Why Does Employment Discrimination Persist against People with Mental Illness? Effects of Negative Stereotypes, Power, and Differential Discrimination

Mental illness affects a sizable minority of Americans at any given time, yet many people with mental illness (hereafter PWMI) remain unemployed or underemployed relative to the general population. Research has suggested that part of the reason for this is discrimination toward PWMI. This research investigated mechanisms that affect employment discrimination against PWMI. Drawing from theories on stigma and power, three studies assessed 1) the stereotyping of workers with mental illness as unfit for workplace success, 2) the impact of positive information on countering these negative stereotypes, and whether negatively-stereotyped conditions elicited discrimination; and 3) the effects of power on mental illness stigma components. I made a series of predictions related to theories on the Stereotype Content Model, illness attribution, the contact hypothesis, gender and mental health, and power. Studies tested predictions using, 1) an online vignette survey measuring attitudes, 2) an online survey measuring responses to fictitious applications for a middle management position, and 3) a laboratory experiment in which some participants were primed to feel powerful and some were not. Results of Study 1 demonstrated that PWMI were routinely stigmatized as incompetent, dangerous, and lacking valued employment attributes, relative to a control condition. This was especially evident for workers presented as having PTSD from wartime service and workers with schizophrenia, and when the worker was a woman. Study 2 showed that, although both war-related PTSD and schizophrenia evoke negative stereotypes, only schizophrenia evoked hiring discrimination. Finally, Study 3 found no effect of being primed to feel powerful on stigmatizing attitudes toward a person with symptoms of schizophrenia. Taken together, findings suggest that employment discrimination towards PWMI is driven by negative stereotypes; but, stereotypes might not lead to actual hiring discrimination for some labeled individuals.

The Origins and Legacy of a Currency Board in Argentina

This is a qualitative case study of the adoption of a currency board in Argentina in 1991. It presents a discursive analysis and intellectual history of four overlaying and mutually influencing stories of Convertibility’s adoption. It is (1) the story of how Menem aligned himself to the Washington Consensus as a means to win a Presidential election. This ideological alignment influences and is influenced by a (2) reconstitution of the Peronist Party’s historically entrenched identity. This in turn re-fashion the whole system of interest articulation and relative power of interest groups in Argentina. The adoption of a currency board also marks the pace of (3) the entrenchment neoliberal interests across a domestic network of neoliberal think-tanks, technocrats, politicians, and “technopoles” articulating neoliberal interests outside of the Washington Consensus, within an International Neoliberal Network. Argentina’s adoption of a currency board falls in line with the Corner Solutions, a neoliberal doctrine promoted to influence developing countries to adopt two forms of exchange rate regimes that allow for less government involvement, including a currency board. Argentina starts as a test country and then becomes (4) an ideological stepping stone to help promote the creation of currency boards across more “developing” countries. These stories are not sequential but concurrent, and they help advance an alternative critique of neoliberalism that focuses on specifics to induce case-specific lessons versus a theory claiming to provide any universal truth.

Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.