As robots begin to receive citizenship, are treated as beloved pets, and given a place at Japanese family tables, it is becoming clear that these machines are taking on increasingly social roles. While human-robot interaction research relies heavily on self-report measures for assessing people’s perception of robots, a distinct lack of robust cognitive and behavioural measures to gauge the scope and limits of social motivation towards artificial agents exists. Here we adapted Conty and colleagues’ (2010) social version of the classic Stroop paradigm, in which we showed four kinds of distractor images above incongruent and neutral words: human faces, robot faces, object faces (for example, a cloud with facial features) and flowers (control). We predicted that social stimuli, like human faces, would be extremely salient and draw attention away from the to-be-processed words. A repeated-measures ANOVA indicated that the task worked (the Stroop effect was observed), and a distractor-dependent enhancement of Stroop interference emerged. Planned contrasts indicated that specifically human faces presented above incongruent words significantly slowed participants’ reaction times. To investigate this small effect further, we conducted a second experiment (N=51) with a larger stimulus set. While the main effect of the incongruent condition slowing down participants’ reaction time replicated, we did not observe an interaction effect of the social distractors (human faces) drawing more attention than the other distractor types. We question the suitability of this task as a robust measure for social motivation and discuss our findings in the light of recent conflicting results in the social attentional capture literature.