Evaluating models of recognition memory using first- and second-choice responses

Swets, Tanner Jr., and Birdsall (1961) proposed a 4-alternative forced-choice task with two choices (4AFC-2R) for distinguishing between the Equal-Variance Signal Detection model and the One-High Threshold model. This task was recently implemented in the field of recognition memory (Parks & Yonelinas, 2009), a field in which several candidate models have been proposed. One advantage of the 4AFC-2R task is that it permits parameter estimation and goodness of fit testing, something which so far was only possible through the use of Receiver Operating Characteristic (ROC) functions for the more complex candidate models. The present article provides a thorough characterization and comparison of the main recognition memory models in the context of this task. Results are illustrated by a reanalysis of Parks and Yonelinas’ original data, revealing a preference for hybrid approaches to recognition memory, more specifically for the dual-process model (Yonelinas, 1997), whereas pure signal detection models performed poorly. The present analysis provides an assessment of the merits and limitations of this task, highlighting future research applications.     

Learning to allocate limited time to decisions with different expected outcomes

The goal of this article is to investigate how human participants allocate their limited time to decisions with different properties. We report the results of two behavioral experiments. In each trial of the experiments, the participant must accumulate noisy information to make a decision. The participants received positive and negative rewards for their correct and incorrect decisions, respectively. The stimulus was designed such that decisions based on more accumulated information were more accurate but took longer. Therefore, the total outcome that a participant could achieve during the limited experiments’ time depended on her “decision threshold”, the amount of information she needed to make a decision. In the first experiment, two types of trials were intermixed randomly: hard and easy. Crucially, the hard trials were associated with smaller positive and negative rewards than the easy trials. A cue presented at the beginning of each trial would indicate the type of the upcoming trial. The optimal strategy was to adopt a small decision threshold for hard trials. The results showed that several of the participants did not learn this simple strategy. We then investigated how the participants adjusted their decision threshold based on the feedback they received in each trial. To this end, we developed and compared 10 computational models for adjusting the decision threshold. The models differ in their assumptions on the shape of the decision thresholds and the way the feedback is used to adjust the decision thresholds. The results of Bayesian model comparison showed that a model with time-varying thresholds whose parameters are updated by a reinforcement learning algorithm is the most likely model. In the second experiment, the cues were not presented. We showed that the optimal strategy is to use a single time-decreasing decision threshold for all trials. The results of the computational modeling showed that the participants did not use this optimal strategy. Instead, they attempted to detect the difficulty of the trial first and then set their decision threshold accordingly.     

Modeling storage and retrieval processes with clinical populations with applications examining alcohol-induced amnesia and Korsakoff amnesia

Multinomial processing tree models can provide for measures of underlying cognitive processes. In this paper, the Chechile [Chechile, R. A. (2004). New multinomial models for the Chechile-Meyer task. Journal of Mathematical Psychology, 48, 364-384] 6P model is described and applied to several applications involving clinical populations. The model provides for separate measures of storage and retrieval. Monte Carlo studies were conducted to examine the relative accuracy of two methods for obtaining an overall condition estimate for the 6P model, i.e., averaging estimates found for individuals versus pooling the multinomial frequency data before estimating the model parameters. The sampling studies showed that the pooling of frequencies resulted in more accurate parameter estimates. However, psychological assessment in clinical psychology requires precise measurement on an individual basis. In order to recover information about individuals from pooled frequency information, a modified jackknife method was advanced. The jackknife method is based on a contrast between the overall pooled frequency information and the pool frequency without the observations from a single individual. Another series of Monte Carlo simulations demonstrate that the new jackknife method resulted in better recovery of the correct individual parameter values relative to estimates based on only the data from the individual. Finally, the 6P model was used to examine the data from two previously reported studies with clinical populations. One application addressed the effect of alcohol-induced amnesia, and the other application dealt with Korsakoff amnesia. In both cases the pattern of storage and retrieval measurements resulted in a clarification of the underlying storage and retrieval differences between the clinical group and the control group.     

Questioning Chase and Simon's (1973) “Perception in Chess”: The “experience recognition” hypothesis

Pattern recognition lies at the heart of the cognitive science endeavor. In this paper, we provide some criticism of this notion, using studies of chess as an example. The game of chess is, as significant evidence shows, a game of abstractions: pressures; force; open files and ranks; time; tightness of defense; old strategies rapidly adapted to new situations. These ideas do not arise on current computational models, which apply brute force by rote-memorization. In this paper we assess the computational models of CHREST and CHUMP, and argue that chess chunks must contain semantic information. This argument leads to a new and contrasting claim, as we propose that key conclusions of Chase and Simon's (1973) influential study stemmed from a non-sequitur. In the concluding section, we propose a shift in philosophy, from “pattern recognition” to a framework of “experience recognition”.     

The Fortieth Anniversary of Women's Ordination in the Lutheran Church

Abstract : This article offers a perspective on the present and future roles of women in the ELCA. It gives some analysis of women's roles in the trend of declining traditional congregations and rising megachurches while imagining the role of clergywomen in the church's next forty years as new models of ministry emerge.     

Using person matching to predict career specialty choice

Person matching promotes career exploration and choice by linking persons to persons in occupations based on inventory profile score similarity. We examined the efficacy of the procedure for career specialty choice. Medical students (N = 196 women, 224 men) responded to the Sixteen Personality Factor Questionnaire (16PF) in their first year of training. After graduating and selecting a medical residency, members of a reference subgroup (n = 62) of the total sample were matched with members of a criterion subgroup (n = 358) based on 16PF score equivalencies determined by the D² statistic. Person matching predicted medical specialty choice 43-60% of the time. Using broader specialty group categories and adding criterion persons increased the number of specialty matches. Additional refinement and analysis should enhance the efficacy of this idiographic approach as an alternative to nomothetic P-E matching for career exploration. Future research should examine person matching in terms of consequential validity.     

Connectionist Models of Word Reading

Abstract— Connectionist models of word reading attempt to explain the computational mechanisms underlying this important skill. The goal of this research is an integrated theory of reading and its brain bases, with the computational model as the interface between the two. The models are governed by computational principles that differ considerably from naive intuitions but nonetheless account for many aspects of normal and impaired (dyslexic) reading.