No abstract

The linguistic category model (LCM) seeks to understand social psychological processes through the lens of language use. Its original development required human judges to analyze natural language to understand how people assess actions, states, and traits. The current project sought to computerize the LCM assessment based on an idea of language abstraction with a previously published data set. In the study, a computerized LCM analysis method was built using an LCM verb dictionary and a part-of-speech tagging program that identified relevant adjectives and nouns. This computerized method compared open-ended texts written in first-person and third-person perspectives from 130 college students. Consistent with construal-level theory, third-person writing resulted in higher levels of abstraction than first-person writing. Implications of relying on an automated LCM method are discussed.

Detecting changes, in performance, sales, markets, risks, social relations, or public opinions, constitutes an important adaptive function. In a sequential paradigm devised to investigate detection of change, every trial provides a sample of binary outcomes (e.g., correct vs. incorrect student responses). Participants have to decide whether the proportion of a focal feature (e.g., correct responses) in the population from which the sample is drawn has decreased, remained constant, or increased. Strong and persistent anomalies in change detection arise when changes in proportional quantities vary orthogonally to changes in absolute sample size. Proportional increases are readily detected and nonchanges are erroneously perceived as increases when absolute sample size increases. Conversely, decreasing sample size facilitates the correct detection of proportional decreases and the erroneous perception of nonchanges as decreases. These anomalies are however confined to experienced samples of elementary raw events from which proportions have to be inferred inductively. They disappear when sample proportions are described as percentages in a normalized probability format. To explain these challenging findings, it is essential to understand the inductivelearning constraints imposed on decisions from experience.

The influence of judges' behaviors on procedural justice was analyzed in a field study, observing the judges' behaviors during n = 129 trials and assessing the defendants and the audiences' justice perceptions. The observed judicial behavior was unrelated to the defendants' justice perceptions. However, the more respectful the judge treated the defendants, the fairer the audience perceived the trial. In general, the effect size of the relationship between observational measures and subjective justice ratings was small in comparison to the relationship within defendants' or audiences' ratings. There were striking differences in the justice perception between the two data sources, namely defendants and audience. Thus, the source matters, and to avoid a same-source bias, should be taken into account when analyzing justice perceptions.