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Cognitive Dissonance

Ledia Kazaz, University of Elbasan "Aleksandër Xhuvani", Albania
lediakazazi@gmail.com

Gal Harpaz, The Open University of Israel
doctorharpaz@gmail.com




Cognitive dissonance theory developed in the late 1950s by US psychologist Leon Festinger refers to the psychological discomfort that arises from holding conflicting beliefs, attitudes, or behaviours simultaneously. It claims that people tend to avoid information and situations that are likely to increase dissonance. According to Festinger, pairs of cognitions (elements of knowledge) can be relevant or irrelevant to one another. If two cognitions are relevant to one another, they are either consonant or dissonant. Two cognitions are consonant if one follows from the other, and they are dissonant if the obverse (opposite) of one cognition follows from the other. To alleviate cognitive dissonance, individuals may engage in various cognitive and behavioural strategies, such as rationalisation, denial, or attitude change.

Festinger proposed strategies for dealing with a situation in which cognitive dissonance arises. First, when experiencing dissonance, and a feeling of psychological discomfort arises, the person will be motivated to try to reduce the dissonance and achieve consonance. For example, this underlying tension could motivate an individual to make an attitude change that would produce consistency between thoughts and behaviours.

Second, in addition to trying to reduce it, the person will actively avoid situations and information which would likely increase the dissonance for him/her.



Keywords: rationalisation, denial, attitude change

Related Entries: Belief, Cognition/Cognitive Linguistics, Epistemic/Truth

References:
Festinger, L. (1957). A theory of cognitive dissonance. Stanford University Press.
Festinger, L. (1962). Cognitive dissonance. Scientific American, 207(4), 93-106.‏ https://www.jstor.org/stable/24936719
Li, K., Liang, H., Kou, G., & Dong, Y. (2020). Opinion dynamics model based on the cognitive dissonance: An agent-based simulation. Information Fusion, 56, 1-14.‏ https://doi.org/10.1016/j.inffus.2019.09.006