Andrew L. Cohen

Trust plays a critical role in visual data communication and decision-making, yet existing visualization research employs varied trust measures, making it challenging to compare and synthesize findings across studies. In this work, we first took a bottom-up, data-driven approach to understand what visualization readers mean when they say they “trust” a visualization. We compiled and adapted a broad set of trust-related statements from existing inventories and collected responses to visualizations with varying degrees of trustworthiness. Through exploratory factor analysis, we derived an operational definition of trust in visualizations. Our findings indicate that people perceive a trustworthy visualization as one that presents credible information and is comprehensible and usable. Building on this insight, we developed an eight-item inventory: four core items measuring trust in visualizations and four optional items controlling for individual differences in baseline trust tendency. We established the inventory’s internal consistency reliability using McDonald’s omega, confirmed its content validity by demonstrating alignment with theoretically-grounded trust dimensions, and validated its criterion validity through two trust games with real-world stakes. Finally, we illustrate how this standardized inventory can be applied across diverse visualization research contexts. Utilizing our inventory, future research can examine how design choices, tasks, and domains influence trust, and how to foster appropriate trusting behavior in human-data interactions.

Changes in temporal context have been shown to impact episodic memory of the 'boundary' items involved in creating this temporal context. However, in most prior studies, changes in temporal context have been operationalized explicitly, typically through perceptually obvious visual or auditory stimuli that form a boundary between the previous and current context. In this work, we explore how recognition of items is impacted when temporal context change is operationalized implicitly through the order of presentation during the study phase. 57 participants, were exposed to a rotation judgment cover task for 15 randomly generated polygon items. Around half of the participants were presented with these items in a random order. The other half were presented with items such that 6 items formed ‘boundaries’ that led in and out of 3 temporal clusters. Similar to findings where boundaries are explicitly operationalized, we show that implicit boundaries are also better remembered than non-boundary items.

Background: Borderline personality disorder (BPD) has been associated with decision-making deficits, yet such deficits may be context dependent, particularly emotional state and social context. Reinforcement learning models offer an avenue to pinpoint decision-making impairments. The current study used reinforcement learning models to examine whether feedback type (social vs. nonsocial) or emotional state (neutral vs. negative) influence the association between BPD and decision making. Method: Adults with BPD (N = 37), subthreshold BPD (N = 28), and without BPD (N = 66) completed a diagnostic interview and a computerized learning task after neutral and negative emotion inductions. We examined accuracy, learning rate, and exploration. Results: We conducted linear models to examine the association between BPD criteria, feedback type, and emotional state on learning parameters and learning accuracy. We found that the negative emotion condition was associated with greater exploration, particularly for those with elevated BPD features. Furthermore, elevated BPD features were associated with impaired accuracy when aiming to avoid loss. A 3-way interaction between BPD, emotion, and feedback indicated that, for people with higher BPD features, learning performance was further impaired when receiving social feedback in the negative emotion condition. Limitations: Several limitations warrant mention, including a relatively homogenous sample, possible co-occurring diagnoses, and methodological consideration with the learning task. Conclusions: The present study underscored the link between BPD and learning impairments. Amplified learning alterations under negative social contexts has important implications for identifying optimal venues to teach new skills (of relevance to treatment) for those with BPD.

We present a method for measuring the efficacy of eyewitness identification procedures by applying fundamental principles of information theory. The resulting measure evaluates the expected information gain (EIG) for an identification attempt, a single value that summarizes an identification procedure's overall potential for reducing uncertainty about guilt or innocence across all possible witness responses. In a series of demonstrations, we show that EIG often disagrees with existing measures (e.g., diagnosticity ratios or area under the receiver operating characteristic) about the relative effectiveness of different identification procedures. Each demonstration is designed to highlight key distinctions between existing measures and EIG. An overarching theme is that EIG provides a complete measure of evidentiary value, in the sense that it factors in all aspects of identification performance. Collectively, these demonstrations show that EIG has substantial potential to inspire new discoveries in eyewitness research and provide a new perspective on policy recommendations for the use of identifications in real investigations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Recognition memory researchers are extending Receiver Operating Characteristic (ROC) methods to complex tasks that require new analysis tools and a deeper theoretical understanding of the principles of ROC construction and interpretation. ROCs from an eyewitness lineup task are a great example of this trend. The current paper establishes some key theoretical principles relevant to lineup ROC research and applies these principles to establish a set of guidelines for memory ROCs. Theoretical demonstrations show that creating ROCs based on likelihood or diagnosticity ratios (DRs) provides a general method for tasks that are not amendable to traditional methods. The demonstrations also establish an alternative interpretation for area under the curve (AUC) for an ROC that connects the observed operating points with straight lines, reinforcing the value of this measure for eyewitness research and revealing other potential applications. Although DR-based ROCs are theoretically justified, parameter recovery simulations show that creating lineup ROCs with observed sample DRs leads to biased estimation of AUC. We propose a new ROC method to correct for this bias, whereby the observed data are fit with a decision model, and ROCs are constructed based on the DRs from the model. Finally, we show how a DR-based ROC can be used to generate a full theoretical ROC for lineup tasks, and note that these theoretical curves reveal a previously unappreciated property of lineup decision making: changing response conservativeness changes the evidence distributions that determine lineup responses.

We present a method for measuring the efficacy of eyewitness identification procedures by applying fundamental principles of information theory. The resulting measure evaluates the Expected Information Gain (EIG) for an identification attempt, a single value that summarizes an identification procedure’s overall potential for reducing uncertainty about guilt or innocence across all possible witness responses. In a series of demonstrations, we show that EIG often disagrees with existing measures (e.g., diagnosticity ratios or area under the ROC) about the relative effectiveness of different identification procedures. Each demonstration is designed to highlight key distinctions between existing measures and EIG. An overarching theme is that EIG provides a complete measure of evidentiary value, in the sense that it factors in all aspects of identification performance. Collectively, these demonstrations show that EIG has substantial potential to inspire new discoveries in eyewitness research and provide a new perspective on policy recommendations for the use of identifications in real investigations.

We present a method for measuring the efficacy of eyewitness identification procedures by applying fundamental principles of information theory. The resulting measure evaluates the Expected Information Gain (EIG) for an identification attempt, a single value that summarizes an identification procedure’s overall potential for reducing uncertainty about guilt or innocence across all possible witness responses. In a series of theoretical demonstrations, we show that EIG often disagrees with existing measures (e.g., diagnosticity ratios or area under the ROC) about the relative effectiveness of different identification procedures. Each demonstration is designed to highlight “blind spots” of the existing measures as a contrast to EIG, which considers every factor relevant to a procedure’s potential for decreasing uncertainty about guilt or innocence. Collectively, these demonstrations show that EIG has substantial potential to inspire new discoveries in eyewitness research. For research designed to identify procedures that will be most effective in criminal investigations, EIG supersedes all other measures, on both theoretical and practical grounds.

We examine whether judgments of posterior probabilities in Bayesian reasoning problems are affected by reasoners' beliefs about corresponding real-world probabilities. In an internet-based task, participants were asked to determine the probability that a hypothesis is true (posterior probability, e.g., a person has a disease, given a positive medical test) based on relevant probabilities (e.g., that any person has the disease and the true and false positive rates of the test). We varied whether the correct posterior probability was close to, or far from, independent intuitive estimates of the corresponding 'real-world' probability. Responses were substantially closer to the correct posterior when this value was close to the intuitive estimate. A model in which the response is a weighted sum of the intuitive estimate and an additive combination of the probabilities provides an excellent account of the results.

The goal of the current research is to use the experimental methods and mathematical models of the information integration framework to precisely determine how category and feature information are combined when making an inference. In three experiments, participants were trained on a probabilistic relationship between a category label and the presence of a property and, separately, the relationship between a visual feature and the presence of the property. Participants were then shown the category label alone, the feature alone, or both in combination, and asked to infer the presence or absence of the property. Two information integration models, the fuzzy logical model of perception and the linear integration model, were fit to the data. The modeling results show that participants were non-Bayesian in their combination of the two sources of information, showed diversity in the relative weight placed on category information, and consistently used each source of information to the extent to which it was known.