Explicit discrimination and ingroup favoritism, but no implicit biases in hypothetical triage decisions during COVID-19.

Disturbingly realistic triage scenarios during the COVID-19 pandemic provide an opportunity for studying discrimination in moral reasoning. Biases and favoritism do not need to be explicit and overt, but can remain implicit and covert. In addition to assessing laypeople's propensity for engaging in overt discrimination, the present study examines whether they reveal implicit biases through seemingly fair random allocations. We present a cross-sectional online study comprising 6 timepoints and a total of 2296 participants. Each individual evaluated 19 hypothetical scenarios that provide an allocation dilemma between two patients who are in need of ventilation and differ only in one focal feature. Participants could either allocate the last ventilator to a patient, or opt for random allocation to express impartiality. Overall, participants exhibited clear biases for the patient who was expected to be favored based on health factors, previous ethical or caretaking behaviors, and in-group favoritism. If one patient had been pre-allocated care, a higher probability of keeping the ventilator for the favored patient indicates persistent favoritism. Surprisingly, the absence of an asymmetry in random allocations indicates the absence of covert discrimination. Our results demonstrate that laypeople's hypothetical triage decisions discriminate overtly and show explicit biases.

How to convince the vaccine-hesitant? An ease-of-access nudge, but not risk-related information increased Covid vaccination-related behaviors in the unvaccinated.

In this study, we contrast how different benefit and harm information formats and the presence or absence of an ease-of-access nudge may facilitate COVID vaccination uptake for a sample of 620 unvaccinated Dutch adults at a timepoint when the vaccine had been widely available for more than a month. Using a 2 × 2 between-subjects factorial design, we varied the information format on mRNA COVID vaccination statistics (generic text vs. facts box) and an affirmative nudge emphasizing the ease of making a vaccination appointment (absent vs. present). We assessed the acceptance of the vaccination information provided, perceptions on the vaccination, and whether participants directly visited a COVID vaccination appointment website. Whereas the facts box did not significantly affect participants' information acceptance, vaccination attitudes, intentions, and link clicking, the affirmative nudge alongside an online link systematically increased the likelihood of clicking on the link to make a vaccination appointment. A verbal nudge emphasizing the ease of vaccine accessibility is more likely to increase vaccination uptake in an unvaccinated population than informational campaigns on vaccine effectiveness.

Task-Based Visual Interactive Modeling: Decision Trees and Rule-Based Classifiers.

Visual analytics enables the coupling of machine learning models and humans in a tightly integrated workflow, addressing various analysis tasks. Each task poses distinct demands to analysts and decision-makers. In this survey, we focus on one canonical technique for rule-based classification, namely decision tree classifiers. We provide an overview of available visualizations for decision trees with a focus on how visualizations differ with respect to 16 tasks. Further, we investigate the types of visual designs employed, and the quality measures presented. We find that (i) interactive visual analytics systems for classifier development offer a variety of visual designs, (ii) utilization tasks are sparsely covered, (iii) beyond classifier development, node-link diagrams are omnipresent, (iv) even systems designed for machine learning experts rarely feature visual representations of quality measures other than accuracy. In conclusion, we see a potential for integrating algorithmic techniques, mathematical quality measures, and tailored interactive visualizations to enable human experts to utilize their knowledge more effectively.

Determinants of information diffusion in online communication on vaccination: The benefits of visual displays.

OBJECTIVE: Social media are an increasingly important source of information on the benefits and risks of vaccinations, but the high prevalence of misinformation provides challenges for informed vaccination decisions. It is therefore important to understand which messages are likely to diffuse online and why, and how relevant aspects-such as scientific facts on vaccination effectiveness-can be made more comprehensible and more likely to be shared. In two studies, we (i) explore which characteristics of messages on flu vaccination facilitate their diffusion in online communication, and (ii) whether visual displays (i.e., icon arrays) facilitate the comprehension and diffusion of scientific effectiveness information. METHODS: In Study 1, 208 participants each rated a random sample of 15 out of 63 messages on comprehensibility, trustworthiness, persuasiveness, familiarity, informativeness, valence, and arousal, and then reported which information they would share with subsequent study participants. In Study 2 (N = 758), we employed the same rating procedure for a selected set of 9 messages and experimentally manipulated how scientific effectiveness information was displayed. RESULTS: Study 1 illustrated that scientific effectiveness information was difficult to understand and thus did not diffuse well. Study 2 demonstrated that visual displays improved the understanding of this information, which could, in turn, increase its social impact. CONCLUSIONS: The comprehensibility of scientific information is an important prerequisite for its diffusion. Visual displays can facilitate informed vaccination decisions by rendering important information on vaccination effectiveness more transparent and increasing the willingness to share this information.

Perspectives on the 2 × 2 Matrix: Solving Semantically Distinct Problems Based on a Shared Structure of Binary Contingencies.

Cognition is both empowered and limited by representations. The matrix lens model explicates tasks that are based on frequency counts, conditional probabilities, and binary contingencies in a general fashion. Based on a structural analysis of such tasks, the model links several problems and semantic domains and provides a new perspective on representational accounts of cognition that recognizes representational isomorphs as opportunities, rather than as problems. The shared structural construct of a 2 × 2 matrix supports a set of generic tasks and semantic mappings that provide a unifying framework for understanding problems and defining scientific measures. Our model's key explanatory mechanism is the adoption of particular perspectives on a 2 × 2 matrix that categorizes the frequency counts of cases by some condition, treatment, risk, or outcome factor. By the selective steps of filtering, framing, and focusing on specific aspects, the measures used in various semantic domains negotiate distinct trade-offs between abstraction and specialization. As a consequence, the transparent communication of such measures must explicate the perspectives encapsulated in their derivation. To demonstrate the explanatory scope of our model, we use it to clarify theoretical debates on biases and facilitation effects in Bayesian reasoning and to integrate the scientific measures from various semantic domains within a unifying framework. A better understanding of problem structures, representational transparency, and the role of perspectives in the scientific process yields both theoretical insights and practical applications.

The echo in flu-vaccination echo chambers: Selective attention trumps social influence.

BACKGROUND: Online discussions may impact the willingness to get vaccinated. This experiment tests how groups of individuals with consistent and inconsistent attitudes towards flu vaccination attend to and convey information online, and how they alter their corresponding risk perceptions. METHODS: Out of 1859 MTurkers, we pre-selected 208 people with negative and 221 people with positive attitudes towards flu vaccinations into homogeneous or heterogeneous 3-link experimental diffusion chains. We assessed (i) which information about flu vaccinations participants conveyed to the subsequent link, (ii) how flu-vaccination related perceptions were altered by incoming messages, and (iii) how participants perceived incoming information. RESULTS: Participants (i) selectively conveyed attitude-consistent information, but exhibited no overall anti-vaccination bias, (ii) were reluctant to alter their flu-vaccination related perceptions in response to messages, and (iii) evaluated incoming information consistent with their prior attitudes as more convincing. DISCUSSION: Flu-vaccination related perceptions are resilient against contradictions and bias online communication. Contrary to expectations, there was no sign of amplification of anti-vaccine attitudes by online communication.

Visual Working Memory Resources Are Best Characterized as Dynamic, Quantifiable Mnemonic Traces.

Visual working memory (VWM) is a construct hypothesized to store a small amount of accurate perceptual information that can be brought to bear on a task. Much research concerns the construct's capacity and the precision of the information stored. Two prominent theories of VWM representation have emerged: slot-based and continuous-resource mechanisms. Prior modeling work suggests that a continuous resource that varies over trials with variable capacity and a potential to make localization errors best accounts for the empirical data. Questions remain regarding the variability in VWM capacity and precision. Using a novel eye-tracking paradigm, we demonstrate that VWM facilitates search and exhibits effects of fixation frequency and recency, particularly for prior targets. Whereas slot-based memory models cannot account for the human data, a novel continuous-resource model does capture the behavioral and eye tracking data, and identifies the relevant resource as item activation.

Social influence and the collective dynamics of opinion formation.

Social influence is the process by which individuals adapt their opinion, revise their beliefs, or change their behavior as a result of social interactions with other people. In our strongly interconnected society, social influence plays a prominent role in many self-organized phenomena such as herding in cultural markets, the spread of ideas and innovations, and the amplification of fears during epidemics. Yet, the mechanisms of opinion formation remain poorly understood, and existing physics-based models lack systematic empirical validation. Here, we report two controlled experiments showing how participants answering factual questions revise their initial judgments after being exposed to the opinion and confidence level of others. Based on the observation of 59 experimental subjects exposed to peer-opinion for 15 different items, we draw an influence map that describes the strength of peer influence during interactions. A simple process model derived from our observations demonstrates how opinions in a group of interacting people can converge or split over repeated interactions. In particular, we identify two major attractors of opinion: (i) the expert effect, induced by the presence of a highly confident individual in the group, and (ii) the majority effect, caused by the presence of a critical mass of laypeople sharing similar opinions. Additional simulations reveal the existence of a tipping point at which one attractor will dominate over the other, driving collective opinion in a given direction. These findings have implications for understanding the mechanisms of public opinion formation and managing conflicting situations in which self-confident and better informed minorities challenge the views of a large uninformed majority.

Melioration as rational choice: sequential decision making in uncertain environments.

Melioration-defined as choosing a lesser, local gain over a greater longer term gain-is a behavioral tendency that people and pigeons share. As such, the empirical occurrence of meliorating behavior has frequently been interpreted as evidence that the mechanisms of human choice violate the norms of economic rationality. In some environments, the relationship between actions and outcomes is known. In this case, the rationality of choice behavior can be evaluated in terms of how successfully it maximizes utility given knowledge of the environmental contingencies. In most complex environments, however, the relationship between actions and future outcomes is uncertain and must be learned from experience. When the difficulty of this learning challenge is taken into account, it is not evident that melioration represents suboptimal choice behavior. In the present article, we examine human performance in a sequential decision-making experiment that is known to induce meliorating behavior. In keeping with previous results using this paradigm, we find that the majority of participants in the experiment fail to adopt the optimal decision strategy and instead demonstrate a significant bias toward melioration. To explore the origins of this behavior, we develop a rational analysis (Anderson, 1990) of the learning problem facing individuals in uncertain decision environments. Our analysis demonstrates that an unbiased learner would adopt melioration as the optimal response strategy for maximizing long-term gain. We suggest that many documented cases of melioration can be reinterpreted not as irrational choice but rather as globally optimal choice under uncertainty.

Feedback design for the control of a dynamic multitasking system: dissociating outcome feedback from control feedback.

OBJECTIVE: We distinguish outcome feedback from control feedback to show that suboptimal performance in a dynamic multitasking system may be caused by limits inherent to the information provided rather than human resource limits. BACKGROUND: Tardast is a paradigm for investigating human multitasking behavior, complex system management, and supervisory control. Prior research attributed the suboptimal performance of Tardast operators to poor strategic task management. METHODS: We varied the nature of performance feedback in the Tardast paradigm to compare continuous, cumulative feedback (global feedback) on performance outcome with feedback limited to the most recent system state (local feedback). RESULTS: Participants in both conditions improved with practice, but those with local feedback performed better than those with global feedback. An eye gaze analysis showed increased visual attention directed toward the feedback display in the local feedback condition. CONCLUSION: Predicting performance in the control of a dynamic multitasking system requires understanding the interactions between embodied cognition, the task being performed, and characteristics of performance feedback. In the current case, at least part of what had been diagnosed as a deficit caused by limited cognitive resources has been shown to be data limited. APPLICATION: Perfect outcome feedback can provide inadequate control feedback. Instances of suboptimal performance can be alleviated by better feedback design that takes into account the temporal dynamics of the human-system interaction.

Discretionary task interleaving: heuristics for time allocation in cognitive foraging.

When participants allocated time across 2 tasks (in which they generated as many words as possible from a fixed set of letters), they made frequent switches. This allowed them to allocate more time to the more productive task (i.e., the set of letters from which more words could be generated) even though times between the last word and the switch decision ("giving-up times") were higher in the less productive task. These findings were reliable across 2 experiments using Scrabble tasks and 1 experiment using word-search puzzles. Switch decisions appeared relatively unaffected by the ease of the competing task or by explicit information about tasks' potential gain. The authors propose that switch decisions reflected a dual orientation to the experimental tasks. First, there was a sensitivity to continuous rate of return--an information-foraging orientation that produced a tendency to switch in keeping with R. F. Green's (1984) rule and a tendency to stay longer in more rewarding tasks. Second, there was a tendency to switch tasks after subgoal completion. A model combining these tendencies predicted all the reliable effects in the experimental data.