Orthogonal but linked neural codes for value
Maisson, D. J.; Fine, J. M.; Yoo, S. B. M.; Cash-Padgett, T. D.; Wang, M. Z.; Sleezer, B. J.; Zimmermann, J.; Hayden, B. Y.
Show abstract
Our ability to effectively choose between dissimilar options implies that information regarding the options values must be available, either explicitly or implicitly, in the brain. Explicit realizations of value involve single neurons whose responses depend on value and not on the specific features that determine it. Implicit realizations, by contrast, come from the coordinated action of neurons that encode specific features. One signature of implicit value coding is that population responses to offers with the same value but different features should occupy semi- or fully orthogonal neural subspaces that are nonetheless linked. Here, we examined responses of neurons in six core value-coding areas in a choice task with risky and safe options. Using stricter criteria than some past studies have used, we find, surprisingly, no evidence for abstract value neurons (i.e., neurons with the response to equally valued risky and safe options) in any of these regions. Moreover, population codes for value resided in orthogonal subspaces; these subspaces were linked through a linear transform of each of their constituent subspaces. These results suggest that in all six regions, populations of neurons embed value implicitly in a distributed population.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Value-related neuronal responses in the human amygdala during observational learning 97%
- Dissociable representations of decision variables within subdivisions of macaque orbitofrontal and ventrolateral frontal cortex 97%
- Neuronal Activity in Orbitofrontal Cortex during Trinary Choices under Risk 97%
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.