Individual Differences in Holistic Processing Predict the Own-Race Advantage in Recognition Memory

Individuals are consistently better at recognizing own-race faces compared to other-race faces (other-race effect, ORE). One popular hypothesis is that this recognition memory ORE is caused by differential own- and other-race holistic processing, the simultaneous integration of part and configural face information into a coherent whole. Holistic processing may create a more rich, detailed memory representation of own-race faces compared to other-race faces. Despite several studies showing that own-race faces are processed more holistically than other-race faces, studies have yet to link the holistic processing ORE and the recognition memory ORE. In the current study, we sought to use a more valid method of analyzing individual differences in holistic processing by using regression to statistically remove the influence of the control condition (part trials in the part-whole task) from the condition of interest (whole trials in the part-whole task). We also employed regression to separately examine the two components of the ORE: own-race advantage (regressing other-race from own-race performance) and other-race decrement (regressing own-race from other-race performance). First, we demonstrated that own-race faces were processed more holistically than other-race faces, particularly the eye region. Notably, using regression, we showed a significant association between the own-race advantage in recognition memory and the own-race advantage in holistic processing and that these associations were weaker when examining the other-race decrement. We also demonstrated that performance on own- and other-race faces across all of our tasks was highly correlated, suggesting that the differences we found between own- and other-race faces are quantitative rather than qualitative. Together, this suggests that own- and other-race faces recruit largely similar mechanisms, that own-race faces more thoroughly engage holistic processing, and that this greater engagement of holistic processing is significantly associated with the own-race advantage in recognition memory.Psycholog

Two Graphs Walk into a Bar: Readout-based Measurement Reveals the Bar-tip Limit Error, a Common, Categorical Misinterpretation of Bar Graphs

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What’s really wrong with bar graphs of mean values: variable and inaccurate communication of evidence on three key dimensions

Human behavioral data are frequently communicated via bar graphs of mean values. Such “mean bar graphs” are presumed to communicate empirical results effectively to non-experts. Yet direct evidence for or against this presumption remains sparse. Here, we ask how a set of widely-consumed scientific mean bar graphs are interpreted by a demographically diverse sample of 133 participants. We use four mean bar graphs of research results, taken from major introductory psychology textbooks, which vary in content (developmental, clinical, social, cognitive), form (unidirectional bars, bidirectional bars), visual aesthetics (four different textbooks’ look and feel), data type (objective performance, survey ratings), and study design (experimental, non-experimental). Participants created a detailed sketch of each graph, adding datapoints for their best guess of individual values that were averaged to produce the mean values. Drawn data values were then analyzed as if they were real data. Results were examined for deviations from the ground truth of the published data and for variability between participants. On three separate dimensions—location of the mean, variability around the mean, and normality of distribution shape—we found large, systematic deviations from ground truth and high inter-participant variability. Together, the combination of systematic deviations and inter-participant variability yielded common, extreme misunderstandings, or fallacies, on all three dimensions. We call these fallacies: (1) the Bar-Tip Limit Error: most or all data plotted inside the bar, as if the bar’s tip represented the outside limit of the data rather than its balanced center point; (2) the Dichotomization Fallacy: little to no overlap between distributions that should show substantial overlap; (3) the Uniformity Fallacy: data distributed uniformly over its entire range, absent the tails that were present in the real data. These results replicated across the four varying stimulus graphs, suggesting that they are not limited to specific graph form, content, visual aesthetic, data type, or study design. We conclude that the choice to communicate human behavioral data via a mean bar graph carries with it at least two major risks. First, different viewers may walk away from the same graph with widely divergent interpretations of the presented evidence. Second, interpretations may deviate systematically, and, for many viewers, to an extreme degree, from ground truth