A systematic review of the relationship between religion and attitudes toward transgender and gender-variant people

Background: Prejudice against transgender people is widespread, yet in spite of the prevalence of this negativity relatively little is known about the antecedents and predictors of these attitudes. One factor that is commonly related to prejudice is religion, and this is especially true for prejudice targets that are considered to be “value violating” (as is the case for transgender individuals).

Method: In this paper, we present the findings of our systematic search of the literature on this topic and present the synthesized evidence. Our search strategy was conducted across five databases and yielded 29 studies (across 28 articles).

Results: We found consistent evidence that self-identifying as with either being “religious” or as Christian (and to a lesser extent, being Muslim) was associated with increased transprejudice relative to being nonreligious (and to a lesser extent, being Jewish). Additionally, we found consistent evidence that certain forms of religiosity were also related to transprejudice – specifically religious fundamentalism, church attendance, and interpretations of the bible as literal (transprejudice was unrelated to religious education).

Conclusion: Although this young, but important field of research is growing, more empirical exploration is needed to fully understand that nuances of the religion-transprejudice relationship.     

Separating figure from ground with a parallel network

The differentiation of figure from ground plays an important role in the perceptual organization of visual stimuli. The rapidity with which we can discriminate the inside from the outside of a figure suggests that at least this step in the process may be performed in visual cortex by a large number of neurons in several different areas working together in parallel. We have attempted to simulate this collective computation by designing a network of simple processing units that receives two types of information: bottom-up input from the image containing the outlines of a figure, which may be incomplete, and a top-down attentional input that biases one part of the image to be the inside of the figure. No presegmentation of the image was assumed. Two methods for performing the computation were explored: gradient descent, which seeks locally optimal states, and simulated annealing, which attempts to find globally optimal states by introducing noise into the computation. For complete outlines, gradient descent was faster, but the range of input parameters leading to successful performance was very narrow. In contrast, simulated annealing was more robust: it worked over a wider range of attention parameters and a wider range of outlines, including incomplete ones. Our network model is too simplified to serve as a model of human performance, but it does demonstrate that one global property of outlines can be computed through local interactions in a parallel network. Some features of the model, such as the role of noise in escaping from nonglobal optima, may generalize to more realistic models.     

Perceptual grouping in two visually reliant species: humans (Homo sapiens) and Australian sea lions (Neophoca cinerea).

Kurylo, van Nest, and Knepper (1997 Journal of Comparative Psychology 111 126-134) have recently shown that hooded rats are able to judge the global orientation of an array of elements if orientation is signalled by the perceptual-grouping principle of proximity, but not if it is signalled by element alignment. Using a procedure designed to overcome some potential problems with the experiment of Kurylo et al, we found the same distinction in the perceptual processing of Australian sea lions. The sea lions were able to judge the orientation of arrays containing strong proximity and similarity information, but performed at chance levels judging arrays in which element alignment signalled global orientation. Human subjects were able to judge all three pattern types quickly and accurately. This is strong evidence of a qualitative distinction in the way in which perceptual grouping operates in humans and the non-human species tested. Whether this distinction is a consequence of evolutionary or experiential factors is a question for future research, but the mere fact of a qualitative difference holds important implications for our understanding of perceptual grouping.     

A learning algorithm for boltzmann machines

The computational power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections can allow a significant fraction of the knowledge of the system to be applied to an instance of a problem in a very short time. One kind of computation for which massively parallel networks appear to be well suited is large constraint satisfaction searches, but to use the connections efficiently two conditions must be met: First, a search technique that is suitable for parallel networks must be found. Second, there must be some way of choosing internal representations which allow the preexisting hardware connections to be used efficiently for encoding the constraints in the domain being searched. We describe a general parallel search method, based on statistical mechanics, and we show how it leads to a general learning rule for modifying the connection strengths so as to incorporate knowledge about a task domain in an efficient way. We describe some simple examples in which the learning algorithm creates internal representations that are demonstrably the most efficient way of using the preexisting connectivity structure.