We explored the frequency with which typical adults make Theory of Mind attributions, and under what circumstances these attributions occur. We used an experience sampling method to query 30 typical adults about their everyday thoughts. Participants carried a Personal Data Assistant that prompted them to categorize their thoughts as Action, Mental State, or Miscellaneous at approximately 30 pseudo-random times during a continuous 10-h period. Additionally, participants noted the direction of their thought and degree of socializing at the time of inquiry. (...) We were interested in the relative frequency of ToM and how prominent they were in immediate social exchanges. Analyses of multiple choice answers suggest that typical adults: spend more time thinking about actions than mental states and miscellaneous things, exhibit a higher degree of own- versus other-directed thought when alone, and make mental state attributions more frequently when not interacting than while interacting with others . A significant 3-way interaction between thought type, direction of thought, and socializing emerged because action but not mental state thoughts about others occurred more frequently when participants were interacting with people versus when alone; whereas there was an increase in the frequency of both action and mental state attributions about the self when participants were alone as opposed to socializing. A secondary analysis of coded free text responses supports findings 1–3. The results of this study help to create a more naturalistic picture of ToM use in everyday life and the method shows promise for future study of typical and atypical thought processes. (shrink)

Could computers ever be conscious? Will they ever have ideas that one could attribute to them and not to the programmer? Will robots be able to 'feel pain', instead of processing bits from sensors informing about danger? Will they have true emotions? These questions may never be answered, but it makes sense to ask whether humans will ever attribute mind to artifacts. This paper suggests introducing a third level of claims regarding artificial intelligence (AI), which is in between 'weak AI' (...) and 'strong AI', the so-called 'attributed AI'. This level requires more than weak AI ('behave as if', which could be said of any desktop calculator), but is less presumptuous than strong AI ('computers that think', a claim that is hard to prove). Attributed AI can be measured. This paper discusses behavioral paradigms for measurements of attributed AI and presents first experimental data. (shrink)