#neurochoice — Public Fediverse posts

Spearheaded by the unstoppable Leili Mortazavi with Elnaz Ghasemi and Charlene C. Wu , this culminates > 15 years of work!
The findings imply that some #FMRI findings are more robust than commonly thought (with respect to reliability, validity, and generalizability).
Special thanks to the editors and reviewers at PNAS Nexus and for support from Stanford University 's #Neurochoice Initiative and the #ToyotaResearchInstitute ! (#AffectiveNeuroscience , #Neuroeconomics )

Now out in @[email protected] :
"#Neuroforecasting reveals generalizable components of choice" (link below)!

Led by the intrepid Alex Genevsky , we examined how deep brain activity (in the Nucleus Accumbens or #NAcc ) can forecast choices out of sample in much larger groups (e.g., in #crowdfunding and #videoviewing markets).

We found that while behavioral forecasts depended on demographic match, neural forecasts (from the NAcc) did not, potentially supporting inferences from smaller samples. So, beyond considering #representativeness of a sample, researchers might also benefit from considering representativeness of different choice components.

Thanks to the Wu Tsai Neurosciences Institute #Neurochoice Initiative for support, to the reviewers for constructive suggestions, and to #PNASNexus for a smooth publishing experience!

https://academic.oup.com/pnasnexus/article/4/2/pgaf029/8016018