was more widely distributed and especially prominent in vmPFC. Our findings position the human preSMA as central to the implementation of value-based decisions.
Aquino, T. G., Minxha, J., Dunne, S., Ross, I. B., Mamelak, A. N., Rutishauser, U., & O’Doherty, J. P. (2020). Value-related neuronal responses in the human amygdala during observational learning. Journal of Neuroscience, 40(24), 4761-4772.
Single-neuron studies of the human brain provide a unique window into the computational mechanisms of cognition. In this study, epilepsy patients implanted intracranially with hybrid depth electrodes performed an observational learning (OL) task. We measured single-neuron activity in the amygdala and found a representation for observational rewards as well as observational expected reward values. Additionally, distinct subsets of amygdala neurons represented self-experienced and observational values. This study provides a rare glimpse into the role of human amygdala neurons in social cognition.
Aquino, T. G., de Camargo, R. Y., & Reyes, M. B. (2018). Approaching subjective interval timing with a non-Gaussian perspective. Journal of Mathematical Psychology, 84, 13-19.
Perceiving time intervals is an essential ability of many animals, whose psychophysical properties have yet to be fully understood. A common theoretical approach is to consider that internal representations of time intervals are reflected in probability distribution functions. Depending on the mechanism proposed for interval timing inverse Gaussian and log-normal probability distributions are candidate distributions to represent internal representations of time. In this article, we show that these two distributions approximate each other under the assumptions of mean accuracy and scalar timing when considering experimentally-relevant Weber fractions. We then show that both distributions may be used in the description of the temporal bisection task, predicting bisection times approximately at the geometric mean of reference time intervals for the experimental range of Weber fractions.