Summary: A new study uncovered neural mechanisms used in planning, revealing an interplay between the prefrontal cortex and hippocampus. The study shows how the brain imagines future outcomes to guide decisions.
This research sheds light on the cognitive processes behind planning, with potential implications for treating disorders affecting decision-making.
Key Facts:
- The prefrontal cortex and hippocampus interact to simulate potential outcomes for decision-making.
- The study used a computational model validated with data from both humans and rats.
- Findings highlight how these brain regions enable us to think before acting, crucial for adaptive behavior.
Source: NYU
In pausing to think before making an important decision, we may imagine the potential outcomes of different choices we could make. While this “mental simulation” is central to how we plan and make decisions in everyday life, how the brain works to accomplish this is not well understood.
An international team of scientists has now uncovered neural mechanisms used in planning.
Its results, published in the journal Nature Neuroscience, suggest that an interplay between the brain’s prefrontal cortex and hippocampus allows us to imagine future outcomes in order to guide our decisions.
“The prefrontal cortex acts as a ‘simulator,’ mentally testing out possible actions using a cognitive map stored in the hippocampus,” explains Marcelo Mattar, an assistant professor in New York University’s Department of Psychology and one of the paper’s authors.
“This research sheds light on the neural and cognitive mechanisms of planning—a core component of both human and animal intelligence. A deeper understanding of these brain mechanisms could ultimately improve the treatment of disorders affecting decision-making abilities.”
The roles of both the prefrontal cortex—used in planning and decision-making—and hippocampus—used in memory formation and storage—have long been established. However, their specific duties in deliberative decision-making, which are the types of decisions that require us to think before acting, are less clear.
To illuminate the neural mechanisms of planning, Mattar and his colleagues—Kristopher Jensen, a computational neuroscientist at University College London, and Guillaume Hennequin, a professor of computational neuroscience at the University of Cambridge—developed a computational model to predict brain activity during planning.
They then analyzed data from both humans and laboratory rats* to confirm the validity of the model—a recurrent neural network (RNN), which learns patterns based on incoming information.
The model took into account existing knowledge of planning and added new layers of complexity, including “imagined actions,” thereby capturing how decision-making involves weighing the impact of potential choices—similar to how a chess player envisions sequences of moves before committing to one.
These mental simulations of potential futures, modeled as interactions between the prefrontal cortex and hippocampus, enable us to rapidly adapt to new environments, such as taking a detour after finding that a road is blocked.
The scientists validated this computational model using both behavioral and neural data. To assess the model’s ability to predict behavior, the scientists conducted a novel experiment measuring how humans navigated an online maze on a computer screen and how long they had to think before each step.
To validate the model’s predictions about the role of the hippocampus in planning, they analyzed neural recordings from rodents navigating a physical maze configured in the same way as in the human experiment.
By giving a similar task to humans and rats, the researchers could draw parallels between the behavioral and neural data—a particularly innovative aspect of this research.
The experimental results were consistent with the computational model, showing an intricate interaction between the prefrontal cortex and hippocampus. In the human experiments, participants’ brain activity reflected more time thinking before acting in navigating the maze.
In the experiments with laboratory rats, the animals’ neural responses in moving through the maze resembled the model’s simulations.
“Overall, this work provides foundational knowledge on how these brain circuits enable us to think before we act in order to make better decisions,” observes Mattar.
“In addition, a method in which both human and animal experimental participants and RNNs were all trained to perform the same task offers an innovative and foundational way to gain insights into behaviors.”
About this decision-making and neuroscience research news
Author: James Devitt
Source: NYU
Contact: James Devitt – NYU
Image: The image is credited to Neuroscience News
Original Research: Open access.
“A recurrent network model of planning explains hippocampal replay and human behavior” by Marcelo Mattar et al. Nature Neuroscience
Abstract
A recurrent network model of planning explains hippocampal replay and human behavior
When faced with a novel situation, people often spend substantial periods of time contemplating possible futures. For such planning to be rational, the benefits to behavior must compensate for the time spent thinking.
Here, we capture these features of behavior by developing a neural network model where planning itself is controlled by the prefrontal cortex.
This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences from its own policy, which we call ‘rollouts’.
In a spatial navigation task, the agent learns to plan when it is beneficial, which provides a normative explanation for empirical variability in human thinking times.
Additionally, the patterns of policy rollouts used by the artificial agent closely resemble patterns of rodent hippocampal replays.
Our work provides a theory of how the brain could implement planning through prefrontal–hippocampal interactions, where hippocampal replays are triggered by—and adaptively affect—prefrontal dynamics.
