Loss Aversion and Preferences in Interaction

Abstract

Many interfaces attempt to assist users by offering suggestions, predicting actions, or correcting user input. Although these attempts may often be beneficial, they will occasionally fail, and prior research from psychology and behavioral economics on loss aversion suggests that such failures may have an overweighted impact on subjective preferences. To better understand the relationship between interface outcomes and subjective preferences, we adapted to interaction a model from behavioral economics describing reference-dependent preferences. Two experiments examined our model’s predictions: both involved word-snapping interface assistance during text selection, with subjects choosing whether they preferred unassisted text selection (no snapping) or assisted text selection (snapping). In Experiment 1, the word-snapping feature could be disabled when it was unhelpful by backtracking the selection, which required progress losses in terms of target characters selected. In Experiment 2, word-snapping could be disabled by pressing a modifier key and waiting for an animation to complete, without need for the loss of target characters. Time and error performance with both experimental methods were comparable. The model predicts an aversion to progress losses in Experiment 1 that should be neutralized in Experiment 2, and the results of both experiments conformed to these predictions. We discuss the implications of the model as a platform for understanding user preferences more broadly, especially when considering interfaces that risk periodically failing to meet user expectations.

Notes

¹ In Section 7 we discuss how this summation is knowingly naïve due to psychological effects (such as peak-end), but it is sufficient for now.

² All $t$-tests use Welch–Satterthwaite degrees-of-freedom approximations for unequal variances.

Additional information

Notes on contributors

Philip Quinn

Philip Quinn ([email protected]) is a researcher with an interest in human factors measurement and modeling; he recently completed a doctorate at the University of Canterbury, New Zealand, and is currently a Senior Research Scientist at Google Inc. Andy Cockburn ([email protected], http://www.cosc.canterbury.ac.nz/~andy) is a computer scientist with interests in human factors measurement and modeling, and the design of user interfaces that accommodate and exploit specific human capabilities; he is a Professor in the Department of Computer Science and Software Engineering at the University of Canterbury, New Zealand.