When Collaboration Falters, Insensitivity to How Our Actions Affect Others Drives Inflated Self-evaluations
Year: 2025
Type: article
Abstract: During high-stake interactions, people not only evaluate policies or outcomes, but also themselves and others. Such evaluations may be crucial for long-term outcomes, such as harmonious marriage, confident leadership and indeed mental health. Powerful evaluations occur during interactions, where people can support or let each other down. Thus, we implemented an interactive decision-making game, wherein two real-life participants explicitly evaluated themselves and their play-partner while playing an ecologically framed, probabilistic, iterated prisoner's dilemma. To separate preferences from abilities, participants did not interact with the other directly, but instructed a computer avatar on how to play on their behalf. We tested a range of computational models of participants' person-evaluations. In some, self-evaluation relied on regret or satisfaction regarding one's decisions. However, the winning models relied directly on observed gains and losses. Here, evaluation of the self was proportional to how much one's partner benefited, and vice versa. We found a marked self-positivity bias, which was most prominent in dyads where both partners often defected. Between participants, a self-positivity bias was explained by a reduced weight of one's partner's benefits onto self-evaluation. This suggests that the negative outcomes claimed to attract defensive, external attribution by attribution theorists are one's partner's poor outcomes. Further analysis suggested that a reduced sensitivity to others' outcomes was associated with reduced earnings for the self, hinting at a functional role for person-evaluations in decision-making. Thus, we introduce a novel computational model that provides a concise account of self-serving bias in evaluations, as observed during risky dyadic interactions. Supplementary Information: The online version contains supplementary material available at 10.1007/s42113-025-00250-y.
Source: Computational Brain & Behavior
Authors: Michael Moutoussis, Meera Gosalia, Geert-Jan Will, Giles Story, Tobias U. Hauser, Aislinn Bowler, Siobhan Edinboro, Edward Bullmore, Raymond Dolan, Ian Goodyer, Peter Fonagy, Peter Jones, Michael Moutoussis, Tobias U. Hauser, Sharon Neufeld, Rafael Romero-Garcia, Michelle St Clair, Petra Vértes, Kirstie Whitaker, Barry Widmer, Gita Prabhu, Umar Toseeb, Junaid Bhatti, Laura Villis, Becky Inkster, Cinly Ooi, Pasco Fearon, John Suckling, Anne-Laura van Harmelen, Rogier Kievit, Ayesha Alrumaithi, Sarah Birt, Aislinn Bowler, Kalia Cleridou, Hina Dadabhoy, Emma Davies, Ashlyn Firkins, Sian Granville, Elizabeth Harding, Alexandra Hopkins, Daniel Isaacs, Janchai King, Danae Kokorikou, Christina Maurice, Cleo McIntosh, Jessica Memarzia, Harriet Mills, Ciara O’Donnell, Sara Pantaleone, Jenny Scott, Gita Prabhu, Raymond Dolan
Language: English
Subfield: General Decision Sciences
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
OpenAlex ID: W4411411006