Iranians' beliefs about COVID-19: a cross-sectional study

Background We sought to investigate people's beliefs, decision dynamics, and future consequences of the current COVID-19 pandemic. Methods The present cross-sectional study was conducted from January 10th to April 30th, 2020. The data collection tool was a researcher-made electronic questionnaire that was designed in Porsline.com. The test-retest reliability of the questionnaire was good, and consisted of three sections: introduction, demographic characteristics, and belief questions about COVID-19. Results In 17 of the 43 beliefs, more than two-thirds of the subjects chose the "correct belief", and less than one-third chose the "I have no idea" or "I disagree" options. There was a significant correlation between age, gender, education, residential area, occupational status and correct belief about COVID-19. Conclusions Accurate knowledge of policymakers, managers, health care workers, and the public, beliefs about COVID-19 is important in promoting community health and disease prevention.

As Within, so Without; As Above, so Below: Common Mechanisms Can Support Between- and Within-Trial Learning Dynamics

Two fundamental difficulties when learning is deciding 1) what information is relevant, and 2) when to use it. To overcome these difficulties, humans continuously make choices about which dimensions of information to selectively attend to, and monitor their relevance to the current goal. Although previous theories have specified how observers learn to attend to relevant dimensions over time, those theories have largely remained silent about how attention should be allocated on a within-trial basis, which dimensions of information should be sampled, and how the temporal ordering of information sampling influences learning. Here, we use the Adaptive Attention Representation Model (AARM) to demonstrate that a common set of mechanisms can be used to specify: 1) how the distribution of attention is updated between trials over the course of learning; and 2) how attention dynamically shifts among dimensions within-trial. We validate or proposed set of mechanisms by comparing AARM’s predictions to observed behavior across five case studies, which collectively encompass different theoretical aspects of selective attention. Importantly, we use both eye-tracking and choice response data to provide a stringent test of how attention and decision processes dynamically interact. Specifically, how does attention to selected stimulus dimensions gives rise to decision dynamics, and in turn, how do decision dynamics influence our continuous choices about which dimensions to attend to via gaze fixations?

Dinámica de participación en esquemas de pago por servicios ambientales urbanos: análisis de la intención

This work aims to understand landowners’ participation decision dynamics in an urban Payment for Environmental Services scheme under different payment scenarios. Based on the Theory of Planned Behavior, we formulated a simulation model at the individual level with interaction graphs, conformed by diverse agents in attributes, and parameterized with data of a Colombian Andes zone. The results confirm the relevance of the differentiation processes in the payment offer. This work constitutes the first approach to simulate participation in PES schemes in an urban context.

Dynamic decision policy reconfiguration under outcome uncertainty

Humans and other mammals flexibly select actions under noisy and unstable conditions. To shed light on the mechanism driving this flexibility, we evaluated how the underlying decision policy evolves when humans change their minds about the most rewarding action. Participants performed a dynamic variant of the two-armed bandit task that manipulated the certainty in relative reward probabilities (conflict) and the reliability of action-outcome contingencies (volatility). We found that conflict and volatility contributed to shifts in exploratory states by changing both the rate of evidence accumulation (drift rate) and the amount of evidence needed to make a decision (boundary height). Following a switch in the optimal choice, the drift rate and the boundary height reduce, allowing variability in the accumulation process to predominate action selection, leading to a fast exploratory state. These changes facilitate the discovery of the new optimal choice, with a quick recovery of the boundary height to baseline. In parallel, the drift rate gradually returns to its asymptotic value as the belief in the value of the optimal choice stabilizes. Together, these decision dynamics suggest that, in the context of volatile two-choice decisions, humans adopt a combined information-threshold and drift rate mechanism in response to environmental changes. Unlike previous observations, we found no evidence that fluctuations in norepinephrine, as measured by pupillometry, associated with this adaptive shift toward an exploratory policy. We conclude that the multifaceted processes underlying a decision can rapidly reconfigure to adapt action selection policy under multiple forms of environmental uncertainty.

Auditory information enhances post-sensory visual evidence during rapid multisensory decision-making

Despite recent progress in understanding multisensory decision-making, a conclusive mechanistic account of how the brain translates the relevant evidence into a decision is lacking. Specifically, it remains unclear whether perceptual improvements during rapid multisensory decisions are best explained by sensory (i.e., ‘Early’) processing benefits or post-sensory (i.e., ‘Late’) changes in decision dynamics. Here, we employ a well-established visual object categorisation task in which early sensory and post-sensory decision evidence can be dissociated using multivariate pattern analysis of the electroencephalogram (EEG). We capitalize on these distinct neural components to identify when and how complementary auditory information influences the encoding of decision-relevant visual evidence in a multisensory context. We show that it is primarily the post-sensory, rather than the early sensory, EEG component amplitudes that are being amplified during rapid audiovisual decision-making. Using a neurally informed drift diffusion model we demonstrate that a multisensory behavioral improvement in accuracy arises from an enhanced quality of the relevant decision evidence, as captured by the post-sensory EEG component, consistent with the emergence of multisensory evidence in higher-order brain areas.

Adaptive Circuit Dynamics Across Human Cortex During Evidence Accumulation in Changing Environments

Many decisions under uncertainty entail the temporal accumulation of evidence that informs about the state of the environment. When environments are subject to hidden changes in their state, maximizing accuracy and reward requires non-linear accumulation of the evidence. How this adaptive, non-linear computation is realized in the brain is unknown. We analyzed human behavior and cortical population activity (measured with magnetoencephalography) recorded during visual evidence accumulation in a changing environment. Behavior and decision-related activity in cortical regions involved in action planning exhibited hallmarks of adaptive evidence accumulation, which could also be implemented by a recurrent cortical microcircuit. Decision dynamics in action-encoding parietal and frontal regions were mirrored in a frequency-specific modulation of the state of visual cortex that depended on pupil-linked arousal and the expected probability of change. These findings link normative decision computations to recurrent cortical circuit dynamics and highlight the adaptive nature of decision-related feedback to sensory cortex.