A nonlinear regression approach to estimating signal detection models for rating data

Modeling Experimentally Induced Strategy Shifts

In dynamic decision-making environments, observers must continuously adjust their decision-making strategies. Previous research has focused on internal fluctuations in decision mechanisms, without regard to how these changes are induced by environmental changes. We developed a simple paradigm in which we manipulated task difficulty, thereby inducing changes in decision processes. We applied this paradigm to recognition memory, manipulating task difficulty by changing the similarity of lures to targets. More difficult decision environments caused participants to make more careful decisions, but these changes did not appear immediately. We propose a simple theoretical account for these data, using a dynamic version of signal detection theory fitted to individual subjects. Our model represents a significant departure from existing models because it incorporates subject-controlled parameters that may adjust over time in response to environmental changes.

ASSESSMENT OF NEUROPSYCHOLOGICAL PARAMETERS AND QUALITY OF LIFE TO EVALUATE OUTCOME IN PATIENTS WITH SURGICALLY TREATED SUPRATENTORIAL MENINGIOMAS

OBJECTIVE

According to current outcomes research programs, assessment of a broad spectrum of parameters, including quality of life indices, is required to adequately reflect the results of a given treatment. We performed a comprehensive evaluation in patients after supratentorial meningioma surgery in a retrospective study.

METHODS

In 91 consecutive patients, outcome was assessed in individual sessions in patients' homes an average of 15 months (standard deviation, ± 3.6 months) after surgery. The survey included tests of cognitive performance, coping strategies, satisfaction with life, and a structured interview.

RESULTS

We found a significant negative correlation between patient age and cognitive performance (P < 0.001), with a major decline beginning at the age of 55 years. Despite normal cognitive performance, 73% of younger patients (younger than 55 years) compared with 20% of older patients (P < 0.001) were not satisfied with life. As a major problem, 68% of younger patients described an inability to accept having this severe disease as a young person. Patients living as singles had a higher frequency of depressive coping (P < 0.05) and less satisfaction with life (P < 0.05).

CONCLUSION

Comprehensive evaluation after meningioma surgery is required to prevent poor long-term results after apparently successful surgery. In our study, tests and structured interviews revealed different aspects, especially concerning patient age. Because demographic variables clearly influenced satisfaction with life, evaluation of quality of life must account for these factors to improve comparison of different studies. However, prospective studies with larger cohorts and control groups are required to prove our hypotheses.

Analyzing recognition performance with sparse data

Experiments in which recognition performance is measured sometimes involve only a small number of observations per subject, rendering d' analysis unreliable (Schooler & Shiffrin, 2005). Here, we introduce, in signal detection models, subject-specific random variables to account for heterogeneous hit and false alarm rates among individuals. Population d' effects for comparing groups are estimated, in this approach, by pooling information from a sample of subjects across experimental conditions. The method is validated by a simulation study and is illustrated with an analysis of the effect of neutral and emotional words on recognition performance, employing the emotional Stroop task (Lee & Shih, 2007).

Word frequency and word likeness mirror effects in episodic recognition memory

Estes and Maddox (2002) suggested that the word frequency mirror effect in episodic recognition memory might be due to word likeness rather than to the frequency of experience with a word per se. We examined their suggestion using a factorial manipulation of frequency and neighborhood density, a measure used in lexical memory research to measure orthographic word likeness. For study with no specified task, main effects of density and frequency were in the mirror order, confirming the hypothesized mirror effect of word likeness but not its role in producing the frequency mirror effect. Lexical decision study increased the size of both mirror effects, even though the density manipulation had a negligible effect on lexical decision performance for words. Post hoc analyses showed that neither mirror effect could be explained by differences in lower order measures of word likeness (letter and bigram frequency). The joint orders of frequency and density results were mirrored across new and old conditions in accordance with attention likelihood theory (ALT), but density effects on z-ROC slope suggest that ALT may require extension to accommodate the effect of word likeness on response confidence.

The dynamics of experimentally induced criterion shifts

Investigations of decision making have typically assumed stationarity, even though commonly observed "context effects" are dynamic by definition. Mirror effects are an important class of context effects that can be explained by changes in participants' decision criteria. When easy and difficult conditions are blocked alternately and a mirror effect is observed, participants must repeatedly change their decision criteria. The authors investigated the time course of these criterion changes and observed the buildup of mirror effects on a trial-by-trial basis. The data are consistent with slow, systematic changes in decision criteria that lag behind stimulus changes. The length of this lag is considerable: analysis of a simple dynamic signal-detection model suggests participants take an average of around 14 trials to adjust to new decision environments. This trial-level measurement of experimentally induced changes has implications for traditional blockwise analyses of data and for models of decision making.

Using the PLUM procedure of SPSS to fit unequal variance and generalized signal detection models

The recent addition of aprocedure in SPSS for the analysis of ordinal regression models offers a simple means for researchers to fit the unequal variance normal signal detection model and other extended signal detection models. The present article shows how to implement the analysis and how to interpret the SPSS output. Examples of fitting the unequal variance normal model and other generalized signal detection models are given. The approach offers a convenient means for applying signal detection theory to a variety of research.

Fitting mixed-effects models for repeated ordinal outcomes with the NLMIXED procedure

This paper presents an analysis of repeated ordinal outcomes arising from two psychological studies. The first case is a repeated measures analysis of variance; the second is a mixed-effects regression in a longitudinal design. In both, the subject-specific variation is modeled by including random effects in the linear predictor (inside a link function) of a generalized linear model. The NLMIXED procedure in SAS is used to fit the mixed-effects models for the categorical response data. The presentation emphasizes the parallel between the model specifications and the SAS statements. The purpose of this paper is to facilitate the use of mixed-effects models in the analysis of repeated ordinal outcomes.