We recorded 203 neurons while the monkeys were performing a random-dot motion discrimination task (n=115 and 88 for monkeys C and F, respectively). The behavioral performance of both monkeys has been documented extensively (Ding and Gold, 2010; Ding and Gold, 2012a; Fan et al., 2018). Their performance in three example sessions is shown in Figure 4A–C (black data points). In general, both monkeys made more contralateral choices with increasing signed motion strength (positive for motion toward the contralateral target; negative for motion toward the ipsilateral target) and had lower RTs (i.e. faster responses) for higher absolute motion strength.

STN neurons showed diverse response profiles. Figure 2A shows average activity patterns of three example neurons. The top neuron showed an initial suppression of activity after motion onset and became active, in a choice-dependent manner, before saccade onset. The middle neuron showed choice- and motion coherence-dependent activation during the motion-viewing period before saccade onset. The bottom neuron exhibited activation after motion onset that was similar for both choices and all coherence levels, which then decayed in a choice- and coherence-dependent manner around saccade onset.

The diversity of response profiles can be seen in the summary heatmaps for the population (Figure 2B). When activity was averaged across all trial types, STN neurons can become activated or suppressed (warm vs. cool colors, respectively), relative to pre-stimulus baseline, during motion viewing and around saccade onset. The timing of peak modulation also spanned the entire motion-viewing period and extended beyond saccade generation, including a substantial fraction of neurons that also responded to target onset before the motion stimulus appeared. These diverse spatiotemporal response profiles suggest that the STN as a whole may serve multiple functions in perceptual decision-making.

Across the population, a substantial fraction of neurons was sensitive to choice, motion coherence, and RT (Figure 2C–E, Figure 2—figure supplement 1). We performed multiple linear regressions, separately for coherence and RT (Equations 1 and 2), for each neuron and used the regression coefficients to measure these decision-related sensitivities. For choice sensitivity (Figure 2C, first row), both contralateral and ipsilateral preferences were commonly observed.

The overall fraction of neurons showing choice sensitivity increased after motion onset and peaked at saccade onset (Figure 2D). For coherence sensitivity, modulations were observed for trials with contralateral or ipsilateral choices and with similar tendencies for positive and negative coefficients (Figure 2C, rows 2 and 3). The fraction of neurons showing reliable coherence sensitivity was also higher around saccade onset (Figure 2D).

Despite the diverse distributions of regression coefficients, there were systematic patterns in when and how these forms of selectivity were evident in the neural responses. Notably, neurons showing choice sensitivity were more likely to show coherence modulation during early motion viewing, especially for trials when the monkey chose the neuron’s preferred choice (Figure 2E, purple). In contrast, coherence modulation emerged later for neurons that did not show choice sensitivity (Figure 2E, gray lines). These systematic interactions in modulation types suggest that the STN population does not simply reflect a random mix of selectivity for decision-related quantities. Instead, there appears to exist subpopulations with distinct decision-related modulation patterns, which we detail below.

Using two forms of cluster analysis, we identified three subpopulations of neurons in the STN with distinct activity patterns that conform to predictions of each of the three previously published sets of models. For the first analysis, we represented a neuron’s activity pattern with a 30-dimensional (30D) vector, consisting of normalized average activity associated with two choices, five coherence levels, and three task epochs. We generated three artificial vectors based on the predicted activity patterns of each model, as follows (Figure 3A). Bogacz and Gurney, 2007, posited that STN neurons, through their reciprocal connections with the external segment of GPe, pool and normalize evidence-related signals, leading to the prediction of choice and coherence-modulated activity during motion viewing (Figure 3A, top; based on simulations using equations in their Appendix B). Ratcliff and Frank, 2012, posited that STN neurons, through their direct innervation by cortical regions, provide an early signal to suppress immature choices, leading to the prediction of a choice-independent signal that appears soon after motion onset and dissipates over time (Figure 3A, middle; based on their Figure 5). Wei et al., 2015, posited that the STN balances evidence-related signals in the GPe until near decision time, leading to the prediction of coherence-dependent ramping activity with no or weak choice selectivity (Figure 3A, bottom; based on their Figure 2D). We performed k-means clustering using these three vectors and another arbitrary vector as the seeds to group the population into four clusters.

Figure 3B shows the average activity from each of the resulted clusters. Consistent with the design of this analysis, the first cluster tends to show choice- and coherence-dependent activity that also ramps up during motion viewing (Figure 3B, first row; Figure 3—figure supplement 1). The second cluster tends to show an early, sharper rise in activity during motion viewing and this activity gradually decreases toward saccade onset (Figure 3B, second row). The third cluster tends to show ramping activity during motion viewing with similar coherence modulation for both choices and a short burst of activity for one choice just before saccade onset (Figure 3B, third row; Figure 3—figure supplement 1). The last cluster shows mixed and, on average, weak task-related modulation (Figure 3B, bottom row; Figure 3—figure supplement 1). The first three clusters contained similar numbers of neurons. When visualized using the t-distributed stochastic neighbor embedding (t-SNE) technique, these clusters did not form a single continuum but instead reflected separable features between clusters (Figure 3C). In other words, the clustering did not simply force a uniform distribution with random-mixed selectivity into four groups.

For the second cluster analysis, we used random seeds without considering any of the model predictions and obtained almost identical clusters. As detailed in Methods, we explored a wide range of settings for clustering, including: (1) using directly the 30D vectors or their principal component projections, (2) basing the clustering on three different distance metrics, and (3) varying the number of presumed clusters. To identify the best setting, we assessed the goodness of clustering using the silhouette score and the stability of clustering using the Rand index (Rand, 1971; Figure 4). The silhouette score quantifies for each member the relative distance between its average within-cluster distance and distance to those in its closest neighboring cluster (a higher score indicates better cluster separation). The silhouette plots favored the combination of using the 30D vector directly and correlation distance (Figure 4A, third row), which generated less variability across clusters and few small-magnitude negative silhouette scores (negative scores indicate that a member is closer to its neighboring cluster than its own cluster).

The Rand index measures how consistently two members are assigned to the same clusters from different iterations of clustering (a high index indicates greater stability). The Rand index was generally high (above 0.95 out of a max of 1; blue box) except for the combination of using the 30D vector and cosine distance (Figure 4B). Finally, using the raw vector-correlation combination, an assumption of four clusters resulted in the highest average Rand index and assuming four to six clusters generally resulted in higher mean silhouette score and lower number of negative scores (Figure 4C; blue arrow). We thus considered that the raw vector-correlation combination and an assumption of four clusters produced the most stable and plausible results.

As shown in Figure 3D and E, the four clusters thus identified closely matched those obtained using model predicted seeds, in terms of the average activity, the cluster sizes, and their locations in the t-distributed stochastic neighbor embedding (t-SNE) space. Increasing the assumed number of clusters caused changes mostly in the gray cluster, with some changes in the blue cluster, and little effects on the red and purple clusters (Figure 4—figure supplements 1 and 2). Together these results suggest that, absent the ground truth on the number of subpopulations in STN, there exist at least three subpopulations that each corresponds to the predictions of one of three previously published models. As a consequence, STN appears in principle to be able to support multiple decision-related functions.

To better understand STN’s functional roles in the decision process, we perturbed STN activity using electrical microstimulation while monkeys performed the task. Specifically, we applied a train of current pulses at identified STN sites during decision formation, lasting from motion onset to saccade onset. Figure 5 shows microstimulation effects on choices and RTs in three example sessions. In the first example session, STN microstimulation caused a leftward horizontal shift (more contralateral choices) and slope reduction (more variable choices) in the psychometric curve (Figure 5A, top), as well as a substantial flattening of RT curves (faster responses that depended less on motion coherence) for both choices (bottom). In the second example session, STN microstimulation induced a minor leftward shift in the psychometric curve and asymmetric changes in RT for the two choices (Figure 5B). In the third example session, STN microstimulation did not change the psychometric curve but caused reductions in RT for both choices (Figure 5C).

Across 54 different STN sites, microstimulation caused variable choice biases and tended to reduce the dependence of choice on motion strength. We fitted a logistic function to the choice data and measured choice bias (horizontal shift) and motion strength dependence (slope). In 23 sessions, microstimulation induced a reliable choice bias (Figure 5D). The induced bias was toward the contralateral or ipsilateral choice in 15 and 8 sessions, respectively, and the median value for bias was not significantly different from zero (Wilcoxon signed-rank test, p=0.15). In 18 sessions, microstimulation induced a change in the slope. The slope was reduced in 15 sessions and the median value was negative (p=0.008). These tendencies were robust across different variants of logistic functions, with or without lapse terms to capture errors independent of motion strength (Figure 5—figure supplement 1). Of the sessions where inclusion of lapse terms for the control and microstimulation trials produced lower Akaike information criteria (AICs), very few showed significant microstimulation-induced changes in lapses (2 sessions each for the ‘Symmetric Lapse’ and ‘Asymmetric Lapse’ variants). Thus, based on fitting results using logistic functions, STN microstimulation most consistently reduced the choice dependence on motion strength, caused session-specific choice biases, and had minimal effects on lapses.

Microstimulation also tended to reduce RT. We fitted linear functions separately for RTs associated with the two choices, in which offset and slope terms measure coherence-independent and -dependent changes in RTs, respectively. Microstimulation caused changes in RT offsets in 25 sessions for contralateral choices (18 were reductions in RT, with a median change across all sessions of –36 ms; Wilcoxon signed-rank test for H₀: median change = 0, p<0.0001) and 18 sessions for ipsilateral choices (15 reductions, mean change = –23 ms, p<0.0001; Figure 5E). Microstimulation caused changes in RT slopes in 6 sessions for contralateral choices (5 were positive, implying a weaker coherence dependence; p<0.0001) and 4 sessions for ipsilateral choices (all 4 were positive; Figure 5F). Thus, based on fitting results using linear functions, STN microstimulation can induce choice-specific changes in RT, with overall tendencies to reduce both the coherence-independent component and the RT’s dependence on coherence for the contralateral choice.

To infer STN’s computational roles in the decision process, we examined the microstimulation effects using a drift-diffusion model (DDM) framework. This framework has been widely used in studies of perceptual decision-making and can provide a unified, computational account of both choice and RT (Gold and Shadlen, 2007). It assumes that noisy evidence is accumulated over time and a decision is made when the accumulated evidence reaches a certain decision bound. The overall RT is the sum of the time needed to reach the bound and non-decision times reflecting perceptual and motor latencies. Previous theoretical models also made predictions about the effects of perturbing STN activity that can be interpreted in the DDM framework. The model by Bogacz and Gurney, 2007, predicted that the perturbation would reduce the effect of task difficulty on decision performance by eliminating a nonlinear transformation that is needed for appropriate evidence accumulation (Green et al., 2013). The model by Ratcliff and Frank, 2012, predicted that the perturbation, by causing changes in the STN’s influence onto the substantia nigra pars reticulata, would change temporal dynamics of the decision bound and influence non-decision time. The model by Wei et al., 2015, predicted that the perturbation would result in a reduction in the decision bound.

To test whether these predictions, and/or other effects, were present in our microstimulation data, we fitted a DDM to choice and RT data simultaneously (Figure 6A). We performed AIC-based model selection and found that, in 40 of 54 sessions, the Full model, which included microstimulation effects on any model parameters, outperformed the None model, which assumed that there was no microstimulation effect on any parameters (Figure 6B). This result implies that, in these sessions, STN microstimulation affected one or more computational components of the decision process. To better characterize these effects, we compared AICs between the Full model and six reduced models to identify sessions with reliable microstimulation-induced changes in particular model parameters (Figure 6—figure supplement 1A).

We found that STN microstimulation resulted in reliable changes in several model parameters over different subsets of sessions (Figure 6C and D). Consistent with model predictions from Bogacz and Gurney, 2007, microstimulation reduced the scale factor for evidence accumulation, k, in 14 sessions. This effect contributed to a decreased motion coherence dependence of choice and RT (Figure 6C, first histogram; Wilcoxon signed-rank test for H₀: zero median effect, p=0.021). Consistent with model predictions from Ratcliff and Frank, 2012, and Wei et al., 2015, microstimulation affected parameters that controlled the decision bound (a, B_collapse, B_t) in 16 sessions each (not necessarily in the same sessions for each parameter, see Figure 6D). The changes in the maximal decision bound (a) were variable across sessions (p=0.68). The changes in the collapsing bound dynamics (B_collapse, B_t) tended to indicate faster and earlier decreases in bounds (p=0.039 and 0.088, respectively). Consistent with model predictions from Ratcliff and Frank, 2012, microstimulation caused changes in non-decision times in 30 sessions (t0_Contra and t0_Ipsi). These changes varied from session to session (p=0.28 and 0.75, respectively). Statistical tests on fitted parameters of all sessions, regardless of whether a microstimulation effect was necessary to account for the behavioral data, showed similar trends (Figure 6—figure supplement 1B).

STN microstimulation had two additional effects beyond those predicted by the previous modeling studies. First, consistent with the above-demonstrated microstimulation-induced choice biases (Figure 5), microstimulation induced offsets in momentary (me; n=16 sessions; p=0.61) and accumulated (z; n=12 sessions; p=0.016) evidence. Second, the microstimulation effects involved changes in more than one model parameter in the majority of sessions (Figure 6D). We did not observe any dominant combinations of effects. These results suggest that the STN is causally involved in multiple decision-related functions, including those mediating the dependence on evidence, choice biases, and bound dynamics.

The multi-faceted microstimulation effects, combined with the fact that the kind of microstimulation we used tends to activate not just one neuron, but rather groups of neurons near the tip of the electrode (Tehovnik, 1996), suggested that STN neurons with different functional roles are located close to one another. Consistent with this idea, neurons that were classified as belonging to different clusters tended to be intermingled (Figure 7). We did not observe any consistent topographical organization patterns within or between the two monkeys. At certain locations, neurons belonging to different clusters were recorded using the same electrode. We calculated silhouette scores to quantify whether the activity pattern-based neuron clusters also formed clusters in the 3D physical space. The mean values were –0.09 and –0.11 for the two monkeys, respectively, indicating that neurons were often closer to others from a different cluster than those within the same cluster. In other words, STN subpopulations did not segregate from each other and instead tended to be intermingled, and thus microstimulation likely activated multiple neurons with different functional properties.

Although the intermingled organization of STN subpopulations, defined based on their task-related activity patterns (Figure 3), made it challenging to relate a specific microstimulation effect to a specific subpopulation, we did observe certain trends that could contribute to the site-specific microstimulation effects. We assigned the single- or multi-unit activity at the stimulation sites to the clusters identified using the random-seeded clustering (Figure 8A). We then grouped the sites by neuron clusters (Figure 8C). When neurons of different clusters were recorded at the same site, the same microstimulation effects were assigned to each cluster. We found that the second cluster was associated with lower overall likelihood of observing microstimulation effects compared to other clusters (Figure 8B; Chi-square test, H₀: the likelihood is the same for the first cluster and the other clusters; p=0.003), while the third cluster had higher overall likelihood (p=0.035). For the first three clusters, no microstimulation effect dominated (p>0.3 for all), whereas for the fourth cluster, it was more likely to observe effects on non-decision times (i.e. with neural activity patterns not related to the three models; p=0.001).

The sign of microstimulation effects depended weakly on neuron clusters. For example, it was more likely to observe an increase in maximal bound height (‘a’) for the third neuron cluster (Chi-square test, H₀: same fractions of increase/decrease for all clusters; p=0.073; Chi-square test, H₀: equal fractions of increase/decrease within the cluster; p=0.036). Microstimulation decreased the scale factor (‘k’) for the third and fourth clusters but caused variable changes for the first cluster (p=0.070 and 0.021, respectively). Microstimulation effects on the non-decision time for the contralateral choices were dominated by increases for the fourth cluster (p=0.04 and 0.007, respectively). Together, these results suggested that microstimulation effects reflected multiple contributions of intermingled STN subpopulations to decision- and non-decision-related processes.

Another potential source of heterogeneity in our data may reflect variations in the monkeys’ motivational state across sessions. In two sets of analyses, we did not observe any significant influence of motivational state on the recording or microstimulation data. For these analyses, we used the rate of fixation breaks, overall error rate, and mean RT as indices of motivational state. None of these measurements differed among sessions when different subpopulations were encountered (Figure 3—figure supplement 2), suggesting that the motivational state cannot predict which type of activity pattern would be observed. Similarly, none of these measurements significantly correlated with the microstimulation effects in any DDM component (Supplementary file 1), suggesting that the motivational state did not modulate the magnitude of microstimulation effects. Together, these results suggest that the diverse activity patterns and microstimulation effects cannot be accounted for by variations in the monkeys’ task engagement.

The behavioral task (Figure 1A), general surgical procedure, and data acquisition methods have been described in detail previously (Ding and Gold, 2010; Ding and Gold, 2012b). Briefly, the monkey was required to report the perceived motion direction of the random-dot stimulus with a saccade at a self-determined time. Trials with different motion coherences (drawn from five levels) and directions were interleaved randomly. The monkey’s eye position was monitored with a video-based eye tracker and provided reward/error feedback online based on comparisons between the monkey’s eye position and task-relevant locations. Saccade RT was measured offline with established velocity and acceleration criteria. Neural activity was recorded using glass-coated tungsten electrodes (Alpha-Omega) or polyamide-coated tungsten electrodes (FHC, Inc), using a grid system through a recording chamber with access to the STN. For microstimulation sessions, lower-impedance FHC electrodes were used to record and stimulate at the same sites. Single units were identified by offline spike sorting (Offline Sorter, Plexon, Inc). Electrical microstimulation was delivered using Grass S88 stimulator as a train of negative-leading bipolar current pulses (250 µs pulse duration, 200 Hz) from motion onset to saccade onset. For most sessions, a current intensity of 50 µA was used. In other sessions, we lowered the intensity to ensure that microstimulation did not abolish the monkey’s ability to complete the trials. We randomly interleaved trials with and without microstimulation at a 1:1 ratio.

We obtained structural MRI scans using T1-MPRAGE and/or T2-SPACE sequences. We estimated the likely chamber coordinates with access to the STN from these images (and 3D reconstruction using BrainSight from Rogue Research, Inc) and mapped the surrounding areas electrophysiologically. Specifically, we identified several putative landmark regions, including: (1) thalamus, which showed characteristic bursts of activity in a low-firing background while the monkey dozed off; (2) reticular nucleus of the thalamus, where neurons exhibited high baseline firing rates (with bursts sometimes >100 Hz); (3) zona incerta, where neurons exhibited low, tonic baseline firing and briefly paused their activity around saccades (Ma, 1996); (4) substantia nigra, pars reticulata, where some neurons showed high baseline firing rates and suppression in activity around visual stimulus or saccade onset (Hikosaka and Wurtz, 1983); and (5) substantia nigra, pars compacta, where neurons showed low baseline firing and responded to unexpected reward. Based on a macaque brain atlas (Saleem and Logothetis, 2007) and previously reported STN activity patterns (Matsumura et al., 1992; Wichmann et al., 1994b; Isoda and Hikosaka, 2008), we defined STN as the area that: (1) was surrounded by these landmark regions, (2) was separated from them by gaps with minimal activity (white matter), and (3) exhibited irregular firing patterns with occasional short bursts. The baseline firing rate, measured within 50 ms before fixation point onset, had a mean ± SD magnitude of 15.4±12.4 spikes/s in our sample.

We measured the firing rates for each neuron and trial condition in running windows (300 ms) aligned to motion and saccade onsets. To visualize the overall activation/suppression, we averaged the firing rates across trial conditions and computed the z-scores using a 300 ms window before motion onset as the baseline. To quantitatively measure each neuron’s task-related modulation, we performed two multiple linear regressions for each running window, separately for coherence and RT because monkeys’ RT strongly depends on coherence on our task: (1)Spikecount=β0+βChoice×IChoice+βCoh−Contra×ICoh−Contra+βCoh−Ipsi×ICoh−Ipsi (2)Spikecount=β0+βChoice×IChoice+βRT−Contra×IRT−Contra+βRT−Ipsi×IRT−Ipsi

where,IChoice={1forcontralateralchoice,−1foripsilateralchoice}ICoh−Contra={coherenceforcontralateralchoice,0foripsilateralchoice},,ICoh−Ipsi={0contralateralchoice,coherenceforipsilateralchoice},IRT−Contra={RTforcontralateralchoice,0foripsilateralchoice}.IRT−Ipsi={0forcontralateralchoice,RTforipsilateralchoice}

Contralateral/ipsilateral choices refer to saccades toward the target contralateral/ipsilateral to the recording sites. Significance of non-zero coefficients was assessed using a t-test (criterion: p=0.05).

We converted each neuron’s activity into a 30D vector consisting of the average firing rate within three 200 ms windows for all trial conditions (i.e. 2 choices × 5 coherence levels). The windows were selected as early motion viewing (100–300 ms after motion onset), late motion viewing (300–500 ms after motion onset), and peri-saccade (100 ms before to after saccade onset). The choice identity was designated as either ‘preferred’ and ‘other’, based on the relative average activity in the peri-saccade window. Note that this designation was used so that neurons with similar general modulation patterns except for the polarity of their choice selectivity would be grouped together. This designation was not based on any statistical test and did not imply that the peri-saccade activity was reliably choice selective. The average firing rate for each neuron was then z-scored based on baseline rates measured in a 300 ms window ending at motion onset.

We explored multiple method variations using k-means clustering and present results from the variation with the highest stability. These variations included: (1) whether or not the vectors were projected onto 11 principal components that together explained at least 95% of total variance; and (2) calculation of vector distance, including squared Euclidean, cosine, and correlation metrics. We determined the best settings using: (1) the Rand index (Rand, 1971), which quantifies the stability of clusters in repeated clustering; (2) silhouette scores, which quantify the quality of grouping and separation between clusters; and (3) visual inspection of clustering results in terms of both cluster distribution in a t-SNE space and average activity of the clusters. To compute the Rand index, we performed 50 runs of clustering, assuming three to nine clusters, for each combination of variations. The Rand index was computed as the fraction of consistent grouping between a pair of units between two clustering runs. For two runs of clustering results, Rand index = Nsame−same+Ndifferent−differentNall pairs, where Nsame−same counts the number of neuron pairs that share clusters in both runs, Ndifferent−different counts the number of neuron pairs that do not share clusters in either run, and Nallpairs counts the total number of neuron pairs. To compute the silhouette scores, we chose the best of 100 repetitions of clustering for each combination of variations. For each neuron, silhouette score = max(Dinter−cluster,Dintra−cluster)Dinter−cluster−Dintra−cluster, where Dinter−cluster is the average distance to the neuron’s nearest neighboring cluster, and Dintra-cluster is the average distance to other neurons in the same cluster. A positive score implies that, for the given neuron, its activity was more similar to other neurons within the same cluster than those in its nearest neighboring cluster. A negative score implies that the neuron’s activity was more similar to those outside its own cluster.

To classify activity recorded at a microstimulation site, we calculated the correlation between its 30D vector and the centroids from random-seeded clustering. The centroid with the highest correlation value determined the cluster identity of the activity.

We analyzed microstimulation effects in several ways. To characterize the effects without assumptions about the underlying decision process, we fitted logistic functions to the choice data and linear functions to the RT data. We used three variants of the logistic functions that differed in their use of lapse rates, which measure the probability of errors independent of motion strength:

No Lapse:(3)p(contralateralchoice)=11+e−(Slope0+Slopeestim)×(Coh+Bias0+Biasestim)

Symmetric Lapse:(4)p(contralateralchoice)=λ0+λestim+1−2×(λ0+λestim)1+e−(Slope0+Slopeestim)×(Coh+Bias0+Biasestim)

Asymmetric Lapse:(5)p(contralateralchoice)=λIpsi0+λIpsi−estim+1−λIpsi0−λIpsi−estim−λContra0−λContra−estim1+e−(Slope0+Slopeestim)×(Coh+Bias0+Biasestim)

where Coh is the signed coherence (positive/negative for motion toward the contralateral/ipsilateral choice). Contralateral/ipsilateral choices refer to saccades toward the targets contralateral/ipsilateral to the microstimulation sites, respectively. To assess the significance of the ‘estim’ terms, we used bootstrap methods. Specifically, we generated 200 sets of data by shuffling the microstimulation status of trials within each session. We fitted these artificial data using the same logistic functions to estimate null distributions for each parameter and performed a one-tailed test to determine if the actual fit value exceeded chance (criterion, p<0.05).

We fitted linear functions to the RT data, separately for the two choices: (6)RT=Offest0+Offsetestim+(Slope0+Slopeestim)×Cohunsigned

We assessed significance using t-tests (criterion, p<0.05).

To infer microstimulation effects on decision-related computations, we fitted DDM to choice and RT data simultaneously. We used DDM variants with collapsing bounds (DDM; Figure 7A), following previously established procedures (Fan et al., 2018; Doi et al., 2020). Briefly, the DDM assumes that motion evidence is accumulated over time into a DV, which is compared to two collapsing choice bounds. A choice is made when the DV crosses either bound, such that the time of crossing determines the decision time and the identity of the bound determines the choice identity. The model has eight basic parameters (presented here in six groups): (1) a, the maximal bound height; (2) B_collapse and B_t, the decay speed and onset specifying the time course of the bound ‘collapse’, respectively; (3) k, a scale factor governing the rate of evidence accumulation; (4) me, an offset specifying a bias in the rate of evidence accumulation; (5) z, an offset specifying a bias in the DV, or equivalently, asymmetric offsets of equal magnitude for the two choice bounds; and (6) t0contra and t0ipsi, non-decision times for the two choices that capture RT components that do not depend on evidence accumulation (e.g. visual latency and motor delay).

We used eight variants of DDM. In the Full model, all eight parameters were allowed to change with microstimulation. In the None model, all eight parameters did not change with microstimulation. In six reduced models (NoA, NoCollapse, NoK, NoME, NoZ, NoT), the corresponding group of parameters (specified above) were fixed while the other parameters were allowed to change with microstimulation. We fitted each model using the maximum a posteriori estimate method and previously established prior distributions (Wiecki et al., 2013). We performed five runs for each fit and used the best run (highest likelihood) for analyses here. We used the AIC for model selection. We considered an AIC difference >3 to indicate that the smaller-AIC model significantly outperformed the larger-AIC model. For a given sessions, if the Full model outperformed a reduced model and the None model, we considered that session to show significant microstimulation effect(s) on the corresponding model parameter(s). For example, we considered STN microstimulation to induce significant changes in k if the Full model outperformed both None and NoK models for a given session.