Rare genetic diseases collectively impact millions of individuals in the United States. These patients and their families share many challenges including delayed diagnosis, lack of knowledgeable providers, and limited economic incentives to develop new therapies for small patient groups. As such, rare disease patients and families often must rely on advocacy, including both self-advocacy to access clinical care and public advocacy to advance research. However, these demands raise serious concerns for equity, as both care and research for a given disease (...) can depend on the education, financial resources, and social capital available to the patients in a given community. In this article, we utilize three case examples to illustrate ethical challenges at the intersection of rare diseases, advocacy and justice, including how reliance on advocacy in rare disease may drive unintended consequences for equity. We conclude with a discussion of opportunities for diverse stakeholders to begin to address these challenges. (shrink)

We present a self-organizing approach to sentence processing that sheds new light on notional plurality effects in agreement attraction, using pseudopartitive subject noun phrases. We first show that notional plurality ratings predict verb agreement choices in pseudopartitives, in line with the “Marking” component of the Marking and Morphing theory of agreement processing. However, no account to date has derived notional plurality values from independently needed principles of language processing. We argue on the basis of new experimental evidence and a dynamical (...) systems model that the theoretical black box of notional plurality can be unpacked into objectively measurable semantic features. With these semantic features driving structure formation, our model reproduces the human verb production patterns as a byproduct of normal processing. Finally, we discuss how the self-organizing approach might be extended to other agreement attraction phenomena. (shrink)

Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.

Sixty‐one million Americans and approximately a billion people worldwide live with some form of disability that limits one or more major life activities. The field of precision medicine continues to grapple with how to best serve disability communities. In this paper, we suggest that precision medicine faces an ethical tension between its goal to treat or cure disabling conditions and views that consider disability as a marginalized identity. We appeal to the concepts of recognition justice and distributive justice to argue (...) that the ELSI community should take a more proactive role in promoting disability inclusion in precision medicine's practice and research. We also highlight two priorities for the ELSI community moving forward: facilitating greater collaboration between genetics and genomic professionals and disability communities and advocating for inclusive research design and disability accommodations in the research process. (shrink)

The rapid advances in genomics over the last decade have come to fruition amid intense public discussions of justice in medicine and health care. While much emphasis has been placed on increasing diversity in genomics research participation, an overly narrow focus on recruitment eschews recognition of the disparities in health care that will ultimately shape access to the benefits of genomic medicine. In this essay, we suggest that achieving a just genomics, both now and in the future, requires an explicit (...) ELSI of translation—normative and pragmatic scholarship that embraces the interconnectedness of research and clinical care and centers the obligations of researchers, institutions, and funders to mitigate inequities throughout the translational pipeline. We propose core principles to guide an ELSI of translation and to ensure that this work balances the value of the generalizable knowledge that genomics research generates and the value of the individuals and communities who make this research possible. (shrink)

We suggest that the theory of dynamical systems provides a revealing general framework for modeling the representations and mechanism underlying syntactic processing. We show how a particular dynamical model, the Visitation Set Gravitation model of Tabor, Juliano, and Tanenhaus (1997), develops syntactic representations and models a set of contingent frequency effects in parsing that are problematic for other models. We also present new simulations showing how the model accounts for semantic effects in parsing, and propose a new account of the (...) distinction between syntactic and semantic incongruity. The results show how symbolic structures useful in parsing arise as emergent properties of connectionist dynamical systems. (shrink)

Drawing from conservation of resources theory, we theorize that spiritual leadership serves as both a resource to enhance employees’ organizational commitment and a passageway to mitigate the negative effects of work–family conflict. Using primary triadic data from leaders, family employees, and nonfamily employees in 77 family firms, results support our theorizing that organizational commitment is enhanced by spiritual leadership but is decreased by work–family conflict. Contrary to theory, however, spiritual leadership exacerbated the negative effects of work–family conflict. Further analysis reveals (...) that family and nonfamily employees respond differently to leadership and work–family stressors. While spiritual leadership has positive effects for family employees, it has negative effects for nonfamily employees experiencing high work–family conflict. (shrink)

The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a (...) dynamical landscape of attractors corresponding to the potential eye-movement behaviors of the system. We test three unique predictions of our approach in an empirical study in the VWP, and describe an implementation in an artificial neural network. We discuss the Impulse Processing framework in relation to other models of the VWP. (shrink)

We examine two connectionist networks—a fractal learning neural network (FLNN) and a Simple Recurrent Network (SRN)—that are trained to process center-embedded symbol sequences. Previous work provides evidence that connectionist networks trained on infinite-state languages tend to form fractal encodings. Most such work focuses on simple counting recursion cases (e.g., anbn), which are not comparable to the complex recursive patterns seen in natural language syntax. Here, we consider exponential state growth cases (including mirror recursion), describe a new training scheme that seems (...) to facilitate learning, and note that the connectionist learning of these cases has a continuous metamorphosis property that looks very different from what is achievable with symbolic encodings. We identify a property—ragged progressive generalization—which helps make this difference clearer. We suggest two conclusions. First, the fractal analysis of these more complex learning cases reveals the possibility of comparing connectionist networks and symbolic models of grammatical structure in a principled way—this helps remove the black box character of connectionist networks and indicates how the theory they support is different from symbolic approaches. Second, the findings indicate the value of future, linked mathematical and empirical work on these models—something that is more possible now than it was 10 years ago. (shrink)

Volume 20, Issue 4, May 2020, Page 41-43.

Human participants and recurrent (“connectionist”) neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular (“strong”) classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks’ encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can (...) be directly compared with the mechanisms of non-connectionist, rule-based accounts. The results reveal that the networks “contain” structures related to mechanisms posited by rule-based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models. (shrink)

In the target article, McGuire and colleagues (2009) found that 54% of social networkers would consider using direct-to-consumer personal genome testing (DTC PGT) for their child and that 63% agree...