Network model for implicit measures of attitudes

Network model for implicit measures of attitudes

Study: Network model for implicit measures of attitudes
Credit: Alina Grubnyak/Unsplash

Our attitudes are composed of an interacting constellation of feelings, beliefs, and behaviors, and these elements can be in conflict with each other. For instance, a person might believe in principles like justice and equality while simultaneously harboring negative feelings toward a minority group.

Building on a network theory of human attitudes, SFI Postdoctoral Fellow Jonas Dalege and co-author Han L. J. van der Maas have shown why implicit measures are better suited to assess such conflicting attitude elements. Their findings are published in a new paper in the journal Social Cognition.

If a researcher wants to know what a person thinks, or how they feel, or where their biases lie, there are two general approaches: a direct assessment that gives the person time to reflect on their answers, or an indirect test that requires that the person respond quickly with little thought. These indirect measurements offer researchers a snapshot of a person’s spontaneous judgments and implicit biases. Several well-known indirect tests that measure implicit biases ask participants to match, rapidly, images of people with positive or negative words. The results are often noisier than direct assessments, but, somewhat paradoxically, that noisy data may also be the strength of the indirect approach, say the authors of the new paper.

When given the chance to really consider our responses, as with direct attitude assessments, our attitude elements—our beliefs, feelings, and behaviors—become more interdependent. As a result of this interdependency, our overall attitudes become more stable and more extreme.

Implicit measures, on the other hand, capture a person’s feelings without asking the person to self-reflect on those feelings. This makes it less likely that any single feeling is suppressed by another conflicting feeling and decreases interdependency of attitude elements. Because of this, implicit measures assess attitudes in high-entropy states, where they may be inconsistent and unstable.

Our beliefs often outweigh feelings when we ponder them, but we tend to act on our feelings and implicit biases when making quick decisions. Implicit measures, which assess attitudes in a noisier state, give a fuller, more accurate, picture of someone’s attitudes, says Dalege.

So, which is a person’s “true” attitude—the implicit or the explicit? It’s a point of contention in social psychology, says Dalege, but it might be the wrong question. “This model suggests there are actually multiple processes going on. Our model implies that we each have many different attitudes, and different processes determine which attitude you’ll actually express.”


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More information:
Jonas Dalege et al. Accurate by Being Noisy: A Formal Network Model of Implicit Measures of Attitudes, Social Cognition (2020). DOI: 10.1521/soco.2020.38.supp.s26

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Santa Fe Institute


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Study: Network model for implicit measures of attitudes (2020, December 4)
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