Using unobserved causes to explain unexpected outcomes: The effect of existing causal knowledge on protection from extinction by a hidden cause.

Abstract

People often rely on the covariation between events to infer causality. However, covariation between cues and outcomes may change over time. In the associative learning literature, extinction provides a model to study updating of causal beliefs when a previously established relationship no longer holds. Prediction error theories can explain both extinction and protection from extinction when an inhibitory (preventive) cue is present during extinction. In three experiments using the allergist causal learning task, we found that protection could also be achieved by a hidden cause that was inferred but not physically present, so long as that cause was a plausible preventer of the outcome. We additionally showed complete protection by a physically presented cue that was neutral rather than inhibitory at the outset of extinction. Both findings are difficult to reconcile with dominant prediction error theories. However, they are compatible with the idea of theory protection, where the learner attributes the absence of the outcome to the added cue (when present) or to a hidden cause, and therefore does not need to revise their causal beliefs. Our results suggest that prediction error encourages changes in causal beliefs, but the nature of the change is determined by reasoning processes that incorporate existing knowledge of causal mechanisms and may be biased toward preservation of existing beliefs.

Instruction on the Scientific Method Provides (Some) Protection Against Illusions of Causality

“…It is also worth noting that the present study involved instructing participants to evaluate a fictitious drug of which participants had no prior knowledge. Previous research has found an interactive effect of expectations and direct experience of cue-outcome events (Alloy & Tabachnik, 1984), with an asymmetrical contribution, such that people who had strong prior beliefs were less likely to change their causal judgements in light of contradictory covariational information (Fugelsang & Thompson, 2000), and were more likely to 'explain away' evidence that conflicts with their prior beliefs by invoking alternative unobserved causes (Chow et al, 2023;Luhmann & Ahn, 2007;Rottman et al, 2011). The influence of prior knowledge on causal beliefs is explicitly incorporated in Griffiths and Tenenbaum's (2009) theory based causal induction, where causal inference is thought to be guided by both prior knowledge about the causal relationship, and an estimation of the strength of that causal relationship based on experienced information.…”