Resource allocation for visual metaphor processing: A study of political cartoons

ABSTRACT

This paper is a study of visual metaphor processing in political cartoons, while secondary task reaction time was used to assess resource allocation when processing three types of visual metaphors, namely juxtapositions, fusions and replacements. Participants viewed a series of visual metaphors. At various intervals, they were invited to push a button whenever a probe appeared. Reaction times to detect the probes were recorded. Subsequently, participants performed recall and recognition tasks. Results showed that participants reacted faster to each successive probe, while metaphor type had no influence on reaction times. Concerning the recall task, the percentage of correctly recalled items was significantly lower for juxtapositions compared to replacements; results for fusions were in between the two. Recognition scores for juxtapositions and fusions were lower than those for replacements. These findings are discussed in the framework of visual metaphors literature, thus providing empirical support to visual metaphor processing.     

Endogenous cueing effects for detection can be accounted for by a decision model of selective attention

Psychonomic Bulletin & Review - Spatial cues help participants detect a visual target when it appears at the cued location. One hypothesis for this cueing effect, called selective perception,...     

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.