Department of Anatomy and Neurobiology, University of Maryland, School of Medicine, Baltimore, MD, USA.
Proc Biol Sci. 2011 Sep 7;278(1718):2553-61. doi: 10.1098/rspb.2011.0836. Epub 2011 Jun 8.
Theories of selective attention in associative learning posit that the salience of a cue will be high if the cue is the best available predictor of reinforcement (high predictiveness). In contrast, a different class of attentional theory stipulates that the salience of a cue will be high if the cue is an inaccurate predictor of reinforcement (high uncertainty). Evidence in support of these seemingly contradictory propositions has led to: (i) the development of hybrid attentional models that assume the coexistence of separate, predictiveness-driven and uncertainty-driven mechanisms of changes in cue salience; and (ii) a surge of interest in identifying the neural circuits underpinning these mechanisms. Here, we put forward a formal attentional model of learning that reconciles the roles of predictiveness and uncertainty in salience modification. The issues discussed are relevant to psychologists, behavioural neuroscientists and neuroeconomists investigating the roles of predictiveness and uncertainty in behaviour.
在联想学习的选择性注意理论中，假设如果线索是强化（高预测性）的最佳可用预测器，则线索的显着性将会很高。相比之下，另一类注意力理论规定，如果线索是强化的不准确预测器（高不确定性），则线索的显着性将会很高。支持这些看似矛盾的命题的证据导致：（i）混合注意力模型的发展，假设单独的、由预测驱动和不确定性驱动的线索显着性变化机制的共存；和（ii）对识别这些机制背后的神经回路的兴趣激增。在这里，我们提出了一个学习的正式注意力模型，该模型调和了预测性和不确定性在显着性改变中的作用。讨论的问题与研究预测性和不确定性在行为中的作用的心理学家、行为神经科学家和神经经济学家有关。
