ECONOMICS OF INNOVATION The Effects of Prize Structures on Innovative Performance

Successful innovation is essential for the survival and growth of organizations, but how best to incentivize innovation is poorly understood. We compare how two common incentive schemes affect innovative performance in a field experiment run in partnership with a large life sciences company. We find that a winner-takes-all compensation scheme generates significantly more novel innovation relative to a compensation scheme that offers the same total compensation but shares it across the ten best innovations. Moreover, the winner-takes-all scheme does not reduce innovative output on average and, among teams of innovators, generates more output than the less risky prize structure

Variability, negative evidence, and the acquisition of verb argument constructions

We present a hierarchical Bayesian framework for modeling the acquisition of verb argument constructions. It embodies a domain-general approach to learning higher-level knowledge in the form of inductive constraints (or overhypotheses), and has been used to explain other aspects of language development such as the shape bias in learning object names. Here, we demonstrate that the same model captures several phenomena in the acquisition of verb constructions. Our model, like adults in a series of artificial language learning experiments, makes inferences about the distributional statistics of verbs on several levels of abstraction simultaneously. It also produces the qualitative learning patterns displayed by children over the time course of acquisition. These results suggest that the patterns of generalization observed in both children and adults could emerge from basic assumptions about the nature of learning. They also provide an example of a broad class of computational approaches that can resolve Baker's Paradox

Neuroenhancement of exposure therapy in anxiety disorders

Although exposure-based treatments and anxiolytic medications are more effective than placebo for treating anxiety disorders, there is still considerable room for further improvement. Interestingly, combining these two modalities is usually not more effective than the monotherapies. Recent translational research has identified a number of novel approaches for treating anxiety disorders using agents that serve as neuroenhancers (also known as cognitive enhancers). Several of these agents have been studied to determine their efficacy at improving treatment outcome for patients with anxiety and other psychiatric disorders. In this review, we examine d-cycloserine, yohimbine, cortisol, catecholamines, oxytocin, modafinil, and nutrients such as caffeine and amino fatty acids as potential neuroenhancers. Of these agents, d-cycloserine shows the most promise as an effective neuroenhancer for extinction learning and exposure therapy. Yet, the optimal dosing and dose timing for drug administration remains uncertain. There is partial support for cortisol, catecholamines, yohimbine and oxytocin for improving extinction learning and exposure therapy. There is less evidence to indicate that modafinil and nutrients such as caffeine and amino fatty acids are effective neuroenhancers. More research is needed to determine their long term efficacy and clinical utility of these agents.R34 MH086668 - NIMH NIH HHS; R01 AT007257 - NCCIH NIH HHS; R21 MH101567 - NIMH NIH HHS; R34 MH099311 - NIMH NIH HHS; R21 MH102646 - NIMH NIH HHS; K23 MH100259 - NIMH NIH HHS; R01 MH099021 - NIMH NIH HH