Trust in Institutions, Not in Political Leaders, Determines Compliance in COVID-19 Prevention Measures within Societies across the Globe.

A core assumption often heard in public health discourse is that increasing trust in national political leaders is essential for securing public health compliance during crises such as the COVID-19 pandemic (2019-ongoing). However, studies of national government trust are typically too coarse-grained to differentiate between trust in institutions versus more interpersonal trust in political leaders. Here, we present multiscale trust measurements for twelve countries and territories across the West, Oceania and East Asia. These trust results were used to identify which specific domains of government and social trust were most crucial for securing public health compliance (frequency of mask wearing and social distancing) and understanding the reasons for following health measures (belief in effectiveness of public health measures). Through the use of linear regression and structural equation modeling, our cross-cultural survey-based analysis (N = 3369 subjects) revealed that higher trust in national and local public health institutions was a universally consistent predictor of public health compliance, while trust in national political leaders was not predictive of compliance across cultures and geographical regions. Institutional trust was mediated by multiple types of transparency, including providing rationale, securing public feedback, and honestly expressing uncertainty. These results highlight the importance of distinguishing between components of government trust, to better understand which entities the public gives the most attention to during crises.

Confidence guides priority between forthcoming tasks.

Humans can estimate confidence in their decisions, and there is increasing interest on how this feeling of confidence regulates future behavior. Here, we investigate whether confidence in a perceptual task affects prioritizing future trials of that task, independently of task performance. To do so, we experimentally dissociated confidence from performance. Participants judged whether an array of differently colored circles was closer to blue or red, and we manipulated the mean and variability of the circles' colors across the array. We first familiarized participants with a low mean low variability condition and a high mean high variability condition, which were matched in performance despite participants being more confident in the former. Then we made participants decide in which order to complete forthcoming trials for both conditions. Crucially, prioritizing one condition was associated with being more confident in that condition compared to the other. This relationship was observed both across participants, by correlating inter-individual heterogeneity in prioritization and in confidence, and within participants, by assessing how changes in confidence with accuracy, condition and response times could predict prioritization choices. Our results suggest that confidence, above and beyond performance, guides prioritization between forthcoming tasks, strengthening the evidence for its role in regulating behavior.

Confidence can be automatically integrated across two visual decisions.

Humans can estimate their confidence in making correct decisions, but these confidence judgments are biased by their other estimations, an effect known as confidence leak. However, it remains unclear whether this effect arises automatically. Here, we address this issue by having participants make two visual decisions and give confidence ratings for one or for both decisions within each trial. Using the well-known interaction between task difficulty and response accuracy as a proxy for confidence, we found that confidence ratings for one decision were greater when the other decision was also associated with greater confidence, even when the latter was not explicitly rated. For one of the two tasks, this confidence leak also occurred when participants knew in advance that no confidence rating would be required for the other task. Our results support the idea that confidence can be automatically integrated across decisions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Confidence as a Priority Signal.

When dealing with multiple tasks, we must establish the order in which to tackle them. In multiple experiments, including a preregistered replication (Ns = 16-105), we found that confidence, or the subjective accuracy of decisions, acts as a priority signal, both when ordering responses about tasks already completed or ordering tasks yet to be completed. Specifically, when participants categorized perceptual stimuli along two dimensions, they tended to first give the decision associated with higher confidence. When participants selected which of two tasks they wanted to perform first, they were slightly biased toward the task associated with higher confidence. This finding extends to nonperceptual decisions (mental calculation) and cannot be reduced to effects of task difficulty, response accuracy, response availability, or implicit demands. Our results thus support the role of confidence as a priority signal, thereby suggesting a new way in which it may regulate human behavior.

The Confidence Database.

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects.

Decoupling sensory from decisional choice biases in perceptual decision making.

The contribution of sensory and decisional processes to perceptual decision making is still unclear, even in simple perceptual tasks. When decision makers need to select an action from a set of balanced alternatives, any tendency to choose one alternative more often-choice bias-is consistent with a bias in the sensory evidence, but also with a preference to select that alternative independently of the sensory evidence. To decouple sensory from decisional biases, here we asked humans to perform a simple perceptual discrimination task with two symmetric alternatives under two different task instructions. The instructions varied the response mapping between perception and the category of the alternatives. We found that from 32 participants, 30 exhibited sensory biases and 15 decisional biases. The decisional biases were consistent with a criterion change in a simple signal detection theory model. Perceptual decision making, thus, even in simple scenarios, is affected by sensory and decisional choice biases.

Number line estimation in highly math-anxious individuals.

In this study, we aimed to investigate the difficulties highly math-anxious individuals (HMA) may face when having to estimate a number's position in a number line task. Twenty-four HMA and 24 low math-anxiety (LMA) individuals were presented with four lines with endpoints 0-100, 0-1,000, 0-100,000, and 267-367 on a computer monitor on which they had to mark the correct position of target numbers using the mouse. Although no differences were found between groups in the frequency of their best-fit model, which was linear for all lines, the analysis of slopes and intercepts for the linear model showed that the two groups differed in performance on the less familiar lines (267-367 and 0-100,000). Lower values for the slope and higher values for the intercept were found in the HMA group, suggesting that they tended to overestimate small numbers and underestimate large numbers on these non-familiar lines. Percentage absolute error analyses confirmed that HMA individuals were less accurate than their LMA counterparts on these lines, although no group differences were found in response time. These results indicate that math anxiety is related to worse performance only in the less familiar and more difficult number line tasks. Therefore, our data challenge the idea that HMA individuals might have less precise numerical representations and support the anxiety-complexity effect posited by Ashcraft and colleagues.

An object-tracking model that combines position and speed explains spatial and temporal responses in a timing task.

Many tasks require synchronizing our actions with particular moments along the path of moving targets. However, it is controversial whether we base these actions on spatial or temporal information, and whether using either can enhance our performance. We addressed these questions with a coincidence timing task. A target varying in speed and motion duration approached a goal. Participants stopped the target and were rewarded according to its proximity to the goal. Results showed larger reward for responses temporally (rather than spatially) equidistant to the goal across speeds, and this pattern was promoted by longer motion durations. We used a Kalman filter to simulate time and space-based responses, where modeled speed uncertainty depended on motion duration and positional uncertainty on target speed. The comparison between simulated and observed responses revealed that a single position-tracking mechanism could account for both spatial and temporal patterns, providing a unified computational explanation.

Reasoning and choice in the Monty Hall Dilemma (MHD): implications for improving Bayesian reasoning.

The Monty Hall Dilemma (MHD) is a two-step decision problem involving counterintuitive conditional probabilities. The first choice is made among three equally probable options, whereas the second choice takes place after the elimination of one of the non-selected options which does not hide the prize. Differing from most Bayesian problems, statistical information in the MHD has to be inferred, either by learning outcome probabilities or by reasoning from the presented sequence of events. This often leads to suboptimal decisions and erroneous probability judgments. Specifically, decision makers commonly develop a wrong intuition that final probabilities are equally distributed, together with a preference for their first choice. Several studies have shown that repeated practice enhances sensitivity to the different reward probabilities, but does not facilitate correct Bayesian reasoning. However, modest improvements in probability judgments have been observed after guided explanations. To explain these dissociations, the present review focuses on two types of causes producing the observed biases: Emotional-based choice biases and cognitive limitations in understanding probabilistic information. Among the latter, we identify a crucial cause for the universal difficulty in overcoming the equiprobability illusion: Incomplete representation of prior and conditional probabilities. We conclude that repeated practice and/or high incentives can be effective for overcoming choice biases, but promoting an adequate partitioning of possibilities seems to be necessary for overcoming cognitive illusions and improving Bayesian reasoning.