The basal ganglia's contributions to perceptual decision making

Abstract

Perceptual decision making is a computationally demanding process that requires the brain to interpret incoming sensory information in the context of goals, expectations, preferences, and other factors. These integrative processes engage much of cortex but also require contributions from subcortical structures to affect behavior. Here we summarize recent evidence supporting specific computational roles of the basal ganglia in perceptual decision making. These roles probably share common mechanisms with the basal ganglia's other, more well-established functions in motor control, learning, and other aspects of cognition and thus can provide insights into the general roles of this important subcortical network in higher brain function.

Figure 1

Drift-diffusion model (DDM). Noisy sensory evidence (top; independent, identically distributed picks from the Gaussian distribution at the right) is accumulated in time to form a decision variable (bottom). Note the slightly positive mean value of the distribution of evidence, which governs the upward rate of rise of the decision variable. When the decision variable hits one of the choice bounds, this choice is made.

Figure 2

A simplified basal ganglia circuit. The dashed black line represents feedback pathways. Abbreviations: FEF: frontal eye field; GPi and GPe: the internal and external segments of the globus pallidus; LIP: lateral intraparietal area of the parietal cortex; SC: superior colliculus; SNc: substantia nigra pars compacta; SNr: substantia nigra pars reticulata; STN: subthalamic nucleus; VTA: ventral tegmental area.

Figure 3

Decision formation. A,B, Simulated data illustrating the average decision variable trajectories in a DDM for different input strengths, aligned to the beginning (A) or end (B) of the accumulation process. Colors indicate coherence levels. C,D, Average activity of a subset of caudate neurons recorded in monkeys performing the dots task, aligned to stimulus (C) and saccade (D) onset, respectively. IN: choices toward the recorded neuron’s response field (RF). OUT: choices away from the RF. Colors indicate data from trials with different motion coherences. Note the choice- and coherence-dependent modulation of caudate activity during motion viewing and the lack of a final “bound-like” pattern of activity level around saccade time. Modified from Ding and Gold (2010). E,F, Average activity of a subset of FEF neurons recorded in monkeys performing the dots task, aligned to stimulus (E) and saccade (F) onset, respectively. Modified from Ding and Gold (2012a).

Figure 4

Initial bias signals. A, Simulated data illustrating the effects of a non-zero starting value on a DDM-based decision process. Gray lines show trajectories of a decision variable in 15 simulated trials with low- (left) and high- (right) coherence inputs and using either a zero (top) or slightly positive (bottom) starting value. Note the stronger effects of the initial bias on the final choices for the low-versus high-coherence simulations. B, Coherence-dependent predictive index of pre-stimulus activity in a caudate neuron. The predictive index quantifies the correlation between neuronal activity and subsequent behavioral choices. A higher value indicates that the activity is more predictive of the final choice. Colors indicate motion coherence. The right panel shows the mean predictive index from the shaded time window in the left panel, plotted as a function of motion coherence. Modified from Ding and Gold (2010).

Figure 5

Reward prediction error (RPE) signals. A, Population activity of DA neurons in a monkey performing an asymmetric-reward task when large reward was expected. Data are plotted with respect to onset of the visual stimulus (STIM), the saccadic response (SAC), and either reward delivery (REW) or error feedback (FDBK). Colors indicate coherence levels. Modified from Nomoto et al. (2010). B, Reward prediction error (RPE) signals derived from a DDM simulation. At time t during motion viewing, $\mathrm{RPE}=\frac{accumulatedevidence}{\sqrt{t}}$. After motion viewing, RPE decays exponentially with a time constant of 400 ms. After feedback onset, RPE is updated with the difference between feedback value (simulated as a positive square pulse for rewarded trials and 0 for error trials) and the RPE value at 100 ms after feedback onset. C, Activity of a caudate neuron during the dots task for one choice. Data for the other choice showed similar patterns. Modified from Ding and Gold (2010).