Linking neural activity to complex decisions

Abstract

In the 1990s, seminal work from Newsome and colleagues made it possible to study the neuronal mechanisms of simple perceptual decisions. The key strength of this work was the clear and direct link between neuronal activity and choice processes. Since then, a great deal of research has extended these initial discoveries to more complex forms of decision making, with the goal of bringing the same strength of linkage between neural and psychological processes. Here, we discuss the progress of two such research programs, namely our own, that are aimed at understanding memory-guided decisions and reward-guided decisions. These problems differ in the relevant brain areas, in the progress that has been achieved, and in the extent of broader understanding achieved so far. However, they are unified by the use of theoretical insights about how to link neuronal activity to decisions.

Figure 1. MT activity at the time of decision during comparisons of motion directions

(A) The diagrams outline the temporal sequences of the events during the task. The monkeys fixated a spot for 1000ms before being presented with two stimuli, S1 and S2, lasting 500ms each and separated by a 1500ms delay. S1 and S2 moved either in the same (S-trials) or in opposite (D-trials) directions. They pressed one of two pushbuttons to report whether S1 and S2 moved in the same or in different directions. The rectangle around the S2 component highlights the portion of the trial analyzed here. During the task stimuli consisted of random dots displaced in directions chosen from a predetermined distribution. The width of this distribution determined the range of directions within which individual dots move and was varied between 0° (all dots moving in the same direction) and 360° (dots moving in random directions). The data in B & C are based on trials with coherent motion (0^o range), while the data in D are based on trials with S1 consisting of random motion (360^o range). Both stimuli were placed in the neuron’s receptive field (RF) and during S2 moved in the preferred direction for that neuron. (B) Average responses during S-trials (blue curves) and D-trials (red curves). Three distinct groups of cells showed differences in their responses to identical stimuli moving in the neuron’s preferred direction: cells with stronger activity on D-trials early in the response (left plot; early D>S cells, n=34), cells with stronger activity on D trials late in the response (middle plot, late D>S cells, n=27), and cells with stronger activity on S-trials (right plot, S>D cells, n=32). (C) Differences between the two response curves shown in B (i.e. comparison effects), computed with ROC analysis shown separately for each group of cells. (D) Choice probability (CP) computed separately for neurons with comparison effects (CP = 0.53± 0.02; p = 0.145, n = 62) and neurons with no comparison effects (CP = 0.53±0.01, p=0.012; n = 67). Note, that the overall CP for cells with CE failed to reach significance, while neurons with no comparison effects (S=D) showed significant choice-related activity, indicative of higher activity prior to “same” report. However, when CP was computed separately for each cell group, one cell group, showed consistently significant CP (S>D, CP=0.58, p=0.03; see Lui and Pasternak, 2011). (E) Correlation between CE and CP computed for individual cells was weak, failing to reach statistical significance (r=0.24, p=0.06).

Figure 2. DLPFC activity at the time of decision during comparisons of motion directions

(A) Diagram of the direction comparison task showing the two types of trials, S-trials and D-trials (Hussar & Pasternak, 2012). On S-trials, S1 and S2 separated by 1500ms delay moved in the same direction while on D-trials, S1 and S2 moved in different directions. The animals reported whether the two directions were the same or different by pressing one of two response buttons. They were allowed to respond 1000ms after S2 offset. During each session, direction difference thresholds were measured by varying the difference between directions in S1 and S2. The rectangle around the S2 trial components highlights the portion of the trial relevant to the analysis described here. (B) Comparison effects (CEs) recorded during and after S2. Average CE for S>D cells (blue, n=20) and D>S cells (red, n=26) during S2 and post-S2. (C) Dependence of CE on the difference in direction between S1 and S2: D>S (red); S>D cells (blue). The correlation between CE and direction difference was highly significant (p<7.5×10⁶). (D) Choice probability of DLPFC neurons more active before “different” (n=24) and before “same” (n=17) reports. Shadings represent ±SEM. (E) Correlation between CE and CP computed for individual S>D and D>S cells which showed both CE and CP. The two measures were strongly correlated during 200–400ms (p=1.3×10⁷) shown here, as well as later in the trial, 600–800ms after S2 onset (not shown; p=1.2×10⁴).

Figure 3. Schematic of task and results from fictive learning experiment

A. Illustration of structure of fictive learning task (Hayden et al, 2009). Stimuli were presented on a dark computer monitor, illustrated by black rectangle. On each trial, a central fixation spot (yellow) appeared, with eight white squares arrayed in a large circle around it. Following a brief hold period, subjects were free to shift gaze to one of the squares to select it. Selection of the square led to an immediate end of the trial. All squares turned color; seven turned red, one turned one of six other colors (see right hand side of panel). Colors validly predicted the reward associated with the chosen option (rewal reward) or, for the seven unchosen options, the reward that would have been given for that option (fictive reward). B. Schematic of behavior on next trial (likelihood of choosing the optimal target) as a function of reward chosen (gray line) or the value of the oddball unchosen reward (black line). Larger rewards led to larger probability of choosing optimally, regardless of whether they were real or fictive. (Note that there is a strong ceiling effect for real rewards). C. and D. Illustration of peri-stimulus time histogram (PSTH) of firing rate for one neuron aligned to time reward is revealed (time 0) for real (C) and fictive (D) rewards. This neuron (and majority of other neurons, data not shown) exhibit higher firing rates following larger reward, regardle sof whether they were real of fictive.

Figure 4. Variation in preference is predicted by variation in firing rate

Plot of average correlation between firing rate in response to fictive rewards for all neurons in dataset and likelihood that monkey would choose optimally on next trial, separated out by reward sizes. For the four larger fictive rewards (2.7, 3, 3.3, and 3.7), there was a significantly positive correlation between firing rate and strategy on next trial. This analysis has many similarities to the choice probability type of analysis.

Figure 5. Schematic of task and results from risky choice experiment

A. Illustration of risky choice task. On each trial, two options appeared and were freely inspected for 1 second. Then monkey fixated and after a brief delay, selected one option by shifting gaze to it. Each option offered a gamble defined by a specific probability (1–100% of a large reward, remainder of probability to a small reward). Following choice, gamble was resolved. B. Monkeys exhibited a bias towards the dominant normative strategy (choose option with greater probability of large reward) to an inferior strategy (choose option with less probability of large reward). In all sessions, monkeys exhibited a greater tendency to switch from the dominant strategy to the inferior one (y-axis) following small rewards than following large rewards. They also exhibited a greater tendency to switch following unexpected rewards, regardless of their sign (x-axis). C. Neuronal responses following outcomes were greater following small rewards than large rewards and following unexpected rewards than expected ones. The patterns of neuronal responses tracked closely the pattern of behavioral results, suggesting that dACC neurons signal likelihood of strategy adjustment, not reward per se.