Problems with using mechanisms to solve the problem of extrapolation

Proponents of evidence-based medicine and some philosophers of science seem to agree that knowledge of mechanisms can help solve the problem of applying results of controlled studies to target populations (‘the problem of extrapolation’). We describe the problem of extrapolation, characterize mechanisms, and outline how mechanistic knowledge might be used to solve the problem. Our main thesis is that there are four often overlooked problems with using mechanistic knowledge to solve the problem of extrapolation. First, our understanding of mechanisms is often (and arguably, likely to remain) incomplete. Secondly, knowledge of mechanisms is not always applicable outside the tightly controlled laboratory conditions in which it is gained. Thirdly, mechanisms can behave paradoxically. Fourthly, as Daniel Steel points out, using mechanistic knowledge faces the problem of the ‘extrapolator’s circle’. At the same time, when the problems with mechanistic knowledge have been addressed, such knowledge can and should be used to mitigate (nothing can entirely solve) the problem of extrapolation.     

Linking associative and serial list memory: Pairs versus triples

Paired associates and serial list memory are typically investigated separately. An "isolation principle" (J. B. Caplan, 2005) was proposed to explain behavior in both paradigms by using a single model, in which serial list and paired associates memory differ only in how isolated pairs of items are from interference from other studied items. In the present study, 2 experiments identify a critical dissociation between the 2 paradigms, challenging this unified account. Specifically, forward and backward probes were highly correlated for pairs and less so for short lists (triples). The authors asked whether the isolation principle could quantitatively accommodate this type of dissociation. A simulation confirmed that a single model incorporating the isolation principle can adequately explain this and other dissociations, supporting the common processes view.     

Priming effects for affective vs. neutral faces

Affective and Neutral Tasks (faces with negative or neutral content, with different lighting and orientation) requiring reaction time judgments of poser identity were administered to 32 participants. Speed and accuracy were better for the Affective than Neutral Task, consistent with literature suggesting facilitation of performance by affective content. Priming effects were significant for the Affective but not Neutral Task. An Explicit Post-Test indicated no conscious knowledge of the stimulus frequency that was associated with performance facilitation. Faster performance by female vs. male participants, and differential speeds and susceptibility to priming of different emotions were also found. Anger and shock were responded to most rapidly and accurately in several conditions, showed no gender differences, and showed significant priming for both RT and accuracy. Fear and pain were responded to least accurately, were associated with faster female than male reaction time, and the accuracy data showed a kind of reverse priming.     

Combining speed and accuracy to assess error-free cognitive processes

Both the speed and accuracy of responding are important measures of performance. A well-known interpretive difficulty is that participants may differ in their strategy, trading speed for accuracy, with no change in underlying competence. Another difficulty arises when participants respond slowly and inaccurately (rather than quickly but inaccurately), e.g., due to a lapse of attention. We introduce an approach that combines response time and accuracy information and addresses both situations. The modeling framework assumes two latent competing processes. The first, the error-free process, always produces correct responses. The second, the guessing process, results in all observed errors and some of the correct responses (but does so via non-specific processes, e.g., guessing in compliance with instructions to respond on each trial). Inferential summaries of the speed of the error-free process provide a principled assessment of cognitive performance reducing the influences of both fast and slow guesses. Likelihood analysis is discussed for the basic model and extensions. The approach is applied to a data set on response times in a working memory test. The authors wish to thank Roger Ratcliff, Christopher Chabris, and three anonymous referees for their helpful comments, and Aureliu Lavric for providing the data analyzed in this paper.     

Hemispheric cognitive style: a comparison of three instruments

In this study, the author tested the reliability, concurrent validity, and predictive validity of three hemispheric cognitive style instruments: (a) the Preference Test (PT; R. Zenhausern, 1978), (b) the Polarity Questionnaire (PQ; B. E. Morton, 2002), and (c) the Wagner Preference Inventory II (WAPI II; R. F. Wagner & K. A. Wells, 1985). Participants were either teachers or teachers in training. The prediction criterion was in the area of teaching licensure. Scores on the PQ had extremely low reliability, and correlations with the PT and the WAPI II were not significantly different from zero. In addition, the PQ did not correlate with the licensure area. The author found that both the PT and the WAPI II had reasonable levels of reliability, and both instruments were able to explain a small percentage of the variance in the teaching licensure area. Using factor analysis of the PT, the author found a 7-factor structure, which suggests that hemispheric cognitive style might be decomposable into separate holist analytic and visual-verbal dimensions.     

The role of coding time in estimating and interpreting growth curve models

The coding of time in growth curve models has important implications for the interpretation of the resulting model that are sometimes not transparent. The authors develop a general framework that includes predictors of growth curve components to illustrate how parameter estimates and their standard errors are exactly determined as a function of receding time in growth curve models. Linear and quadratic growth model examples are provided, and the interpretation of estimates given a particular coding of time is illustrated. How and why the precision and statistical power of predictors of lower order growth curve components changes over time is illustrated and discussed. Recommendations include coding time to produce readily interpretable estimates and graphing lower order effects across time with appropriate confidence intervals to help illustrate and understand the growth process.     

Personality over time: Methodological approaches to the study of short-term and long-term development and change

Although theories of personality emphasize the integrative, enduring, and dynamic nature of personality, the current modal research design in personality ignores the dimension of time. We consider a variety of recent methods of longitudinal data analysis to examine both short-term and long-term development and change in personality, including mean-level analyses both across and within individuals across time, variance structures across time, and cycles and dynamic models across time. These different longitudinal analyses can address classic as well as new questions in the study of personality and its development. We discuss the linkages among different longitudinal analyses, measurement issues in temporal data, the spacing of assessments, and the levels of generalization and potential insights afforded by different longitudinal analyses.