Liberal representationalism is the view that even some internal states of very simple organisms like plants or bacteria count as genuine representations. This view has been heavily criticized by many authors, including myself. In a recent paper, Marc Artiga attempts to defend liberal representationalism against these criticisms. One of his main targets is an argument of explanatory exclusion that he ascribes to Burge, Ramsey, Rescorla, Sterelny and me (among others). In this paper, I reply to Artiga by distinguishing the exclusion (...) argument that he focuses on from what is, in my view, the real explanatory challenge for liberal representationalism, and I argue that none of the considerations put forward by Artiga in defense of the liberal view provide an adequate answer to this challenge. (shrink)

Representationalists have routinely expressed skepticism about the idea that inflexible responses to stimuli are to be explained in representational terms. Representations are supposed to be more than just causal mediators in the chain of events stretching from stimulus to response, and it is difficult to see how the sensory states driving reflexes are doing more than playing the role of causal intermediaries. One popular strategy for distinguishing representations from mere causal mediators is to require that representations are decoupled from specific (...) stimulus conditions. I believe this requirement on representation is mistaken and at odds with explanatory practices in sensory ecology. Even when sensory states have the job of coordinating a specific output with a specific input, we can still find them doing the work of representations, carrying information needed for organisms to respond successfully to environmental conditions. We can uncover information at work by intervening specifically on the information conveyed by sensory states, leaving their causal role undisturbed. (shrink)

There is an apparent connection between reinforcement learning and agency. Artificial entities controlled by reinforcement learning algorithms are standardly referred to as agents, and the mainstream view in the psychology and neuroscience of agency is that humans and other animals are reinforcement learners. This article examines this connection, focusing on artificial reinforcement learning systems and assuming that there are various forms of agency. Artificial reinforcement learning systems satisfy plausible conditions for minimal agency, and those which use models of the environment (...) to perform forward search are capable of a form of agency which may reasonably be called action for reasons. (shrink)

The teleosemantic indeterminacy problem has generated much discussion but no consensus. One possible solution is to accept indeterminacy as a real feature of some representations. I call this view “indeterminacy realism.” In this paper, I argue that indeterminacy realism should be treated as a serious option. By drawing an analogy with vagueness, I try to show that accepting the reality of indeterminacy would not be catastrophic for teleosemantics. I further argue that there are positive reasons to endorse indeterminacy realism. I (...) end the paper by arguing that indeterminacy realism need not generalize viciously to the case of propositional attitudes. (shrink)