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. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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分层推理中承诺的好处。

人类倾向于对造成某些观察到的证据的原因进行单一解释，而不是考虑所有可能的替代方法。这种趋势可以解释各种形式的认知和感知偏见。但是，仅对单一的高级解释进行承诺似乎是短视且不合理的，因此目前尚不清楚为什么人们有动机使用这种策略。在回答这个问题的第一步中，我们系统地量化了该策略如何在2种常见的层次推理任务，基于类别的感知和因果提示组合的情况下在特征级别上影响估计精度。使用模型仿真，我们证明，虽然仅以单个高级解释为条件，估计准确性通常会受到影响，缩小在整个功能范围上并不均匀。与考虑所有高级解释的完整推理策略相比，准确性仅在相对接近决策边界的特征值更差，而在其他所有地方都更好。即，对于观察者具有相当高的机会就特征的高级解释正确的特征值，完全致力于该特定解释是有利的。我们还表明，以先前的高级解释为条件提供了一种有效的机制，可以部分保护证据免遭推理过程中后期噪声（例如，保留和从工作记忆中撤回）的破坏。我们的结果表明，仅靠最可能的高层解释的自上而下的推理策略在后期噪声和更全面的性能指标方面可能是有利的。（PsycInfo数据库记录（c）2020 APA，保留所有权利）。