Effects of Outcome Predictability on Human Learning
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Cue-outcome learning is a cornerstone of intelligent action. Learning that a stimulus (the cue) can be used to predict a second event (the outcome) affords adaptive decision making. If an animal can use environmental cues to predict the presence of food (an appetitive outcome) or a predator (an aversive outcome), then it can potentially act to maximize the likelihood of the former and minimize the latter. This capacity also allows for the development of complex associative webs of knowledge. For example, it allows humans to associate a visual icon with an auditory phoneme (and thus read), or to connect new information with existing knowledge. These are fundamental capacities of intelligent agents. The capacity to learn cue-outcome mental associations is crucial. However, forming mental associations may be cognitively costly, so it is important to be selective about which associations are learned, and thus to prioritize which stimuli gain access to the learning process. One way to achieve this efficiency is to leverage prior knowledge to focus primarily upon those events (cues and outcomes) that are most likely to be meaningfully associated (e.g., Mackintosh, 1975; Le Pelley, 2004, 2010; Mitchell and Le Pelley, 2010; Esber and Haselgrove, 2011). There are three logically distinguishable components of a person's prior associative knowledge that could be used to guide the selectivity of subsequent cue-outcome learning. First, there is the knowledge about specific cue-outcome associations that have already been learned (represented as associative strength, or V). Second, there is knowledge about the cueing stimuli themselves (represented as cue associability, or α). Finally, people could use prior knowledge about the outcome stimuli. The influence of the first two forms of associative knowledge in guiding subsequent learning has been extensively investigated. However, the influence of the third form (i.e., information about outcome stimuli) has been largely overlooked in the learning literature. We argue herein that this oversight of the potential role of outcome predictability in shaping learning offers fertile new ground for the science of learning. Before making this case, we first briefly note how associative strength and cue associability have been shown to guide learning.
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