Using Multinomial Processing Tree Models to Measure Cognitive Deficits in Clinical Populations.

2007 ◽  
pp. 19-50 ◽  
Author(s):  
William H. Batchelder ◽  
David M. Riefer
Keyword(s):  
Cognitive Deficits ◽  
Multinomial Processing Tree Models ◽  
Multinomial Processing Tree ◽  
Tree Models ◽  
Clinical Populations

Cognitive psychometrics: Assessing storage and retrieval deficits in special populations with multinomial processing tree models.

2002 ◽  
Vol 14 (2) ◽  
pp. 184-201 ◽  
Author(s):  
David M. Riefer ◽  
Bethany R. Knapp ◽  
William H. Batchelder ◽  
Donald Bamber ◽  
Victor Manifold
Keyword(s):  
Special Populations ◽  
Multinomial Processing Tree Models ◽  
Storage And Retrieval ◽  
Multinomial Processing Tree ◽  
Tree Models

Inconsistency effects in source memory and compensatory schema-consistent guessing

2014 ◽  
Vol 67 (10) ◽  
pp. 2042-2059 ◽  
Author(s):  
Viviane Küppers ◽  
Ute J. Bayen
Keyword(s):  
Source Memory ◽  
Source Monitoring ◽  
Memory Item ◽  
Item Memory ◽  
Multinomial Processing Tree Models ◽  
Multinomial Processing Tree ◽  
Tree Models ◽  
Separate Source

The attention–elaboration hypothesis of memory for schematically unexpected information predicts better source memory for unexpected than expected sources. In three source-monitoring experiments, the authors tested the occurrence of an inconsistency effect in source memory. Participants were presented with items that were schematically either very expected or very unexpected for their source. Multinomial processing tree models were used to separate source memory, item memory, and guessing bias. Results show an inconsistency effect in source memory accompanied by a compensatory schema-consistent guessing bias when expectancy strength is high, that is, when items are very expected or very unexpected for their source.

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