Figures and data
Task timeline.
Subjects chose between two snack food lotteries on each trial. Subjects learned about the lotteries through random food draws. Every 4-8 seconds, subjects sampled a new draw from each lottery. They were allowed to sample as many times as they wanted but were incentivized to sample approximately 7 draws per trial. Sampled food draws were presented for 2 seconds, followed by a fixation cross appearing for 2-6 seconds with random jitter. The trial ended when the subject chose the left or right lottery, using the respective index finger. Upon making their choice, subjects were presented with a food drawn from their chosen lottery.
Example trial with the sampled value and accumulated value.
The sampled value (ΔSV; red) and accumulated value (ΔAV; black) are plotted for this example trial. For the first draw, ΔSV and ΔAV are identical. However, as the trial proceeds, the two signals diverge. In the model, a choice is made when the |ΔAV| reaches a pre-specified decision boundary.
Choice data.
(a) The probability of choosing left based on ΔSV and ΔAV. As the value difference becomes greater in favor of one option, the probability of choosing that option increases, for both ΔSV and ΔAV. (b) The effect of gaze on choice. The longer that subjects looked at one lottery over the other, over the course of the whole trial, the more likely they were to choose that lottery.
Regions responding to sampled value, accumulated value, and their gaze-weighted variants in GLM1.
(a) |ΔSV| correlated positively, but not quite significantly, with activity in vmPFC. (b) |ΔAV| correlated positively with activity in pre-SMA and dlPFC and negatively with activity in vmPFC (not shown). (c) |ΔSVGaze| correlated negatively with activity in the striatum. (d) |ΔAVGaze| correlated positively with activity in pre-SMA, vmPFC, and striatum (not shown).
GLM1 beta plots from the vmPFC, striatum, pre-SMA, IPS, and dlPFC.
Displayed are regression coefficients from each region for (a) non-gaze-weighted signals: absolute sampled value difference (|ΔSV|) and absolute lagged accumulated value difference (|ΔAV|), and (b) gaze-weighted signals: gaze-weighted sampled value (|ΔSVGaze|) and absolute lagged gaze-weighted accumulated value (|ΔAVGaze|). Note: Bar heights reflect mean beta estimates averaged across all voxels within each ROI. Statistical significance was determined using permutation tests with FWE correction, which identify spatially localized, reliable effects within each ROI.
Non-uniform temporal weighting.
In both gaze-weighted and non-gaze-weighted models, participants showed stronger recency than primacy effects, both in terms of (A) the model parameters, and (B) the resulting temporal weighting functions averaged across all trials. Error bars are standard errors clustered by participant.
Sample-level correlations between value signal regressors.
Pearson correlations computed across samples for different quantifications of sampled value (ΔSV) and accumulated value (ΔAV). Values represent correlations between absolute magnitudes of each measure. |ΔSV| = absolute sampled value difference; |ΔAV| = absolute accumulated value difference; lagged |ΔAV| = absolute accumulated value difference up to the previous sample; |ΔSVGaze| = gaze-weighted sampled value; |ΔAVGaze| = gaze-weighted accumulated value; lagged |ΔAVGaze| = gaze-weighted accumulated value from the previous sample.
