Social norms can be an effective way to promote public health and encourage healthy behaviors among individuals. The global COVID-19 pandemic has prompted health officials to call for new behavioral norms to help prevent the disease’s spread, for example “social distancing” measures. Yet whether people actually intend to engage in these behaviors (behavioral intention) may depend on both whether they personally find them effective (individual beliefs), and whether they think others will engage in them too (perceived norms). In this study we tested three hypotheses on the relationships between individual beliefs, perceived norms, and behavioral intentions related to COVID-19 preventive health behaviors. We predicted: H1) that perceptions of others’ beliefs about the importance and impact of preventive behaviors (perceived norms) would be lower than individuals’ own beliefs (i.e., a pluralistic ignorance effect); H2) that perceived norms and individual beliefs would each predict behavioral intentions; and H3) that the difference between perceived norms and individual beliefs would uniquely predict behavioral intentions. We observed large self-other differences in beliefs about preventive health behaviors (support for H1). However, only individuals' own beliefs were associated with behavioral intention, not perceived norms or the self-other gap (no support for H2 or H3). In short, individuals did not differ in their preventive behavioral intentions based on the differences (or their lack thereof) between perceived social norms and their personal beliefs. Participants who strongly believed that preventive behaviors are important ignored normative pressures from average Americans, who they perceived as less concerned than themselves. Together, these findings highlight the different roles personal beliefs and perceived norms might play in forming behavioral intentions.
In this article we focus on interpreting multidimensional scaling (MDS) configurations using facet theory. The facet theory approach is attempting to partition a representational space, facet by facet, into regions with certain simplifying constraints on the regions’ boundaries (e.g., concentric circular sub-spaces). A long-standing problem has been the lack of computational methods for optimal facet-based partitioning. We propose using support vector machines (SVM) to perform this task. SVM is highly attractive for this purpose as they allow for linear as well as nonlinear classification boundaries in any dimensionality. Using various classical examples from the facet theory literature we elaborate on the combined use of MDS and SVM for facet-based partitioning. Different types of MDS are discussed, and options for SVM kernel specification, tuning, and performance evaluation are illustrated.
Abstract Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural ‘score’ to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.
What aspects of attitudes toward social out-groups predict behavior toward those groups? Social attitudes research typically links favorability toward social out-groups to engagement in intergroup behaviors: the stronger your (un)favorability toward a group, the more costly behaviors you should engage in, concerning that group. But many people make strongly-valenced favorability statements about minoritized out-groups without engaging in corresponding costly actions. We investigate subjective attitude importance as a competing predictor of costly intergroup behaviors. When white respondents rate their attitudes toward minoritized racial out-groups as more personally important to them, they are more likely to give up real money to: (1) prevent biased attitude signaling, (2) preserve their reputations, and (3) increase charitable donation to an out-group-supporting nonprofit. Finally, we find that attitude measurements that centered personal relevance better explained variance in costly behavior than generic favorability or importance measurements. These results indicate that measuring subjective attitude importance, with emphasis on the personal relevance of the attitude, improves prediction of supportive and discriminatory behaviors toward minoritized racial out-groups.
Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural 'score' to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.
Abstract Mental models provide a cognitive framework allowing for spatially organizing information while reasoning about the world. However, transitive reasoning studies often rely on perception of stimuli that contain visible spatial features, allowing the possibility that associated neural representations are specific to inherently spatial content. Here, we test the hypothesis that neural representations of mental models generated through transitive reasoning rely on a frontoparietal network irrespective of the spatial nature of the stimulus content. Content within three models ranges from expressly visuospatial to abstract. All mental models participants generated were based on inferred relationships never directly observed. Here, using multivariate representational similarity analysis, we show that patterns representative of mental models were revealed in both superior parietal lobule and anterior prefrontal cortex and converged across stimulus types. These results support the conclusion that, independent of content, transitive reasoning using mental models relies on neural mechanisms associated with spatial cognition.
Mental models provide a cognitive framework that allows for organizing and manipulating information while reasoning about the world. Deductive reasoning with mental models is supported by a frontoparietal network, including regions of anterior prefrontal cortex associated with relational integration, and superior parietal regions associated with spatial cognition. Based in part on this evidence, mental models are often considered spatial representations. However, studies of transitive reasoning often rely on direct perception of stimuli that are inherently spatial in content, leaving open the possibility that the associated neural representations are specific to content that is inherently spatial or concretely perceivable. Here we directly test the hypothesis that the neural representation of mental models generated through transitive reasoning relies on this same frontoparietal network irrespective of the spatial nature of the stimulus content. Specifically, participants generated three distinct mental models through a transitive reasoning task. The content within the three models ranges from expressly visuospatial to entirely abstract. Moreover, all of the mental models participants generated were based on inferred relationships that were never directly observed. Multivariate representational similarity analysis was used to assess the correlation between these to-be-learned mental models and the patterns of neural activity elicited while viewing individual stimuli after training. Patterns representative of the mental models were revealed in both superior parietal lobule and anterior prefrontal cortex. Notably, these neural patterns were highly convergent across stimulus types. These results support the conclusion that, independent of content, relational reasoning using mental models relies on neural mechanisms associated with spatial processing.
How does STEM knowledge learned in school change students’ brains? Using fMRI, we presented photographs of real-world structures to engineering students with classroom-based knowledge and hands-on lab experience, examining how their brain activity differentiated them from their “novice” peers not pursuing engineering degrees. A data-driven MVPA and machine- learning approach revealed that neural response patterns of engineering students were convergent with each other and distinct from novices when considering physical forces acting on the structures. Furthermore, informational network analysis demonstrated that the distinct neural response patterns of engineering students reflected relevant concept knowledge: learned categories of mechanical structures. Information about mechanical categories was predominantly represented in bilateral anterior ventral occipitotemporal regions. Importantly, mechanical categories were not explicitly referenced in the experiment, nor does visual similarity between stimuli account for mechanical category distinctions. The results demonstrate how learning abstract STEM concepts in the classroom influences neural representations of objects in the world.
Modality specific encoding habits account for a significant portion of individual differences reflected in functional activation during cognitive processing. Yet, little is known about how these habits of thought influence long-term structural changes in the brain. Traditionally, habits of thought have been assessed using self-report questionnaires such as the visualizer-verbalizer questionnaire. Here, rather than relying on subjective reports, we measured habits of thought using a novel behavioral task assessing attentional biases toward picture and word stimuli. Hypothesizing that verbal habits of thought are reflected in the structural integrity of white matter tracts and cortical regions of interest, we used diffusion tensor imaging and volumetric analyses to assess this prediction. Using a whole-brain approach, we show that word bias is associated with increased volume in several bilateral language regions, in both white and grey matter parcels. Additionally, connectivity within white matter tracts within an a priori speech production network increased as a function of word bias. These results demonstrate long-term structural and morphological differences associated with verbal habits of thought.
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