Monkeys and humans quickly learn that patience is rewarding

A pioneering study has shed new light on the process of decision-making within the primate brain during foraging. The research was focused on two rhesus monkeys that were trained to navigate an experimental setup for food rewards, revealing intricate strategies for optimizing their gains.

The study was conducted by a team of neuroscientists, including contributions from the German Primate Center (DPZ) – Leibniz Institute for Primate Research in Göttingen.

The research not only deepens our comprehension of self-paced actions but also has significant implications for understanding neurological disorders, such as Parkinson’s disease.

How the research was conducted 

The experiment allowed the monkeys to roam freely in a room equipped with two food boxes, from which they could obtain food pellets by pressing a button. 

Throughout the experiment, the monkeys learned that delaying their button presses would lead to an increase in the number of pellets dispensed.

This behavior adjustment occurred notably when a button press failed to yield a reward, prompting the monkeys to either wait longer before attempting again or to switch to the alternate box. 

Predicting patience levels in the monkeys

The researchers meticulously recorded the neuronal activity in the front part of the monkeys’ brains, employing a mathematical model to decode the monkeys’ reward expectations based on neural activity.

This approach enabled the team to predict with remarkable accuracy how long the rhesus monkeys were prepared to wait for an increased reward and when they would decide to pursue another option.

“When we started the experiment, we expected that our monkeys would simply choose the box based on how successful they had been with that box before,” said lead author Neda Shahidi, a junior research group leader at the Collaborative Research Center 1528 at the University of Göttingen and the DPZ in Göttingen.

The monkeys’ ability to modulate their waiting times based on previous outcomes and the duration since their last action highlights a sophisticated level of strategic planning.

Monkeys and rewards

“After a while, however, they had learned to pay attention to the time since the last keystroke and also to their previous success at a box. If they had waited a while but not received any pellets, they waited even longer before pressing the next time. However, if they were not rewarded too many times in a row after pressing the button, they moved to the other box. They had apparently decided that this food box was not worth the wait and it was better to look elsewhere,” Shahidi added. 

This indicates a calculated decision-making process, where the monkeys deemed a food box unworthy of further attempts after consecutive unrewarded efforts, choosing instead to explore other options.

Characterizing brain activity patterns

To dissect the underlying neuronal processes, the research team wirelessly recorded the activity of 96 neurons in the prefrontal cortex, a critical area for controlling goal-directed behavior. 

“However, characterizing the activity patterns of individual neurons does not always reveal the whole story when we study complex decision-making processes,” Shahidi explained.

To address this, the team developed a mathematical model that distinguished components of neural activity correlating with the monkeys’ decisions on when to press the button and their previous successes with each box.

Advances in data science

Surprisingly, the model provided highly accurate predictions of the monkeys’ forthcoming actions, including their choices to switch between food boxes. “We were surprised at how well our model could predict what the monkeys would do in the next few seconds,” Shahidi remarked. 

“Our results show not only how the development of wireless recording technologies can improve our understanding of brain mechanisms in natural movement scenarios, but also how advances in data science are transforming neuroscience by extracting the computational components of the brain from the collective activity of neurons. We hope that in the long term, such advances will help to better understand abnormalities in cognitive processes such as self-pacing in Parkinson’s or self-initiating actions in apathy,” she concluded.

The study is published in the journal Nature Neuroscience. 

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