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Understanding How Humans Learn and Adapt to Changing Environments  

Daphne Bavelier and Aaron Cochrane

Compared to other animals or to artificial agents, humans are unique in the extent of their abilities to learn and adapt to changing environments. When focusing on skill learning and model-based approaches, learning can be conceived as a progression of increasing, then decreasing, dimensions of representing knowledge. First, initial learning demands exploration of the learning space and the identification of the relevant dimensions for the novel task at hand. Second, intermediate learning requires a refinement of these relevant dimensions of knowledge and behavior to continue improving performance while increasing efficiency. Such improvements utilize chunking or other forms of dimensionality reduction to diminish task complexity. Finally, late learning ensures automatization of behavior through habit formation and expertise development, thereby reducing the need to effortfully control behavior. While automatization greatly increases efficiency, there is also a trade-off with the ability to generalize, with late learning tending to be highly specific to the learned features and contexts. In each of these phases a variety of interacting factors are relevant: Declarative instructions, prior knowledge, attentional deployment, and cognitive fitness have unique roles to play. Neural contributions to processes involved also shift from earlier to later points in learning as effortfulness initially increases and then gives way to automaticity. Interestingly, video games excel at providing uniquely supportive environments to guide the learner through each of these learning stages. This fact makes video games a useful tool for both studying learning, due to their engaging nature and dynamic range of complexity, as well as engendering learning in domains such as education or cognitive training.

Article

The Interaction of Perception and Memory  

Emma Megla and Wilma A. Bainbridge

Whereas visual perception is the interpretation of the light that enters the retina of the eye, long-term memory is the encoding, storage, and retrieval of perceptual experiences and learned information. Although these are separable processes, they continuously interact and influence each other. For example, the underlying perceptual features of an image result in large consistency in whether people will remember or forget it, and the visual similarities that images share can influence how well they will be remembered. The exaggeration of visual features, such as enlarged eyes on a face, can lead to enhanced memory, and a buildup in perceptual experience can also improve memory. In addition to perception influencing memory, memory also influences perception. Familiarity with an object or object category can result in enhanced perceptual processing, or even lead to the stimuli “looking” different from how they otherwise would. Additionally, learning a new category of objects changes how we perceive its categorical members, and even members of different, related categories. Perception and memory are closely intertwined in the brain as well, with mechanisms that allow similar perceptual items to be distinguished in memory, but also support incomplete perceptual details to be filled in from memory. Additionally, there are divisions in the brain dedicated to the perceptual and mnemonic processing of different object categories, such as faces and scenes. In other words, there are widespread examples in which memory and perception influence each other, with neural mechanisms and areas set in place to deal with these complex interactions.