TY - GEN

T1 - Modelling the IAT

T2 - Implicit Association Test reflects shallow linguistic environment and not deep personal attitudes

AU - Lynott, Dermot

AU - Connell, Louise

AU - O'Brien, Kerry S.

AU - Kansal, Himanshu

PY - 2012

Y1 - 2012

N2 - People often have thoughts, attitudes and biases that are not themselves consciously aware of or that they would rather not share with others. To assess such attitudes, researchers use paradigms like the Implicit Association Test (IAT) that do not rely on explicit responding to determine the level of bias a person holds towards a particular target concept (e.g., race, gender, age). Responses in the IAT are assumed to reflect deeply held beliefs and attitudes, and not shallow, superficial associations. However, as linguistic distributional information has been shown to serve as a viable heuristic in many cognitive tasks, we investigated whether it could be used to predict the level of bias established by the IAT. We used a large corpus of language (Web 1T) and data from 16 IAT studies (N = 1825) to examine whether the degree of linguistic co-occurrence for target concepts and attributes reflected the size of bias observed in human behavioural data. We found that the effect size of the linguistic biases corresponded strongly with the effect sizes from the behavioural data. We suggest that language reflects prevalent cultural attitudes which are captured by tasks such as the IAT, suggesting that the IAT may reflect shallow, linguistic associations rather than deeper conceptual processing.

AB - People often have thoughts, attitudes and biases that are not themselves consciously aware of or that they would rather not share with others. To assess such attitudes, researchers use paradigms like the Implicit Association Test (IAT) that do not rely on explicit responding to determine the level of bias a person holds towards a particular target concept (e.g., race, gender, age). Responses in the IAT are assumed to reflect deeply held beliefs and attitudes, and not shallow, superficial associations. However, as linguistic distributional information has been shown to serve as a viable heuristic in many cognitive tasks, we investigated whether it could be used to predict the level of bias established by the IAT. We used a large corpus of language (Web 1T) and data from 16 IAT studies (N = 1825) to examine whether the degree of linguistic co-occurrence for target concepts and attributes reflected the size of bias observed in human behavioural data. We found that the effect size of the linguistic biases corresponded strongly with the effect sizes from the behavioural data. We suggest that language reflects prevalent cultural attitudes which are captured by tasks such as the IAT, suggesting that the IAT may reflect shallow, linguistic associations rather than deeper conceptual processing.

KW - Cognitive Science

KW - implicit attitudes

KW - IAT

M3 - Conference contribution/Paper

SP - 1948

EP - 1953

BT - Proceedings of the 34th Annual Conference of the Cognitive Science Society

A2 - Miyake, Naomi

A2 - Peebles, David

A2 - Cooper, Rick

CY - Austin, TX

ER -