Michael Tomasello ( 2016 ) prominently defends the view that there are uniquely human capacities required for shared intentions, therefore great apes do not share intentions. I show that these uniquely human capacities for abstraction are not necessary for shared intentionality. Excluding great apes from shared intentions because they lack certain capacities for abstraction assumes a specific interpretation of shared intentionality, which I call the Roleplaying Model. I undermine the necessity of abstraction for shared intentionality by presenting an alternative model (...) of shared intentionality, which I call the Normative Model. It is a modification of Margaret Gilbert’s ( 2013 ) Plural Subject theory, which I modify such that it is, in principle, compatible with nonhuman shared intentionality. To do this, I substitute Gilbert’s account of obligation for Kristin Andrews’ ( 2020 ) account of animal social norms. In this Normative Model, uniquely human capacities for abstraction are not required for shared intentionality. By undermining the necessity of uniquely human abstraction, I make cases of prima-facie cooperation in chimpanzees plausible instances of shared intentionality. The Roleplaying Model’s denial of nonhuman shared intentionality creates a problem insofar as apparent shared intentional activity cannot be fully analyzed. The Normative Model resolves this problem. (shrink)

The paper focuses on the issue of compatibility of social institution and convention. At first, it introduces the modest account of conventionality building on five distinctive features – interdependence, arbitrariness, mind-independence, spontaneity, and normative-neutrality – which constitute conventional behaviour, then it presents the two major theories of social institutions that explain them in terms of rules, or equilibria. The argument is that conventions cover a wide-ranging area and cannot be identified with the category of institutions because it would be too (...) restrictive and contradictory to the initial modest account. (shrink)