Benefits of Commitment in Hierarchical Inference

Humans have the tendency to commit to a single interpretation of what has caused some observed evidence rather than considering all possible alternatives. This tendency can explain various forms of biases in cognition and perception. However, committing to a single high-level interpretation seems short-sighted and irrational, and thus it is unclear why humans are motivated to use such strategy. In a first step toward answering this question, we systematically quantified how this strategy affects estimation accuracy at the feature level in the context of 2 common hierarchical inference tasks, category-based perception and causal cue combination. Using model simulations, we demonstrate that although estimation accuracy is generally impaired when conditioned on only a single high-level interpretation, the reduction is not uniform across the entire feature range. Compared with a full inference strategy that considers all high-level interpretations. accuracy is only worse for feature values relatively close to the decision boundaries but is better everywhere else. That is. for feature values for which an observer has a reasonably high chance of being correct about the high-level interpretation of the feature, a full commitment to that particular interpretation is advantageous. We also show that conditioning on an preceding high-level interpretation provides an effective mechanism for partially protecting the evidence from corruption with late noise in the inference process (e.g.. during retention in and recall from working memory). Our results suggest that a top-down inference strategy that solely relies on the most likely high-level interpretation can be favorable with regard to late noise and more holistic performance metrics.