Abstract
The two high threshold model (2HTM) of recognition memory makes strong predictions regarding differences between receiver operating characteristics (ROC) functions across strength manipulations. Province and Rouder (2012) tested these predictions and showed that the 2HTM provided a better account of the data than a continuous signal detection model using an extended two-alternative forced-choice task. The present study replicates and extends Province and Rouder’s findings at the level of confidence-rating responses as well as their associated response times. Model-mimicry simulations are also reported, ascertaining that the models can be well discriminated in this experimental design. Supplemental files for this article are available at osf.io/zadt6/
References
(2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390–412. doi: 10.1016/j.jml.2007.12.005
(2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68, 255–278. doi: 10.1016/j.jml.2012.11.001
(2013). Validating a two-high threshold model for confidence rating data in recognition memory. Memory, 8, 916–944. doi: 10.1080/09658211.2013.767348
(2009). Recognition ROCs are curvilinear – or are they? On premature arguments against the two-high-threshold model of recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 587–606. doi: 10.1037/a0015279
(2011). The striking similarities standard, distractor-free, and target-free recognition. Memory & Cognition, 39, 925–940. doi: 10.3758/s13421-011-0090-3
(1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11, 671–684. doi: 10.1016/S0022-5371(72)80001-X
(2012). Binary ROCs in perception and recognition memory are curved. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38, 130–151. doi: 10.1037/a0024957
(2012). Beyond ROC curvature: Strength effects and response time data support continuous-evidence models of recognition memory. Journal of Memory and Language, 67, 389–406. doi: 10.1016/j.jml.2012.06.002
(2007). G*Power 3: A flexible statistical power analysis for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. doi: 10.3758/BF03193146
(2011). The diagnosticity of individual data for model selection: Comparing signal-detection models of recognition memory. Psychonomic Bulletin & Review, 18, 751–757. doi: 10.3758/s13423-011-0096-7
(
in press ). Discrete-state and continuous models of recognition memory: Testing core properties under minimal assumptions. Journal of Experimental Psychology: Learning, Memory, and Cognition.(2013). Recognition memory models and binary-response ROCs: A comparison by minimum description length. Psychonomic Bulletin & Review, 20, 693–719. doi: 10.3758/s13423-013-0407-2
(1994). Comments on Batchelder and Riefer’s multinomial model for source monitoring. Psychological Review, 101, 166–171. doi: 10.1037/0033-295X.101.1.166
(2010). Toward a complete decision model of item and source memory: A discrete-state approach. Psychonomic Bulletin & Review, 17, 465–478. doi: 10.3758/PBR.17.4.465
(2009). Using the World-Wide Web to obtain large-scale word norms: 190,212 ratings on a set of 2,654 German nouns. Behavior Research Methods, 41, 13–19. doi: 10.3758/BRM.41.1.13
(2002). On the form of ROCs constructed from confidence ratings. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 380–387. doi: 10.1037/0278-7393.28.2.380
(2000). The importance of complexity in model selection. Journal of Mathematical Psychology, 44, 190–204. doi: 10.1006/jmps.1999.1283
(2010). Some-or-none recollection: Evidence for item and source memory. Journal of Experimental Psychology: General, 139, 341–362. doi: 10.1037/a0018926
(2012). Editors’ introduction to the special section on replicability in psychological science: A crisis of confidence? Perspectives on Psychological Science, 7, 528–530. doi: 10.1177/1745691612465253
(2012). Evidence for discrete-state processing in recognition memory. Proceedings of the National Academy of Sciences USA, 109, 14357–14362. doi: 10.1073/pnas.1103880109
(1988). Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318–339. doi: 10.1037/0033-295X.95.3.318
(2009). The nature of psychological thresholds. Psychological Review, 116, 655–660. doi: 10.1037/a0016413
(2014). From ROC curves to psychological theory. Manuscript submitted for publication
(2002). An experimental test of loss aversion. Journal of Risk and Uncertainty, 25, 233–249. doi: 10.1023/A:1020923921649
(2012). Discovering cognitive architecture by selectively influencing mental processes. New Jersey, NJ: World Scientific.
(2013). MPTinR: Analysis of multinomial processing tree models with R. Behavior Research Methods, 45, 560–575. doi: 10.3758/s1342801202590
(2004). A new on-line resource for psycholinguistic studies. Journal of Memory and Language, 51, doi: dx.doi.org/10.1016/j.jml.2004.03.002 247–250.
(2000). ROC curves and confidence judgments in recognition memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 582–600. doi: 10.1037/0278-7393.26.3.582
(2004). Assessing model mimicry using the parametric bootstrap. Journal of Mathematical Psychology, 48, 28–50. doi: 10.1016/j.jmp.2003.11.004
(2002). Elementary signal detection theory. Oxford, UK: Oxford University Press.
(2007). Dual-process theory and signal-detection theory of recognition memory. Psychological Review, 114, 152–176. doi: 10.1037/0033-295X.114.1.152
(2007). Receiver operating characteristics (ROCs) in recognition memory: A review. Psychological Bulletin, 133, 800–832. doi: 10.1037/0033-2909.133.5.800
