Control Yourself: Broad Implications of Narrowed Attention

The Inversion Problem: Why Algorithms Should Infer Mental State and Not Just Predict Behavior

More and more machine learning is applied to human behavior. Increasingly these algorithms suffer from a hidden-but serious-problem. It arises because they often predict one thing while hoping for another. Take a recommender system: It predicts clicks but hopes to identify preferences. Or take an algorithm that automates a radiologist: It predicts in-the-moment diagnoses while hoping to identify their reflective judgments. Psychology shows us the gaps between the objectives of such prediction tasks and the goals we hope to achieve: People can click mindlessly; experts can get tired and make systematic errors. We argue such situations are ubiquitous and call them "inversion problems": The real goal requires understanding a mental state that is not directly measured in behavioral data but must instead be inverted from the behavior. Identifying and solving these problems require new tools that draw on both behavioral and computational science.

Taming the white bear: Lowering reactance pressures enhances thought suppression

Individuals fail to suppress certain thoughts, especially under conditions that tax cognitive resources. We investigated the impact of modifying psychological reactance pressures on thought suppression attempts. Participants were asked to suppress thoughts of a target item under standard experimental conditions or under conditions designed to lower reactance pressures. In the presence of high cognitive load, weakening associated reactance pressures resulted in greater success at suppression. The results suggest that reducing relevant motivational pressures can facilitate thought suppression, even when an individual experiences cognitive limitation.