Intentional and Phenomenal attributions in the light of the influence of personality traits, and Attitudes towards robots on pro-social behaviour in human-robot interaction
In the decades to come, robots could become more present in the human environment increasing the likelihood to interact with them. When reasoning about them, individuals tend to endow robots with human-like characteristics such as intentions or emotions, they develop attitudes toward them and differ in their likelihood to cooperate with them . However, how these different variables emerge, interact in the human mind and effect actual behaviour in HRI is still poorly understood. In three studies, using the intentional and phenomenal stance theoretical framework, the attitudes toward robots evaluation and the Big-Five personality traits framework we investigated the attribution of intentional and phenomenal experience to robots and the influence of imaginative representation robots on the interpretative attributions (Experiment 1). We also evaluated how the context of evaluation presenting robots with different level of human-likeness as potential social actors compared to mere technological prototypes and the prior attitudes toward them could bias intentional/phenomenal attributions (Experiment 2). Finally, we used a human-robot a prisoner’s dilemma game and developed a structural integrative model using attributions, attitudes and personality traits to evaluate the likelihood of participants to make a prosocial decision in HRI (Experiment 3).Experiment 1, 2 and 3 results showed that intentional stance is more readily adopted than phenomenal stance and that the imaginative type of the stances predicts the interpretative type. In experiment 2 level of attributions were predicted by attitudes toward robots. Also, attributions were influenced by robot human-likeness and the presentation of robots as social, compared to non-social, agents. Finally, experiment 3 structural integrative model showed a predominance of personality traits and attitudes to predict the likelihood to cooperate in an actual HRI.