Interpreting time-series COVID data: reasoning biases, risk perception, and support for public health measures

Effective risk communication during the COVID-19 pandemic is critical for encouraging appropriate public health behaviors. One way that the public is informed about COVID-19 numbers is through reports of daily new cases. However, presenting daily cases has the potential to lead to a dynamic reasoning bias that stems from intuitive misunderstandings of accumulation. Previous work in system dynamics shows that even highly educated individuals with training in science and math misunderstand basic concepts of accumulation. In the context of COVID-19, relying on the single cue of daily new cases can lead to relaxed attitudes about the risk of COVID-19 when daily new cases begin to decline. This situation is at the very point when risk is highest because even though daily new cases have declined, the active number of cases are highest because they have been accumulating over time. In an experiment with young adults from the USA and Canada (N = 551), we confirm that individuals fail to understand accumulation regarding COVID-19, have less concern regarding COVID-19, and decrease endorsement for public health measures as new cases decline but when active cases are at the highest point. Moreover, we experimentally manipulate different dynamic data visualizations and show that presenting data highlighting active cases and minimizing new cases led to increased concern and increased endorsement for COVID-19 health measures compared to a control condition highlighting daily cases. These results hold regardless of country, political affiliation, and individual differences in decision making. This study has implications for communicating the risks of contracting COVID-19 and future public health issues.

Neural Interactions Underlying Visuomotor Associations in the Human Brain

Rapid and flexible learning during behavioral choices is critical to our daily endeavors and constitutes a hallmark of dynamic reasoning. An important paradigm to examine flexible behavior involves learning new arbitrary associations mapping visual inputs to motor outputs. We conjectured that visuomotor rules are instantiated by translating visual signals into actions through dynamic interactions between visual, frontal and motor cortex. We evaluated the neural representation of such visuomotor rules by performing intracranial field potential recordings in epilepsy subjects during a rule-learning delayed match-to-behavior task. Learning new visuomotor mappings led to the emergence of specific responses associating visual signals with motor outputs in 3 anatomical clusters in frontal, anteroventral temporal and posterior parietal cortex. After learning, mapping selective signals during the delay period showed interactions with visual and motor signals. These observations provide initial steps towards elucidating the dynamic circuits underlying flexible behavior and how communication between subregions of frontal, temporal, and parietal cortex leads to rapid learning of task-relevant choices.

Gifted Dyslexics

This chapter explores the intersection of giftedness, dyslexia, creativity, and a pattern of MIND-Strengths, framing dyslexia as a learning difference not a disability. It describes findings from individual cases of gifted dyslexics the authors have seen in their pscyhoeducational clinic through four case vignettes. Specifically, the chapter identifies and describes the unique patterns of MIND-Strengths, visual, and creative strengths of each student with information gathered from a protocol of individual assessments. MIND-Strengths include (a) material reasoning, (b) interconnected reasoning, (c) narrative reasoning, and (d) dynamic reasoning. Following each case description is a table that lists strengths, challenges, recommended strategies, interventions, and common professions. The recommended teaching and learning strategies build upon the MIND-Strengths as well as the visual, and creative strengths of each of the four cases.

Discourse management gestures

Gestures that are used by interlocutors to manage the gist of their ‘discourse interactions’, namely content exchange and floor taking, can have one of two very different pragmatic functions: to signal inclusion and cooperation in friendly conversation, or to establish control in more argumentative conversation. While inclusive-cooperative gestures have been extensively studied (e.g., Bavelas, Chovil, Lavrie, & Wade, 1992; Kendon, 1995; Müller, 2004; Sweetser, 1998), control gestures received little attention (although see Kendon, 1995, 2004) until a recent spark of interest in their form and function (e.g., Calbris, 2011; Müller, 2017; Wehling, 2010, 2012, 2013). However, even though research has detailed important aspects of such discourse managing gestures, to date no comprehensive account of their conceptual foundations and pragmatic functions exists. The present paper fills this gap in the literature. Building on prior analyses of control gestures in argumentative discourse (e.g., Wehling, 2010) and inclusive-cooperative gestures in friendly conversation (e.g., Bavelas et al., 1992; Müller, 2004), it details a typology of discourse management gestures that distinguishes inclusive-cooperative and control gestures as separate pragmatic types and accounts for their forms and functions in terms of their conceptual foundations in primary metaphoric, space-motion schematic, and force dynamic reasoning.

Multiagent Only Knowing in Dynamic Systems

The idea of "only knowing" a collection of sentences, as proposed by Levesque, has been previously shown to be very useful in characterizing knowledge-based agents: in terms of a specification, a precise and perspicuous account of the beliefs and non-beliefs is obtained in a monotonic setting. Levesque's logic is based on a first-order modal language with quantifying-in, thus allowing for de re versus de dicto distinctions, among other things. However, the logic and its recent dynamic extension only deal with the case of a single agent. In this work, we propose a first-order multiagent framework with knowledge, actions, sensing and only knowing, that is shown to inherit all the features of the single agent version. Most significantly, we prove reduction theorems by means of which reasoning about knowledge and actions in the framework simplifies to non-epistemic, non-dynamic reasoning about the initial situation.