Abstract
Psychological and neural distinctions between the technical concepts of “liking” and “wanting” pose important problems for motivated choice for goods. Why could we “want” something that we do not “like,” or “like” something but be unwilling to exert effort to acquire it? Here, we suggest a framework for answering these questions through the medium of reinforcement learning. We consider “liking” to provide immediate, but preliminary and ultimately cancellable, information about the true, long-run worth of a good. Such initial estimates, viewed through the lens of what is known as potential-based shaping, help solve the temporally complex learning problems faced by animals.
Highlights
Berridge and his colleagues [1,2,3,4] have long argued that there is a critical difference between “liking” and “wanting.” The scare quotes are copied from papers such as Morales and Berridge’s paper [1] to distinguish the more precise quantities that these authors have in mind from the arguably more blurry everyday meanings of these terms or subjective reports that humans can provide upon verbal request
Why should we have both “liking” and “wanting”? In this essay, we argue that “liking” systems play the role of what is known as potential-based shaping [26] in the context of reinforcement learning (RL; [27])
“liking” provides a preliminary assessment of the long-run worth of a morsel of food or a drop of liquid. The latter is reported by postoral evaluation mechanisms feeding into the dopamine system and is the substrate for establishing the motivational impact or “wanting” for those foodstuffs
Summary
Berridge and his colleagues [1,2,3,4] have long argued that there is a critical difference between “liking” and “wanting.” The scare quotes are copied from papers such as Morales and Berridge’s paper [1] to distinguish the more precise quantities that these authors have in mind from the arguably more blurry everyday meanings of these terms or subjective reports that humans can provide upon verbal request. The idea is that the shaping function provides a hint about the values of states—being large for states that are associated with large long-run reward.
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