Cognitive computations and digital ruminations

The dissertation has been sent out to the committee. Now I have two fun filled weeks to get settled back outside of D.C. and then figure out how to condense those 150+ pages into a 45 minute talk.Sounds like fun.

Have I mentioned how much I hate modeling dual-tasking? I will concede that using Dario & Niels’s threaded cognition does make things significantly easier – but it is still a royal PIA.

The initial stab at the dual-tasking model (spatial/verbal 1-back and pointing), was ok, but at some point between getting it working and running it at ICCM, something stopped working. Regardless, though, the model wasn’t quite what I was aiming for. The majority of the errors were timeouts, with very few errors.

The new one, is looking much better with spatial interference from other representations (without jacking up the base level noise) – but still not ideal. The threaded cognition, while a step in the right direction, depends upon a very simple but problematic assumption: buffers being empty. The idea is that any goal thread will block while the buffer it needs is occupied. If you follow this, you can get some decent interleaving. However, that kills how many productions work. Often times, chains of productions depend upon a chunk in the retrieval/imaginal/visual buffer.

It’s not such a challenge to harvest and reinstate these bits of information, but it is if the buffer has a capacity greater than one (the configural buffer). I’m relying on the pointing and 1-back to interfere with each other at the buffer level (stepping all over each other), which means I can’t depend on the buffer being empty as a semaphore. It’s an interesting balancing act. My solution for now is to have more productions that test for the occurrence of interference (i.e. buffer is empty but shouldn’t be or the wrong chunk is in the buffer) – which has a nice benefit that I can actually keep track of where the interference is occuring precisely.

The models are running now, we’ll see. One thing that I definitely cannot account for yet is why there are more errors in verbal 1-back & rotate than stationary. There’s just no clear theoretical position from ACT-R’s perspective. meh.

Harrison, A.M. (2007) The Influence of Spatial Working Memory Constraints on Spatial Updating. 8th International Conference on Cognitive Modeling. Doctoral consortium. (Paper)

new versions of jACT-R, IDE, and ACT-R/S have been posted. I will be putting together a screencast soon to illustrate how to set up automatic model fit calculations and parameter space searches.

Check it out.. ACT-R/S bugs crushed and this is the latest, greatest model fit:

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Gotta love cognitive modeling. I’ve been looking at the parameter sensitivity on both egocentric and jrd pointing. The point is to fit the set size 4 and then apply it to set size 8 (I ditched 6 in the analysis, so I’ll ignore them again here).

I had a really nice fit (relatively) with common parameter values. RMSE combined (ego/jrd) of 0.9s, which is much smaller than the standard deviation of either ego or jrd pointing responses (error is in abeyance for now).

So I ran some bulk iterations. Damnit. The performance is dependent upon the activation balance between visually attended and spatially updated representations. Well, the random assignment of configurations and trials within a configuration is sufficient to blow that single test out of the water. Bleck. So, I guess I’m going to do this parameter search with bulk runs.

Ha!

Man, I’d forgotten how interesting modeling could actually be. Since the critical path for the dissertation is currently the models, I’ve shifted from writing to modeling full bore.

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Experiment #1 has been in the can since December. Now I’m up to my eyeballs in #2. Here’s the latest.

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Hiatt, L.M., Trafton, J. G., Harrison, A.M., & Schultz, A. C., (2004) A cognitive model for spatial perspective taking. International Conference on Cognitive Modeling, 2004.

Harrison, A. M., & Schunn, C. D. (2003). ACT-R/S: Look Ma, No “cognitive-map”! International Conference on Cognitive Modeling, 2003 [PDF]