Birkbeck, University of London, United Kingdom.
New York University Medical Center, New York, USA.
J Cogn Neurosci. 2023 Jul 1;35(7):1133-1143. doi: 10.1162/jocn_a_01997.
Perceivers can use past experiences to make sense of ambiguous sensory signals. However, this may be inappropriate when the world changes and past experiences no longer predict what the future holds. Optimal learning models propose that observers decide whether to stick with or update their predictions by tracking the uncertainty or "precision" of their expectations. However, contrasting theories of prediction have argued that we are prone to misestimate uncertainty-leading to stubborn predictions that are difficult to dislodge. To compare these possibilities, we had participants learn novel perceptual predictions before using fMRI to record visual brain activity when predictive contingencies were disrupted-meaning that previously "expected" events became objectively improbable. Multivariate pattern analyses revealed that expected events continued to be decoded with greater fidelity from primary visual cortex, despite marked changes in the statistical structure of the environment, which rendered these expectations no longer valid. These results suggest that our perceptual systems do indeed form stubborn predictions even from short periods of learning-and more generally suggest that top-down expectations have the potential to help or hinder perceptual inference in bounded minds like ours.
感知者可以利用过去的经验来理解模棱两可的感觉信号。然而，当世界发生变化，过去的经验不再预测未来会发生什么时，这种方法可能就不适用了。最优学习模型提出，观察者通过跟踪其预期的不确定性或“精度”来决定是坚持还是更新他们的预测。然而，预测的对比理论认为，我们容易错误估计不确定性，从而导致固执的预测，这些预测很难改变。为了比较这些可能性，我们让参与者在使用 fMRI 记录预测线索被打乱时的视觉大脑活动之前，学习新的感知预测，这意味着以前“预期”的事件变得客观上不太可能发生。多元模式分析显示，尽管环境的统计结构发生了显著变化，使这些预期不再有效，但主要视觉皮层仍能更准确地解码预期事件。这些结果表明，即使在短暂的学习期间，我们的感知系统确实会形成固执的预测，更普遍地表明，自上而下的预期有可能帮助或阻碍像我们这样有限思维的感知推断。
