Vanderbilt University, Nashville, TN.
York University, Toronto, Canada.
J Cogn Neurosci. 2022 Sep 1;34(10):1952-1971. doi: 10.1162/jocn_a_01885.
Prospective gains and losses influence cognitive processing, but it is unresolved how they modulate flexible learning in changing environments. The prospect of gains might enhance flexible learning through prioritized processing of reward-predicting stimuli, but it is unclear how far this learning benefit extends when task demands increase. Similarly, experiencing losses might facilitate learning when they trigger attentional reorienting away from loss-inducing stimuli, but losses may also impair learning by increasing motivational costs or when negative outcomes are overgeneralized. To clarify these divergent views, we tested how varying magnitudes of gains and losses affect the flexible learning of feature values in environments that varied attentional load by increasing the number of interfering object features. With this task design, we found that larger prospective gains improved learning efficacy and learning speed, but only when attentional load was low. In contrast, expecting losses impaired learning efficacy, and this impairment was larger at higher attentional load. These findings functionally dissociate the contributions of gains and losses on flexible learning, suggesting they operate via separate control mechanisms. One mechanism is triggered by experiencing loss and reduces the ability to reduce distractor interference, impairs assigning credit to specific loss-inducing features, and decreases efficient exploration during learning. The second mechanism is triggered by experiencing gains, which enhances prioritizing reward-predicting stimulus features as long as the interference of distracting features is limited. Taken together, these results support a rational theory of cognitive control during learning, suggesting that experiencing losses and experiencing distractor interference impose costs for learning.
前瞻性收益和损失会影响认知加工,但它们如何调节变化环境中的灵活学习仍未得到解决。收益的前景可能通过优先处理奖励预测刺激来增强灵活学习,但当任务需求增加时,这种学习益处能延伸多远还不清楚。同样,当损失引发注意力从导致损失的刺激上重新定向时,它们可能有助于学习,但损失也可能通过增加动机成本或当负面结果被过度泛化而损害学习。为了澄清这些不同的观点,我们测试了在通过增加干扰物体特征的数量来改变注意力负荷的环境中,不同大小的收益和损失如何影响特征值的灵活学习。通过这种任务设计,我们发现较大的预期收益提高了学习效果和学习速度,但前提是注意力负荷较低。相比之下,期望损失会损害学习效果,而且在注意力负荷较高时,这种损害更大。这些发现从功能上区分了收益和损失对灵活学习的贡献,表明它们通过单独的控制机制发挥作用。一种机制是由损失引起的,它降低了减少干扰的能力,损害了将信用分配给特定导致损失的特征的能力,并减少了学习过程中的有效探索。第二种机制是由收益引起的,只要分散注意力的特征的干扰受到限制,它就会增强对奖励预测刺激特征的优先排序。总的来说,这些结果支持了学习期间认知控制的理性理论,表明体验损失和体验干扰会给学习带来成本。