Abstract
A theoretical pillars of vision science in the information-processing tradition is that perception involves unconscious inference. The classic support for this claim is that, since retinal inputs underdetermine their distal causes, visual perception must be the conclusion of a process that starts with premises representing both the sensory input and previous knowledge about the visible world. Focus on this “argument from underdetermination” gives the impression that, if it fails, there is little reason to think that visual processing involves unconscious inference. Here an alternative means of support for this pillar is proposed, based on another foundational challenge for the visual system: recognizing invariant properties of objects in the environment even though anything we encounter is never seen exactly the same way twice. Explaining how the visual system solves this invariance problem requires positing visual processes that exhibit many commonalities with inductive inference. Thus, this novel “argument from invariance” reveals one way in which visual processing clearly involves unconscious inference.
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Notes
The idea of unconscious inference is one of many insights about vision first made by Ibn Al-Haytham (latinized Alhazen) that were later rediscovered (Howard , 1996).
All of these diagnostic features could be interpreted in a manner that does not require explicit representation. Instead, the information or knowledge from prior experiences is somehow “implicitly” represented in the operation of the visual system. However, this broader interpretation would not seem to describe a form of inferential process and is closer to the sort of metaphorical usages that have often been criticized (Hatfield , 2002; Orlandi , 2014). In the present discussion, I only consider these features in the more restricted sense that requires explicit mental representation of the inputs to the process.
There are two senses in which the diagnostic features I have enumerated might be thought to apply to the solutions of mapping problems, depending on how each is characterized. First, in Fig. 1A, we might want to explain how one comes to guess that the birds are ospreys, given the evidence available. The answer, or “solution”, in this case, is that one has used inductive reasoning. Second, we might then seek to explain how this deliberation is achieved, from an information-processing perspective. In which case, the “problem” itself is a mapping achieved via inductive deliberation and the information-processing “solution” must also exhibit the features, assuming it explains (rather than explains away) this deliberation. It is this second sense of mapping problems/solutions, which I have in mind.
The content must also presumably be original, in the sense of not being determined by convention or the intentions of a separate agent (Searle , 1983). Furthermore, the internal state of the visual system that is the vehicle for the content must serve a representational function, like being used by the visual system to stand-in for what it represents to aid in further information-processing or action (Ramsey , 2007). Here I take these conditions for granted and focus on the conditions of distality and robustness.
Quilty-Dunn and Mandelbaum (2018, pp. 6–8) require that an inferential transition be not just rule-following but also “logic-obeying”. The notion of logic-obeying they have in mind is tied to the idea of discursive representational formats in which a representation can be decomposed into a canonical contituent structure. Thus, they include the requirement that bare inferential transitions occur in virtue of the architecture of a system being sensitive to the constituent structure of the representations involved. I have excluded this requirement because the same notion of logic-obeying would seem to be inherent in the very idea of information-processing as a species of computation. For under a very general characterization, all computations operate in accordance with rules that are sensitive to only the constituent structure of the symbols over which they are defined (Piccinini & Scarantino , 2010). To put the point simply: if visual information-processing operations are carried out over mental representations they will have a discursive format.
Matters may ultimately depend on the sense of “innateness” being employed or how one characterizes the debate between empiricist and nativist hypotheses, both of which are topics of discussion in their own right (Linquist , 2018). Here I assume that a psychological capacity is innate just in case it is not learned (Ritchie , 2020; Samuels , 2002) and that the debate concerns domain-specific vs domain-general learning processes in development (Margolis & Laurence , 2013). As to how much learning, or what style of learning, is required by my characterization of an induction problem, I remain agnostic. For example, it is compatible with the possibility of zero-shot learning constrained by inductive biases built into the visual system.
Though any connection of Bayesian modeling to the actual work of figures like von Helmholtz is rather tenuous (Westheimer , 2008).
Priors being reflected in natural constraints also makes sense of how they might be innate, but not in a way that supports an inferential interpretation of Bayesian models (cf. Scholl , 2005).
Buckner (2019b) argues that categorization behavior picks out the the lower bound on rational practical inference. There are commonalities between Buckner’s argument and the one present here, as he also acknowledges that it may be grounded in similar claims about theoretical inference (Buckner, 2019b, p. 702). However, a notable difference is that his argument identifies a role for metacognitive feelings in guiding the deliberative process and so does not concern unconscious inference as such.
Object recognition, so characterized, should be distinguished from object detection, which concerns whether we see an object, but not what it is. Instances of object (or visual feature) detection are unlikely to involve unconscious inductive inference in the sense I have spelled out if they reflect hardcoded natural constraints that leave no room for learning and generalization—especially when they lead to a reflex-like behavior. For example, “sign stimuli” that cause fixed action plans by organisms involve detection of a target that is innately specified and not open to learning or modulation from experience. Hence, the processes that control the release of behavior in such cases will not qualify as instances of unconscious inductive inference.
Our ability to discriminate colors is also typically considered distinct from the phenomenon of color categorization (see e.g. Witzel & Gegenfurtner, 2018).
Of course, “in the wild” object recognition does not involve an explicit partition between training and test experiences with explicit feedback. Some behavioral paradigms also exclude explicit feedback during training, such as those that involve passive viewings of sequential viewpoint images of objects where learning is via temporal association (Cox et al. , 2005; Tian & Grill-Spector , 2015; Wallis & Bülthoff , 2001).
If the state space is encoded in distributed patterns of neural activity (say) then the information-processing rules will also be defined with respect to the sub-symbols that make up the pattern, rather than the dimensions of the state space themselves. Thus, it must be further assumed that operations over distributions of sub-symbols is one way in which inferential transitions over state spaces can be implemented.
A response I will not consider is that there is no invariance problem. For example, Gibson (1979), and many following in the ecological perception tradition (e.g. Burton & Turvey, 1990), reject the existence of the invariance problem because they posit a unique mapping between the distal world, proximal stimulation, and perception. However even to some within the ecological psychology tradition he started, the existence of such a “one-to-one-to-one” mapping is empirically untenable (Withagen & Chemero , 2009).
For a philosophical introduction to DNNs, see Buckner (2019a). Briefly, architecturally what distinguishes DNNs from earlier generations of neural networks is the following: first, they are “deep” in the sense that they have more than one hidden layer (sometimes even hundreds of them). Second, they involve a mixture of different kinds of layers, such as convolutional and fully connected layers. And third, they are sparsely connected. For example, convolutional layers may only be connected with a subset of nodes in the next layer. Technologically, the initial critical advance was to leverage GPUs to train networks with several convolutional layers on complex stimulus sets using error back propagation, which had not previously been feasible (Krizhevsky et al. , 2012).
Note that this is consistent with the earlier claim that priors being reflected in natural constraints undermines the underdetermination argument. Under the characterization I have offered of induction problems and their solutions, (i) the inputs must be overtly represented, but (ii) not the transition rules that govern the relationship between them. Priors as natural constraints is inconsistent with (i), but inferential transitions as natural constraints is consistent with (ii).
Cermeño-Aínsa (2021) rejects Beck’s stimulus-dependence condition based in part on visual categorization as a case study. However, his critique rests on two mistaken claims about visual categorization and how it is explained. The first is that the neural basis of categorization is not specific to visual cortex (Cermeño-Aínsa, 2021, p. 13). This claim runs counter to the vast majority of research in visual neuroscience (DiCarlo et al. , 2012). The second is to not properly distinguish between cases like Fig. 1A, B. Cermeño-Aínsa (2021, p. 14) claims that visual categorization is not perceptually grounded because, on the one hand, we can visually categorize without seeing all the distinctive properties of an object so it is not proximally constrained; and on the other, that visual categories involves our conceptual capacities. However, Beck precludes cases like Fig. 1A as perceptual because in such a case no diagnostic visual properties of ospreys themselves are visible; being proximally constrained only requires that some of these properties are visible. Furthermore, as also pointed out in the text, attributing appearances does not require conceptual capacities.
Another consideration is that evidence of cognitive penetration may even be compatible with (or even provide evidence in favor of) information encapsulation, despite the common assumption to the contrary (Clarke , 2020).
Thank you to Bence Nanay, Bryce Huebner, Cameron Buckner, David Barack, Evan Westra, and the anonymous referees of this journal, for their helpful feedback on earlier versions of this manuscript. This work was also previously presented at Johns Hopkins University. Thank you to the audience there, and in particular, Chaz Firestone, Jorge Morales, and Steve Gross, for their feedback on the project. This research was supported by the Intramural Research Program of the National Institute of Mental Health (ZIAMH002909 awarded to Chris I. Baker).
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Brendan Ritchie, J. Recognizing why vision is inferential. Synthese 200, 25 (2022). https://doi.org/10.1007/s11229-022-03508-1
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DOI: https://doi.org/10.1007/s11229-022-03508-1