Racial bias in pediatric pain perception.

Racial disparities in pediatric pain care are prevalent across a variety of healthcare settings, and likely contribute to broader disparities in health, morbidity, and mortality. The present research expands on prior work demonstrating potential perceptual contributions to pain care disparities in adults and tests whether racial bias in pain perception extends to child targets. We examined the perception and hypothetical treatment of pain in Black and White boys (Experiment 1), Black and White boys and girls (Experiment 2), Black and White boys and adult men (Experiment 3), and Black, White, Asian, and Latinx boys (Experiment 4). Across this work, pain was less readily perceived on Black (versus White) boys' faces-though this bias was not observed within girls. Moreover, this perceptual bias was comparable in magnitude to the same bias measured with adult targets and consistently predicted bias in hypothetical treatment. Notably, bias was not limited to Black targets-pain on Hispanic/Latinx boys' faces was also relatively underperceived. Taken together, these results offer strong evidence for racial bias in pediatric pain perception. PERSPECTIVE: This article demonstrates perceptual contributions to racial bias in pediatric pain recognition. Participants consistently saw pain less readily on Black boys' faces, compared to White boys, and this perceptual bias consistently predicted race-based gaps in treatment. This work reveals a novel factor that may support pediatric pain care disparities.

The Resilience of the Phonological Network May Have Implications for Developmental and Acquired Disorders.

A central tenet of network science states that the structure of the network influences processing. In this study of a phonological network of English words we asked: how does damage alter the network structure (Study 1)? How does the damaged structure influence lexical processing (Study 2)? How does the structure of the intact network "protect" processing with a less efficient algorithm (Study 3)? In Study 1, connections in the network were randomly removed to increasingly damage the network. Various measures showed the network remained well-connected (i.e., it is resilient to damage) until ~90% of the connections were removed. In Study 2, computer simulations examined the retrieval of a set of words. The performance of the model was positively correlated with naming accuracy by people with aphasia (PWA) on the Philadelphia Naming Test (PNT) across four types of aphasia. In Study 3, we demonstrated another way to model developmental or acquired disorders by manipulating how efficiently activation spread through the network. We found that the structure of the network "protects" word retrieval despite decreases in processing efficiency; words that are relatively easy to retrieve with efficient transmission of priming remain relatively easy to retrieve with less efficient transmission of priming. Cognitive network science and computer simulations may provide insight to a wide range of speech, language, hearing, and cognitive disorders.

What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach.

Cognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. In the present study, we used computer simulations to compare the ability of a well-known model of spoken word recognition, TRACE, to the ability of a cognitive network model with a spreading activation-like process to account for the findings from several previously published behavioral studies of language processing. In all four simulations, the TRACE model failed to retrieve a sufficient number of words to assess if it could replicate the behavioral findings. The cognitive network model successfully replicated the behavioral findings in Simulations 1 and 2. However, in Simulation 3a, the cognitive network did not replicate the behavioral findings, perhaps because an additional mechanism was not implemented in the model. However, in Simulation 3b, when the decay parameter in spreadr was manipulated to model this mechanism the cognitive network model successfully replicated the behavioral findings. The results suggest that models of cognition need to take into account the multi-scale structure that exists among representations in memory, and how that structure can influence processing.