Criterion-selective neurons in the human medial frontal cortex track decision thresholds during memory-based decision making
Layher, E.; Skelin, I.; Reed, C. M.; Chung, J. M.; Bateman, L. M.; Valiante, T. A.; Mamelak, A. N.; Miller, M. B.; Rutishauser, U.
Show abstract
The decision criterion is foundational to theories of decision-making, yet little is known about its neural underpinnings. In memory-based decision making, the criterion sets the minimal memory strength for something to be familiar, but whether or how it is distinctly represented from memory strength is unknown. We recorded single neurons in the medial frontal cortex (MFC) and medial temporal lobe (MTL), both implicated in memory-based decisions, while participants made decisions under different decision criteria. We identified criterion-selective (CS) neurons in the MFC that tracked the criterion regardless of memory strength, and memory-selective (MS) neurons in both regions that tracked memory strength regardless of the criterion. CS neurons signaled the criterion before MS neurons signaled memory strength, and a race model incorporating both neuron types outperformed one using MS neurons alone. These findings reveal two independent cellular substrates, one for the decision criterion and one for memory strength, whose joint activity underlies memory-based decisions.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Inhibitory and excitatory populations in parietal cortex are equally selective for decision outcome in both novices and experts 96%
- Canonical decision computations underlie behavioral and neural signatures of cooperation in primates 95%
- A learning-evoked slow-oscillatory architecture paces population activity for offline reactivation across the human medial temporal lobe 95%
Similar papers in this journal
Similar papers in this journal
"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.