Recognition Memory Models

Recognition memory is concerned with the ability to discriminate between previously encountered information and new information. A central question is how to disentangle response tendencies (e.g., the tendency to respond “old”) from memory performance (i.e., the ability to discriminate between old and new information). Several measurement models with markedly different assumptions about the underlying memory process exist.

reco_models

Three different SDT based measurement models of recognition memory and a prototypical ROC plot.

In recognition memory experiments participants are first presented with a list of stimuli, one after each other, which they are asked to encode (= learning phase). In the subsequent test phase participants are again presented with stimuli and have to decide which of those stimuli had been presented during the study phase (i.e., are old) and which are new. In the simplest task, the Old/New task, stimuli are presented individually and for each item participants have to decide if the item is old or new. Responding “old” to an old item is called a hit and responding “old” to a new item is called a false alarm. As the proportion of new responses to old items, termed misses, adds up to one with the hit rate, those can be ignored (the same is true with new responses to new items, called correct rejections).

Unfortunately, hit and false alarm rates are no unbiased measures of memory performance (). Take for example a decision maker with no memory of the studied items whatsoever who only responds randomly with “old” and “new”. We would expect this individual to show a hit and false alarm rate of .5. Now imagine another decision maker who also has no memory of the studied items but with the tendency to respond “old” in 80% of the trials. Clearly we would expect that individual to show a hit and false alarm rate of .8. As can be easily seen, memory performance can only be assessed by combining hits and false rate. Several different measurement models try to accomplish this.

The figure on the left shows three measurement models for recognition memory based on signal-detection theory (SDT; ). According to SDT, the memory information of a previously encountered stimulus acts as a continuous signal, usually termed familiarity, which needs to be detected by the decision maker. If the familiarity value of a given stimulus exceeds a criterion set by the decision maker, response “old” is given. New items also elicit familiarity which can sometimes exceed the criterion leading to false alarms. The familiarity distributions are usually assumed to be Gaussian. The smaller the overlap of the old and new items familiarity distributions the better the memory of the decision maker. The placement of the criterion reflects the response tendencies. The models displayed here have multiple criteria reflecting the fact that responses need to be given on a graded scale (here from 1 to 6).

The most popular version of the SDT based models is the unequal variance signal-detection (UVSD; ) model. The dual-process signal detection (DPSD; ) model assumes a combination of familiarity based recognition judgments and threshold-based episodic retrieval judgments, termed recollection. With probability $$R$$, an item is recollected (i.e., the memory episode is retrieved) and the correct response is given with highest confidence. The mixture signal detection (MSD; ) model assumes a mixture of old item familiarity distributions. The familiarity distribution for the items that were attended to during the study phase (with probability $$\lambda$$) are shifted further away from the new item distribution than the familiarity distribution for the unattended items. Our figure displays a version of the MSD where the unattended familiarity distributions is equivalent to the new item familiarity distribution. A fourth model (which is graphically displayed here), the two-high threshold (2HT; ) model, assumes no continuous memory process. Instead it assumes discrete memory states. Either an item is recognized as old or new, with probabilities $$D_o$$ and $$D_n$$, respectively, in which case the correct response is invariably given, or not. In case recognition fails a response is guessed reflecting response tendencies.

I am interested in a variety of aspects concerning recognition memory models (see here for a list or relevant publications). There is a long and ongoing debate on whether continuous memory models or discrete state memory models are more empirically adequate (e.g., ). I have recently published a paper concerning this debate and am currently working on more in this regard. The question I am generally interested in is how can we adequately measure recognition memory performance. This is also important in one of my more applied studies (). Finally, I am interested in specific aspects of recognition memory models. For example, we could show that there is actually little evidence for criteria variability (or criterion noise) in recognition memory experiments.

 

References

Baumann, C., Singmann, H., Gershman, S. J., & Helversen, B. von. (2020). A linear threshold model for optimal stopping behavior. Proceedings of the National Academy of Sciences, 117(23), 12750–12755. https://doi.org/10.1073/pnas.2002312117
Merkle, E. C., & Wang, T. (2018). Bayesian latent variable models for the analysis of experimental psychology data. Psychonomic Bulletin & Review, 25(1), 256–270. https://doi.org/10.3758/s13423-016-1016-7
Overstall, A. M., & Forster, J. J. (2010). Default Bayesian model determination methods for generalised linear mixed models. Computational Statistics & Data Analysis, 54(12), 3269–3288. https://doi.org/10.1016/j.csda.2010.03.008
Llorente, F., Martino, L., Delgado, D., & Lopez-Santiago, J. (2020). Marginal likelihood computation for model selection and hypothesis testing: an extensive review. ArXiv:2005.08334 [Cs, Stat]. Retrieved from http://arxiv.org/abs/2005.08334
Duersch, P., Lambrecht, M., & Oechssler, J. (2020). Measuring skill and chance in games. European Economic Review, 127, 103472. https://doi.org/10.1016/j.euroecorev.2020.103472
Lee, M. D., & Courey, K. A. (2020). Modeling Optimal Stopping in Changing Environments: a Case Study in Mate Selection. Computational Brain & Behavior. https://doi.org/10.1007/s42113-020-00085-9
Xie, W., Bainbridge, W. A., Inati, S. K., Baker, C. I., & Zaghloul, K. A. (2020). Memorability of words in arbitrary verbal associations modulates memory retrieval in the anterior temporal lobe. Nature Human Behaviour. https://doi.org/10.1038/s41562-020-0901-2
Frigg, R., & Hartmann, S. (2006). Models in Science. Retrieved from https://stanford.library.sydney.edu.au/archives/fall2012/entries/models-science/
Greenland, S., Madure, M., Schlesselman, J. J., Poole, C., & Morgenstern, H. (2020). Standardized Regression Coefficients: A Further Critique and Review of Some Alternatives, 7.
Gelman, A. (2020, June 22). Retraction of racial essentialist article that appeared in Psychological Science « Statistical Modeling, Causal Inference, and Social Science. Retrieved June 24, 2020, from https://statmodeling.stat.columbia.edu/2020/06/22/retraction-of-racial-essentialist-article-that-appeared-in-psychological-science/
Rozeboom, W. W. (1970). 2. The Art of Metascience, or, What Should a Psychological Theory Be? In J. Royce (Ed.), Toward Unification in Psychology (pp. 53–164). Toronto: University of Toronto Press. https://doi.org/10.3138/9781487577506-003
Gneiting, T., & Raftery, A. E. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association, 102(477), 359–378. https://doi.org/10.1198/016214506000001437
Rouhani, N., Norman, K. A., Niv, Y., & Bornstein, A. M. (2020). Reward prediction errors create event boundaries in memory. Cognition, 203, 104269. https://doi.org/10.1016/j.cognition.2020.104269
Robinson, M. M., Benjamin, A. S., & Irwin, D. E. (2020). Is there a K in capacity? Assessing the structure of visual short-term memory. Cognitive Psychology, 121, 101305. https://doi.org/10.1016/j.cogpsych.2020.101305
Lee, M. D., Criss, A. H., Devezer, B., Donkin, C., Etz, A., Leite, F. P., … Vandekerckhove, J. (2019). Robust Modeling in Cognitive Science. Computational Brain & Behavior, 2(3), 141–153. https://doi.org/10.1007/s42113-019-00029-y
Bailer-Jones, D. (2009). Scientific models in philosophy of science. Pittsburgh, Pa.,: University of Pittsburgh Press.
Suppes, P. (2002). Representation and invariance of scientific structures. Stanford, Calif.: CSLI Publications.
Roy, D. (2003). The Discrete Normal Distribution. Communications in Statistics - Theory and Methods, 32(10), 1871–1883. https://doi.org/10.1081/STA-120023256
Ospina, R., & Ferrari, S. L. P. (2012). A general class of zero-or-one inflated beta regression models. Computational Statistics & Data Analysis, 56(6), 1609–1623. https://doi.org/10.1016/j.csda.2011.10.005
Uygun Tunç, D., & Tunç, M. N. (2020). A Falsificationist Treatment of Auxiliary Hypotheses in Social and Behavioral Sciences: Systematic Replications Framework (preprint). PsyArXiv. https://doi.org/10.31234/osf.io/pdm7y
Murayama, K., Blake, A. B., Kerr, T., & Castel, A. D. (2016). When enough is not enough: Information overload and metacognitive decisions to stop studying information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(6), 914–924. https://doi.org/10.1037/xlm0000213
Jefferys, W. H., & Berger, J. O. (1992). Ockham’s Razor and Bayesian Analysis. American Scientist, 80(1), 64–72. Retrieved from https://www.jstor.org/stable/29774559
Maier, S. U., Raja Beharelle, A., Polanía, R., Ruff, C. C., & Hare, T. A. (2020). Dissociable mechanisms govern when and how strongly reward attributes affect decisions. Nature Human Behaviour. https://doi.org/10.1038/s41562-020-0893-y
Nadarajah, S. (2009). An alternative inverse Gaussian distribution. Mathematics and Computers in Simulation, 79(5), 1721–1729. https://doi.org/10.1016/j.matcom.2008.08.013
Barndorff-Nielsen, O., BlÆsild, P., & Halgreen, C. (1978). First hitting time models for the generalized inverse Gaussian distribution. Stochastic Processes and Their Applications, 7(1), 49–54. https://doi.org/10.1016/0304-4149(78)90036-4
Ghitany, M. E., Mazucheli, J., Menezes, A. F. B., & Alqallaf, F. (2019). The unit-inverse Gaussian distribution: A new alternative to two-parameter distributions on the unit interval. Communications in Statistics - Theory and Methods, 48(14), 3423–3438. https://doi.org/10.1080/03610926.2018.1476717
Weichart, E. R., Turner, B. M., & Sederberg, P. B. (2020). A model of dynamic, within-trial conflict resolution for decision making. Psychological Review. https://doi.org/10.1037/rev0000191
Bates, C. J., & Jacobs, R. A. (2020). Efficient data compression in perception and perceptual memory. Psychological Review. https://doi.org/10.1037/rev0000197
Kvam, P. D., & Busemeyer, J. R. (2020). A distributional and dynamic theory of pricing and preference. Psychological Review. https://doi.org/10.1037/rev0000215
Blundell, C., Sanborn, A., & Griffiths, T. L. (2012). Look-Ahead Monte Carlo with People (p. 7). Presented at the Proceedings of the Annual Meeting of the Cognitive Science Society.
Leon-Villagra, P., Otsubo, K., Lucas, C. G., & Buchsbaum, D. (2020). Uncovering Category Representations with Linked MCMC with people. In Proceedings of the Annual Meeting of the Cognitive Science Society (p. 7).
Leon-Villagra, P., Klar, V. S., Sanborn, A. N., & Lucas, C. G. (2019). Exploring the Representation of Linear Functions. In Proceedings of the Annual Meeting of the Cognitive Science Society (p. 7).
Ramlee, F., Sanborn, A. N., & Tang, N. K. Y. (2017). What Sways People’s Judgment of Sleep Quality? A Quantitative Choice-Making Study With Good and Poor Sleepers. Sleep, 40(7). https://doi.org/10.1093/sleep/zsx091
Hsu, A. S., Martin, J. B., Sanborn, A. N., & Griffiths, T. L. (2019). Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people. Behavior Research Methods, 51(4), 1706–1716. https://doi.org/10.3758/s13428-019-01201-9
Martin, J. B., Griffiths, T. L., & Sanborn, A. N. (2012). Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories. Cognitive Science, 36(1), 150–162. https://doi.org/10.1111/j.1551-6709.2011.01204.x
Gronau, Q. F., Wagenmakers, E.-J., Heck, D. W., & Matzke, D. (2019). A Simple Method for Comparing Complex Models: Bayesian Model Comparison for Hierarchical Multinomial Processing Tree Models Using Warp-III Bridge Sampling. Psychometrika, 84(1), 261–284. https://doi.org/10.1007/s11336-018-9648-3
Wickelmaier, F., & Zeileis, A. (2018). Using recursive partitioning to account for parameter heterogeneity in multinomial processing tree models. Behavior Research Methods, 50(3), 1217–1233. https://doi.org/10.3758/s13428-017-0937-z
Jacobucci, R., & Grimm, K. J. (2018). Comparison of Frequentist and Bayesian Regularization in Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 639–649. https://doi.org/10.1080/10705511.2017.1410822
Raftery, A. E. (1993). Bayesian model selection in structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing Structural Equation Models (pp. 163–180). Beverly Hills: SAGE Publications.
Lewis, S. M., & Raftery, A. E. (1997). Estimating Bayes Factors via Posterior Simulation With the Laplace-Metropolis Estimator. Journal of the American Statistical Association, 92(438), 648–655. https://doi.org/10.2307/2965712
Mair, P. (2018). Modern psychometrics with R. Cham, Switzerland: Springer.
Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(1), 1–36. https://doi.org/10.18637/jss.v048.i02
Kaplan, D., & Lee, C. (2016). Bayesian Model Averaging Over Directed Acyclic Graphs With Implications for the Predictive Performance of Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, 23(3), 343–353. https://doi.org/10.1080/10705511.2015.1092088
Schoot, R. van de, Verhoeven, M., & Hoijtink, H. (2013). Bayesian evaluation of informative hypotheses in SEM using Mplus: A black bear story. European Journal of Developmental Psychology, 10(1), 81–98. https://doi.org/10.1080/17405629.2012.732719
Lin, L.-C., Huang, P.-H., & Weng, L.-J. (2017). Selecting Path Models in SEM: A Comparison of Model Selection Criteria. Structural Equation Modeling: A Multidisciplinary Journal, 24(6), 855–869. https://doi.org/10.1080/10705511.2017.1363652
Shi, D., Song, H., Liao, X., Terry, R., & Snyder, L. A. (2017). Bayesian SEM for Specification Search Problems in Testing Factorial Invariance. Multivariate Behavioral Research, 52(4), 430–444. https://doi.org/10.1080/00273171.2017.1306432
Matsueda, R. L. (2012). Key advances in the history of structural equation modeling. In Handbook of structural equation modeling (pp. 17–42). New York, NY, US: The Guilford Press.
Bollen, K. A. (2005). Structural Equation Models. In Encyclopedia of Biostatistics. American Cancer Society. https://doi.org/10.1002/0470011815.b2a13089
Tarka, P. (2018). An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences. Quality & Quantity, 52(1), 313–354. https://doi.org/10.1007/s11135-017-0469-8
Sewell, D. K., & Stallman, A. (2020). Modeling the Effect of Speed Emphasis in Probabilistic Category Learning. Computational Brain & Behavior, 3(2), 129–152. https://doi.org/10.1007/s42113-019-00067-6