- Information Integration Theory
**Information Integration Theory**was proposed byNorman H. Anderson to describe and model how a person integrates information from a number of sources in to make an overall judgment. The theory proposes three functions. [*1*]The "valuation function" V(S) is an empirically derived mapping of stimuli to an interval scale. It is unique up to an interval transformation (y = ax + b).

The "integration function" r = I{s1,s2, .. ,sn} is an algebraic function combining the subjective values of the information. "Cognitive Algebra" refers to the class of functions that are used to model the integration process. They may be adding, averaging, weighted averaging, multiplying, etc.

The "response production function" R = M(r) is the process by which the internal impression is translated into an overt response.

Information Integration Theory differs from others theories in that it is not erected on a consistency principle such as balance or congruity but rather relies on algebraic models.

**Integration Models**There are three main types of algebraic models used in Information Integration Theory. Adding, Averaging, and Multiplying.

**Adding Models**

R = Reaction/Overt Behavior

F/G = Contributing Factors

R_{1}= F_{1}+ G_{1}(Condition 1)

R_{2}= F_{2}+ G_{2}(Condition 2)Typically an experiment is designed so that:

R_{1}= R_{2}, and

F_{1}> F_{2}, so that

G_{1}< G_{2}There are two special cases known as discounting and augmentation.

Discounting: The value of any factor is reduced if other factors that produce the same effect are added.

Example: F_{2}is not present or has a value of zero. If F_{1}is positive, then G_{1}must be less than G_{2}.

Augmentation: An inverse version of the typical model.

Example: If F_{1}is negative, then G_{1}must be greater than G_{2}Two advantages of adding models; (1) Participants are not required to have an exact intuitive calculation, (2) The adding model itself need not be completely valid. Certain kinds of interaction among the factors would not affect the qualitative conclusions.

**References**

*Anderson, N. H. Application of an Additive Model to Impression Formation. "Science", 1962, 138, 817-818

*Anderson, N. H. On the Quantification of Miller's Conflict Theory. "Psychological Review", 1962, 69, 400-414

*Anderson, N. H. A Simple Model for Information Integration. In R.P. Abelson, E. Aronson, W.J. McGuire, T.M. Newcomb, M.J. Rosenberg, & P.H. Tannenbaum (Eds.), "Theories of Cognitive Consistency: A Sourcebook." Chicago: Rand McNally, 1968

*Anderson, N. H. Functional Measurement and Psychophysical Judgment. "Psychological Review", 1970, 77, 153- 170.

*Anderson, N. H. Integration Theory and Attitude Change. "Psychological Review", 1971, 78, 171-206.

*Anderson, N. H. (1981). "Foundation of information integration theory". New York: Academic Press.

*Norman, K. L. (1973). "A method of maximum likelihood estimation for information integration models". (CHIP No. 35). La Jolla, California: University of California, San Diego, Center for Human Information Processing.

*Norman, K. L. (1976). A solution for weights and scale values in functional measurement. "Psychological Review, 83", 80-84.**External links*** [

*http://lap.umd.edu/simile/ Stimulus Integration Models Iterated for Likelihood Estimates*]

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