The equation of happiness

\[H(t) = w_0 + w_1 \sum_{j=1}^t \gamma^{t-j} CR_j + w_2 \sum_{j=1}^t \gamma^{t-j} EV_j + w_3 \sum_{j=1}^t \gamma^{t-j} RPE_j\] I don't know if my intuition is correct, but the equation from Rutledge et al. reminds me of a neural network, or more correctly a sum of three different neural networks. In every case, this could became an important step in order to mathematically describe our brain.

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A common question in the social science of well-being asks, "How happy do you feel on a scale of 0 to 10?" Responses are often related to life circumstances, including wealth. By asking people about their feelings as they go about their lives, ongoing happiness and life events have been linked, but the neural mechanisms underlying this relationship are unknown. To investigate it, we presented subjects with a decision-making task involving monetary gains and losses and repeatedly asked them to report their momentary happiness. We built a computational model in which happiness reports were construed as an emotional reactivity to recent rewards and expectations. Using functional MRI, we demonstrated that neural signals during task events account for changes in happiness.

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Rutledge R.B., Skandali N., Dayan P. & Dolan R.J. (2014). A computational and neural model of momentary subjective well-being., Proceedings of the National Academy of Sciences of the United States of America, PMID: http://www.ncbi.nlm.nih.gov/pubmed/25092308

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via design & trends