Encoding, maintenance, and retrieval processes in the lag effect: a multinomial processing tree analysis

Short-term studies on repeated learning of verbatim material have typically revealed an overall benefit of long lags compared to short lags between repetitions. This has been referred to as the lag effect. On educationally relevant time scales, however, an inverted-U-shaped relation between lag and memory performance is often observed. Recently, Cepeda et al. (2009) showed that the optimal lag for relearning depends heavily on the time interval between the last learning session and the final memory test (i.e., the retention interval; RI). In order to explore the cognitive mechanisms underlying this result in more detail we independently manipulated both the lag and the RI in a 3×2 experimental design and analysed our data using a multinomial processing tree model for free-then-cued-recall data. Our results reveal that the lag effect trends are mainly driven by encoding and maintenance processes rather than by retrieval mechanisms. Our findings have important implications for theories of the lag effect.     

A Multinomial Model to Assess Fluency and Recollection in a Sequence Learning Task

We suggest that well-formedness judgements in conjunction with L.L. Jacoby's (1991) process dissociation procedure and an appropriate measurement model can be used to obtain measures of implicit and explicit sequence knowledge. We introduce a new measurement model designed specifically for the sequence learning task. The model assumes that sequence identification is based on recollection, perceptual or motor fluency, systematicity detection, and guessing. The model and the application of the process dissociation procedure were empirically evaluatedusing auditory eventsequences. In Experiment 1, the parameterreflecting recollection was higher in an intentional than in an incidental learning condition. Experiment 2 showed that random sequences interspersed among the systematic sequences during the acquisition phase may change this pattern of results. A manipulation of processing fluency in Experiment 3 was reflected in the appropriate model parameter. In sum, the new measurement model and the application of the process dissociation procedure appear to be useful tools in sequence learning research.     

On the plasticity of the survival processing effect

Nairne, Thompson, and Pandeirada (2007) discovered a strong and rather general memory advantage for word material processed in a survival-related context. One possible explanation of this effect conceives survival processing as a special form of encoding: Nature specifically "tuned" our memory systems to process and remember fitness-relevant information. We tested this explanation by studying whether the survival processing effect is robust against encoding manipulations that do not affect the fitness relevance of information. Three experiments replicated a strong survival processing effect under standard conditions but showed that the mnemonic benefit of survival processing diminishes or even vanishes when participants focus on a single problem (Experiments 1 and 2) or technique (Experiment 3) of survival. We argue that it is not survival processing per se that facilitates recall but the richness and distinctiveness with which information is encoded.     

Fluent, fast, and frugal? A formal model evaluation of the interplay between memory, fluency, and comparative judgments

A new process model of the interplay between memory and judgment processes was recently suggested, assuming that retrieval fluency-that is, the speed with which objects are recognized-will determine inferences concerning such objects in a single-cue fashion. This aspect of the fluency heuristic, an extension of the recognition heuristic, has remained largely untested due to methodological difficulties. To overcome the latter, we propose a measurement model from the class of multinomial processing tree models that can estimate true single-cue reliance on recognition and retrieval fluency. We applied this model to aggregate and individual data from a probabilistic inference experiment and considered both goodness of fit and model complexity to evaluate different hypotheses. The results were relatively clear-cut, revealing that the fluency heuristic is an unlikely candidate for describing comparative judgments concerning recognized objects. These findings are discussed in light of a broader theoretical view on the interplay of memory and judgment processes.     

The nature of memory processes underlying recognition judgments in the process dissociation procedure

The hypothesis is tested that the memory processes involved in recognition judgments in the process dissociation procedure are the same as those involved in standard source-monitoring tasks. It is shown how source-monitoring response categories can be mapped onto process dissociation response categories. On the basis of this observation, an experiment was conducted in which it was possible to compare, using a multinomial modeling approach, the parameters representing memory processes in the process dissociation procedure with those involved in source monitoring. For the two different encoding conditions realized, the results are compatible with the hypothesis that the same processes are involved in source monitoring and in recognition judgments in the process dissociation procedure. Implications for the interpretation of the model’s parameters are discussed.     

Use of the recognition heuristic depends on the domain’s recognition validity,not on the recognition validity of selected sets of objects

According to the recognition-heuristic theory, decision makers solve paired comparisons in which one object is recognized and the other not by recognition alone, inferring that recognized objects have higher criterion values than unrecognized ones. However, success—and thus usefulness—of this heuristic depends on the validity of recognition as a cue, and adaptive decision making, in turn, requires that decision makers are sensitive to it. To this end, decision makers could base their evaluation of the recognition validity either on the selected set of objects (the set’s recognition validity), or on the underlying domain from which the objects were drawn (the domain’s recognition validity). In two experiments, we manipulated the recognition validity both in the selected set of objects and between domains from which the sets were drawn. The results clearly show that use of the recognition heuristic depends on the domain’s recognition validity, not on the set’s recognition validity. In other words, participants treat all sets as roughly representative of the underlying domain and adjust their decision strategy adaptively (only) with respect to the more general environment rather than the specific items they are faced with.     

BINOMIX: A BASIC program for maximum likelihood analyses of finite and beta-binomial mixture distributions

If a discrete random variableY is binomially distributed for each observation unit in a sample, and if the binomial parameterp varies between different units, then the distribution ofY underlying a sample of observations based on different units is called a binomial mixture distribution. BINOMIX is a BASIC program to estimate parameters and to evaluate the goodness of fit for the two most important types of binomial mixtures, finite and beta-binomial.     

CAML—Maximum likelihood consensus analysis

Consensus analysis enables estimation of individual differences in competencies and response tendencies when answer keys to dichotomous forced-choice questions are unknown. CAML, a set of functions written in R, implements maximum likelihood estimation for the general Condorcet model that underlies consensus analysis. CAML avoids problems of alternative approaches that have often rendered consensus analysis impractical or unfeasible in the past. It provides (1) measures of model fit, (2) a measure of consensus, (3) point and interval estimates of competencies and response tendencies, and (4) an estimate of the unknown answer key. The present article describes the general Condorcet model, the CAML algorithms, and the handling of the software. In addition, the validity of CAML results is tested in a recognition memory study using selective experimental manipulations of the parameters. The results show that CAML works very well in practice and provides valid estimates of competencies, response tendencies, and answer keys.