People more frequently select norm-violating factors, relative to norm- conforming ones, as the cause of some outcome. Until recently, this abnormal-selection effect has been studied using retrospective vignette-based paradigms. We use a novel set of video stimuli to investigate this effect for prospective causal judgments—i.e., judgments about the cause of some future outcome. Four experiments show that people more frequently select norm- violating factors, relative to norm-conforming ones, as the cause of some future outcome. We show that the abnormal-selection effects (...) are not primarily explained by the perception of agency (Experiment 4). We discuss these results in relation to recent efforts to model causal judgment. (shrink)

People often reason about omissions. One line of research shows that people can distinguish between the semantics of omissive causes and omissive enabling conditions: for instance, not flunking out of college enabled you (but didn’t cause you) to graduate. Another line of work shows that people rely on the normative status of omissive events in inferring their causal role: if the outcome came about because the omission violated some norm, reasoners are more likely to select that omission as a cause. (...) We designed a novel paradigm that tests how norms interact with the semantics of omissive enabling conditions. The paradigm concerns the circuitry of a mechanical device that plays music. Two experiments used the paradigm to stipulate norms and present a distinct set of possibilities to participants. Participants chose which causal verb best described the operations of the machine. The studies revealed that participants’ responses are best predicted by their tendency to consider the semantics of omissive relations. In contrast, norms had little to no effect in participants’ responses. We conclude by marshaling the evidence and considering what role norms may play in people’s understanding of omissions. (shrink)

Lieto’s Minimal Cognitive Grid (MCG) for assessing artificial agents, augmented as the method MCG+, has two implications: (1) MCG+ can advance the mathematical science of universal intelligence/cognition. (2) (a) pre-Lieto, this science lacks of coverage of perception; (b) heralded artificial agents of today are devoid of human-level perceptual intelligence.

For decades AI researchers have built agents that are capable of carrying out tasks that require human-level or human-like intelligence. During this time, questions of how these programs compared in kind to humans have surfaced and led to beneficial interdisciplinary discussions, but conceptual progress has been slower than technological progress. Within the past decade, the term agency has taken on new import as intelligent agents have become a noticeable part of our everyday lives. Research on autonomous vehicles and personal assistants (...) has expanded into private industry with new and increasingly capable products surfacing as a matter of routine. This wider use of AI technologies has raised questions about legal and moral agency at the highest levels of government (National Science and Technology Council 2016) and drawn the interest of other academic disciplines and the general public. Within this context, the notion of an intelligent agent in AI is too coarse and in need of refinement. We suggest that the space of AI agents can be subdivided into classes, where each class is defined by an associated degree of control. (shrink)