A Theological Challenge: Coordinating Biological, Social, and Religious Visions of Humanity

This paper attempts two tasks. First, it sketches how the natural sciences (including especially the biological sciences), the social sciences, and the scientific study of religion can be understood to furnish complementary, consonant perspectives on human beings and human groups. This suggests that it is possible to speak of a modern secular interpretation of humanity (MSIH) to which these perspectives contribute (though not without tensions). MSIH is not a comprehensive interpretation of human beings, if only because it adopts a posture of neutrality with regard to the reality of religious objects and the truth of theological claims about them. MSIH is certainly an impressively forceful interpretation, however, and it needs to be reckoned with by any perspective on human life that seeks to insert its truth claims into the arena of public debate. Second, the paper considers two challenges that MSIH poses to specifically theological interpretations of human beings. On the one hand, in spite of its posture of religious neutrality, MSIH is a key element in a class of wider, seemingly antireligious interpretations of humanity, including especially projectionist and illusionist critiques of religion. It is consonance with MSIH that makes these critiques such formidable competitors for traditional theological interpretations of human beings. On the other hand, and taking the religiously neutral posture of MSIH at face value, theological accounts of humanity that seek to coordinate the insights of MSIH with positive religious visions of human life must find ways to overcome or manage such dissonance as arises. The goal of synthesis is defended as important, and strategies for managing these challenges, especially in light of the pluralism of extant philosophical and theological interpretations of human beings, are advocated.

An Invariant Representation for Matching Trajectories across Uncalibrated Video Streams

We introduce a view-point invariant representation of moving object trajectories that can be used in video database applications. It is assumed that trajectories lie on a surface that can be locally approximated with a plane. Raw trajectory data is first locally approximated with a cubic spline via least squares fitting. For each sampled point of the obtained curve, a projective invariant feature is computed using a small number of points in its neighborhood. The resulting sequence of invariant features computed along the entire trajectory forms the view invariant descriptor of the trajectory itself. Time parametrization has been exploited to compute cross ratios without ambiguity due to point ordering. Similarity between descriptors of different trajectories is measured with a distance that takes into account the statistical properties of the cross ratio, and its symmetry with respect to the point at infinity. In experiments, an overall correct classification rate of about 95% has been obtained on a dataset of 58 trajectories of players in soccer video, and an overall correct classification rate of about 80% has been obtained on matching partial segments of trajectories collected from two overlapping views of outdoor scenes with moving people and cars.

Temporal Dynamics of Decision-Making during Motion Perception in the Visual Cortex

How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons." A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probablistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.