A reconsideration of the theological conception of sin in the light of the psychoanalytic conceptions of shame and guilt

Boston University Graduate School of Arts and Sciences. Department of Religion.

Peace and Peacemaking in Paul Against the Backdrop of Greco-Roman Conceptions of Peace

Peace in the ancient world has been studied primarily from the perspective of pacifism and questions related to war and peace. This study employs a socio-historical method to determine how peace was understood in itself, not just with respect to war. It demonstrates that the Greco-Roman world viewed peace as brief periods of tranquility in an existence where conflict was the norm, while Paul regarded peace as the norm and conflict as an intrusive aberration. Through a historical and literary survey of Greco-Roman thought and culture, this study shows that myth, legend, religion, education, philosophy, and science created and perpetuated the idea that conflict was necessary for existence. Wars were fought to attain peace, which meant periods of calm, quiet, and security with respect to the gods, one's inner self, nature, others who are insiders, and others who are outsiders. Despite the desirability of peace, genuine peace was seldom experienced, and even then, only briefly, as underlying enmity persisted without resolution. While Paul supports the prevailing conception of peace as tranquility and felicity in relation to God, self, nature, and others, he differs as to the origin, attainment, and maintenance of peace. In Paul, peace originates in God and is graciously given to those who are justified and reconciled to God through Jesus Christ. God removes the enmity caused by sin and provides the indwelling Spirit to empower believers to think and behave in ways that promote and maintain peace. This study also examines how three social dynamics (honor-shame, patron-client, friendship-enmity) affect Paul's approach to conflict resolution with Philemon and Onesimus, Euodia and Syntyche, believers who are prosecuting one another in civil courts, and Peter. Rather than giving specific procedures for resolving conflict, Paul reinforces the believer's new identity in Christ and the implications of God's grace, love, and peace upon their thoughts, words, and behavior toward one another. Paul uses these three social dynamics to encourage believers in the right direction, but their ultimate accountability is to God. The study concludes with four strategic principles for educating the church and developing an atmosphere and attitude within the church for peacemaking.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.