Primate Amygdala Neurons Simulate Decision Processes of Social Partners.

By observing their social partners, primates learn about reward values of objects. Here, we show that monkeys' amygdala neurons derive object values from observation and use these values to simulate a partner monkey's decision process. While monkeys alternated making reward-based choices, amygdala neurons encoded object-specific values learned from observation. Dynamic activities converted these values to representations of the recorded monkey's own choices. Surprisingly, the same activity patterns unfolded spontaneously before partner's choices in separate neurons, as if these neurons simulated the partner's decision-making. These "simulation neurons" encoded signatures of mutual-inhibitory decision computation, including value comparisons and value-to-choice conversions, resulting in accurate predictions of partner's choices. Population decoding identified differential contributions of amygdala subnuclei. Biophysical modeling of amygdala circuits showed that simulation neurons emerge naturally from convergence between object-value neurons and self-other neurons. By simulating decision computations during observation, these neurons could allow primates to reconstruct their social partners' mental states.

Utility functions predict variance and skewness risk preferences in monkeys.

Utility is the fundamental variable thought to underlie economic choices. In particular, utility functions are believed to reflect preferences toward risk, a key decision variable in many real-life situations. To assess the validity of utility representations, it is therefore important to examine risk preferences. In turn, this approach requires formal definitions of risk. A standard approach is to focus on the variance of reward distributions (variance-risk). In this study, we also examined a form of risk related to the skewness of reward distributions (skewness-risk). Thus, we tested the extent to which empirically derived utility functions predicted preferences for variance-risk and skewness-risk in macaques. The expected utilities calculated for various symmetrical and skewed gambles served to define formally the direction of stochastic dominance between gambles. In direct choices, the animals' preferences followed both second-order (variance) and third-order (skewness) stochastic dominance. Specifically, for gambles with different variance but identical expected values (EVs), the monkeys preferred high-variance gambles at low EVs and low-variance gambles at high EVs; in gambles with different skewness but identical EVs and variances, the animals preferred positively over symmetrical and negatively skewed gambles in a strongly transitive fashion. Thus, the utility functions predicted the animals' preferences for variance-risk and skewness-risk. Using these well-defined forms of risk, this study shows that monkeys' choices conform to the internal reward valuations suggested by their utility functions. This result implies a representation of utility in monkeys that accounts for both variance-risk and skewness-risk preferences.

The random dot tachistogram: a novel task that elucidates the functional architecture of decision.

Reaction times are long and variable, almost certainly because they result from a process that accumulates noisy decision signals over time, rising to a threshold. But the origin of the variability is still disputed: is it because the incoming sensory signals are themselves noisy? Or does it arise within the brain? Here we use a stimulus - the random dot tachistogram - which demands spatial integration of information presented essentially instantaneously; with it, we demonstrate three things. First, that the latency distributions still show the variability characteristic of LATER, implying that there must be two integrators in series. Secondly, that since this variability persists despite removal of all temporal noise from the stimulus, or even trial-to-trial spatial variation, it must come from within the nervous system. Finally, that the average rate of rise of the decision signal depends linearly on how many dots move in a given direction. Taken together, this suggests a rather simple, two-stage model of the overall process. The first, detection, stage performs local temporal integration of stimuli; the local, binary, outcomes are linearly summed and integrated by LATER units in the second stage, that perform the final global decision by a process of racing competition.