The Structuring Causes of Behavior: Has Dretske Saved Mental Causation?

Hofmann, Frank; Schulte, Peter

doi:10.1007/s12136-014-0218-8

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Other liberal representationalists may try to adopt the same strategy. Unfortunately for those theorists, I think that the Dretskean strategy is almost certainly doomed to failure (for reasons laid out in Hofmann and Schulte ; cf. also Cummins and Kim for criticism).…”

Section: The Exclusion Argument and The Real Explanatory Challenge Fomentioning

confidence: 99%

Challenging Liberal Representationalism: A Reply to Artiga

Schulte

2019

Dialectica

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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.

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Section: The Exclusion Argument and The Real Explanatory Challenge Fomentioning

confidence: 99%

Challenging Liberal Representationalism: A Reply to Artiga

Schulte

2019

Dialectica

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“…Hofmann and Schulte (2014) argue that Dretske's theory does not succeed in explaining how the content of mental states can cause behaviour. But this is compatible with it providing an attractive account of a minimal form of agency.…”

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confidence: 95%

Reinforcement learning and artificial agency

Butlin

2023

Mind & Language

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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.

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“…8 8 However, not everyone agrees that teleosemantics can account for the explanatory role of content, at least not in the way suggested. Compare (Hofmann & Schulte, 2014; Shea, 2007). …”

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confidence: 99%

Should the teleosemanticist be afraid of semantic indeterminacy?

Bergman

2021

Mind & Language

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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.

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The Structuring Causes of Behavior: Has Dretske Saved Mental Causation?

Cited by 4 publications

References 20 publications

Challenging Liberal Representationalism: A Reply to Artiga

Challenging Liberal Representationalism: A Reply to Artiga

Reinforcement learning and artificial agency

Should the teleosemanticist be afraid of semantic indeterminacy?

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