| Sumario: | Face stereotypes are prevalent, consequential, yet oftentimes inaccurate. How do false first impressions arise and persist despite counter-evidence? Building on the overgeneralization hypothesis, we propose a domain-general cognitive mechanism: insufficient statistical learning, or Insta-learn. This mechanism posits that humans are quick statistical learners but insufficient samplers. Humans extract statistical regularities from very few exemplars in their immediate context and prematurely decide to stop sampling, creating and perpetuating locally accurate—but globally inaccurate—impressions. Six experiments (N = 1,565) tested this hypothesis using novel pairs of computer-generated faces and social behaviors by fixing the population-level statistics of face–behavior associations to zero (i.e., no relationship). The initial sample contained either 11, five, or three examples with either a positive, zero, or negative linear relationship between facial features and social behaviors. The sampling procedure contained a free-sampling condition in which participants were free to decide when to stop viewing more examples and a fixed-sampling condition in which participants were forced to view all stimuli before making decisions. Consistent with the Insta-learn mechanism, participants learned novel face stereotypes quickly, with as few as three examples, and did not sample enough when they were given the freedom to do so. This domain-general cognitive mechanism provides one plausible origin of false face stereotypes, demonstrating negative consequences when people learn too much from too little. Statement of Limitations: Although we aim to study a general psychological phenomenon, our ideas and findings are bound by the available literature, methodological choices, and samples of participants. All of these elements add potential subjectivity. First, most references in this article come from the United States and Western Europe. This may constrain the accumulated knowledge to certain historical contexts, which may not be applicable to other contexts. Second, we used tightly controlled experiments because we needed to pin down the precise mechanism and to quantify the proposed causal effect. This design allows for high internal validity at the cost of some external validity. Third, our participants come from one standard crowdsourcing platform; therefore, they possess certain characteristics, such as being a mostly White, English-speaking sample. Although our analysis controlled for individual-level covariates such as age, race, gender, and socioeconomic status, the generalizability of the findings to other samples remains an open question.
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