The neural foundation of moral decision-making

The nature of moral judgments has received considerable attention not only in philosophy and psychology but lately in neuroscience as well. There are two major paradigms that consider moral judgments either mainly rational, or as emotional-/ intuition-based processes. Relatively recent neuroimaging studies revealed however that both rational and emotional processes may support moral judgments. In line with these results, this doctoral thesis focused on ways that could better elucidate the supporting cognitive and/ or emotional processes of moral judgments. In a first study, moral judgments were compared to esthetic judgments by employing a whole-brain analysis. This idea was based on the philosophical and the psychological frameworks of moral sense theory and social intuitionist model respectively. Both models view moral judgments akin to esthetic judgments, as decision-making processes based on emotions/ subjective feelings. The fMRI data suggest a common denominator between the judgment modalities - a network involved in both cognitive and emotion processing. However, moral judgments seem to rely on an additional social component. In a second fMRI study, the two main paradigms of moral research were investigated. A main difference between the paradigms is the perspective the participants have towards the moral stimuli (i.e. first- or third-perspective). The fMRI data revealed that neural differences may emerge, and that they may be related to the so-called “actor-observer bias”, a tendency to attribute one’s own behavior to the situation, and the behaviors of others to their inner characteristics. Several hypotheses are put forth, which try to explain the complex neural mechanisms of moral decision-making.Die Natur moralischer Urteile hat nicht nur in der Philosophie und Psychologie, sondern neuerdings auch in den Neurowissenschaften beträchtliche Aufmerksamkeit erhalten. Es gibt zwei Haupt-Paradigmen, die moralische Urteile entweder als vorwiegend rationale, oder als emotionale und auf Intuition basierende Prozesse betrachten. Bildgebende Studien haben jedoch gezeigt, dass moralische Urteile sowohl durch rationale als auch durch emotionale Prozesse beschrieben werden können. Auf diesen Befunden aufbauend ist die vorliegende Doktorarbeit einer vertiefenden Untersuchung der zugrundeliegenden neuro-kognitiven und emotionalen Prozesse moralischer Urteile gewidmet. In einer ersten Studie wurden moralische und ästhetische Urteile durch den Einsatz einer „whole brain“ Analyse verglichen. Dieser Idee liegen philosophische und psychologische Hypothesen der „Moral Sense Theorie“ und dem „Social Intuitionist Model“ zu Grunde. Die fMRT-Daten legen einen gemeinsamen Nenner der beiden Urteilsarten nahe; es konnte ein Netzwerk identifiziert werden, das sowohl für kognitive und als auch für emotionale Verarbeitung zuständig ist. Bei moralischen Urteilen werden allerdings weitere neuronale Areale kooptiert, die eine soziale Komponente des Urteilens repräsentieren. In einer zweiten fMRT-Studie wurden zentrale Paradigmen der moralischen Forschung untersucht. Ein Hauptunterschied zwischen den Paradigmen ist die Perspektive der Teilnehmer auf die moralischen Stimuli (d.h. der ersten oder dritten Perspektive). Die fMRT-Daten legen nahe, dass Unterschiede in neuronalen Aktivierungen auf den sogenannten „Actor-Observer-Bias“ zurückgeführt werden können. Dieser Bias stellt eine Tendenz dar, das eigene Verhalten jeweils der äußeren Situation zuzuschreiben, und das Verhalten der anderen jeweils deren persönlichen Merkmalen. Auf der Grundlage neuro-kognitiver und psychologischer Hypothesen werden die komplexen neuronalen Mechanismen der moralischen Entscheidungsfindung zu erklären versucht

Neural correlates of moral judgments in first- and third-person perspectives: implications for neuroethics and beyond

Background: There appears to be an inconsistency in experimental paradigms used in fMRI research on moral judgments. As stimuli, moral dilemmas or moral statements/ pictures that induce emotional reactions are usually employed; a main difference between these stimuli is the perspective of the participants reflecting first-person (moral dilemmas) or third-person perspective (moral reactions). The present study employed functional magnetic resonance imaging (fMRI) in order to investigate the neural correlates of moral judgments in either first-or third-person perspective. Results: Our results indicate that different neural mechanisms appear to be involved in these perspectives. Although conjunction analysis revealed common activation in the anterior medial prefrontal cortex, third person-perspective elicited unique activations in hippocampus and visual cortex. The common activation can be explained by the role the anterior medial prefrontal cortex may play in integrating different information types and also by its involvement in theory of mind. Our results also indicate that the so-called "actor-observer bias" affects moral evaluation in the third-person perspective, possibly due to the involvement of the hippocampus. We suggest two possible ways in which the hippocampus may support the process of moral judgment: by the engagement of episodic memory and its role in understanding the behaviors and emotions of others. Conclusion: We posit that these findings demonstrate that first or third person perspectives in moral cognition involve distinct neural processes, that are important to different aspects of moral judgments. These results are important to a deepened understanding of neural correlates of moral cognition-the so-called "first tradition" of neuroethics, with the caveat that any results must be interpreted and employed with prudence, so as to heed neuroethics "second tradition" that sustains the pragmatic evaluation of outcomes, capabilities and limitations of neuroscientific techniques and technologies

Attractor Dynamics in Feedforward Neural Networks

We study the probabilistic generative models parameterized by feedforward neural networks. An attractor dynamics for probabilistic inference in these models is derived from a mean field approximation for large, layered sigmoidal networks. Fixed points of the dynamics correspond to solutions of the mean field equations, which relate the statistics of each unit to those of its Markov blanket. We establish global convergence of the dynamics by providing a Lyapunov function and show that the dynamics generate the signals required for unsupervised learning. Our results for feedforward networks provide a counterpart to those of Cohen-Grossberg and Hopfield for symmetric networks. 1 Introduction Attractor neural networks lend a computational purpose to continuous dynamical systems. Celebrated uses of these networks include the storage of associative memories (Amit, 1989), the reconstruction of noisy images (Koch et al, 1986), and the search for shortest paths in the traveling salesman proble..

CANCER DETECTION FOR LOW GRADE SQUAMOUS ENTRAEPITHELIAL LESION

The National Cancer Institute estimates in 2012, about 577,190 Americans are expected to die of cancer, more than 1,500 people a day. Cancer is the second most common cause of death in the US, accounting for nearly 1 of every 4 deaths. Cancer diagnosis has a very important role in the early detection and treatment of cancer. Automating the cancer diagnosis process can play a very significant role in reducing the number of falsely identified or unidentified cases. The aim of this thesis is to demonstrate different machine learning approaches for cancer detection. Dr. Tawfik, pathologist from University of Kansas medical Center (KUMC) is an inventor of a novel pathology tissue slicer. The data used in this study comes from this slicer, which successfully allows semi-automated cancer diagnosis and it has the potential to improve patient care. In this study the slides are processed and visual features are computed and the dataset is made from scratch. After features extraction, different machine learning approaches are applied on the dataset which has shown its capability of extracting high-level representations from high-dimensional data. Support Vector Machine and Deep Belief Networks (DBN) are the concentration in this study. In the first section, Support vector machine is applied on the dataset. Next Deep Belief Network which is capable of extracting features in an unsupervised manner is implemented and with back-propagation the network is fine tuned. The results show that DBN can be effective when applied to cytological cancer diagnosis by increasing the accuracy in cancer detection. In the last section a subset of DBN features are selected and then appended with raw features and Support Vector Machine is trained and tested with that. It shows improvement over the first section results. In the end the study infers that Deep Belief Network can be successfully used over other leading classification methods for cancer detection

The neural foundation of moral decision-making

The nature of moral judgments has received considerable attention not only in philosophy and psychology but lately in neuroscience as well. There are two major paradigms that consider moral judgments either mainly rational, or as emotional-/ intuition-based processes. Relatively recent neuroimaging studies revealed however that both rational and emotional processes may support moral judgments. In line with these results, this doctoral thesis focused on ways that could better elucidate the supporting cognitive and/ or emotional processes of moral judgments. In a first study, moral judgments were compared to esthetic judgments by employing a whole-brain analysis. This idea was based on the philosophical and the psychological frameworks of moral sense theory and social intuitionist model respectively. Both models view moral judgments akin to esthetic judgments, as decision-making processes based on emotions/ subjective feelings. The fMRI data suggest a common denominator between the judgment modalities - a network involved in both cognitive and emotion processing. However, moral judgments seem to rely on an additional social component. In a second fMRI study, the two main paradigms of moral research were investigated. A main difference between the paradigms is the perspective the participants have towards the moral stimuli (i.e. first- or third-perspective). The fMRI data revealed that neural differences may emerge, and that they may be related to the so-called “actor-observer bias”, a tendency to attribute one’s own behavior to the situation, and the behaviors of others to their inner characteristics. Several hypotheses are put forth, which try to explain the complex neural mechanisms of moral decision-making.Die Natur moralischer Urteile hat nicht nur in der Philosophie und Psychologie, sondern neuerdings auch in den Neurowissenschaften beträchtliche Aufmerksamkeit erhalten. Es gibt zwei Haupt-Paradigmen, die moralische Urteile entweder als vorwiegend rationale, oder als emotionale und auf Intuition basierende Prozesse betrachten. Bildgebende Studien haben jedoch gezeigt, dass moralische Urteile sowohl durch rationale als auch durch emotionale Prozesse beschrieben werden können. Auf diesen Befunden aufbauend ist die vorliegende Doktorarbeit einer vertiefenden Untersuchung der zugrundeliegenden neuro-kognitiven und emotionalen Prozesse moralischer Urteile gewidmet. In einer ersten Studie wurden moralische und ästhetische Urteile durch den Einsatz einer „whole brain“ Analyse verglichen. Dieser Idee liegen philosophische und psychologische Hypothesen der „Moral Sense Theorie“ und dem „Social Intuitionist Model“ zu Grunde. Die fMRT-Daten legen einen gemeinsamen Nenner der beiden Urteilsarten nahe; es konnte ein Netzwerk identifiziert werden, das sowohl für kognitive und als auch für emotionale Verarbeitung zuständig ist. Bei moralischen Urteilen werden allerdings weitere neuronale Areale kooptiert, die eine soziale Komponente des Urteilens repräsentieren. In einer zweiten fMRT-Studie wurden zentrale Paradigmen der moralischen Forschung untersucht. Ein Hauptunterschied zwischen den Paradigmen ist die Perspektive der Teilnehmer auf die moralischen Stimuli (d.h. der ersten oder dritten Perspektive). Die fMRT-Daten legen nahe, dass Unterschiede in neuronalen Aktivierungen auf den sogenannten „Actor-Observer-Bias“ zurückgeführt werden können. Dieser Bias stellt eine Tendenz dar, das eigene Verhalten jeweils der äußeren Situation zuzuschreiben, und das Verhalten der anderen jeweils deren persönlichen Merkmalen. Auf der Grundlage neuro-kognitiver und psychologischer Hypothesen werden die komplexen neuronalen Mechanismen der moralischen Entscheidungsfindung zu erklären versucht

A cortical model of object perception based on Bayesian networks and belief propagation.

Evidence suggests that high-level feedback plays an important role in visual perception by shaping the response in lower cortical levels (Sillito et al. 2006, Angelucci and Bullier 2003, Bullier 2001, Harrison et al. 2007). A notable example of this is reflected by the retinotopic activation of V1 and V2 neurons in response to illusory contours, such as Kanizsa figures, which has been reported in numerous studies (Maertens et al. 2008, Seghier and Vuilleumier 2006, Halgren et al. 2003, Lee 2003, Lee and Nguyen 2001). The illusory contour activity emerges first in lateral occipital cortex (LOC), then in V2 and finally in V1, strongly suggesting that the response is driven by feedback connections. Generative models and Bayesian belief propagation have been suggested to provide a theoretical framework that can account for feedback connectivity, explain psychophysical and physiological results, and map well onto the hierarchical distributed cortical connectivity (Friston and Kiebel 2009, Dayan et al. 1995, Knill and Richards 1996, Geisler and Kersten 2002, Yuille and Kersten 2006, Deneve 2008a, George and Hawkins 2009, Lee and Mumford 2003, Rao 2006, Litvak and Ullman 2009, Steimer et al. 2009). The present study explores the role of feedback in object perception, taking as a starting point the HMAX model, a biologically inspired hierarchical model of object recognition (Riesenhuber and Poggio 1999, Serre et al. 2007b), and extending it to include feedback connectivity. A Bayesian network that captures the structure and properties of the HMAX model is developed, replacing the classical deterministic view with a probabilistic interpretation. The proposed model approximates the selectivity and invariance operations of the HMAX model using the belief propagation algorithm. Hence, the model not only achieves successful feedforward recognition invariant to position and size, but is also able to reproduce modulatory effects of higher-level feedback, such as illusory contour completion, attention and mental imagery. Overall, the model provides a biophysiologically plausible interpretation, based on state-of-theart probabilistic approaches and supported by current experimental evidence, of the interaction between top-down global feedback and bottom-up local evidence in the context of hierarchical object perception

Bayesian Unsupervised Learning of Higher Order Structure

Multilayer architectures such as those used in Bayesian belief networks and Helmholtz machines provide a powerful framework for representing and learning higher order statistical relations among inputs. Because exact probability calculations with these models are often intractable, there is much interest in finding approximate algorithms. We present an algorithm that efficiently discovers higher order structure using EM and Gibbs sampling. The model can be interpreted as a stochastic recurrent network in which ambiguity in lower-level states is resolved through feedback from higher levels. We demonstrate the performance of the algorithm on benchmark problems