Leveraging people's proficiency in extracting summary statistics from ensembles, we conducted two studies in which we presented rating information from consumer feedback systems through color-coded icon arrays. The investigation aims to explore how different icon array arrangements (ascending, descending, random) influence decision making and average estimation across varying levels of ensemble means (average ratings) and ensemble sizes (review volume). Our results revealed four key insights: 1) Preferences for rating variance differed, particularly at the extremes of the average rating spectrum when ensembled were dominated by one or two rating categories. 2) Structured information yielded greater certainty in responses, with confidence increasing when the task setup aligned with task goals. 3) Ensemble size prompted individuals to adapt strategies based on contextual needs. 4) Unstructured presentations led to higher estimation accuracy, suggesting that a lack of structure may encourage heightened processing effort.

applied contexts, focusing on online shopping with consumer feedback. The richness of crowdsourced information and the growth of e-commerce highlight the importance of understanding how different consumer feedback signals are weighted across product types. Participants allocated 100 points among eight common consumer feedback signals for products differing in emotional, commitment, and monetary values. A pilot study confirmed the product choice validity, assessing a separate group of participants' inclination to purchase target products for emotional needs and long-term use. Results reveal an increased reliance on crowdsourced information weight heightened decision stakes. While the overall signal importance ranking remains consistent across products, negative information gains significance, and average ratings diminish in importance for high-stakes decisions. The findings carry theoretical and practical implications, shedding light on the nuanced decision dynamics in applied contexts.

When purchasing products online, often two products may have similar mean ratings and numbers of reviews, but suchapparent similarities may hide important differences. Sometimes, the distribution of star ratings is also available to decisionmakers in addition to these two attributes. Will the decision still be as undifferentiated as before or will the distributionsof stars engender a preference towards one of the products? To answer this question, the current study manipulated thedisplayed variability of ratings for choices with the same average rating. The behavioral studies showed that participantsexhibited distinctive choice patterns when the distribution of ratings was provided even when the average rating andtotal number of reviews were the same between two compared products. A utility-based cognitive model was thereforedeveloped to identify the underlying mechanism as to why people chose the way they did.

Despite its omnipresence in this information-laden society, statistics is hard. The present study explored the applicability of a grounded cognition approach to learning basic statistical concepts. Participants in 2 experiments interacted with perceptually rich computer simulations designed to foster understanding of the relations between fundamental statistical concepts and to promote the ability to reason with statistics. During training, participants were asked to estimate the probability of two samples coming from the same population, with sample size, variability, and difference between means independently manipulated. The amount of learning during training was measured by the difference between participants’ confidence judgments and those of an Ideal Observer. The amount of transfer was assessed by the increase in accuracy from a pretest to a posttest. Learning and transfer were observed when tailored guidance was given along with the perceptually salient properties. Implications of our quantitative measures of human sensitivity to statistical concepts were discussed.

Building upon previous literature that demonstrates the effect of average rating and number of reviews on consumerbehavior, the present study begged the question of how rating distributions influence perception of product quality at theindividual consumer level. To address this question, we presented a wide range of rating variances for each average ratingfrom 1.1 to 4.9 in a 5-star system and asked participants to indicate their perceived quality of each product on a scale of1 10. The behavioral study revealed an interaction between average rating and rating variance: Among all products of thesame average rating, when the average rating was low (below 2.5), people judged less-variable products to be of higherquality, whereas when the average rating was high (above 2.5), people judged more-variable products to be of higherquality. A utility-based cognitive model was developed to identify the underlying mechanisms of this reversed preference.

We investigated whether, and in what, ways people use visual structures to evaluate mathematical expressions. We also explored the relationship between strategy use and other common measures in mathematics education. Participants organized long sum/products when visual structure was available in algebraic expressions. Two experiments showed a similar pattern: One group of participants primarily calculated from left to right, or combined identical numbers together. A second group calculated adjacent pairs. A third group tended to group terms which either produced easy sums (e.g., 6+4), or participated in a global structure. These different strategies were associated with different levels of success on the task, and, in Experiment 2, with differential math anxiety and mathematical skill. Specifically, problem solvers with lower math anxiety and higher math ability tend to group by chunks and easy calculation. These results identify an important role for the perception of coherent structure and pattern identification in mathematical reasoning.

It may seem pointless to compare two products with the exact same average rating and total number of reviews without other review information. Now imagine a scenario in which the distribution of star ratings is also available to decision makers in addition to these two attributes. Will the decision still be uncertain as it is before or the distributions of stars will engender a preference towards one of the products? To answer this question, the current study used variability of star ratings as an approximation of a product’s distribution. The behavioral studies showed that participants exhibited distinctive choice patterns when the distribution of ratings was provided even when the average rating and total number of reviews were the same between two products involved in a comparison. A utility-based cognitive model was therefore developed to identify the underlying mechanism as to why people chose the way they did.

Optimizing the study of vocabulary words for high-stakes tests such as the SAT or GRE prep can beproblematic, given that many words are semantically,orthographically, or phonologically confusable. Companiesmarketing test preparation programs make multiplerecommendations, such as clustering words on some basis,but little research has been carried out to examine what thatbasis should be. Across two experiments, we compare theefficacy of different types of clustering—categorical,alphabetical, and confusable--for the learning ofsemantically related words (Experiment 1) and confusablewords (Experiment 2). We demonstrate that, in contrastto most learners’ intuitions, an alphabetical sequence yieldssuperior learning.