Linking dynamic patterns of neural activity in orbitofrontal cortex with decision making

Abstract

Humans and animals demonstrate extraordinary flexibility in choice behavior, particularly when deciding based on subjective preferences. We evaluate options on different scales, deliberate, and often change our minds. Little is known about the neural mechanisms that underlie these dynamic aspects of decision-making, although neural activity in orbitofrontal cortex (OFC) likely plays a central role. Recent evidence from studies in macaques shows that attention modulates value responses in OFC, and that ensembles of OFC neurons dynamically signal different options during choices. When contexts change, these ensembles flexibly remap to encode the new task. Determining how these dynamic patterns emerge and relate to choices will inform models of decision-making and OFC function.

Figure 1. The locus of fixation modulates OFC reward responses

A) After an initial fixation period, monkeys were shown images associated with no reward, a small reward or a large reward. Immediately after an image appeared, the monkeys were free to move their eyes. The image remained on screen for 4s before the corresponding outcome was delivered. B) Taking a standard analytic approach to the data in which firing is aligned to stimulus onset, McGinty et al. found that the firing rates of neurons in OFC were modulated by the amount of reward associated with the different stimuli, most prominently around the time of initial stimulus presentation. C) When the data were aligned to where the subjects were looking during the period of free viewing, a different pattern emerged. When monkeys’ gaze was directed close to the stimuli, this OFC neuron encoded the reward amount associated with the stimuli (left), whereas this encoding was suppressed when gaze was directed away from the stimuli (right). Adapted from [20].

Figure 2. OFC ensembles dynamically represent potential outcomes during decision making

A) Monkeys viewed and chose between different pictures, each predicting one of 4 values of reward. On single picture trials, one image, chosen at random, was shown and monkeys had to fixate the picture for 450 ms to complete the trial and obtain the predicted reward. A linear discriminant analysis (LDA) was trained to classify the 4 picture values from ensemble activity collected on these trials, during the time when each picture was fixated. On interleaved trials, monkeys were presented with 2 pictures drawn randomly from the set of 8, and allowed to make a choice. The trained classifiers were then used to decode picture values from the same neural ensemble in short epochs of time on individual choice trials. B) One example trial, in which the monkey chose between pictures of value 3 and 4. The color bar at the top shows the categorical classification at each time point, and the plot below shows the posterior probabilities associated with each of the 4 values. The dotted line shows when a choice was made. C) Choice times were predicted by the posterior probabilities associated with the chosen values (red) and unchosen values (blue). In the first column, a regression model was used to predict choice times from chosen and unchosen probabilities at each time point. The top panel shows beta coefficients, the bottom quantifies the variance in choice times explained by each factor as coefficients of partial determination (CPDs). Higher chosen probabilities predicted faster choices, while higher unchosen probabilities predicted slower choices. In the right column, the same regression predicted choice times, but unchosen probabilities were replaced with probabilities associated with options that were not available (NA). D) During choices, single OFC neurons encoded both picture values similarly. The plot shows the beta coefficients from a regression for each neuron when it did not contribute to the ensemble used for decoding. The value of the picture presented on the left side of the screen was encoded at times when the left picture value was decoded from ensemble activity (x-axis), and the value of the picture on the right was encoded when the corresponding picture value was decoded (y-axis). Adapted from [23].

Figure 3. Neural encoding in OFC across tasks

A) To investigate how neurons in macaque OFC encoded decision factors across tasks, Xie and Padoa-Schioppa tested monkeys in two settings: context 1 where subjects chose between juices A and B (e.g. apple versus orange juice) and context 2 where they chose between juices C and D (e.g. cranberry versus grape). On each trial two stimuli were presented, each associated with different amounts of juice. Monkeys’ subjective preferences were computed by systematically varying the amounts of juice of each type over the course of a block. Then the relative values of each option could be derived. B) The matrix plots show the number of OFC neurons that either encoded offer value (positive or negative encoding association), chosen value (positive or negative encoding association), and chosen juice or were unselective for any decision variables across the two contexts. Red borders represent where proportions were statistically above chance based on an odds ratio test on the joint probability. C) Peri-event time histograms and raster plots from Rudebeck et al., showing an example neuron recorded across two tasks: the first where stimuli were well-learned (top panels) and a second where novel stimuli were presented and subjects had to learn how much reward was associated with each image (bottom panels). The neuron encoded the amount of reward received on each trial in the familiar and novel settings, albeit with different encoding schemes. Inset figure shows waveform shape in familiar and novel settings. D) Proportion of neurons encoding the amount of reward in the familiar setting (blue) and in both familiar and novel (red). Yellow numbers represent the percentage of neurons that had persistent encoding across tasks. Adapted from [33,34].

Figure 4. Amygdala lesions do not affect independent value coding of choice options in OFC

A) Neural activity was recorded in OFC while monkeys performed a two-option choice task where stimuli associated with different amounts of reward were sequentially presented [17]. After the stimuli were presented, monkeys were allowed to choose between them and receive the reward amount associated with the chosen stimulus. Many OFC neurons encoded the amount of reward associated with both the first stimulus (S1) and second stimulus (S2). B) Percent of neurons in OFC before (blue) and after (red) lesions of amygdala classified by a sliding hierarchical ANOVA as encoding the S2 reward value, S1 relative value or interaction between these two factors during the stimulus-2 period. A high proportion of neurons encoding the interaction between S2 reward and S1 relative value would indicate that OFC correlates of reward-value are relative. A low proportion would suggest that encoding is independent of other options. Inset figure shows across all neurons the maximum slope of encoding taken from a regression conducted on neurons that encoded the reward-value of S1 and S2 before (blue) and after lesions (red). Regression lines are fitted to the data. Non-significant neurons are in gray. Adapted from [17].