where t is the trial number, w0 is a constant term, other weights w capture the influence of different event types, 0 ≤ γ ≤ 1 is a forgetting factor that makes events in more recent trials more influential than those in earlier trials, CRj is the CR if chosen instead of a gamble on trial j, EVj is the EV of a gamble (average reward for the gamble) if chosen on trial j, and RPEj is the RPE on trial j contingent on choice of the gamble. If the CR was chosen, then EVj = 0 and RPEj = 0; if the gamble was chosen, then CRj = 0. Parameters were fit to happiness ratings in individual subjects. We found that CR, EV, and RPE weights were on average positive [all t(25) > 4.6, P < 0.0001] with EV weights lower than RPE weights [t(25) = 4.3, P < 0.001; Fig. 2A]. The forgetting factor γ was 0.61 ± 0.30 (mean ± SD). This model explained moment-to-moment fluctuations in happiness well with r2 = 0.47 ± 0.21 (mean ± SD; Fig. 1) and, when judged according to complexity, explained this reactive happiness better than a range of alternative models, including models without exponential constraints, parameters for unchosen options, and utility-based models.

From "A computational and neural model of momentary subjective well-being"
Robb B. Rutledgea, Nikolina Skandalia, Peter Dayanc, and Raymond J. Dolana
http://www.pnas.org/content/early/2014/07/31/1407535111.full.pdf+html

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