Influential recent work aims to ground psychiatric dysfunction in the brain's basic computational mechanisms. For instance, the compulsive symptoms that feature prominently in drug abuse and addiction have been argued to arise from over reliance on a habitual “model-free” system in contrast to a more laborious “model-based” system. Support for this account comes in part from failures to appropriately change behavior in light of new events. Notably, instrumental responding can, in some circumstances, persist despite reinforcer devaluation, perhaps reflecting control by model-free mechanisms that are driven by past reinforcement rather than knowledge of the (now devalued) outcome. However, another line of theory posits a different mechanism – latent causal inference – that can modulate behavioral change. It concerns how animals identify different contingencies that apply in different circumstances, by covertly clustering experiences into distinct groups. Here we combine both lines of theory to investigate the consequences of latent cause inference on instrumental sensitivity to reinforcer devaluation. We show that instrumental insensitivity to reinforcer devaluation can arise in this theory even using only model-based planning, and does not require or imply any habitual, model-free component. These ersatz habits (like laboratory ones) emerge after overtraining, interact with contextual cues, and show preserved sensitivity to reinforcer devaluation on a separate consumption test, a standard control. Together, this work highlights the need for caution in using reinforcer devaluation procedures to rule in (or out) the contribution of different learning mechanisms and offers a new perspective on the neurocomputational substrates of drug abuse.
All Science Journal Classification (ASJC) codes
- Neuroscience (miscellaneous)
- Latent causal inference
- Model based planning