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<p>Decision scientists have grown increasingly interested in how people adaptively control their decision making, exploring how metacognitive factors influence how people accumulate evidence and commit to a choice. A recent study proposed a novel form of such adaptive control, whereby the values of one's options&#xa0; contribute to both the formation of a decision and the effortful invigoration of a response. In this framework, the control process was operationalized in a drift diffusion model as the lowering of the decision threshold on difficult trials. Reanalyzing the data from this experiment, we establish alternative explanations for these findings. We show that the reported evidence for controlled threshold adjustments can be explained away by task confounds, time-dependent collapses in decision thresholds, and stimulus-driven dynamics in an&#xa0;alternative form of evidence accumulation. Our findings challenge the specific evidence for this new theory of motivated control&#xa0;while at the same time revealing paths and pitfalls in computational approaches to&#xa0;a more general understanding when and how control guides decision-making.</p>

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Misspecified models create the appearance of adaptive control during value-based choice

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月14日

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  • Harrison Ritz,
  • Romy Frömer,
  • Amitai Shenhav

摘要

Decision scientists have grown increasingly interested in how people adaptively control their decision making, exploring how metacognitive factors influence how people accumulate evidence and commit to a choice. A recent study proposed a novel form of such adaptive control, whereby the values of one's options  contribute to both the formation of a decision and the effortful invigoration of a response. In this framework, the control process was operationalized in a drift diffusion model as the lowering of the decision threshold on difficult trials. Reanalyzing the data from this experiment, we establish alternative explanations for these findings. We show that the reported evidence for controlled threshold adjustments can be explained away by task confounds, time-dependent collapses in decision thresholds, and stimulus-driven dynamics in an alternative form of evidence accumulation. Our findings challenge the specific evidence for this new theory of motivated control while at the same time revealing paths and pitfalls in computational approaches to a more general understanding when and how control guides decision-making.