Archive for the ‘Posts by P.L.’ Category

Grand Challenges of Neuroscience: Day 6

Monday, July 21st, 2008

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 NSF workshop panel on the Grand Challenges of Mind and Brain highlighted the question of ‘causal understanding’ as their 6th research topic.   (This was the final topic in their report.)

In addition to studying causal understanding, it is probably just as important to study causal misunderstanding: that is, why do individuals infer the wrong causes for events?  Or incorrect results from causes? Studying the errors we make in causal inference and understanding may help us discover the underlying neural mechanisms.  

It probably isn’t too difficult to imagine that progress on causal understanding, and improvements in our ability to be correct about causation, will probably be important for the well-being of humanity.  But what kinds of experiments and methods could be used to human brain mechanisms of  causal understanding?

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A Brief Introduction to Reinforcement Learning

Monday, June 2nd, 2008

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 of learning hypothesized to occur in various parts of the brain, i.e., Hebbian-like LTP (here and here) and error-correction learning (here and here). The computational model described in this post contains hypotheses about how we learn to make choices based on reward.

The goal of this post is to introduce a third type of learning: Reinforcement Learning (RL).  RL is hypothesized by a number of cognitive neuroscientists to be implemented by the basal ganglia/dopamine system.  It has become somewhat of a hot topic in Cognitive Neuroscience and received a lot of coverage at this past year’s Computational Cognitive Neuroscience Conference. (more…)

Levels of Analysis and Emergence: The Neural Basis of Memory

Friday, May 30th, 2008

A square 'emerges' from its surroundings (at least in our visual system)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 talk about the brain at any of a number of levels: atoms/molecules, ion channels and other proteins, cell compartments, neurons, networks, columns, modules, systems, dynamic equations, and algorithms.

However, a description at too low a level might be too detailed, causing one to lose the forest for the trees.  Alternatively, a description at too high a level might miss valuable information and is less likely to generalize to different situations.

For example, one might theorize that cars work by propelling gases from their exhaust pipes.  Although this might be consistent with all of the observed data, by looking “under the hood” one would find evidence that this model of a car’s function is incorrect.

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Combining Simple Recurrent Networks and Eye-Movements to study Language Processing

Saturday, April 5th, 2008

BBS image of GLENMORE model

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 affect eye movements. Although these models have focused on relatively low-level cognitive, perceptual and motor variables, there has been a concerted effort in the past few years (spurred by psycholinguists) to extend these computational models to syntactic processing.

During a modeling symposium at ECEM2007 (the 14th European Conference on Eye Movements), Dr. Ronan Reilly presented a first attempt to take syntax into account in his eye-movement control model (GLENMORE; Reilly & Radach, Cognitive Systems Research, 2006). (more…)

The role of reward and cognitive control in decision making

Monday, September 24th, 2007

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 in Reward and Decision-Making
To choose appropriately between distinct courses of action requires the ability to integrate an estimate of the causal relationship between an action and its consequences, or outcome, with the value, or utility, of the outcome. Any attempt to base decision-making solely on cognition fails fully to determine action selection because any information, such as “action A leads to outcome O,” can be used both to perform A and to avoid performing A. It is interesting to note in this context that, although there is an extensive literature linking the cognitive control of executive functions specifically to the prefrontal cortex (Goldman-Rakic, 1995; Fuster, 2000), more recent studies suggest that these functions depend on reward-related circuitry linking prefrontal, premotor, and sensorimotor cortices with the striatum (Chang et al., 2002; Lauwereyns et al., 2002; Tanaka et al.,2006).


Email 2 (from MC):

It sounds like they are distinguishing cognition from reward processing. I’m not sure why, since ‘cognition’ typically encompasses reward processing now days.

The distinction I think they’re really trying to make is between cognitive control and reward processing. Given that, it’s still a ridiculous paragraph. Why must it be either cognitive control or reward processing? It’s likely (no, virtually certain!) that the two interact during reward-based decision making. For instance, O’Reilly’s stuff shows how this might happen.

Another problem with this paragraph: They equate causal knowledge with cognitive control. Well-known causal knowledge doesn’t involve cognitive control! For instance, routine decision making would involve lower perceptuo-motor circuits, and if it involved differential reward then reward circuits would be engaged as well. Cognitive control has little/no role here.

When cognitive control is involved it’s probably doing a lot more than just retrieving causal relations from semantic memory. For instance, perceptual decision making studies show that cognitive control is involved even in deciding what is being perceived when uncertainty arises.

I guess what they’re trying to do is show that cognitive control doesn’t explain all of decision making since there must be a reward component as well. Perhaps this is a good point to make; they just didn’t do it well.


Email 3 (from PL):

Ahhh, ok I think I see now what they’re trying to say.  It really just struck me as an excessively divisive statement to start out what appeared to be an interesting article.  Can you say “flamebait”?  Perhaps they’re trying to be provocative.

- PL & MC