Off-campus UMass Amherst users: To download campus access theses, please use the following link to log into our proxy server with your UMass Amherst user name and password.
Non-UMass Amherst users: Please talk to your librarian about requesting this thesis through interlibrary loan.
Master of Science (M.S.)
Year Degree Awarded
Month Degree Awarded
recognition memory, remember-know models, ex-Gaussian distribution
The remember-know paradigm is widely used in recognition memory research to explore the mechanisms underlying recognition judgments. The most intriguing question about the paradigm that needs to be answered is: Are the processes that underlie “remember” and “know” responses the same or different? The extant remember-know models provide different answers. The dual-process model (Yonelinas, 1994) assumes that “remember” and “know” judgments are made with qualitatively different underlying processes. The one-dimensional Signal Detection Theory (SDT) model (Donaldson, 1996; Hirshman & Master, 1997) and the Sum-difference Theory of Remembering and Knowing (STREAK) model assume that “remember” and “know” judgments are made with same underlying processes but different response criteria. In this thesis, three experiments were conducted to evaluate these models. The remember-know models were fit to the accuracy data to see which model provides the best account for the ROC data. In addition, the reaction time data were fit with ex-Gaussian distributions and the best-fit skew parameters were used to reveal whether the underlying strategic processes for “remember” and “know” judgments are same or not.
The results of the remember-know model fit were mixed: In the first experiment with list length manipulation, 6 out of 8 cases were best fit with the one-dimensional models and the other 2 cases were best fit with the dual-process models; in the second experiment with list strength manipulation, 11 out of 18 cases were best fit with the one-dimensional models, another 6 cases were best fit with the dual-process models and the rest one case were best fit with the STREAK model; in the third experiment with response bias manipulation, 6 out of 16 cases were best fit with the one-dimensional models and the other 10 cases were best fit with the dual-process models.
The results of ex-Gaussian fit to RT data supported the one-dimensional model better: for the subjects who provide enough overlapping data in comparison of the distributions of hits followed by “remember” and “know” judgments, the values of skew parameter did not differ for “remember” and “know” responses in 7 out of 8 cases. This indicates that the same process underlies “remember” and “know” responses.
Advisor(s) or Committee Chair