Error and bias in attribution

Although the likes of Heider and Kelly would have us believe that like ‘little professors’ we are completely logical, systematic and rational when interpreting behaviour, in fact we are far less scientific than the ‘normative’ or idealised models of attributions would have us believe. (Fiske 2004).

Zebrowitz (1990) describes attributional bias as…

“The tendency to favour one cause over another when explaining some effect. Such favouritism may result in causal attributions that deviate from predictions derived from rational attributional principles like covariation”

Most behaviour is a product of both individual and situation but our causal attributions often favour one of these causes over the other.

Jones and Nisbett (1971) suggests that we all want to see ourselves as competent interpreters of human behaviour and so we naively assume that simple explanations are better than complex ones.

The actor observer effect: One commonly observed bias in attribution is the actor-observer effect. Jones and Nisbett (1971) noted that actors and observers frequently make differing attributions about the same event.

• Actors tend to see their own behaviour as a reaction to circumstances; thus variable from situation to situation (external attributions-situational)
• Observers attribute the same behvaiour to dispositional factors and intentions and thus more consistent across situations; this is an example of FAE – see below

For example, Nisbett (1973) explained that students…

• would assume that actors would behave in the future in ways similar to those they had just witnessed
• describe friends’ choices of girlfriends and degree subjects by referring to the friend’s disposition but describe their own choice girlfriends or course based only qualities of the girlfriend or course.

For this learning objective you need to know about 2 attributional errors. We will focus on:

The Fundamental Attribution Error

Self serving bias

Conclusion

These errors serve as good evaluation of the normative models as they describe what actually happens as opposed to what is predicted by the models.