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Face stereotypes are prevalent and consequential. In this paper, we investigate an experience sampling account of face stereotyping. In two experiments, we show that, in an experience-sampling-based Trust Game, participants in the role of an investor were much more likely to play the game with trustees with trustworthy faces than those with untrustworthy faces (Experiment 1). Crucially, this endogenous experience sampling bias has amplified the behavioral consequences of the facial trustworthiness stereotype. In contrast, when the information was directly described with no sampling constraint for the participants, the face stereotype had a very limited effect on investment decisions (Experiment 2). We conclude by suggesting that the description-experience gap paves a promising avenue for studying sampling-based accounts in social cognition and behavior.

Correlation judgments are at the core of belief formation. In previous studies of correlation judgment from 2D scatterplots, observers underestimate correlations, and display stronger underestimation biases when the scatterplot is shown in a landscape view than in a portrait view. Yet, it is unclear how these biases arise. Here, we propose that observers are Bayesian learners who perform “mental regression” using the observed data points in graph. Accordingly, judgment errors can arise from biased visual information sampling. We test our model’s predictions with two eye-tracking experiments and find that the Bayesian learning model, applied to information obtained from visual fixation data, replicates classic behavioral findings. The model also predicts trial-level estimation biases at a high accuracy level. Our study shows how computational models trained on process-level data can shed light on the cognitive mechanisms underlying belief formation, and yield theory-driven practical implications for data visualization and statistical communication.

We introduce a new empirical paradigm for studying naturalistic active learning, as well as new computational tools for jointly modeling algorithmic and rational theories of information search. Subjects in our task can ask questions and learn about hundreds of everyday items, but must retrieve queried items from memory. In order to maximize information gain, subjects need to retrieve sequences of dissimilar items. We find that subjects are not able to do this. Instead, associative memory mechanisms lead to the successive retrieval of similar items, an established memory effect known as semantic congruence. The extent of semantic congruence (and thus suboptimality) is unaffected by task instructions and incentives, though subjects are able to identify efficient query sequences when given a choice. Overall, our results indicate that subjects can distinguish between optimal and suboptimal search if explicitly asked to do so, but have difficulty implementing optimal search from memory. We conclude that associative memory processes place critical restrictions on people’s ability to ask good questions in naturalistic active learning tasks.

Decision models are formal algorithms that are used to represent decision processes and predict choice across a wide rangeof disciplines. These models are often highly complex, which makes it difficult to understand the relationships betweendifferent models, the unique features of individual models and, in turn, the fundamental properties of choice behaviorcaptured by these models. We address this issue in a large-scale computational analysis that uses parameter bootstrappingcross-fitting techniques to derive pairwise measures of decision model distances. Our analysis includes over 80 prominentmodels of risky and intertemporal choice, and results in an ontology of decision models, with data-driven model clustersand hierarchies that synthesize over seven decades of quantitative research on human choice behavior.

People’s desire to be patient or impatient can fluctuate frommoment to moment, yet little is known about the effects ofvariability in time preference on intertemporal choicebehavior. We examine this issue through the lens of anexponential discounting model with noisy discount factors. Weshow that such a model can generate decreasing patience overtime, accounting for behavioral patterns typically attributed tohyperbolic discounting, while also making reasonablepredictions regarding violations of intertemporal dominance.Additionally, two experiments reveal that many participants dodisplay noise in their discount factors, and that a noisy discountfactor model outperforms hyperbolic models in terms ofquantitative fit. Ultimately the majority of participants are bestdescribed by some type of exponential discounting model(with or without noisy discount factors). These results indicatethat it may not be necessary to assume alternate forms of non-exponential discounting, as long as the discount factors in anexponential model are permitted to vary at random. Theseresults also highlight the importance of allowing for differentsources of noise in choice modeling.People’s desire to be patient or impatient can fluctuate frommoment to moment, yet little is known about the effects ofvariability in time preference on intertemporal choicebehavior. We examine this issue through the lens of anexponential discounting model with noisy discount factors. Weshow that such a model can generate decreasing patience overtime, accounting for behavioral patterns typically attributed tohyperbolic discounting, while also making reasonablepredictions regarding violations of intertemporal dominance.Additionally, two experiments reveal that many participants dodisplay noise in their discount factors, and that a noisy discountfactor model outperforms hyperbolic models in terms ofquantitative fit. Ultimately the majority of participants are bestdescribed by some type of exponential discounting model(with or without noisy discount factors). These results indicatethat it may not be necessary to assume alternate forms of non-exponential discounting, as long as the discount factors in anexponential model are permitted to vary at random. Theseresults also highlight the importance of allowing for differentsources of noise in choice modeling.

Deep-sea eukaryotic microorganisms play a vital role in degrading organic matter and geochemically cycling elements in the deep ocean. However, the impact of sampling methods on detection of these microorganisms under high hydrostatic pressure remains uncertain. In this study, we compared a traditional water sampling method using a Niskin bottle, an in situ microbial filtration and fixation method (ISMIFF), and a multiple in situ nucleic acid collection (MISNAC) method to exhibit differences in the community structures that were collected at ~590–3100 m in the South China Sea (SCS). The classification and biodiversity indices of 18S rDNA Illumina sequencing reads from the V9 variation region revealed higher diversity for MISNAC DNA absorption column samples compared to others. Importantly, the relative abundance of Ciliophora (19.49%), Copepoda (23.31%), and Diplonemea (10.67%) was higher in MISNAC adsorption column samples, while Retaria (48.86%) were dominant in the MISNAC membrane samples. This indicates that MISNAC columns might collect more DNA in situ for the naked protists, while Retaria with a carbonate shell were more likely retained on the membrane. In conclusion, MISNAC is an effective method for DNA collection of deep-sea eukaryotic microorganisms and provides valuable materials for studying deep-sea microbial ecosystems. [ABSTRACT FROM AUTHOR]

Amphipods are an important group of invertebrates in marine ecosystems due to their high abundance and diversity. As an essential part of the marine food web, amphipods play a vital role in nutrient recycling and provide large amounts of detritus-derived fine-particulate organic matter for other invertebrates. Although the importance of gut microbiota and the necessity to consider them has been increasingly recognized, the gut microbial community and diversity of amphipods have not been well studied. Here, we comparatively studied the gut microbiota of diverse amphipod species inhabiting from coastal to hadopelagic zones. The results showed that four phyla, including Proteobacteria, Firmicutes, Bacteroidota, and Actinobacteriota, occupied more than 90% of the total microbes in the studied amphipod guts, with Firmicutes being dominant in the hadal amphipods. The gut microbiome of amphipods from the hadal zone displayed the lowest richness, lowest diversity, and shared few microorganisms with the surrounding seawater compared to others. Amphipods in different inhabiting regions have discriminant taxa for their gut microbial communities. Taken together, amphipod gut microbiota was affected by both biological and abiotic factors, yet these factors are not independent. This article provides us with a further understanding of the structure and characteristics of the gut microbiota of invertebrate organisms. [ABSTRACT FROM AUTHOR]

Facial fracture management dates as early as Hippocratic era. Comminuted mandibular fractures are one of the challenging clinical condition requiring high surgical expertise to achieve a good functional and esthetic outcome. In presence of infection and other facial fractures managing comminuted mandibular fracture becomes more challenging.Here we present a case of grossly comminuted and infected mandibular fracture with delayed presentation managed by using maxillary distractor as stabilizing agent. Using a maxillary distractor for managing a fractured mandible has been seldom reported in literature. Current case report gives idea to practicing clinician about the possibility of treatment beyond the established principles. Keywords: Mandibular fracture, Maxillary distractor, Infection