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. 2020 Jun 22;9:e56694. doi: 10.7554/eLife.56694

Figure 5. Microstimulation induced correlated changes in the reward modulation of drift and bound.

(A) Scatter plots of changes in drift and bound induced by electrical microstimulation (abscissa and top histograms) and by interactions between electrical microstimulation and reward condition (ordinate and right histograms). Solid lines in histograms: mean values across sessions, t-test, p>0.05. Labels (a, b) correspond to the example sessions in Figure 3A and B, respectively. (B) On trials without microstimulation, the differences in Δdrift and Δbound between the two reward contexts were negatively correlated. Line and shaded area: linear regression and 95% confidence interval, t-test, p<0.0001. “a” and “b” indicate the data points for the examples in Figure 3A and B. Data are color-coded by the values of Δbound (rew) no estim. (C) Scatter plots of reward effects on trials without microstimulation (abscissa) and interaction effects (ordinate) for Δdrift (left) and Δbound (right). Lines represent results of linear regression (shaded area: 95% confidence interval). (D) The interaction effects (Δdrift (rew x estim) and Δbound (rew x estim)), equivalent to the difference between “Δdrift/bound (rew) with estim” and “Δdrift/bound (rew) no estim”, were negatively correlated. Same format as B. t-test, p<0.0001. Red dashed line re-plots the linear regression results from Figure 5B, using the appropriate range of Δdrift (rew x estim) as x-values. Data are color-coded by the values of Δbound (rew) no estim. Note that the roughly reversed orders of color progressions in B and D is most consistent with simulated effects in Figure 4F.

Figure 5—source data 1. Fitting results for choice and RT data using the DDM for sessions with significant microstimulation effects.
The same data were used for plots in Figure 5—figure supplement 2, Figure 5—figure supplement 4, Figure 6A and E, Figure 6—figure supplement 3, and for the summary statistics in Supplementary file 1b.

Figure 5.

Figure 5—figure supplement 1. DDM fits to example sessions in Figure 3.

Figure 5—figure supplement 1.

(A) DDM fit for the example session in Figure 3A. Lines are DDM fits to both choice and single-trial RT data (circles, correct trials; crosses, error trials). Same format as Figure 3A. (B) Illustration of average timecourses of decision variable, using the fitted DDM parameters. Thick and thin lines represent high and low coherence levels, respectively. Same line colors and types as in A. Dotted lines indicate collapsing bounds. (C, D) DDM fit for the example session in Figure 3B. Same format as A,B.
Figure 5—figure supplement 2. DDM fitting results.

Figure 5—figure supplement 2.

(A) Histogram of the difference in AIC between the full model, in which all DDM parameters were allowed to vary by reward context and microstimulation status, and a reduced model, in which all DDM parameters were allowed to vary by reward context but not microstimulation status. Negative ΔAIC implies that the full model is better. The red arrow indicates the criterion we used and corresponds to the gap in the histogram. (B) Map showing the best DDM variant (lowest AIC, black bar) for sessions with significant microstimulation effects (n = 39 sessions to the left of the red arrow in A). In the reduced models, parameters associated with collapsing bounds (β_alpha and β_d, “NoCollapse”), total bound height (a, “NoA”), drift rate scalor (k, “NoK”), bias in drift rate (me, “NoME”), relative bound height (z, “NoZ”), and non-decision times (t_contra and t_ipsi, “NoT0”) were allowed to vary based on reward context, but not microstimulation status. (C) Histograms of differences in AIC between the full model and reduced models for the 39 sessions to the left of the red arrow in A. Mean ΔAIC values were negative for reduced models (t-test, p<0.001 for all). (D) Scatter plots of changes in DDM parameters induced by electrical microstimulation in the Ipsi-LR blocks (abscissa and top histograms) or in the Contra-LR blocks (ordinate and right histograms). Solid lines in histograms: mean values across sessions. Red lines, t-test p<0.05. Labels (a, b) correspond to the example sessions in Figure 3A and B, respectively.
Figure 5—figure supplement 3. Biases in drift and bounds together accounted for biases measured in logistic fits.

Figure 5—figure supplement 3.

Scatter plots of microstimulation effects on choice bias, measured with logistic fit (abscissa) and DDM fits (ordinate). Bias values for the DDM were simulated using fitted parameters. Lines: linear regression.
Figure 5—figure supplement 4. Both monkeys showed similar correlation patterns between ∆drift and ∆bound.

Figure 5—figure supplement 4.

(A-C) Results from monkey C. Same format as Figure 5B and D. Data points were color-coded based on ∆bound (rew) no estim values. Red dashed lines replotted the linear regression in A, with x-values centered on ∆drift (rew) with estim and ∆drift (rew × estim), respectively. Solid lines, linear regressions with t-test, p<0.0001 ,=0.0058, and 0.004. respectively. (D-F) Results from monkey F. Linear regression, t-test, p<0.0001 for all.