Associative Networks Definition
Associative networks are cognitive models that incorporate long-known principles of association to represent key features of human memory. When two things (e.g., “bacon” and “eggs”) are thought about simultaneously, they may become linked in memory. Subsequently, when one thinks about bacon, eggs are likely to come to mind as well. Over 2,000 years ago, Aristotle described some of the principles governing the role of such associations in memory. Similar principles were elaborated by British philosophers in the 1700s, and contributed to a variety of psychological theories, including those developed by contemporary cognitive psychologists to model memory.
Basic Models of Associative Networks
In associative network models, memory is construed as a metaphorical network of cognitive concepts (e.g., objects, events and ideas) interconnected by links (or pathways) reflecting the strength of association between pairs of concepts. Such models commonly incorporate ideas about “spreading activation” to represent the processes of memory retrieval. According to such models, concepts that are currently being thought about are said to be “activated,” and “excitation” spreads from these down connecting pathways to associated concepts. Associations that have been encountered more frequently in the past are likely to be stronger and are represented in associative network models by pathways through which excitation can spread more quickly. Once sufficient excitation has passed from previously activated concepts to a new concept, so that its level of accumulated excitation surpasses some threshold, that new concept will also be brought to mind.
Associative Networks Model Details
Serial search models assume that excitation traverses one pathway after another until needed concepts are discovered and retrieved from memory. More common are parallel processing models, which view excitation as simultaneously traversing all connecting pathways, converging most quickly at concepts that have multiple connections to those already activated. Consequently, thinking about “bacon,” “eggs,” and “juice” is more likely to activate “breakfast” than might any of those concepts alone.
Once activated, a concept retains excitation as long as it receives attention, after which activation declines as excitation flows away. Because this decay in activation takes time, however, a concept may retain an elevated level of residual excitation, even after passing from thought. Consequently, concepts that have been thought about recently may be primed, and require relatively little excitation to achieve activation. Inhibitory processes are also sometimes posited, to further control the number and relevance of concepts activated at one time. As they have been refined, associative network models have become increasingly complex, mathematical, and tied to neurological mechanisms involved in learning and memory.
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