Bejjanki Vikranth R, Knill David C, Aslin Richard N
J Vis. 2016;16(5):9. doi: 10.1167/16.5.9.
A large body of research has established that, under relatively simple task conditions, human observers integrate uncertain sensory information with learned prior knowledge in an approximately Bayes-optimal manner. However, in many natural tasks, observers must perform this sensory-plus-prior integration when the underlying generative model of the environment consists of multiple causes. Here we ask if the Bayes-optimal integration seen with simple tasks also applies to such natural tasks when the generative model is more complex, or whether observers rely instead on a less efficient set of heuristics that approximate ideal performance. Participants localized a "hidden" target whose position on a touch screen was sampled from a location-contingent bimodal generative model with different variances around each mode. Over repeated exposure to this task, participants learned the a priori locations of the target (i.e., the bimodal generative model), and integrated this learned knowledge with uncertain sensory information on a trial-by-trial basis in a manner consistent with the predictions of Bayes-optimal behavior. In particular, participants rapidly learned the locations of the two modes of the generative model, but the relative variances of the modes were learned much more slowly. Taken together, our results suggest that human performance in a more complex localization task, which requires the integration of sensory information with learned knowledge of a bimodal generative model, is consistent with the predictions of Bayes-optimal behavior, but involves a much longer time-course than in simpler tasks.
大量研究已证实，在相对简单的任务条件下，人类观察者会以近似贝叶斯最优的方式将不确定的感官信息与习得的先验知识相结合。然而，在许多自然任务中，当环境的潜在生成模型由多种原因构成时，观察者必须进行这种感官加先验的整合。在此，我们要探讨的是，当生成模型更为复杂时，在简单任务中所观察到的贝叶斯最优整合是否也适用于此类自然任务，或者观察者是否转而依赖一套效率较低的启发式方法来近似理想表现。参与者要定位一个“隐藏”目标，该目标在触摸屏上的位置是从一个位置相关的双峰生成模型中采样得到的，每个模式周围具有不同的方差。在反复接触此任务的过程中，参与者学习到了目标的先验位置（即双峰生成模型），并在逐次试验的基础上，以与贝叶斯最优行为预测相一致的方式，将这种习得的知识与不确定的感官信息进行整合。具体而言，参与者迅速了解了生成模型两种模式的位置，但模式的相对方差则学习得慢得多。总体而言，我们的结果表明，在更复杂的定位任务中，人类的表现需要将感官信息与双峰生成模型的习得知识相结合，这与贝叶斯最优行为的预测相一致，但与简单任务相比，涉及的时间进程要长得多。
