Anterior cingulate cortex in complex associative learning: monitoring action state and action content
Huang, W.; Hall, A. F.; Kawalec, N.; Opalka, A. N.; Liu, J.; Wang, D. V.
Show abstract
Environmental changes necessitate adaptive responses, and thus the ability to monitor ones actions and their connection to specific cues and outcomes is crucial for survival. The anterior cingulate cortex (ACC) is implicated in these processes, yet its precise role in action monitoring versus outcome tracking remains unclear. To investigate this, we developed a novel discrimination-avoidance task for mice, designed with clear temporal separation between actions and outcomes. Our findings show that ACC neurons primarily encode post-action variables over extended periods, reflecting the animals preceding actions rather than the outcomes or values of those actions. Specifically, we identified two distinct subpopulations of ACC neurons: one encoding the action state (whether an action was taken) and the other encoding the action content (which action was taken). Importantly, increased post-action ACC activity was associated with better performance in subsequent trials. These findings suggest that the ACC supports complex associative learning through extended signaling of rich action-relevant information, thereby bridging cue, action, and outcome associations.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics 97%
- The retrosplenial cortex combines internal and external cues to encode head velocity during navigation 96%
- Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.