Human inductive biases for aversive continual learning — a hierarchical Bayesian nonparametric model

Publication Year


Conference Paper

Humans and animals often display remarkable continual learning abilities, adapting quickly to changing environments while retaining, reusing, and accumulating old knowledge over a lifetime. Unfortunately, in environments with adverse outcomes, the inductive biases supporting such forms of learning can turn maladaptive, yielding persistent negative beliefs that are hard to extinguish, such as those prevalent in anxiety disorders. Here, we present and model human behavioral data from a fear-conditioning task with changing latent contexts, in which participants had to predict whether visual stimuli would be followed by an aversive scream. We show that participants’ learning in our task spans three different regimes — with old knowledge either being updated, discarded (forgotten) or retained and reused in new contexts (remembered) by different participants. The latter regime corresponds to (maladaptive) spontaneous recovery of fear. We demonstrate using simulations that these behavioral regimes can be captured by varying inductive biases in Bayesian non-parametric models of contextual learning. In particular, we show that the “remembering” regime can be produced by “persistent” variants of hierarchical Dirichlet process priors over contexts and negatively biased “deterministic” beta distribution priors over outcomes. Such inductive biases correspond well to widely observed “core beliefs” that may have adaptive value in some lifelong-learning environments, at the cost of being maladaptive in other environments and tasks such as ours. Our work offers a tractable window into human inductive biases for continual learning algorithms, and could potentially help identify individual differences in learning strategies relevant for response to psychotherapy. 


Conference Name
Proceedings of Machine Learning Research