Dichhaptic laterality and field dependence

Relationships among laterality, field dependence, and sex were investigated using right handed subjects. Dichhaptic measures of laterality showed a significant left-hand advantage for the discrimination of irregular shapes. No significant relationships were found between laterality and sex or sex role perceptions nor between laterality and field dependence. A modality of response effect on the laterality task was interpreted in terms of the functional cerebral space principle.     

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.