The inference capabilities of humans suggest that they might be using algorithms with high degrees of parallelism. This paper develops a completely parallel connectionist inference mechanism. The mechanism handles obvious inferences, where each clause is only used once, but may be extendable to harder cases.

Recent successful robotic models of complex tasks are characterized by use of deictic primitives and frequent access to the sensory input. Such models require only limited memory representations, a well- known characteristic of human cognition. We show, using a sensori-motor copying task, that human performance is also characterized by deictic strategies and limited memory representations. This suggests that the deictic approach is afruitful one for under- standing human brain mechanisms; it also suggests a computational rationale for the limitations on human short term memory