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Abstract:
Behavior is typically highly variable across individuals. To deal with this complexity, average data from many an-
imals, grouped by genotype or experience, is analyzed for differences. This approach, while powerful, obscures
the individual differences within groups. To link the workings of individual brains to their behavioral outputs, we
have to identify and quantify inter-individual behavioral differences. Therefore, we designed an automated six-
arm behavioral apparatus to minimize and control extraneous variables. Initially, wild type (WT) and fragile-X
syndrome (FXS) model Long-Evans rats received reward on all arms, allowing us to examine their innate biases. Subsequently, reward was restricted to just three arms, and was only delivered if the rats visited the arms in a
specific sequence. Four different sets of rewarded arms were employed in sequential blocks. We then developed
a generative model for the entire behavior, using a variant of a reinforcement learning (RL) model with short-term
memory. Conventional RL implementations learned markedly slower than the animals. The addition of two types
of arm biases that were apparent during the exploratory behavior produced a model with three free parameters–
learning, forgetting, and temporal discounting rates–that could be fit to describe the behavior of each animal. A
2D subspace of the parameters identified systematic variation of parameters between individuals, such as where
a group of animals had similar parameters for their learning rates, but varied in their temporal discounting rates.
These analyses revealed that individual differences far outweighed genotypic differences across the full cohort of
FXS and WT animals, but that when subsets of animals with similar sets of model parameters were identified,
genotype-related differences emerged. These findings suggest that relatively simple behavioral models can ex-
plain individual differences and thereby reveal otherwise hidden patterns of variability across different groups of
animals.
