Affective Brain Lab, Department of Experimental Psychology, University College London, London, United Kingdom.
Google, Mountain View, California, United States of America.
PLoS Comput Biol. 2019 Jun 27;15(6):e1007089. doi: 10.1371/journal.pcbi.1007089. eCollection 2019 Jun.
To make good judgments people gather information. An important problem an agent needs to solve is when to continue sampling data and when to stop gathering evidence. We examine whether and how the desire to hold a certain belief influences the amount of information participants require to form that belief. Participants completed a sequential sampling task in which they were incentivized to accurately judge whether they were in a desirable state, which was associated with greater rewards than losses, or an undesirable state, which was associated with greater losses than rewards. While one state was better than the other, participants had no control over which they were in, and to maximize rewards they had to maximize accuracy. Results show that participants' judgments were biased towards believing they were in the desirable state. They required a smaller proportion of supporting evidence to reach that conclusion and ceased gathering samples earlier when reaching the desirable conclusion. The findings were replicated in an additional sample of participants. To examine how this behavior was generated we modeled the data using a drift-diffusion model. This enabled us to assess two potential mechanisms which could be underlying the behavior: (i) a valence-dependent response bias and/or (ii) a valence-dependent process bias. We found that a valence-dependent model, with both a response bias and a process bias, fit the data better than a range of other alternatives, including valence-independent models and models with only a response or process bias. Moreover, the valence-dependent model provided better out-of-sample prediction accuracy than the valence-independent model. Our results provide an account for how the motivation to hold a certain belief decreases the need for supporting evidence. The findings also highlight the advantage of incorporating valence into evidence accumulation models to better explain and predict behavior.
为了做出正确的判断，人们会收集信息。代理需要解决的一个重要问题是何时继续采样数据，何时停止收集证据。我们研究了人们想要持有某种信念的愿望如何影响他们形成这种信念所需的信息量。参与者完成了一个顺序采样任务，他们有动力准确判断自己是否处于理想状态，这种状态与更多的奖励而不是损失相关联，或者处于不理想状态，这种状态与更多的损失而不是奖励相关联。虽然一种状态比另一种状态好，但参与者无法控制自己所处的状态，为了最大化奖励，他们必须最大限度地提高准确性。结果表明，参与者的判断偏向于相信他们处于理想状态。他们需要较少的支持证据来得出这个结论，并且在达到理想结论时更早地停止收集样本。在另一个参与者样本中复制了这些发现。为了研究这种行为是如何产生的，我们使用漂移扩散模型对数据进行建模。这使我们能够评估两种可能的机制，这些机制可能是这种行为的基础：（i）效价依赖的反应偏差和/或（ii）效价依赖的过程偏差。我们发现，一个具有反应偏差和过程偏差的效价依赖模型比一系列其他替代模型更能拟合数据，包括效价独立模型和只有反应或过程偏差的模型。此外，效价依赖模型比效价独立模型提供了更好的样本外预测准确性。我们的结果提供了一种解释，说明为什么持有某种信念的动机降低了对支持性证据的需求。这些发现还强调了在证据积累模型中纳入效价的优势，以更好地解释和预测行为。
