Quentin Frederik Gronau1 Generalization of Recognition Memory Models across YES/NO and Two-Alternative Forced-Choice Tasks Using Ternary Response Options Quentin Frederik Gronau1 Henrik Singmann2 David Kellen3 Karl Christoph Klauer4 1 University of Amsterdam; 2 University of Zurich; 3 University of Basel; 4 Albert-Ludwigs-Universität Freiburg

YES/NO Recognition Memory Task

HOUSE BOAT FOOD SEAL RAIN RAIN CAT KING FOOD SEAL HEAVEN Learning Phase: HOUSE BOAT FOOD SEAL RAIN … Test phase: old? new? RAIN old? new? CAT old? new? KING old? new? FOOD old? new? SEAL old? new? HEAVEN …

HOUSE BOAT FOOD SEAL RAIN RAIN CAT KING FOOD SEAL HEAVEN Learning Phase: HOUSE BOAT FOOD SEAL RAIN … Old Word New Word old? new? RAIN old? new? CAT old? new? KING old? new? FOOD old? new? SEAL old? new? HEAVEN Hit Miss Correct Rejection False Alarm …

Measurement Models Two-High Threshold (discrete state) Model (2HTM; Snodgrass & Corwin, 1988) Unequal-Variance Signal Detection (continuous) Model (UVSD; Green & Swets, 1966)

How can we adequately measure recognition memory?

Validating a Ternary Response Scale HOUSE old unsure new Such a ternary response scale has already been used (but not validated) by: Singmann, Kellen, & Klauer (2013, CogSci Proceedings) Kellen, Singmann, Klauer, & Flade (in revision) Kellen, Singmann, & Klauer (2014) Klauer, Dittrich, Scholtes, & Voss (2015)

YES/NO Task Two-High Threshold (discrete state) Model Unequal-Variance Signal Detection (continuous) Model 4 parameters (D0, Dn, gunsure, gold), 4 available df → fully identified 4 parameters (μo, σo, c1, c2), 4 available df → fully identified

Validating a Ternary Response Scale HOUSE BOAT left unsure right HOUSE old unsure new Is this ternary response scale valid? The experiment: Generalization approach Test ability to be generalized across two most common recognition memory tasks: YES/NO and Two-Alternative Forced-Choice (2AFC) task When presenting participants with YES/NO and 2AFC trials for a set of studied words, memory parameters should be the same for both tasks Jang, Wixted, & Huber (2009) provided evidence that the UVSD can adequately be generalized across these tasks using a 6-point-confidence rating scale To our knowledge, now such generalization study exists for 2HTM

(within participants) 2 tasks (within participants) Learning Phase: HOUSE Old/New Task per Block: 50 strong (4 × learned) 50 weak (1 ×) 50 new (0 ×) HOUSE old unsure new BOAT Experiment: 29 participants 550 trials: 2 blocks with 275 words 150 Old/New & 125 2AFC memory strength manipulation 0.75 s study time + 0.25 ISI 2AFC Task per Block: 50 strong (4 × learned) 50 weak (1 ×) 25 new/new (0 ×) old words: 50% old word left 50% old word right WHITE RAVEN left unsure old FOOD SEAL RAIN Test phase: … old? unsure? new? RAIN old? unsure? new? CAT left? unsure? right? KING FOOD left? unsure? right? COFFEE BED old? unsure? new? SEAL old? unsure? new? HEAVEN left? unsure? right? HOUSE SPOON …

Two-Alternative Forced-Choice (2AFC) Task Two-High Threshold (discrete state) Model Unequal-Variance Signal Detection (continuous) Model

Unrestricted Models 2HTM separate parameters: G²(174)= 187.21, p=.23 6.9% individual misfits UVSD separate parameters: G²(174)= 186.02, p=.25 10.34% individual misfits

Correlation Memory Parameters Different Tasks

Memory Parameters Restricted Across Tasks 2HTM: Do and Dn restricted to be equal across tasks G²(261)= 338.65, p= .0008 17.24% individual misfits ΔG²(87)= 151.44, p < .001 Critical χ² in compromise power analysis (Faul, Erdfelder, Lang, & Buchner (2007): 148.34 24.14% individual misfits UVSD: μo and σo restricted to be equal across tasks G²(261)= 324.91, p = .004 17.24% individual misfits ΔG²(87)= 138.89, p < .001 Critical χ² in compromise power analysis (Faul, Erdfelder, Lang, & Buchner (2007): 148.34 24.14% individual misfits

Conclusion Both the Two-High Threshold Model (2HTM) and the Unequal-Variance Signal Detection Model (UVSD) are able to adequately account for participants‘ performance across YES/NO and 2AFC task Ternary response scale is a parsimonious and valid way to measure recognition memory that should be integrated in researchers‘ toolbox which yields full model identifiablity for the simplest recognition memory tasks

Thanks to my Collaborators