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If perception is probabilistic, why does it not seem probabilistic? rstb.royalsocietypublishing.org Opinion piece Cite this article: Block N. 2018 If perception is probabilistic, why does it not seem probabilistic? Phil. Trans. R. Soc. B 373: 20170341. http://dx.doi.org/10.1098/rstb.2017.0341 Accepted: 5 June 2018 One contribution of 17 to a theme issue ‘Perceptual consciousness and cognitive access’. Subject Areas: cognition, behaviour Keywords: perception, consciousness, probability Author for correspondence: Ned Block e-mail: ned.block@nyu.edu Ned Block Department of Philosophy, New York University, New York, NY, USA NB, 0000-0003-0587-6899 The success of the Bayesian perspective in explaining perceptual phenomena has motivated the view that perceptual representation is probabilistic. But if perceptual representation is probabilistic, why does normal conscious perception not reflect the full probability functions that the probabilistic point of viewendorses? Forexample,neuronsincorticalareaMTthat respond to the direction of motion are broadly tuned: a patch of cortex that is tuned to vertical motion also responds to horizontal motion, but when we see vertical motion, foveally, in good conditions, it does not look at all horizontal. The standard solution in terms of sampling runs into the problem that sampling is an account of perceptual decision rather than perception. This paper argues that the best Bayesian approach to this problem does not require probabilistic representation. This article is part of the theme issue ‘Perceptual consciousness and cognitive access’. 1. Introduction Onemotivationfortreating neural representations as probabilistic isthat neurons are stochastic devices: identical inputs to identical neurons will inevitably yield variation in firing patterns. That applies to all neural representation, but there is a reason to expect perceptual representation, in particular, to be probabilistic because, given any activation of a perceptual system, there are many different environmental situations with different perceptible properties that could have produced it, some more probable than others. The visual system is said to cope with these facts by representing many of the possible environmental situations, each with a certain probability [1,2]. Perceptual representation of a range of environmental situations, each with a certain probability, is what is meant in this article by ‘probabilistic representation’. Forexample,Vuletal.saythat‘…thatinternalrepresentationsare madeupof multiple simultaneously held hypotheses, each with its own probability of being correct …’ [3]. Gross & Flombaum [4] describe ‘…a growing body of work that emphasizes the probabilistic nature of the computations and representations involved in a perceiver’s attempts to “infer” the distal scene from noisy signals and then store the representations it constructs’. They advocate probabilistic representations in which perceptual properties are attributed to places or things with a certain probability. It is often noted that perception does not normally seem probabilistic [3,5–7]. But how would perception seem if it did seem probabilistic? The phenomenology of perception would reflect the probability distributions of probabilistic perceptual representations. An example from motion-sensitive area MT that illustrates the problem is in figure 1. Tuning curves in individual neurons for direction of motion have broad sensitivities. (I will discuss populations of neurons later.) Tuning curves for neurons tuned to vertical downward motion respond also to a range of other motions, from horizontal motion to the left to horizontal motion to the right [8]. Still, when you look at a close medium-size object moving vertically in good conditions, you do not normally see any hint of horizontal motion. &2018 The Author(s) Published by the Royal Society. All rights reserved.visually evoked activity area MT Figure 1. The response of a patch of cortex tuned to downward motion in area MT of monkey cortex. (The curve is representative but hypothetical.) The height of the curve represents level of neuronal discharge. The shaded area indicates the most active neurons. Reproduced with permission from [8] the Society for Neuroscience. The same point applies to detectors for orientation in early cortical areas of the visual system. Seeing a vertical bar or grid activates neurons whose maximum response is to vertical grids. Simple cells tuned to vertical respond to a wide range of other orientations, but to a lesser degree, typically with substantial activations by grids tilted up to 308 in either direction, clockwise and anticlockwise [9,10]. But when one views a vertical grid foveally in normal conditions, there is no hint of the 308 tilts. You can verify this for yourself by looking at figure 2. (The fovea is the centre of the retina where cones are the densest. A thumb at arm’s length is seen entirely foveally.) The probabilistic point of view as applied to individual neurons dictates that these degrees of activation in neurons are representations of probabilities that the stimulus has one or another of these orientations, so the representation of the orientation of a grid is often thought of as a set of hypotheses attributing different probabilities to various different grids, or, alternatively, as a probability function over orientations. The reader may wonder whether the cortical areas mentioned really do support conscious perception rather than unconscious perception. It is controversial whether visual area V1 supports conscious perception [11,12], but other orientation-specific areas (e.g. V2 and V3) do support conscious perception. The evidence is overwhelming that MT/MST supports conscious perception. Micro-stimulation to MT/MST affects direction perception in monkeys according to the dominant tuning of the cells stimulated [13]. Damage to this area causes deficits in motion perception, including the total inability to see motion [11]. Subjects’ perception of motion are correlated with the dominant tuning of motion-selective cells including three-dimensional (3D) as well as 2D motion [14]. Even illusory motion correlates with activation in this area [15]. In brief, the problem to be discussed here is that conscious perception does not normally reflect the probabilistic hypotheses other than the dominant one. The response that I have often gotten from Bayesians is that it is a mistake to focus on the level of individual neurons. A Bayesian computation that combines information from many neurons in a population can be used to decide which hypothesis or narrow band of hypotheses best predicts the overall neural activations. (These are ‘likelihoods’ in a sense to be explained.) If the perceiver is in a scanner, combining responses of many neurons allows for decoding of the visual input orientation [16]. However, it is unclear how the procedures that allow the neuroscientist to decode from a population relate to the perceiver’s decoding from a population. There is no inner eye that looks at populations. There has to be some mechanism of combination. Unlesssomesuchmechanismisfound,weshouldnotsuppose there is any explicit representation [17] of these likelihoods. That is, we should treat the likelihoods or likelihood functions ‘instrumentally’, i.e. as ‘as if’ constructs. The subject of this article is probabilistic representation in perception, not cognition (thinking, reasoning, deciding). And it is probabilistic representation, not representation of probabilities. Let me explain the difference. The probabilistic perceptual representations at issue here are of this sort: ,red, therei,0.7.,to be read as a representation of redness at the location indicated by‘therei’,witha0.7probability.Butwhatifwhatisrepresented in perception is not redness but itself a probability, say that the probability is 0.3 that something is red? This is a representation of a probability. Humanscertainlyhave cognitive representations of probabilities. We know that if A causally influences B, then thepresenceofAmakesBmoreprobable.And,weusesuchrepresentations in reasoning and problem solving [18,19]. There is some evidence of representations of probabilities in perception [20], thoughIamnotpersuadedthatthisstudyconcernsperception as opposed to perceptual judgement. If there is perception of probability, the question arises astowhether there could bea probabilistic representation of probability; for example, a representation of the form: ,probability of redness of 0.3, therei, 0.7.. (If this seems unintelligible, note that I can have a 0.9 credence that the probabilityof decayof acertain subatomic particle is 0.1.) In any case, this article concerns probabilistic representation, not representation of probabilities; and in perception, not cognition. I will mention two proposals that have been made concerning the role of probabilistic representation in the phenomenologyofperception,confidenceandprecision,arguing that they do not deal with the problem at hand. Then I will return to a discussion of more promising approaches which focus on populations of neurons. 2. Confidence Some say that a conscious reflection of probabilistic representation can be found in conscious confidence. One can have a conscious sense of a low degree of confidence that that is Isaac in the distance. As one gets closer, one’s conscious confidence that it is Isaac might increase [21]. However, such confidences involve cognitive categorization of perception (where cognition is the domain of thought and reasoning). One can be very confident that one sees something, less confident that one sees a person, still less confident that one sees a guy in a ill-fitting suit and still less confident that one sees Isaac [6]. The fact that conscious confidence depends on the imposition of cognitive categories raises the question of the extent to which the phenomenology of confidence is perceptual phenomenology. Morrison has countered byappealing to perceptual categorization, saying it is the perceptual categories that make the confidences perceptual [22]. However, it is not the case that perceptual categorization is involved in all perception. The operational index of perceptual categorization is faster and more accurate discrimination across perceptual categories than within perceptual categories. And that obtains in only some cases of perception, for example, colour perception and phoneme perception. Using the example of oriented grids, there may be categorical perception of cardinal orientations, but the same issue arises for +158 from a 258 tilt where no cardinal orientations are involved. 2 rstb.royalsocietypublishing.org Phil. Trans. R. Soc. B 373: 201703413–30°–20°–10° 0° 10° 20° 30° Figure 2. Oriented grids tilted from minus 308 to plus 308. Ask yourself whether in viewing the central grid, you see any hint of the minus 308 or plus 308 grids. In any case, perceptual categorization does nothing to solve the problem of the perception of direction or orientation that we started with. In normal foveal perception of a vertical grid, we are not aware of the 308 tilts at all, so we are certainly not aware of them with low confidences. Another confidence-based approach would be metacognitive confidence, the confidence that a certain probability estimate is correct. Confidence in this sense is strongly conceptual; for example, it requires the concept of probability. So, metacognitive confidence is unlikely to be perceptual. 3. Precision Another proposal about the manifestation of probabilistic representation in the phenomenology of perception is that what Vul et al. and Gross & Flombaum interpret probabilistically should instead be seen in terms of representational precision [6]. In the case of the orientation cells that are tuned to verticality but are also activated by a wide range of other orientations, the manifestation of this wide responsiveness might be blur. Of course, vertical things may look blurry in a fog, but the problem at hand is why they do not look blurry in foveal perception in standard conditions, despite the wide tuning of individual neurons. It has been suggested that the low quality of colour information in the peripheral retina shows that perception really is highly indeterminate. Our impressionof determinacy is supposedtobeanillusion[23–27].Myfirstpointagainstthisclaim is that it is a myth that there are insufficient colour receptors in the periphery of the retina to see vivid colours. Discrimination of one hue from another is as good at 508 as in the fovea if the colour stimuli are large enough [28]. And there is some colour sensitivity out to 808–908. I called this a myth ([29, p. 534]) and a recent article describes it as a ‘widespread misconception even among vision scientists’ [30]. Second, it is well known that there is integration of colour information over time within visual cortex. Third, ‘memory colour’ effects are well known. Similar points are made in [29,31]. It might be said that, rather than blur, the perception represents a determinable rather than a determinate [32]. A determinate is a more specific way of having a determinable, as red is a more specific way of being coloured. The determinable/determinate relation is relative—red is determinate relative to coloured but determinable relative to scarlet. The suggestion that we are aware of determinables does have the advantage of predicting that we do not see low probability alternatives, but it throws out the baby with the bathwater by denying that we see the high probability alternatives as well. How would the determinable hypothesis apply to the vertical grid or vertical motion examples? Perhaps, the determinable would be motion that deviates from vertical at most by a small acute angle. If ‘small’ is supposed to cover the full range of putatively represented angles, the problem is just restated, and if ‘small’ covers a smaller range, the proposal does not face upto the problemhowitisthatperception does notreflect the probabilistic representations outside that range. 4. Populations Thus far, it may seem that I am arguing that if perception is probabilistic, it would seem probabilistic; it does not seem so, so it is not probabilistic. That is not my argument. There are a number of ways in which probabilistic perception might not seemprobabilistic. The most promising candidates involve populations of neurons. I mentioned earlier that the information required to determine the conscious perception is spread over populations. The question at hand is whether the mechanisms by which this information is integrated requires actual probabilistic representation. My overall point is that the best approach to population responses does not involve commitment to actual explicit probabilistic representations because they are to be understood in terms of Marr’s ‘computational’ level, to be explained below. The next two sections concern two population-based approaches: sampling and competition. I endorse the latter and go on to explain that it is compatible with Bayesian approaches. 5. Sampling Samplingisawayofmovingfromprobabilisticrepresentations to narrower probability distributions or to non-probabilistic representations in populations of neurons. Any such process can bedescribedassamplingbutaswewillseeinthenextsection, there is another approach that is less naturally described as sampling. The big attraction of sampling from a Bayesian perspective is that optimal Bayesian inference is intractable but sampling is not. My objection to sampling is that standard sampling models model perceptual decision rather than perception itself. The term ‘sampling’ covers any process in which items are chosen fromadistribution, e.g. drawing balls from an urn. The ‘standard’ form of sequential sampling according to a recent review [33] isthe diffusion decision model in which the subject is given a task, say of deciding whethera bar istilted to the left or to the right. A threshold of evidence is set for each of the choices, and the system samples from the distribution of responses. Samples are the input and the output is a decision. (Some writers treat the decision itself as a sample [34].) In one version, each sample is treated as an item of evidence in a Bayesian calculation of posterior probability. If the accumulation of evidence reaches the threshold for clockwise before the threshold for anticlockwise, the perceptual decision is clockwise [33,35]. rstb.royalsocietypublishing.org Phil. Trans. R. Soc. B 373: 20170341Applied to the problem at hand, the suggestion would be that probabilistic representations are unconscious, but conscious perception reflects the sampling, not the probabilistic representations themselves. The sampling answer to ‘If perception is probabilistic, why does it not seem probabilistic?’ then is that unconscious perception is probabilistic but conscious perception is not. Vuletal.say‘Internalrepresentationsaregradedprobability distributions, yet responses about, and conscious access to, these representations is limited to discrete samples. Our mind appears to perform Bayesian inference without our knowing it’. Gross & Flombaum (p. 361), referencing Vul et al., put it this way: ‘perceivers construct from noisy transduced signals probabilistic representations (assignments of credences over a space of possibilities concerning the distal scene) that take into account, as best they can, expected relationships among the scene’s various features; performance, in response to a specific query, then involves “sampling” from the probabilistic representations stored in visual memory’ (italics added). However, we do not need a query for the vertical grid or vertical motion to look vertical. Without any particular task or query or focused attention, vertical objects in the world seen foveally in normal conditionstend to look vertical. You may be reading this onacomputerscreenwhosesidesareverticalandlookvertical despite the fact that there is no task concerning them and you are not attending directly to them. Further, Bayesian models of sampling standardly require cognitive categories imposedinadvanceaspartofthesubject’stask.Intheexample above, the categories were tilted left and tilted right. But then the same problem arises as already mentioned in connection with confidence. When sampling depends on the imposition of cognitive categories, that raises the question of the extent to which the phenomenology of the conscious state is genuinely perceptual phenomenology. The basic problem is that sampling models model perceptual decision rather than perception, i.e. the formation of a percept. Perception takes place routinely with no task, explicit or implicit, and without any need for perceptual decision as to which cognitive category to apply. I am appealing here and in what follows to the difference between perception and cognition—where cognition includes thought, reasoning and decision-making. Although I cannot argue for it here, I believe that perceptual representations are constitutively iconic, non-conceptual and non-propositional in content, whereas cognitive representations do not have these properties. There is an important divide between the types of representations involved in perception and cognition [36–38]. An advocate of sampling might suggest that there is always sampling in conscious perception, independently of any task. If there are many samples, the problem this article started with arises: The samples will be samples of different orientations, so why does not vision reflect all the samples? The supposition that there is a single random sample leading to a point estimate would predict widespread illusion. The evidence for probabilistic representation in ordinary perception is problematic. To get evidence for sampling, Vul et al. had to produce a perceptual situation in which subjects were making weakly informed guesses, something that does not happen in prototypical foveal vision. They presented 26 letters in quick succession for 20 mseachwith47 msinbetween letters. In the series, one letter was surrounded by a circle and the subjects’ task was to say which letter was circled. The innovation of Vul et al. was to ask for multiple guesses about the 4 rstb.royalsocietypublishing.org sameperception,the results ofwhichthey describeassampling from a distribution of hypotheses in that very perception. However, anyone who has been a subject in such a rapid series of percepts—15 letters in a single second—knows that the subjective impressionis one of guessing. A similarproblem arises for a second experiment in which letters were ‘crowded’ together in space. In crowded perception, most notably in the periphery of the visual field, there is more than one object in an ‘integration field’, making the perception bewildering. One subject in a (different) crowding experiment was quoted as saying ‘It looks like one big mess…I seem to take features of one letter and mix them up with those of another’. Another subject said: ‘I know that there are three letters. But for some reason, I can’t identify the middle one, which looks like it’s being stretched and distorted by the outer flankers’ ([39, p. 1139]) . The evidence for probabilistic perception in a case in which subjects are subjectively guessing does not automatically apply to ordinary foveal perception in which a vertical line looks vertical, despite representations in the visual system of lower probability tilts. In the Vul et al. cases, competition has broken down and there are many simultaneously present percepts. Further, what Vul et al. describe as sampling from a distribution was a matter of making a series of four decisions about what letter was cued. Subjects got monetary rewards, more money for getting the letter right on the first guess and less for the subsequent three guesses, so they had to evaluate which of these conceptual categorizations they were most sure of. Their responses required complex cognition involving theimpositionofconceptsonwhateverperceptualinformation they had. In sum, standard forms of sequential sampling require the imposition of cognitive categories, something that may never be involved in genuine perception. Sampling is more of a model of perceptual decision than of perception, i.e. the formation of percepts. And a highly cited item of evidence for sampling involves uncertain perception that is quite different from the kind of perception that gives rise to the original problem. The problems with the sampling approach motivate looking at another approach to populations of neurons, competition.