UCSF research upends 100-year-old assumption about Pavlov’s dog and associative learning. How we learn to predict an outcome isn’t determined by how many times a cue and reward happen together. Instead, how much time passes between rewards is also important. Understanding this process has implications for how we think about addiction, and could also point to faster AI algorithms.summary
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