James Jones-Rounds, HEP Lab Manager is the test subject for undergraduate researcher, Sofia Ribolla, from the Laboratory of Brain and Cognition directed by Nathan Spreng

Memory and Neuroscience Research

Charles BrainerdThroughout my career, my research and teaching have revolved around a single broad theme: the scientific study of human cognition. I have concentrated most extensively on the development of cognitive processes in normal and atypical children, but I have also published considerable research on adult cognition and have taught widely in that area. In recent years, my research and teaching have also encompassed questions about how cognitive processes are affected by normal aging and by the diseases of late adulthood. This, in turn, has stimulated a research program in cognitive neuroscience. Across all of these areas, a particular focus has been the relationship between memory processes and higher reasoning abilities. After several years of research and teaching on the memory/reasoning interface, I began to develop, with the collaboration of my colleague V. F. Reyna, a general model of how memory influences reasoning and how reasoning influences memory, which is known as fuzzy-trace theory. Fuzzy-trace theory, which seeks to explain some of the most counterintuitive aspects of memory and reasoning, is now widely used by investigators in fields such as forensic psychology, judgment and decision making, and human memory.

Conjoint recognition model

Program and Model Files:

1.General Processing Tree (GPT) (Hu & Phillips, 1999): GPT is a Windows based computer program used for analyzing multinomial processing tree models. The program allows conducting various modeling tasks, including parameter estimation, hypothesis testing, and power analysis. For a description of the program, see Hu and Phillips (1999). The program is available at http://www.xiangenhu.info upon request and here.

Model Files:

CR model.zip

2.MPT in R package (requires the R program, http://cran.r-project.org): An alternative program to the GPT. For more information, please click here.

Model Files: .EQN files

CR model.eqn 
CR model - reduced sentences.eqn 
CR model - all sentences.eqn

Dual-retrieval model

Tutorial: Dual-retrieval model tutorial.pdf

The purpose of this tutorial is to outline the application of a group of two-stage Markov models that have been used to quantify recollective and nonrecollective retrieval processes (Brainerd, Aydin, & Reyna, 2012; Brainerd & Reyna, 2010; Brainerd, Reyna, & Howe, 2009; Gomes, Brainerd, & Stein, 2013). The tutorial provides a step-by-step guide on how to compute the relevant statistics to obtain parameter estimates and goodness-of-fit statistics. Because the models measure retrieval operations that can be broadly separated into recollective (direct access, D) and nonrecollective ones (reconstruction, R, and familiarity judgment, J), we also refer to them as dual-retrieval models. For specific information about the models and the theory underlying them, please see Brainerd et al. (2009).

Additional files used in examples:

Database Section I.xls
Database Section II.xls

Program and Model Files:

1.Microsoft Excel: Used to compute the frequencies of correct recall (C) and incorrect recall (E) across trials using a simple Visual Basic (VB) macro

Click here to download the file that contains the VB macro

2.General Processing Tree (GPT)

Model Files:

11 parameters - 4 fixed trials.zip
Error model - 6p - 3 fixed trials.zip
Alternative 3Js Error model - 6 parameters - 3 fixed trials.zip
Success model - 6p - 3 fixed trials.zip
Both model - 6p - 3 fixed trials.zip

OPTIONAL file:
Data entry using GPT can be time consuming when the total number of experimental conditions is large or, in the case of individual data analysis, the sample size is large. Below is a VB macro that creates a GPT model file containing the frequencies of C-E patterns stored on a .TXT or .CSV file (only available for the reduced dual-retrieval models). The file contains a data file example (.txt and .csv) and the GPT model structure file of each reduced dual-retrieval model (the program uses them to generate a new GPT model file containing the entries in the data file).

Transfer data to GPT.zip


3.MPT in R package (requires the R program, http://cran.r-project.org): An alternative program to the GPT. For more information, please click here.

Model Files: .EQN files

11 parameters - 4 fixed trials.eqn
Error model - 6 parameters - 3 fixed trials.eqn
Alternative 3Js Error model - 6 parameters - 3 fixed trials.eqn
Success model - 6 parameters - 3 fixed trials.eqn
Both model - 6 parameters - 3 fixed trials.eqn

Dual-recollection model

Model files for the conjoint recognition procedure: The multinomial, mixed, and signal detection versions of the dual-recollection model can be found below. EQN files are compatible with most multinomial processing tree programs, but only the multinomial version of the model is available in such format. The Excel file, on the other hand, contains all three versions of the dual-recollection model (one in each tab) that can be used to analyze the data from related and unrelated distractors in the conjoint recognition paradigm. To use the Excel file, it is necessary to enable VB macros and activate the Solver add-in.

EQN model files:

- Dual-recollection multinomial model - RD, UD.eqn
- Dual-recollection multinomial model - TG, RD, UD.eqn

Microsoft Excel:

- Multinomial, mixed, and signal detection models - RD, UD - CR procedure.xlsm
- Multinomial, mixed, and signal detection models - TG, RD, UD.xlsm

Model files for the conjoint process dissociation procedure: The multinomial, mixed, and signal detection versions of the dual-recollection model can be found below. MODEL files are compatible with the MPT in R package.

EQN model files:

- Dual-recollection multinomial model - Conjoint PDP.eqn

MPT in R model files:

- Dual-recollection mixed multinomial and signal detection model - Conjoint PDP.model
- Dual-recollection signal detection model - Conjoint PDP.model

References

Brainerd, C. J., Aydin, C., & Reyna, V. F. (2012). Development of dual-retrieval processes in recall: Learning, forgetting, and reminiscence. Journal of Memory and Language, 66, 763-788. doi:10.1016/j.jml.2011.12.002

Brainerd, C. J., & Reyna, V. F. (2010). Recollective and nonrecollective recall. Journal of Memory and Language, 63, 425-445. doi:10.1016/j.jml.2010.05.002

Brainerd, C. J., Reyna, V. F., & Howe, M. L. (2009). Trichotomous processes in early memory development, aging, and cognitive impairment: A unified theory. Psychological Review, 116, 783-832. doi:10.1037/a0016963

Gomes, C. F. A., Brainerd, C. J., & Stein, L. M. (2013). Effects of emotional valence and arousal on recollective and nonrecollective recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 663-677. doi:10.1037/a0028578

Hu, X., & Phillips, G. A. (1999). GPT.EXE: A powerful tool for the visualization and analysis of general processing tree models. Behavior Research Methods, Instruments, & Computers, 31, 220-234. doi:10.3758/BF03207714