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1. How Red Is a Ladybeetle? Examining People’s Notions of Biological Variability

   Leon-Villagra, P; Mathiaparanam, O; Rosengren, K; Buchsbaum, D (August 2024, Cognitive Science Society)

   Samuelson, L K; Frank, S; Toneva, M; Mackey, A; Hazeltine, E (Ed.)

   People often display essentialist biases, which can lead them to underestimate within-species variability. This bias is espe- cially pronounced when traits are described as advantageous for survival. However, it is unclear whether this bias is limited to the specified trait or encompasses complex trait interactions. We used Markov Chain Monte Carlo with People (MCMCp) to analyze people’s representations of biological variability, using ladybeetles as a model species. Participants either re- ceived contextual information about the benefits of ladybeetle color for survival, or survival-irrelevant information. Overall, participants held consistent beliefs about ladybeetle features, but those with survival-relevant context produced lighter and larger ladybeetles; this difference was consistent with survey responses. However, we found no significant interaction be- tween MCMCp variability and essentialism scores, given our context manipulation. We discuss potential explanations for these results and highlight advantages of MCMCp for assess- ing biological variability, particularly when studying the devel- opment of essentialist biases. 

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2. How Red Is a Ladybeetle? Examining People's Notions of Biological Variability.

   León-Villagrá, P; Mathiaparanam, O; Rosengren, K; Buchsbaum, D (August 2024, Proceedings of the Annual Meeting of the Cognitive Science Society)

   Samuelson, L K; Frank, S; Tonevam, M; Mackey, A; Hazeltine, E (Ed.)

   People often display essentialist biases, which can lead them to underestimate within-species variability. This bias is especially pronounced when traits are described as advantageous for survival. However, it is unclear whether this bias is limited to the specified trait or encompasses complex trait interactions. We used Markov Chain Monte Carlo with People (MCMCp) to analyze people’s representations of biological variability, using ladybeetles as a model species. Participants either received contextual information about the benefits of ladybeetle color for survival, or survival-irrelevant information. Overall, participants held consistent beliefs about ladybeetle features, but those with survival-relevant context produced lighter and larger ladybeetles; this difference was consistent with survey responses. However, we found no significant interaction be- tween MCMCp variability and essentialism scores, given our context manipulation. We discuss potential explanations for these results and highlight advantages of MCMCp for assess- ing biological variability, particularly when studying the development of essentialist biases. 

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3. General and own-species attentional face biases

   https://doi.org/10.3758/s13414-020-02132-w  

   Jakobsen, Krisztina V.; White, Cassidy; Simpson, Elizabeth A. (October 2020, Attention, Perception, & Psychophysics)

   null (Ed.)

   Humans demonstrate enhanced processing of human faces compared with animal faces, known as own-species bias. This bias is important for identifying people who may cause harm, as well as for recognizing friends and kin. However, growing evidence also indicates a more general face bias. Faces have high evolutionary importance beyond conspecific interactions, as they aid in detecting predators and prey. Few studies have explored the interaction of these biases together. In three experiments, we explored processing of human and animal faces, compared with each other and to nonface objects, which allowed us to examine both own-species and broader face biases. We used a dot-probe paradigm to examine human adults’ covert attentional biases for task-irrelevant human faces, animal faces, and objects. We replicated the own-species attentional bias for human faces relative to animal faces. We also found an attentional bias for animal faces relative to objects, consistent with the proposal that faces broadly receive privileged processing. Our findings suggest that humans may be attracted to a broad class of faces. Further, we found that while participants rapidly attended to human faces across all cue display durations, they attended to animal faces only when they had sufficient time to process them. Our findings reveal that the dot-probe paradigm is sensitive for capturing both own-species and more general face biases, and that each has a different attentional signature, possibly reflecting their unique but overlapping evolutionary importance. 

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4. The role of trait reappraisal in response to emotional ambiguity: A meta-analysis.

   Harp, Nicholas R.; Gross, James J.; Uusberg, Andero; Neta, Maital (November 2023, Emotion)

   Individuals exhibit a systematic valence bias—a specific form of interpretation bias—in response to emo- tional ambiguity. Accumulating evidence suggests most people initially respond to emotional ambiguity negatively and differ only in subsequent responses. We hypothesized that trait-level cognitive reap- praisal—an emotion regulation strategy involving the reinterpretation of affective meaning of stimuli— might explain individual differences in valence bias. To answer this question, we conducted a random- effects meta-analysis of 14 effect sizes from 13 prior studies (n = 2,086), identified via Google Scholar searches. We excluded studies (a) in languages other than English, (b) from non-peer-reviewed sources, or (c) nonempirical sources. We included studies with (a) the Emotion Regulation Questionnaire, (b) a puta- tive measure of valence bias prior to any study-specific manipulations, and (c) adult human participants (i.e., 17+). Supporting our prediction, we found individuals with higher trait reappraisal exhibited a less negative bias (r = −.18, z = −4.04, p , .001), whereas there was a smaller, opposite effect for trait expressive sup- pression (r = .10, z = 2.14, p = .03). The effects did, however, vary across tasks with stronger effects observed among studies using the scrambled sentences task compared to the valence bias task. Although trait reappraisal accounted for only a small amount of variance, reappraisal may be one mechanism contrib- uting to variability in response to ambiguity. 

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5. How can geologic decision-making under uncertainty be improved?

   https://doi.org/10.5194/se-10-1469-2019  

   Wilson, Cristina G.; Bond, Clare E.; Shipley, Thomas F. (January 2019, Solid Earth)

   Abstract. In the geosciences, recent attention has been paid to the influence of uncertainty on expert decision-making. When making decisions under conditions of uncertainty, people tend to employ heuristics (rules of thumb) based on experience, relying on their prior knowledge and beliefs to intuitively guide choice. Over 50 years of decision-making research in cognitive psychology demonstrates that heuristics can lead to less-than-optimal decisions, collectively referred to as biases. For example, the availability bias occurs when people make judgments based on what is most dominant or accessible in memory; geoscientists who have spent the past several months studying strike-slip faults will have this terrain most readily available in their mind when interpreting new seismic data. Given the important social and commercial implications of many geoscience decisions, there is a need to develop effective interventions for removing or mitigating decision bias. In this paper, we outline the key insights from decision-making research about how to reduce bias and review the literature on debiasing strategies. First, we define an optimal decision, since improving decision-making requires having a standard to work towards. Next, we discuss the cognitive mechanisms underlying decision biases and describe three biases that have been shown to influence geoscientists' decision-making (availability bias, framing bias, anchoring bias). Finally, we review existing debiasing strategies that have applicability in the geosciences, with special attention given to strategies that make use of information technology and artificial intelligence (AI). We present two case studies illustrating different applications of intelligent systems for the debiasing of geoscientific decision-making, wherein debiased decision-making is an emergent property of the coordinated and integrated processing of human–AI collaborative teams. 

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