A Brief Introduction to Reinforcement Learning

Computational models that are implemented, i.e., written out as equations or software, are an increasingly important tool for the cognitive neuroscientist.  This is because implemented models are, effectively, hypotheses that have been worked out to the point where they make quantitative predictions about behavior and/or neural activity. In earlier posts, we outlined two computational models …

Grand Challenges of Neuroscience: Day 2

Topic 2: Conflict and Cooperation Generally, cognitive neuroscience aims to explain how mental processes such as believing, knowing, and inferring arise in the brain and affect behavior.  Two behaviors that have important effects on the survival of humans are cooperation and conflict. According to the NSF committee convened last year, conflict and cooperation is an …

History’s Top Brain Computation Insights: Day 18

18) Behavior exists on a continuum between controlled and automatic processing (Schneider & Shiffrin – 1977) During the 1970s those studying the cognitive computations underlying visual search were at an impasse. One group of researchers claimed that visual search was a flat search function (i.e., adding more distracters doesn't increase search time), while another group …

History’s Top Brain Computation Insights: Day 8

8) Reward-based reinforcement learning can explain much of behavior (Skinner – 1938, Thorndike – 1911, Pavlov – 1905) B. F. Skinner showed that reward governs much of human and animal behavior. He discovered operant conditioning, a method for manipulating behavior so powerful he could teach a pigeon to bowl (or a dog to jump on …