Topic 6: Causal Understanding Causal understanding is an important part of human cognition. How do we understand that a particular event or force has caused another event? How do realize that inserting coins into a soda machine results in a cool beverage appearing below? And ultimately, how do we understand people’s reactions to events? The …
Category Archives: Posts by P.L.
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 …
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Levels of Analysis and Emergence: The Neural Basis of Memory
Cognitive neuroscience constantly works to find the appropriate level of description (or, in the case of computational modeling, implementation) for the topic being studied. The goal of this post is to elaborate on this point a bit and then illustrate it with an interesting recent example from neurophysiology. As neuroscientists, we can often choose to …
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Combining Simple Recurrent Networks and Eye-Movements to study Language Processing
Modern technologies allow eye movements to be used as a tool for studying language processing during tasks such as natural reading. Saccadic eye movements during reading turn out to be highly sensitive to a number of linguistic variables. A number of computational models of eye movement control have been developed to explain how these variables …
The role of reward and cognitive control in decision making
Here’s an exchange of emails between PL and MC on a recently published paper (Balleine et al., 2007). Email 1 (from PL): Have a look at this introductory paragraph from a recent (Aug 2007) J Neurosci article by Balleine, Delgado and Hikosaka. What do they mean by “cognition” here? The Role of the Dorsal Striatum …
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