This study advances our understanding of the brain by identifying the key roles of different regions of the prefrontal cortex in the ability to adaptively persist. For example, the vmPFC appears to be crucial for assessing the value of reward and motivating waiting, while the dmPFC and anterior insula are important for adjusting our behavior to environmental conditions.
Making decisions about how long to wait for an uncertain future reward is a common and complex challenge. To decide whether to continue waiting or to give up, our brains make a continuous assessment, comparing the subjective value of the expected reward with the costs of continuing to wait, such as wasted time and energy.
Research on decisions involving future rewards has a long history in neuroscience and psychology. Previous work has focused primarily on the initial choice between smaller immediate rewards and larger delayed rewards.
Studies have shown that areas such as the ventromedial prefrontal cortex (vmPFC) and striatum are crucial for assessing the subjective value of these options, while the dorsolateral prefrontal cortex (dlPFC) helps with cognitive control, allowing us to resist impulses for quick rewards.
Meanwhile, the amygdala and dopaminergic system influence impulsivity, making it harder to choose to wait for future rewards. These studies contributed to the concept of “temporal discounting,” which describes how the perceived value of a reward decreases as its delay increases.
However, sustaining motivation during the period of waiting for a reward is equally important and has received more attention recently.
Studies in humans and animals suggest that the vmPFC continues to assess the value of the reward over time, while the dorsomedial prefrontal cortex (dmPFC) and anterior insula increase their activity when a person is considering giving up.
Computational models have helped explain this behavior by demonstrating how the brain calculates the cost-benefit of continuing to wait.
Brain damage offers another valuable perspective. Research shows that damage to the vmPFC leads to increased impulsivity and decreased willingness to wait, while damage to the dmPFC and anterior insula impairs the ability to adjust persistence to the environment. This underscores the importance of these areas in the dynamic calibration process during waiting.
Cortical midline structures – anatomical definition. Perigenual anterior cingulate cortex (PACC), ventro-dorsomedial prefrontal cortex (VMPFC, DMPFC), supragenual anterior cingulate cortex (SACC), posterior cingulate cortex (PCC) and precuneus. Since they are all located in the midline of the brain, they have been coined as ‘cortical midline structures’ (CMS). Source: Northoff, G. Brain and self – a neurophilosophical account. Child Adolesc Psychiatry Ment Health 7, 28 (2013). https://doi.org/10.1186/1753-2000-7-28
To explore the role of these brain regions, researchers at the University of Pennsylvania conducted a study with 28 participants (20 females and 8 males) who had lesions in different parts of the frontal lobe.
They were given a task that assessed their ability to adaptively adjust the amount of time they were willing to wait for monetary rewards.
The results revealed significant differences in the behavior of participants with specific lesions. Those with damage to the vmPFC waited less time overall, while participants with lesions in the dmPFC and anterior insula had difficulty adjusting their persistence based on environmental characteristics.
These findings were explained with the help of a computational model that analyzed participants’ decisions. It showed that lesions in these areas of the brain systematically altered the parameters that regulate the balance between persisting and giving up.
Persisting in the pursuit of long-term goals is essential in many areas of life, such as education, health, and finance. Often, these choices involve a trade-off: smaller immediate rewards versus larger future rewards.
Most neuroscience research on future rewards has focused on early decisions, such as choosing between a smaller reward now or a larger one later. However, the real difficulty lies in maintaining the initial decision, persisting during the waiting time necessary to achieve the objective.
This study advances our understanding of the brain by identifying the key roles of different regions of the prefrontal cortex in the ability to adaptively persist.
For example, the vmPFC appears to be crucial for assessing reward value and motivating waiting, while the dmPFC and anterior insula are important for adjusting our behavior to environmental conditions.
These findings provide a broader view of how the brain helps people maximize the benefits of delayed rewards, contributing to success in a variety of life contexts.
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Lesions to different regions of frontal cortex have dissociable effects on voluntary persistence.
Camilla van Geen, Yixin Chen, Rebecca Kazinka, Avinash R. Vaidya, Joseph W. Kable and Joseph T. McGuire
Journal of Neuroscience 25 November 2024, e0068242024
Abstract:
Deciding how long to keep waiting for uncertain future rewards is a complex problem. Previous research has shown that choosing to stop waiting results from an evaluative process that weighs the subjective value of the awaited reward against the opportunity cost of waiting. Activity in ventromedial prefrontal cortex (vmPFC) tracks the dynamics of this evaluation, while activation in the dorsomedial prefrontal cortex (dmPFC) and anterior insula (AI) ramps up before a decision to quit is made. Here, we provide causal evidence of the necessity of these brain regions for successful performance in a willingness-to-wait task. 28 participants (20 female and 8 male) with lesions to different regions of the frontal lobe were tested on their ability to adaptively calibrate how long they waited for monetary rewards. We found that participants with lesions to the vmPFC waited less overall, while participants with lesions to the dmPFC and anterior insula were specifically impaired at calibrating their level of persistence to the environment. These behavioral effects were accounted for by systematic differences in parameter estimates from a computational model of task performance.
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