Constructivist coding: learning from selective feedback

Although much learning in real-life environments relies on highly selective feedback about outcomes, virtually all cognitive models of learning, judgment, and categorization assume complete and representative feedback. We investigated empirically the effect of selective feedback on decision making and how people code experience with selective feedback. The results showed that, in contrast to a commonly raised concern, performance was not impaired following learning with selective and biased feedback. Furthermore, even in a simple decision task, the experience that people acquired was not a mere recording of the observed outcomes, but rather a reconstruction from general task knowledge.     

Adaptive changes between cue abstraction and exemplar memory in a multiple-cue judgment task with continuous cues

The majority of previous studies on multiple-cue judgment with continuous cues have involved comparisons between judgments and multiple linear regression models that integrated cues into a judgment. The authors present an experiment indicating that in a judgment task withadditive combination of multiple continuous cues, people indeed displayed abstract knowledge of the cue criterion relations that was mentally integrated into a judgment, but in a task withmultiplicative combination of continuous cues, people instead relied on retrieval of memory traces of similar judgment cases (exemplars). These results suggest that people may adopt qualitatively distinct forms of knowledge, depending on the structure of a multiple-cue judgment task. The authors discuss implications for theories of multiple-cue judgment.     

Note on the rationality of rule-based versus exemplar-based processing in human judgment

This paper reports a study of the relationship between rule- versus exemplar-based processing and criteria for rationality of judgment. Participants made probability judgments in a classification task devised by S. W. Allen and L. R. Brooks (1991). In the exemplar condition, the miscalibration was accounted for by stochastic components of the judgment with a format-dependence effect, implying simultaneous over- and underconfidence depending on the response scale. In the rule condition, there was an overconfidence bias not accounted for by the stochastic components of judgment. In both conditions the participants were additive on average and reasonably transitive, but the larger stochastic component in the exemplar condition produced somewhat larger absolute deviations. The results suggest that exemplar processes are unbiased but more perturbed by stochastic components, while rule-based processes may be more prone to bias.     

Exemplars, prototypes, and the flexibility of classification models

J. P. Minda and J. D. Smith (2001) showed that a prototype model outperforms an exemplar model, especially in larger categories or categories that contained more complex stimuli. R. M. Nosofsky and S. R. Zaki (2002) showed that an exemplar model with a response-scaling mechanism outperforms a prototype model. The authors of the current study investigated whether excessive model flexibility could explain these results. Using cross-validation, the authors demonstrated that both the prototype model and the exemplar model with a response-scaling mechanism suffered from overfilling in the linearly separable category structure. The results illustrate the need to make sure that the best-fitting model is not chasing error variance instead of variance attributed to the cognitive process it is supposed to model.     

Exemplar effects in categorization and multiple-cue judgment

Categorization and multiple-cue judgment are similar tasks, but the influential models in the two areas are different in terms of the computations, processes, and neural substrates that they imply. In categorization, exemplar memory is often emphasized, whereas multiple-cue judgment generally is interpreted in terms of integration of cues that have been abstracted in training. In 3 experiments the authors investigated whether these conclusions derive from genuine differences in the processes or are accidental to the different research methods. The results revealed large individual differences and a shift from exemplar memory to cue abstraction when the criterion is changed from a binary to a continuous variable, especially for a probabilistic criterion. People appear to switch between qualitatively distinct processes in the 2 tasks.     

Alexithymia,memory of emotion,emotional awareness,and perfectionism

The Toronto Alexithymia Scale (TAS-20; R. M. Bagby, J. D. A. Parker, & G. J. Taylor, 1994) is a self-assessment instrument designed to measure deficits in meta-emotional functioning (e.g., difficulties in identifying and describing emotions). Four studies were carried out to examine the association between the TAS-20 and (a) performance measures of meta-emotional functioning (memory of emotion and emotional awareness; Studies 1-2) and (b) measures of perfectionistic standards that may possibly be involved in the self-assessment of abilities-difficulties (Studies 3-4). The TAS-20 failed to correlate in the predicted direction with the performance measures but showed sizable correlations with measures of perfectionism. Moreover, perfectionism was found to predict TAS-20 scores independent of depression, anxiety, and somatic complaints. The results are discussed in terms of the TAS-20 measuring primarily certain aspects of meta-emotional self-efficacy.     

A comparison of correlation, calibration, and diagnosticity as measures of the confidence-accuracy relationship in witness identification

The relationship between witness confidence and accuracy (CA) has traditionally been measured by the point-biserial correlation (rpb). Recently, 2 alternative indices for measuring the CA relation have been proposed, namely calibration and diagnosticity analyses (e.g., P. Juslin, N. Olsson, & A. Winman, 1996). In this study, the 3 measures were compared quantitatively using 52 independent data sets. The measures rpb and calibration were weakly correlated, whether computed across earwitness data sets, eyewitness data sets, or all data. Thus, when applied to the same data, these 2 measures sometimes yield different conclusions. A modest relation was observed between the rpb and the diagnosticity of confidence. Finally, calibration and degree of over- and underconfidence covaried with task difficulty, consistent with K. A. Deffenbachers' (1980) optimality hypothesis.