Regression results and model validation for GDX and sham mice.

<p>We also fit regression and computational models to the GDX and sham data. For regression #1 (A) and regression #3 (B) GDX (purple) and sham (green) sessions for individual mice for both set size = 2 and set size = 4 were averaged together and the mean of each individual was used to generate...

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Bibliographic Details
Main Author:	Juliana Chase (20469427) (author)
Other Authors:	Liyu Xia (11061087) (author), Lung-Hao Tai (20469430) (author), Wan Chen Lin (14055537) (author), Anne G. E. Collins (4011860) (author), Linda Wilbrecht (11061099) (author)
Published:	2024
Subjects:	Neuroscience Sociology Science Policy Mental Health Biological Sciences not elsewhere classified using logistic regression stay strategy increased softmax beta parameters single behavioral session aid comparative study reinforcement learning alongside rapid working memory odor set sizes incremental reinforcement learning adult mice use back strategies may associate odor stimuli strategies mice use learning concurrent stimulus wm task translated short term memory working memory adult mice back strategies associate stimuli back perseverative learning systems learning rate learning performance incremental rl task experience simple form right port rewards within receive reward new models gonadal hormones first acquire findings advance computational models comparable task 4 odors
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Regression results and model validation for GDX and sham mice.

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