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Temporal Context Model

Eugen G Tarnow  March 30 2011 05:26:38 PM
I got the following request from a referee rejecting my paper (presumably the referee was Marc Howard):

"The model really needs to be compared against other models of free recall rather than SOB which is a model of serial recall. So, the obvious candidate is the Temporal Context Model (Howard and Kahana). In particular, I can't see how this model is going to be able to handle conditional response curves which are key data for free recall."

I took a little time to address this issue and wrote:

Kahana (1996) found that the chance that a recalled item is followed by the next item in the list is higher than that it is followed by a previous item in the list.  He constructed “conditional recall probability” (CRP) graphs that he claimed could not be reproduced with the then current computer models.

This was followed by the Temporal Context Model (Howard & Kahana, 2001).  It argues that the u-shaped free recall curve is really not u-shaped.  To minimize the primacy effect and make the u-shape a j-shape they add a distracting task at each item presentation: deciding whether a word is concrete or abstract.  The primacy effect is still there for the first list item and Howard & Kahana (2001) concede that “the model has no mechanism to generate primacy, it fails to capture the small one-position primacy effect in the data.”  .  

The CRP graphs can be accommodated in the Dynamic Tagging theory as a result of chunking, i.e. the subject associates two items together and makes one memory out of them.  Howard & Kahana (201) though write that “it is unlikely that the lag-recency effect is a consequence of direct interitem associations” because there seems to be what they term time scale invariance, i.e. the CRP graphs look the same even if the interitem interval is changed from 2 to 16 seconds.  The evidence for this invariance is limited to Fig. 2 in Howard & Kahana (2002) in which the interitem interval is varied from 2 to 16 seconds.  Unfortunately, the data in the figure has error bars much too large to prove their point.  The data behind the Dynamic Tagging model (Tarnow, 2008) shows the slow decay of short term memory on a logarithmic time scale which makes this point even harder to prove.  

But the main weakness of the Temporal Context Model is that it was created purely to fit the CRP curves using abstract concepts without any regard to the underlying biology.  It has choices, for example, “ff one prefers the response–suppression approach, then TCM can be used to generate a set of activations, which can in turn be modulated by response suppression” (Howard & Kahana, 2001).  It can be anything to anyone which also means it has no predictive power and carries no information.

Do you agree or disagree?  Feel free to post a comment!
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