Reliable misrepresentation and tracking theories of mental representation

Mendelovici, Angela

doi:10.1007/s11098-012-9966-8

Cited by 48 publications

(36 citation statements)

References 11 publications

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“…For recent critical discussions, see Schellenberg (2010), (2011a) and Echeverri (2016). 2 For views according to which hallucinating subjects stand in awareness or acquaintance relations to property-clusters, see Johnston (2004); for (uninstantiated) properties, see Dretske (1995), Byrne (2001), Tye (2002), Hill (2006), (2009), Pautz (2007), Siegel (2011), Mendelovici (2013, and Statzicker (2016); for phenomenal properties, see Chalmers (2006), Block (2007); for propositions, see Russell (1913); for intentional objects, see Harman (1990), Lycan (1996); for sense-data, see Robinson (1994); for qualia, see Levine (1983), Chalmers (1996), Block (2003), McLaughlin (2007); for Meinongian objects, see Parsons (1980). It is important to note that one could argue that hallucinating subjects represent intentional objects without arguing that perceivers stand in awareness or acquaintance relations to such objects.…”

Section: Codamentioning

confidence: 99%

Perceptual Consciousness as a Mental Activity

Schellenberg

2017

Nous

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I argue that perceptual consciousness is constituted by a mental activity. The mental activity in question is the activity of employing perceptual capacities, such as discriminatory, selective capacities. This is a radical view, but I hope to make it plausible. In arguing for this mental activist view, I reject orthodox views on which perceptual consciousness is analyzed in terms of (sensory awareness relations to) peculiar entities, such as, phenomenal properties, external mind‐independent properties, propositions, sense‐data, qualia, or intentional objects.

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Section: Codamentioning

confidence: 99%

Perceptual Consciousness as a Mental Activity

Schellenberg

2017

Nous

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“…This version of representationalism will meet with difficulty if misrepresentation turns out to be widespread and systematic, as it is on Watzl's view. To see why, consider Mendelovici's (2012) distinction between reliability and veridicality. Reliable misrepresentation involves a similar response in similar circumstances, even though the world isn't really the way it is represented to be.…”

Section: Widespread Errormentioning

confidence: 99%

Perceptual content is indexed to attention

Prettyman

2016

Synthese

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Attention seems to raise a problem for pure representationalism, the view that phenomenal content supervenes on representational content. The problem is that shifts of attention sometimes seem to bring about a change in phenomenal content without a change in representational content. I argue that the representationalist can meet this challenge, but that doing so requires a new view of the representational content of perception. On this new view, the representational content of perception is always relative to a way of attending. I call this the attention-indexed view of perceptual content.

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“…First, because only a subset of the states and contingencies in an environment will be relevant for behaviour, action-oriented models need not exhaustively model their environment (Baltieri and Buckley, 2017). Moreover, specific misrepresentations may prove to be useful for action (Wiese, 2017;McKay and Dennett, 2009;Mendelovici, 2013;M. Zehetleitner and Schönbrodt, 2015), indicating that action-oriented models need not be accurate.…”

Section: Introductionmentioning

confidence: 99%

Learning action-oriented models through active inference

Tschantz

Seth

Buckley

2019

Preprint

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Converging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms.

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Reliable misrepresentation and tracking theories of mental representation

Cited by 48 publications

References 11 publications

Perceptual Consciousness as a Mental Activity

Perceptual Consciousness as a Mental Activity

Perceptual content is indexed to attention

Learning action-oriented models through active inference

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