scholarly journals A show about nothing: No-signal processes in systems factorial technology.

2021 ◽  
Vol 128 (1) ◽  
pp. 187-201
Author(s):  
Zachary L. Howard ◽  
Paul Garrett ◽  
Daniel R. Little ◽  
James T. Townsend ◽  
Ami Eidels
Keyword(s):  
Systems Factorial Technology

Systems Factorial Technology analysis of mixtures of processing architectures

2019 ◽  
Vol 92 ◽  
pp. 102229 ◽  
Author(s):  
Daniel R. Little ◽  
Ami Eidels ◽  
Joseph W. Houpt ◽  
Paul M. Garrett ◽  
David W. Griffiths
Keyword(s):  
Systems Factorial Technology ◽  
Technology Analysis ◽  
Processing Architectures

scholarly journals What do cows drink? A systems factorial technology account of processing architecture in memory intersection problems

Cognition ◽  
2020 ◽  
Vol 202 ◽  
pp. 104294
Author(s):  
Zachary L. Howard ◽  
Bianca Belevski ◽  
Ami Eidels ◽  
Simon Dennis
Keyword(s):  
Systems Factorial Technology ◽  
Processing Architecture

An examination of age-related differences in attentional control by systems factorial technology

2019 ◽  
Vol 92 ◽  
pp. 102280 ◽  
Author(s):  
Cheng-Ta Yang ◽  
Shulan Hsieh ◽  
Cheng-Ju Hsieh ◽  
Mario Fifić ◽  
Yen-Ting Yu ◽  
...  
Keyword(s):  
Attentional Control ◽  
Systems Factorial Technology ◽  
Age Related

scholarly journals Survivor interaction contrasts for errored response times: Non-parametric contrasts for serial and parallel systems

2020 ◽  
Author(s):  
Haiyuan Yang ◽  
Daniel R. Little ◽  
Ami Eidels ◽  
James T. Townsend
Keyword(s):  
Diffusion Model ◽  
Response Times ◽  
Parallel Systems ◽  
P Systems ◽  
Formal Proofs ◽  
Free Response ◽  
Systems Factorial Technology ◽  
Counter Model ◽  
Survivor Interaction Contrast ◽  
Processing Architecture

Systems Factorial Technology (SFT) is a theoretically-derived methodology that allows for strong inferences to be made about the underlying processing architecture (e.g., whether processing occurs in a pooled, coactive fashion or independently, in serial or in parallel). Measures of mental architecture using SFT have been restricted to the use of error-free response times. In this paper, through formal proofs and demonstrations, we extended the measure of architecture, the survivor interaction contrast (SIC), to response times conditioned on whether they are correct or incorrect. We show that so long as an ordering relation (between stimulus conditions of different difficulty) is preserved, unique conditional SIC predictions are found for several classes of processing models. We further prove that this ordering relation holds for the popular Wiener diffusion model for both correct and error RTs but fails under some instantiations of a Poisson counter model.

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