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. Author manuscript; available in PMC: 2021 Feb 8.
Published in final edited form as: Nat Neurosci. 2020 Aug 10;23(10):1267–1276. doi: 10.1038/s41593-020-0688-5

Figure 1. A subset of ventral pallidum neurons signal preference-based reward prediction errors.

Figure 1.

(a, c-f) are adapted from (9). (a) Task: entering the reward port during a 10s cue triggered reward delivery. (b) The cue indicated 50/50 probability of receiving sucrose or maltodextrin solutions, as seen in example session (right). (c) Percentage sucrose of total solution consumption in a two-bottle choice, before (“Initial”) and after (“Final”) recording. (d) Mean(+/−SEM) lick rate relative to pump onset. (e) Mean(+/−SEM) activity of all recorded neurons on sucrose (Suc) and maltodextrin (Mal) trials.Gray rectangle indicates window used for analysis in (g-h,j) and all equivalent analyses in subsequent figures. (f) Mean(+/−SEM) activity of all recorded neurons on trials sorted by previous and current outcome. (g) Coefficients(+/−SE) from a linear regression fit to the z-scored activity of all neurons (n=436 neurons) and the outcomes on the current and preceding 10 trials. (h) Schematic of model-fitting and neuron classification process. For each neuron, the reward outcome and spike count following reward delivery on each trial were used to fit three models: RPE, Current outcome, and Unmodulated. Akaike information criterion (AIC) was used to select which model best fit each neuron’s activity (right). (i) Mean(+/−SEM) activity of neurons best fit by each of the three models, plotted according to previous and current outcome. (j) Coefficients(+/−SE) for outcome history linear regression for each class of neurons (n=72 RPE, 126 Current outcome, and 238 Unmodulated neurons).