The current information ecosystem has made it difficult to judge the veracity of information. Presumably, evaluating an informational source’s expertise aids in this judgment process. However, whether these judgements influence memory for that information is unclear. In this study, we investigate the extent to which a source's expertise influences recognition memory for explanations given by that source. Participants viewed 20 vignettes describing events and two potential explanations for each event. Each explanation was either said by a source (domain expert vs. non-expert) or given without a source. Participants were exposed to both source and no-source conditions. We investigated whether participants’ recognition of explanations were predicted by condition and source expertise. Our findings indicated that neither condition nor source expertise influenced participants' performance on the recognition task. This suggests that people either do not attend to source in their evaluations of explanations or do not encode these evaluations.

Reading comprehension research has identified sources of children’s difficulty with inference-making: lack of semantic/content knowledge and logical reasoning difficulty. NLP tools modeling semantic knowledge (e.g. BERT) can predict adult inference-making, but it is unclear whether they can predict children’s inference-making difficulty. In our ongoing study, we will examine whether our new inference difficulty metric can predict kindergarten students’ inference-making, using empirical data from a classroom intervention (ELCII). Students were given verbal information on a topic and multiple-choice questions, which require students to draw an inference from two given scaffolds. To develop this metric, we will train BERT on children’s books and ELCII content to compute an additive inference vector, the sum of the two vectorized scaffolds. The cosine distance between the additive and correct inferences may indicate inference difficulty. Results will indicate whether a probabilistic semantic space can model children’s inferences or if other components (e.g. logic) should be considered.

We investigated individuals’ representation of composite numbers (i.e., those with factors other than 1 and themselves). Some numbers are “more composite” (have more factors) than others (e.g., 72 vs. 74). We hypothesized that people mentally represent numbers in factor neighborhoods. This should result in a compositeness penalty: performance on factoring problems (e.g., x * 9 = 72) should be slower for more composite products, because retrieving the corresponding factor requires inhibiting neighboring factor pairs. We measured 50 undergraduates’ time to correctly solve factoring problems for composite products (6 to 100). We found that for well-practiced facts (i.e., the 12x12 multiplication table), performance slowed as compositeness increased, even after controlling for problem size and factor size. However, there was no compositeness penalty for “non-table” facts. These findings support our factor neighborhood hypothesis for well-practiced facts and suggest that people's representations of composite numbers are influenced by mathematics instruction (i.e., “multiplication tables”).

Research suggests that peoples learning may be influenced by individual differences in their epistemic beliefs, such asFaith in Intuition (FiN), Need for Evidence (NfE), and belief that Truth is Political(TiP). This study investigated the extentto which these epistemic beliefs influenced belief in scientific information about global warming and trust in sources.Participants read statements about global warming and rated how much they believed the information and trusted thesource. Each statement was presented with a conservative, liberal, or scientific source and framed in certain or tentativelanguage. We found that epistemic beliefs significantly interacted with source and language tentativeness. For example,those with low FiN believed certain language statements more than tentative language statements. Those with low NfEbelieved conservative sources more than scientific or liberal sources. These findings demonstrate how individuals epistemicbeliefs interact with source and language factors to influence belief and trust of scientific information.

Understanding variables and solving algebraic equations are essential to advanced mathematical thinking. Missing-operand problems (e.g., x + 3 = 5) are solvable via two strategies: 1) pattern-matching, or direct arithmetic fact retrieval(e.g., 2 + 3 = 5), and 2) algebraic symbol-manipulation, or performing the inverse operation (e.g., 5 3 = 2). U.S. undergrad-uates made speeded verifications of arithmetic sentences like 2 + 3 = 5 and 5 3 = 2. They then solved missing-operandproblems like x + 3 = 5. We decoded individual differences in strategy choice by whether speed on missing-operandproblems was better predicted by speed on verifying direct- or inverse-matched arithmetic facts. We found individualdifferences in strategy choice, although these were not significantly associated with mathematical achievement.

The traveling salesperson problem (TSP) is an NP-Hard problem that computers find difficult to solve. Humans are surprisingly good at solving the TSP, with solutions within 10% of optimal for problems with up to 100 points, constructed in time linear with the number of points. We propose that humans solve the TSP by initially clustering the points and then connecting them first within and then between clusters. In this study, 67 participants first clustered 40 stimuli and then solved them as TSPs. Strikingly, participants' TSP solutions perfectly followed their clusters for 52% of the stimuli. Further, participants' TSP solutions' were more congruent with their clusters for stimuli with statistically higher levels of clustered structure. This provides strong evidence for the clustering proposal. Random TSP solutions, however, showed no such congruence to cluster structure. These findings suggest that clustering might be a fundamental ability for reasoning about graph-theoretic algorithmic problems.

Human solutions to the Traveling Salesperson Problem (TSP) are surprisingly close to optimal and unexpectedly efficient. We posit that humans solve instances of the TSP by first clustering the points into smaller regions and then solving each cluster as a simpler TSP. Prior research has shown that participants cluster visual stimuli reliably. That is, their clustering and re-clustering of the same stimulus are similar, especially when the stimulus is relatively more clustered. In this study, participants solved the same TSP instances twice. On the second presentation, half of the instances were flipped about the horizontal and vertical axes. Participants solved the TSP reliably, with their two tours of the same instance sharing 77 percent of the same edges on average. In addition, within-participant reliability was higher for more clustered versus more dispersed instances. Our findings are consistent with the proposal that people use clustering strategies to solve the TSP.

Humans efficiently find near-optimal (i.e., near-minimum-length) tours when solving small instances of the TravelingSalesperson Problem (TSP), a problem hard for computers. We hypothesize that this is possible because they use thefollowing strategy: cluster the points, solve the smaller TSPs for each cluster, and then solve the TSP defined by theclusters. This study focused on the antecedent process of human clustering. 42 participants clustered 56 sets of 15 to 40points on two occasions. We found that human clustering is generally reliable (M Fowlkes-Mallows Index = 0.75) forall problem sizes. Reliability was higher for problems that showed statistical evidence of cluster structure versus no suchstructure, and was not affected when the problem was flipped for the second presentation. Thus, humans are sensitive tocluster structure, and clustering is a stable foundation for solving TSP instances. This sets the stage for future research onclustering-based TSP strategies.