Belief polarization in a complex world: A learning theory perspective.
Ontology highlight
ABSTRACT:
SUBMITTER: Haghtalab N
PROVIDER: S-EPMC8126847 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:While affective polarization has been shown to have serious social consequences, there is little evidence regarding its effects on political attitudes and behavior such as policy preferences, voting, or political information accrual. This paper provides evidence that affective polarization impacts misinformation belief, arguing that citizens with higher levels of affective polarization are more likely to believe in-party-congruent misinformation and less likely to believe out-party-congruent misinformation. The argument is supported by data from the ANES 2020 Social Media Study and the ANES 2020 Time Series Study, which speaks to the generalizability of the relationship. Additionally, a survey experiment provides evidence that the relationship is causal. The results hold among Democrats and Republicans and are independent of the effects of partisan strength and ideological extremity. Furthermore, the relationship between affective polarization and misinformation belief is exacerbated by political sophistication rather than tempered by it, implying that education will not solve the issue. The results speak to the need for work on reducing affective polarization.
| S-EPMC9848725 | biostudies-literature
Project description:Belief-importance (belimp) theory hypothesizes that personality traits confer a propensity to perceive convergences or divergences between the belief that we can attain certain goals and the importance that we place on these goals. Belief and importance are conceptualized as two coordinates, together defining the belimp plane. We tested fundamental aspects of the theory using four different planes based on the life domains of appearance, family, financial security, and friendship as well as a global plane combining these four domains. The criteria were from the areas of personality (Big Five and trait emotional intelligence) and learning styles. Two hundred and fifty eight participants were allocated into the four quadrants of the belimp plane (Hubris, Motivation, Depression, and Apathy) according to their scores on four reliable instruments. Most hypotheses were supported by the data. Results are discussed with reference to the stability of the belimp classifications under different life domains and the relationship of the quadrants with the personality traits that are hypothesized to underpin them.
| S-EPMC4395152 | biostudies-literature
Project description: Not available
| S-EPMC10771331 | biostudies-literature
Project description:Dempster-Shafer evidence theory (D-S theory) is suitable for processing uncertain information under complex circumstances. However, how to measure the uncertainty of basic probability distribution (BPA) in D-S theory is still an open question. In this paper, a method of measuring total uncertainty based on belief interval distance is proposed. This method is directly defined in the D-S theoretical framework, without the need of converting BPA into probability distribution by Pignistic probability transformation. Thus, it avoids the loss of information. This paper analyzes the advantages and disadvantages of the previous total uncertainty of measurement, and the uncertainty measurement examples show the effectiveness of the new uncertainty measure. Finally, an information fusion method based on the new uncertainty measure is proposed. The validity and rationality of the proposed method are verified by two classification experiments from UCI data sets.
| S-EPMC8507476 | biostudies-literature
Project description:The Stochastic Becker-DeGroot-Marschak (SBDM) mechanism is a theoretically elegant way of eliciting incentive-compatible beliefs under a variety of risk preferences. However, the mechanism is complex and there is concern that some participants may misunderstand its incentive properties. We use a two-part design to evaluate the relationship between participants' probabilistic reasoning skills, task complexity, and belief elicitation. We first identify participants whose decision-making is consistent and inconsistent with probabilistic reasoning using a task in which non-Bayesian modes of decision-making lead to violations of stochastic dominance. We then elicit participants' beliefs in both easy and hard decision problems. Relative to Introspection, there is less variation in belief errors between easy and hard problems in the SBDM mechanism. However, there is a greater difference in belief errors between consistent and inconsistent participants. These results suggest that while the SBDM mechanism encourages individuals to think more carefully about beliefs, it is more sensitive to heterogeneity in probabilistic reasoning. In a follow-up experiment, we also identify participants with high and low fluid intelligence with a Raven task, and high and low proclivities for cognitive effort using an extended Cognitive Reflection Test. Although performance on these tasks strongly predict errors in both the SBDM mechanism and Introspection, there is no significant interaction effect between the elicitation mechanism and either ability or effort. Our results suggest that mechanism complexity is an important consideration when using elicitation mechanisms, and that participants' probabilistic reasoning is an important consideration when interpreting elicited beliefs.
| S-EPMC8175444 | biostudies-literature
Project description:Mathematical models provide a rational basis to inform how, where and when to control disease. Assuming an accurate spatially explicit simulation model can be fitted to spread data, it is straightforward to use it to test the performance of a range of management strategies. However, the typical complexity of simulation models and the vast set of possible controls mean that only a small subset of all possible strategies can ever be tested. An alternative approach-optimal control theory-allows the best control to be identified unambiguously. However, the complexity of the underpinning mathematics means that disease models used to identify this optimum must be very simple. We highlight two frameworks for bridging the gap between detailed epidemic simulations and optimal control theory: open-loop and model predictive control. Both these frameworks approximate a simulation model with a simpler model more amenable to mathematical analysis. Using an illustrative example model, we show the benefits of using feedback control, in which the approximation and control are updated as the epidemic progresses. Our work illustrates a new methodology to allow the insights of optimal control theory to inform practical disease management strategies, with the potential for application to diseases of humans, animals and plants. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.
| S-EPMC6558566 | biostudies-literature
Project description:A key challenge organisations face when transitioning to agile delivery methods is that of quickly and effectively learning new ways of working. This study posits that fundamental historical, cultural and behavioural aspects affect the transition and contribute to the poor performance of many agile implementations. In order to address such factors, this study applies a modified Activity Theory (AT) based framework to a case study agile implementation within a large public sector organisation. An activity is closely defined, and six generic activities associated with all agile implementations are identified. These are validated against the agile maturity model literature and a set of evaluation criteria of contradictions, congruences and collaboration is established. Evidence is gathered from participant interviews and the framework is used to surface learning and development obstacles and issues within an expansive learning cycle. The study argues that analysis via this modified AT framework brings original insight. Initial findings indicate that there are relatively few learning and development issues associated with the use of agile tools and techniques themselves and that most problems arise at the interface where the “changed” (more agile) delivery teams meet the organisation’s behavioural norms and practices.
| S-EPMC7251609 | biostudies-literature
Project description:Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.
| S-EPMC5661005 | biostudies-literature
Project description:To investigate whether people can implicitly learn regularities in a social context, we developed a new implicit sequence learning task combining elements from classic false belief and serial reaction time tasks. Participants learned that protagonists were offered flowers at four locations. The protagonists' beliefs concerning the flowers were true or false, depending on their orientation, respectively, toward the scene (so that the flowers could be seen) or away from it. Unbeknown to the participants, there was a fixed belief-related sequence involving three dimensions (identity of the two protagonists, true-false belief orientation held by the protagonists, and flower location as believed by the protagonists). Participants had to indicate as fast as possible where the flowers were located (Experiment 1), or how many flowers were given (Experiment 2) according to the protagonists. Experiment 1 combined perceptual and motor processes (as both the belief-related sequence and motor responses referred to location), whereas Experiment 2 unconfounded the sequence and motor responses, allowing to investigate pure perceptual implicit learning. For reasons of comparison, two non-social conditions were created in Experiment 2 by replacing the protagonists with two non-social objects-colored cameras or shapes. Results revealed significant implicit sequence learning of all belief-related dimensions in Experiment 1, and of true-false belief orientation in Experiment 2, even without a motor confound. Importantly, there were faster reaction times and stronger sequence learning effects in the social than in the non-social conditions. The present findings demonstrate for the first time that people are able to implicitly learn belief-related sequences.
| S-EPMC8032999 | biostudies-literature
Project description:To behave adaptively, animals must learn to predict future reward, or value. To do this, animals are thought to learn reward predictions using reinforcement learning. However, in contrast to classical models, animals must learn to estimate value using only incomplete state information. Previous work suggests that animals estimate value in partially observable tasks by first forming "beliefs"-optimal Bayesian estimates of the hidden states in the task. Although this is one way to solve the problem of partial observability, it is not the only way, nor is it the most computationally scalable solution in complex, real-world environments. Here we show that a recurrent neural network (RNN) can learn to estimate value directly from observations, generating reward prediction errors that resemble those observed experimentally, without any explicit objective of estimating beliefs. We integrate statistical, functional, and dynamical systems perspectives on beliefs to show that the RNN's learned representation encodes belief information, but only when the RNN's capacity is sufficiently large. These results illustrate how animals can estimate value in tasks without explicitly estimating beliefs, yielding a representation useful for systems with limited capacity.
| S-EPMC10513382 | biostudies-literature