Contributions in Merlin

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This "best model" approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data.

This article presents a conceptual model of entrepreneurial opportunity production from a constructivist perspective. The model assumes that opportunity production proceeds through several stages, including conceptualization of an opportunity idea by an entrepreneur, objectification of that idea, and enactment of the opportunity into a new venture. However, not all opportunity ideas survive this full process. Between the conceptualization stage and the objectification stage, some ideas are abandoned due to inadequate objectification. Also, between the objectification stage and the enactment stage, some objectified opportunities are abandoned due to insufficient resource support. We identify variables that influence the likelihood that opportunity ideas will be objectified and other variables that influence the likelihood that objectified opportunities will be enacted, and these variables are incorporated into empirically testable propositions. In the discussion section, we describe several boundary conditions for our theory, contrast the theory with objectivist (discovery) theory, and derive implications for future research.

Both perceptual inference and motor responses are shaped by learned probabilities. For example, stimulus-induced responses in sensory cortices and preparatory activity in premotor cortex reflect how (un)expected a stimulus is. This is in accordance with predictive coding accounts of brain function, which posit a fundamental role of prediction errors for learning and adaptive behavior. We used functional magnetic resonance imaging and recent advances in computational modeling to investigate how (failures of) learned predictions about visual stimuli influence subsequent motor responses. Healthy volunteers discriminated visual stimuli that were differentially predicted by auditory cues. Critically, the predictive strengths of cues varied over time, requiring subjects to continuously update estimates of stimulus probabilities. This online inference, modeled using a hierarchical Bayesian learner, was reflected behaviorally: speed and accuracy of motor responses increased significantly with predictability of the stimuli. We used nonlinear dynamic causal modeling to demonstrate that striatal prediction errors are used to tune functional coupling in cortical networks during learning. Specifically, the degree of striatal trial-by-trial prediction error activity controls the efficacy of visuomotor connections and thus the influence of surprising stimuli on premotor activity. This finding substantially advances our understanding of striatal function and provides direct empirical evidence for formal learning theories that posit a central role for prediction error-dependent plasticity.

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Diese Arbeit untersucht Spin-offs aus Hochschulen und der Privatwirtschaft. Ein Spin-off ist ein Unternehmen, das sich aus einem bereits existierenden System, der Mutterorganisation, herausgelöst hat. Ziel dieser Untersuchung ist die Ermittlung der Erfolgsfaktoren für Academic und Corporate Spin-offs.Das Hauptaugenmerk liegt auf der Beziehung zwischen der Mutterorganisation und der Ausgründung. Es wird analysiert, inwieweit sich die Muttereinrichtung auf den Unternehmenserfolg von Spin-offs auswirken kann. Bei den Hochschul-Spin-offs wird zudem der Transferbereich von der Wissenschaft in die Privatwirtschaft genau untersucht. Diese Masterarbeit wurde branchenabhängig durchgeführt. Im Zentrum stehen Unternehmen, die sich im Bereich der Medizintechnik spezialisiert haben. Sowohl Mutterinstitution wie auch Transferobjekt üben eindeutig einen Einfluss auf den Erfolg der Spin-offs aus. Auf der Seite der Academic Spin-offs wird die Mutterinstitution im Durchschnitt zwischen erfolgsneutral und erfolgshemmend eingestuft. Bei den Corporate Spin-offs wird die Mutterorganisation durchschnittlich als erfolgsneutral bewertet.