Collins & Quillian Semantic Network Model
The most prevalent example of the
semantic networkprocessing approach is the Collins & Quillian Semantic Network Model. cite journal
title=Retrieval time from semantic memory
journal=Journal of verbal learning and verbal behavior
last=Allan M. Collins
coauthors= M.R. Quillian
pages=240-248] cite journal
title=Does category size affect categorization time?
journal=Journal of verbal learning and verbal behavior
first=|last=Allan M. Collins
coauthors=M. Ross Quillian
pages=432-438] The semantic network processing approach states that the meanings of words are embedded in networks of other meanings.
Knowledgeis validated and acquires meaning through correlation with other knowledge, (Harley, 1995). The connections within a semantic network are not only associative in nature. The links between informationin a semantic network are qualitative and purposeful. Therefore, the links within the network have semantic value.
In the Collins & Quillian model, concepts are represented as nodes that are interconnected to other nodes within the network. The nodes are accessed when they are heard and then activated in memory causing information that is correlated to the concept to be primed. The ‘ISA’ link is the most common link in this semantic network model. The nodes within the network that are connected by this link have a specific type of relationship that is hierarchical in nature. Therefore the concept at the lower level node is a form/type of the concept at the higher level node. This type of interconnectedness is also evidenced by the ‘HAS A’ link.
The structure of the Collins & Quillian model compensates for many of the deficits identified in earlier theories such as the behaviorist notion of meaning being derived from a network of associations. According to the
Behaviorists, a word is defined based on placement in a network of associations. Meaning is extrapolated from an accumulation of episodic instances involving the word in question. The primary problem with this theoryis the fact that a definition based solely on associative properties alone is inadequate since it does not encompass all aspects of meaning, (Harley, 1995). Other prominent flaws in the network of associations include its lack of structure, failure to evince relationships between words, and the lack of cognitive economy.
Several components of the Collins & Quillian semantic network model address the issues identified in the Behaviorists’ network of associations. The hierarchical schema intrinsic to the semantic network model creates structure and facilitates the use of cognitive economy. Rosch (1999) refers to cognitive economy as the means by which an organism acquires a substantial amount of information without having to undergo a search of all finite resources. The hierarchical structure of the semantic network model eliminates redundancy since access to information stored at one level is not required to process an instance of the category at another level. For example, if 'fur' is stored at the level of ‘dog’ it is not necessary to process ‘Collie’ which is a type of dog. Moreover, the relationships between words are demonstrated by the purposeful interconnectedness of linked activated nodes within the network.
There are a variety of problems with the Collins & Quillian model despite the fact that it ameliorates flaws identified in the network of associations, including a great deal of evidence from research findings that debunk the hierarchical framework. One obvious problem with the model is the fact that not all categories can be represented in hierarchical form. The structure of the model is well suited for itemizing words in naturally occurring categories, i.e., animals, metals, (Harley, 1995). However, the process becomes challenging when it comes to abstract concepts. While this finding is of extreme importance there is also a significant amount of evidence that discredits the validity of the hierarchical model stemming from information processing task research results.
Collins & Quillian employed a sentence verification task to test the efficacy of the semantic network model and the results obtained appeared to support the hierarchical structure of the theory. However, upon scrutinizing the materials used, critics of the theory inferred that the size of the sample category and the words used in the task administered by Collins & Quillian confounded the semantic distance with 'conjoint frequency,' a term which refers to two words with a clear association occurring together frequently. Therefore, according to the critics, the results derived from the sentence verification task did not support the hierarchical model since they were caused by failure to, “properly control for typicality and semantic distance,” (Harley, 1995). Tests that gauge the speed with which subjects make judgments about category membership are preeminent in semantic memory research, (Rosch, 1999). Information processing tasks of this type entail measuring reaction time speed of true-false judgments by subjects to statements of the form: ‘x is a member of category y’. It was postulated that the reaction times were faster for the words in the sentences that substantiated the hierarchical structure of the semantic network as a result of correlative associations between the words selected for the task.
In addition to the fallible results due to conjoint frequency it was also determined that the predictions of the hierarchical framework were also inaccurate. For example, all untrue statements are not rejected at the same speed as suggested by test results elicited by Collins & Quillian. This is especially true in instances where items are compromised by the relatedness effect, which states that, “the more related two things are the harder it is to disentangle them,” (Harley, p. 182). Similarly, true statements of equal semantic distance can yield disparate response times primarily when one item is judged to be a prototypical example. There is a substantial amount of research solidifying the impact of prototypes on reaction time measurements. Rosch (1999) references several studies with results consistently indicating that true judgment responses for items perceived as prototypical are invariably faster during information processing tasks.
The sentence verification task data and the prototypical examples played an integral role in the decision to revise the Collins & Quillian semantic network model. Subsequently, Collins & Loftus embarked on an effort to address and rectify the deficits identified within the hierarchical structure of the original model. The revised model is multidimensional with a separation between the semantic and lexical components. Emphasis is placed on the premise of spreading activation along links to nodes to initiate access and priming in the network. The links between the nodes vary in both strength and distance and the role of hierarchical relationships is minimized. The information compiled suggests that although the hierarchical framework of the original semantic network model developed by Collins & Quillian was exemplary in addressing deficits identified in the earlier theory of network associations it is evident that there are entirely too many flaws in the constructs of the model. The conclusions drawn from scrutiny of the sentence verification task outcomes and the implications of prototypes indicate that the pertinence of the hierarchical infrastructure was not validated by the methods used to elucidate the model.
Bloom, J. (2006). Language and Thought Lecture November 7th 2006. Graduate Faculty-New School of Social Research.
Harley, T. A. (1995). Semantics. Chapter 6 of The psychology of language: From data to theory. East Sussex, UK: Psychology Press, pp. 175-205.
Rosch, E. (1999). Principles of Categorization. In E. Margolis & S. Laurence (Eds.), Concepts: Core Readings (pp. 189-206). Cambridge, MA: MIT Press. Reprinted from (1978), E. Rosch & B. Lloyds (Eds.), Cognition and Categorization. Hillsdale, NJ: Laurence Erlbaum Associates
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