Abstract As recent developments in autism research offer alternative explanations to the mainstream options, it can now be argued that the so-called cognitive deficits in the social domain associated with autism have been mischaracterized or, at least, oversimplified. We will use predictive models within a 4E (i.e., embodied, embedded, enactive and extended) conception of cognition to address the question of cognitive impairment in psychiatrics and autism. Such models force us to reassess what "cognitive deficit" means by integrating the environment not only in its usual sense (evo-developmental), but by understanding all cognitive performances as embedded in environments (or fields of affordances) that shape and sustain them. By adopting a predictive 4E perspective, we aim to show that the "cognitive deficits" associated with autism are in fact mismatches between environmental resources and the particular form of neurological functioning of autistic people (neurodiversity), brought about by the fact that the cultural niches that set up the relevant fields of affordances are structured by and for neurotypicals. This mismatch leads to epistemic injustices, both testimonial and hermeneutic, that feed back into research on autism and clinical approaches, thereby making the "deficits" appear based on individual shortcomings. In this context, autism interventions should partly focus on the development of social policies aimed at modifying those aspects of cultural niches that make environments unsuitable for the full development of all individuals. Introduction There is an ongoing transition in cognitive science from internalist to externalist, or so-called 4E, models of human cognition [1] . We argue that the resulting extension of the very notion of "cognition" requires a reassessment of what is meant by "cognitive deficit". In particular, we focus here on 4E (embodied, embedded, enactive and extended) models of cognition couched in terms of the predictive processing framework [2] , to show how we should now construe the notion of cognitive deficit, and illustrate the resulting notion by showing how the "deficits" traditionally associated with autism should properly be understood. One reason for this suggestion is epistemic coherence: psychiatry's conception of cognitive deficits should stay in touch with theoretical developments in cognitive science. But more importantly, internalist (non 4E) models have mostly failed to give us an understanding of the nature of cognitive psychiatric conditions generally. The current state of psychiatry's Diagnostic and Statistical Manual (DSM) [3] is a case in point: Moreover, internalist models, focusing on internal causes of psychiatric conditions (genes, neurotransmitters, brain regions, cognitive modules) have failed to truly help people. 4E models would allow a re-conceptualization of the question that may shed new light on old conceptual and practical problems plaguing psychiatry. Our intention is not to oppose "entirely external deficits" to "entirely internal deficits", but rather to advocate for a transition from a strictly internalist conception to an externalist conception of deficits, which construes deficits not as properties of organisms but as properties of extended systems that include the organism's brain and body, but also its environment. Introduction In this paper, we thus question the dominant conception of cognitive deficits to propose an alternative view, the latter being the main development we propose here, and then we use the case of autism to illustrate this alternative and discuss some of the issues that emerge from the proposed shift. If cognition is indeed external, or more external that what the dominant view once supposed, 4E models of "cognitive deficits", compared to the old, dominant, conception, should allow better-focused interventions, that is, interventions that are focused on the actual source of the deficit. Moreover, by replacing an essentialist view of deficits, which understands them as intrinsic features of individuals, with a non-essentialist view that understands deficits as deficits of a larger system than the individual, 4E models of cognitive deficits should allow for more just practices and interventions. Recent developments in autism research now offer alternative explanations [4] [5] [6] to the mainstream options [7] [8] [9] . With these models, and our externalist concept of deficits, we believe it can now be argued that the so-called "social deficits" associated with autism have been mischaracterized [10] [11] or, at least, grossly oversimplified. We accomplish this in three main steps. First, we present the new conception of cognition, which we refer to as 4E predictive models. Second, based on this new conception of cognition, we propose a redefinition of the notion of "cognitive deficits". To do so, we discuss the traditional conception of cognitive deficit, that is, the internalist view of deficits, and then sketch the nature of cognitive performance in a 4E predictive framework. This will allow us to properly characterize the nature of the externalist view of deficits. Finally, we review the cognitive deficits associated with autism. For this, we adopt a recently developed family of predictive explanations of autism, and then analyze the so-called social deficit as a mismatch between autistic cognitive processing, as understood by such models, and the autistic individual's environment, built by and for neurotypicals. We end this analysis by showing how the resulting testimonial and hermeneutic injustices [12] feed back into research on autism as well as clinical practice with autistic individuals to entrench the externally construed deficit into the autistic individual's cognitive system. A New Conception of Cognition: 4E Predictive Models We maintain that cognition is predictive and extended (i.e., 4E: embodied, embedded, enactive and extended [13] ; see below), and this entails that cognitive deficits are deficits of extended predictive systems. "Deficit" is a normative notion, meaning that something that should be working in a certain way is not. A New Conception of Cognition: 4E Predictive Models Viewed from a 4E predictive perspective, that "something" can be the brain but also, and equally, it can be the body, the environment or the complex set of relations that link them. The extension of the concept of deficit allowed by predictive models [2] [14] within a 4E conception of cognition opens the opportunity (and responsibility) to view our assumptions on questions of cognitive deficits in psychiatry through new lenses. We look especially at the case of autism, which is the object of a very active and constantly developing scientific literature. Predictive Processing Predictive processing (henceforth PP) is a view of the underlying neurological and computational mechanisms responsible for cognitive capacities. PP inverses the traditional bottom-up conception of cognition: instead of viewing the mind as a mostly passive system set up to receive and process information, PP takes it to be an active inferential system that constantly seeks to predict its incoming sensory input [2] [15] [16] . It does this by adjusting its current model of the environment, developed based on past encounters with the environment, or by acting on its environment. Action (or output), which is traditionally conceived as following perception, is now viewed as simultaneous and intertwined with perception. Predictive Processing By comparing the actual input from the environment with the internally generated predicted input, which may be wildly off the mark in unknown environments, predictive systems are thought to compute an error signal that, on a short time-scale, drives model selection and local action in the environment, as well as, on a longer time-scale, learning (viewed as model adjustment). Ma-Journal of Behavioral and Brain Science thematically, such systems can be understood in Bayesian terms. Their models are then the prior probability distributions (or densities) and likelihoods (together understood as a generative model) used to inferentially generate a prediction on the basis of the computed error signal, which serves as Bayesian evidence for inference. Predictive Processing On the most popular accounts of PP, predictive systems are taken to be highly hierarchical, that is, many-layered or deep. Thus viewed, each layer seeks to predict the activity of the layer just below itself, and so on, until the sensory or motor periphery is reached, in which case predictions are about incoming sensory signals or serve to drive muscles and other effectors (e.g., glands). In hierarchically layered predictive systems, lower level priors are concerned with the most proximal regularities in the organism's environment, that is, those that are at the smallest spatiotemporal scales (e.g., quick local changes in the sensory periphery). Proximal regularities that are perfectly predicted by lower-level priors do not generate any error signal to go up the hierarchy: the error is said to have been explained away. As the signal moves up the hierarchy, higher levels are concerned with more distal regularities, that is larger and larger spatiotemporal scales (objects moving across the field of vision, day changing into night, vision becoming much noisier as myopia progressively sets in, and so on). Although we presented the levels from the bottom up, recall that the primary flow of information in predictive systems is thought to be top-down: higher levels can thus be viewed as providing context for the lower level predictions. It is as if the visual system was saying: on the basis of my predicted current level of my myopia, the predicted current ambient daylight, and the predicted cat moving across the room, my lower level predicts that this current patch of dark grey will be replaced by a patch of lighter gray. Recall also that such systems should never be viewed as exclusively perceptual, for the predictions simultaneously reach the motor periphery, which may be taken to predict how such and such movement will result in such and such sensory input (active inference). Predictive Processing Predictive systems are driven by prediction error. All prediction errors are not created equal however: some reflect inadequate models but some reflect environmental randomness (noise) or volatility (frequent changes in regularities). Predictive Processing While prediction errors caused by poor models and environment volatility is reducible by changing models (model selection) and learning (model adjustment), prediction errors caused by environmental randomness (noise) is not. No amount of model selection and correction can reduce noise. Active inference, however, can reduce all source of error by making the environment less noisy (although this might sometimes be a practical impossibility). For instance, the fact that visual models cannot properly predict the pattern of light as the sun reflects on the ocean, and thus generate a lot of error, is not a sign that the visual model is incorrect but that light patterns on the ocean are mostly visually random. Similarly, poor prediction performance in environments that are perfectly regular but that change often (as in strobe-lit rooms or mood swings) are not a Journal of Behavioral and Brain Science reflection on the quality of the generative model but of the environment's volatility. To properly drive model selection, action and learning, it is thus thought that the error signal should be modulated by another prediction, generated by hyper-priors (priors about priors), called the prediction's precision. Mathemati- cally, model precision is the inverse of model variance (in statistics, variance is standard deviation squared). If the mean is a model of class performance on a given exam, then variance is a measure of the precision of predicting a student's grade based on the mean: the larger the variance, the less precise the prediction. Predictive Processing Finally, it is useful to understand the activity of predictive systems as the use of prediction error to build hierarchical models of the causal structure of the world, and the use of these models to minimize variational free energy, that is the probability that the system finds itself in unexpected states. Organisms whose predictive system minimizes their free energy will, all else being equal, survive longer by maintaining themselves at acceptable values in a range of life-promoting parameters (right temperature, right blood-sugar levels, right distance from walls, right number of predators, and so on). 4E Cognition Although PP is an exciting development both in neuroscience and in cognitive science, many see its most interesting feature as its promise to offer a neurological foundation for 4E cognition, a loose collection of views of cognition that emphasize the deep interaction between the brain, body and environment. Specifically, 4E cognition refers to embodied, embedded, extended and enactive cognition. Proponents of the embodied approach traditionally claim that cognition is not bound within the confines of the skull (i.e., the brain) [17] [18] . The bodily organization of the whole organism is thought to be implicated in cognition. For instance, some embodiment theorists claim that the very shape and organization of the human body shape some of our most basic concepts and cognitive processes. Theories of embedded cognition claim that cognition depends on a complex interaction with and exploitation of the cognizing organism's environment (i.e., some set of external resources) [19] . Enactive cognition, for its part, claims that cognition is not a state of the organism or a product of the brain's activity: it is something performed by the cognizing organism. Finally, theories of extended cognition claim that cognition, mostly-perhaps not exclusively [1] [20]-human cognition, spreads across the confines of the body [21] (the "skin/skull boundary"). On this view, external resources are not simply important for, but constitutive of cognition. 4E Cognition One way to understand 4E cognition is to view brain, body and environment as forming a coupled dynamical system. When two or more systems interact in such a way that they each cause instantaneous changes in the other (changes describable by coupled differential equations), it makes sense to say that they strictly speaking form a single higher-level system (any division between them-e.g. "the skin"-being somewhat arbitrary) and pragmatic-e.g., the pragmatics of ex-Journal of Behavioral and Brain Science planation). Studying 4E cognition means studying those cognitive capacities and situations where brain, body and environment form such a system. Claiming that "cognition is 4E" amounts to the very strong claim that all cognitive capacities do; conversely, claiming that cognition is not 4E (a view often labeled "internalism") amounts to the equally strong (but more in line with orthodox cognitive science) claim that they never do. Our only claim here is that some cognitive capacities are 4E, and that many "disabilities" are disruption of one or more components of the coupled system that sustains a given cognitive capacity. By opting for an externalist conception as opposed to an internalist conception, we are expanding cognition outside the brain, but we are not denying its internal aspects: the goal is in fact to blur the usual inside/outside frontier, if there even is such a frontier, not to deny some things are internal. 4E Cognition Although we stay clear of the debate's stronger claims, even the weak claim has deep consequences for cognitive science. Since systems that bear 4E cognitive capacities, and whose dysfunction are the source of 4E disabilities, are brain-body environment coupled dynamical systems, models of their system-level properties cannot be formulated in the traditional taxonomies of cognitive science so long as they are typologies of capacities that belong to individual organisms. Perception, on the 4E view, and to take but one example, is not a property of organisms (subjects) but of coupled brain-body-environment dynamical systems. At the system level, the main cognitive concept may be the relational brain-body-environment concept of affordances, which are meaningful structures or situations of the environment, whose meaning depends on the organism's body structures, its set of motor skills and its brain's proper attunement to them, and which are organized into fields of affordances 1 through series of movements (active inference) designed to shape the signal inputs. At lower levels of explanation, models of the brain processes that attune organisms to certain affordances in the field, and models of the physical structures that make situations parts of organisms' sets of affordances, will of course be neurological and physicochemical or biological, respectively. For instance, explanations of why complex sugars afford sustenance to a given bacterium will refer to chemical properties of the sugars and of the bacterium's metabolism. Predictive 4E Cognition One reason why many view PP as an exciting foundation for 4E cognition is that it offers a way to see how brain and environment can be dynamically coupled, by supplying models of brain processes that neatly mesh into models of coupled brain body-environment cognitive capacities. Brains are dynamically coupled to the body that houses them and the environment in which this body is embedded by having rich non-linear feedback interaction with its partners (indeed the 1 Affordances are organism-environment relational properties of environmental situations-i.e., the presence of a door affords exiting, the presence of certain plants afford sustenance, etc. A field of affordances is an ecologically structured set of affordances [22] . Journal of Behavioral and Brain Science cognition has long been waiting for (see Clark [2] for an attempt at uniting PP and 4E cognition). Predictive 4E Cognition According to 4E cognition, organisms tend to modify their physical and biological environments so as to alter the affordances they offer, either by introducing new affordances in the environment or by altering those that are already Predictive 4E Cognition there. An instance of the first case is the ubiquitous example of beaver dams, which afford beavers protection against predators and easier access to food sources during the winter months. An instance of the second case is food caching by birds, which affords them food storage. Once the niche is constructed, beaver perceive the pond as affording protection and birds perceive cache locations as affording food sources. Humans are of course champion niche builders (to such an extent that they are currently responsible for the mass extinction of most other species). Like beavers and birds, humans build structures that afford protection against predators and easy access to food sources, and much more, but they also build what are called "cultural niches", that is, niches made up of information. Just like beavers are born in a built niche that affords them protection and access to food, humans are born in built niches that let them know how to build canoes [27] , when is the best time locally to plant seeds, how people greet each other in a certain location, and so on. Predictive 4E Cognition As Constant et al. [28] put it, cultural niche construction in the context of PP corresponds to outsourcing socially relevant information in the environment. Predictive 4E Cognition This information can be defined in terms of cultural affordances. Cultural niche construction is thus a form of disambiguation rather than confirmatory predictive activity [29] , that is, it allows organisms to access information that guides the choice between hypotheses. Accordingly, the process of communal active inference that is cultural niche construction can be defined as a form of meta-learning: "[the niche] can function as a meta-learning mechanism, by which socially relevant cues in the environment come to guide the agent's acquisition of adaptive cultural knowledge and skills" ( [28] , p 6). This suggests niche construction as a prime candidate for an extended (even distributed) cognitive complement for a cognitive system that tolerates less environmental ambiguity (as is the case with autism) by providing insight into the environmental elements (i.e., Journal of Behavioral and Brain Science sensory cues) that should trigger learning. for instance, syntax is said to be built by an innate computational mechanism (the Language Acquisition Device or "LAD"), on the basis of an innate representation (the Universal Grammar), resulting in acquired mechanisms (local syntax; e.g., English) [30] . Similarly, semantics is thought to be built by an innate hypothesis formation and confirmation mechanism, which links innate primitive-concepts to local arrangements of phonemes (spoken words) or graphemes (written words) (e.g. English words) [31] . This view puts constraints to how "cognitive deficits" can be construed: they can derive from false or absent representations, or from ineffective or absent computational mechanisms, whose origin can be cognitive or neurological. To take language again, traditional accounts of cognition will regard absence of proper input at a specific age (the so-called "critical period") as preventing the construction of the local syntax and lexicon because the LAD will miss the syntactic inputs it needs to set the local parameters on the UG and the hypothesis and confirmation learning mechanisms will miss the phonemic inputs it needs to formulate hypotheses regarding the local phonemic form and internal primitive-conceptual structure. Similarly, again to take language, traditional accounts will attribute dyslexia to differences in brain structure (e.g., decreased white matter integrity in bundles that extend from the back of the brain to the parts responsible for speech articulation [32] ). A New Conception of "Cognitive Autism spectrum disorder does not escape this tradition. Traditional models situate the deficit in the individual's internal cognitive system, something that is apparent in, for instance, the search for the genetic causes of autism. This approach, in addition to being localizationist, seeks to reduce various behavioral phenomena (e.g., social and communication deficits, and restricted and repetitive behaviors) associated with autism to a single biological cause. As a prominent example, consider Baron-Cohen's [9] work: he originally [33] example of an internalist conception of autism is the theory of social motivation [7] , which postulates a deficit of social motivation in autistic individuals. These accounts take an internalist stance on deficits (deficits as internal impairments). A New Conception of "Cognitive In the following section, we will propose an externalist stance on cognitive deficits (describing deficits that can depend on something wrong occurring outside the individual organism). 4E predictive models of cognition force us to reassess what "cognitive deficit" means by integrating the environment not only in its usual sense (evo-developmental), but by understanding all cognitive performances as embedded in environments or fields of affordances that shape and sustain them. In the case of autism, we will see in later sections that the 4E predictive conception of cognition steers us towards explanations based in differences in perceptual processing rather than explanations based on social deficits 4E Cognitive Performance By adopting a 4E predictive perspective on cognition, we find ourselves in need to shift our perspective on cognitive performance. Di Paolo and De Jaegher [36] , Many of Baron-Cohen's proposals have since been severely criticized on methodological grounds. Fine [35] , for instance, has shown the methodological flaws in the experiments supporting his gendered models of cognition they presuppose (i.e., studies on gender differences in newborns which did not take precautions to hide the baby's sex ( [35] , p 95)). Journal of Behavioral and Brain Science be: 1) disabling, 2) disruptive, 3) no relation, 4) contextual and 5) enabling. 4E Cognitive Performance We mentioned above that the organism forms a coupled dynamical system with external processes and resources. On this view, cognitive performance depends on the harmonization of the coupled system that sustains a cognitive capacity through a given period of time. At a broad level of explanation, the coupled system can be said to recruit resources or enter into dynamical relations with processes that tend to have a neutral or positive contribution to its current cognitive task. At a lower, more mechanistic or subpersonal, level of explanation, the coupled system can be said to minimize prediction error through cognitive processes (e.g., the correction of its models guided by prediction error) or through action (e.g., performing active inference in order to conform sensory data to prior models). By reducing prediction error, and thus surprisal, the organism harmonizes itself with its environment in the sense that the organism seldom gets into states that it did not anticipate. Since higher level priors are about the statistical nature of the environment at ever higher spatial and temporal scales, an organism that would have completely minimized prediction error (which is strictly speaking impossible) would flow smoothly in its environment: its cognitive performance would be optimal. A harmonized coupled system might be described as one in which the various internal and external contributions to cognitive performance are globally positive. Consider the seemingly simple task of multiplying two three-digit numbers. Human memory alone typically does not allow such a mathematical operation (or to put it in terms of affordances: sets of three-digit numbers do not afford products based on memory skills alone). It requires an additional material substrate (e.g., pen and paper) to serve as external memory, a cultural niche in which culturally designed and numerals and long-multiplication algorithms (at least one) are transmitted, and skills in manipulating the material substrate (e.g., arranging the numerals in a specific form required by the algorithm, focusing attention on relevant parts of the paper, inscribing relevant numerals at specific locations, and so on). When each skill is applied at the right time, brain, body and environment form a coupled system able to perform the algorithm that allows multi-digit multiplications: written outputs on the paper bring inputs for the next skill, whose output bring input for the next one, until the written numeral on the paper corresponds to the products of the multiplied multi-digit numbers. This can only occur when there is a certain degree of match between organism's skills and the environment in which they are deployed. If the numerals disappear from the substrate a bit too quickly (writing on sand in windy conditions) or the individual has difficulty writing legible numerals then the ability to multiply multi-digit numbers slowly dissolves: sets of multi-digit numbers less and less afford products. 4E Cognitive Performance In terms of the typology introduced earlier, the pen and paper, as well as the muscles that allow pens to form symbols on paper, and the bones and articulations that provide the necessary flexible rigidity, can be said to provide anywhere between a contextual and an enabling contribution to the cognitive performance Journal of Behavioral and Brain Science undertaken by the organism. In a predictive 4E framework, the qualitative value of cognitive performance fluctuates as a function of the efficacy of the coupling between organism and environment: if the environment is not adequate, or muscle paralysis prevents writing numerals, or a vascular-cerebral accident prevents remembering the proper form of numerals, then cognitive performance is diminished, and we would then speak of a cognitive deficit: an inability to skillfully multiply multi-digit numbers. Redefining "Cognitive Deficits" According to the PP4E conception of cognitive performance laid out in the previous section, cognitive deficits are no longer dependent on properties (or lack thereof) of the organism alone. We saw that cognitive performance in any given cognitive task is the result of a dynamical process accomplished by a harmonized coupled system encompassing brain, body, world and even cultural resources. A cognitive deficit with respect to a given task is anything in the brain, the body or the environment that disrupts the proper accomplishment of the dynamical process underlying the performance of the task. This is thus not a rejection of the idea that deficits can have a cerebral cause, but recognition that, like cognition itself, deficits are not restricted to the brain, which is all that is needed to move from an internalist to an externalist conception of cognitive deficits. Redefining "Cognitive Deficits" The cognitive deficit literature is replete with examples of potential neural sources of deficits, and so we will concentrate our efforts here on cognitive deficits where the potential source is the body or the environment. Our strategy will be twofold. To explain and illustrate our externalist conception of cognitive deficits, we first start (in this section) with admittedly simple and obvious examples, where everyone would agree that the source of the cognitive deficit is external and does not specifically depend on the brain. Then, to motivate it and make it relevant to cognitive science, we turn our attention (in the next section) to the case of autism where, we will argue, many of the same external sources are potentially at play, although the source of the condition is viewed by most, and especially by cognitive and neurological models, as internal. Redefining "Cognitive Deficits" Let us thus start with an admittedly simple example that brings out the normative nature of the deficit/variation distinction. Human capacities vary as a function of age: babies cannot walk or talk whereas children and adults can. This is normal human variability. Similarly, sensibility to sound frequencies varies as a function of age: younger humans can hear frequencies older humans cannot (a phenomenon known as "presbycusis"). For instance, sounds at about 16 kHz tend to be heard by individuals under thirty years old but not by those over thirty. Let us suppose that society comes to be structured in such a way that virtually all auditory cues (announcements, warnings, alarms, etc.) have a 15 kHz frequency. In regards to public transport announcements or (for a more impactful example) fire alarms, humans over thirty would become disabled because they could no longer function like younger ones: they would have to use alternative Journal of Behavioral and Brain Science cues for public transport (e.g., visual cues), wear specially designed hearing aids, and they might even miss fire alarms entirely. In predictive terms, older humans in such circumstances lose a set of inputs their brains could previously rely on to guide their predictions about upcoming events. Announcements and alarms were regularities that their predictive system had come to integrate as indicative of, say, arrival at desired destination or imminent danger from fires. In both cases, the ability of learned models (prior beliefs, which we could roughly translate as conditional statements like "If I hear this sound, I am under threat from a fire" and "If I am under threat from a fire, I should get to safety") to guide perception and action based on given sets of inputs is impaired. Let's reiterate that this example presupposes no impairment in the auditory systems of people over thirty. Presbycusis is normal human variability, but because it does not significantly impact everyday functioning in current societies, it is not considered a disability. It is instructive to reflect upon why presbycusis is not a disability in current societies. As they are currently organized, societies are built mostly by people at about or over thirty years old: such people would not think of using sounds they cannot hear as signals or alarms and hence no one or very few are disabled by presbycusis. As we will see presently, this is in sharp contrast to other conditions of human variability where one subset of the human population has built structures, set signals and designed cultural affordances that create cognitive disabilities and impairments in another. In terms of cognitive performance, note first that we just described a case where an enabling or contextual contributor was subtracted, showing that cognitive deficits can appear even without disruptive or disabling contributions. Redefining "Cognitive Deficits" The sound frequency example can be extended further if we imagine a situation where there is a constant ambient noise of 16 kHz. Let us say that every technological device (which are ubiquitous in today's-especially western-societies) emits such a sound as a byproduct. We can imagine that such a sound would be so invasive that it would interfere with daily cognitive tasks undertaken by individuals who are less than thirty years old, thus negatively impacting their cognitive performance in many regards. In the present case, the cognitive mechanisms and processes involved in a particular cognitive task, say the computation of long multiplications, are quite functional: memories of multiplication tables are present and the material substrate is suitable to sustain the computation. But here some of the cognitive resources necessary to accomplish the task, such as attention, are recruited to cope with the constant disrupting noise. In this case, an individual's predictive system is constantly trying to reduce prediction error generated by environmental noise (in both the acoustic and informational meanings of the word), thus leaving much less resources for the cognitive task at hand (if we suppose that attention is a finite resource in predictive systems). Redefining "Cognitive Deficits" This specific example is a case of disruptive contribution to cognitive performance. Redefining "Cognitive Deficits" Another possible external source of a cognitive deficit becomes clear through Journal of Behavioral and Brain Science the lenses of the predictive processing framework: cases where the type of prediction error minimization known as active inference is somehow prevented by external factors. As we saw, active inference is a process of prediction error minimization through action that falls under two (non-exclusive) functional categories: confirmatory and disambiguatory [29] . Confirmatory active inference is the confirmation of the organism's beliefs (priors, and thus predictions) about the world through action. If my brain predicts that, given my hand's current movement, it should be getting sensations that it is not getting from it as I grasp a cup of coffee, prediction errors will be generated due to the mismatch between prediction and proprioceptive and visual signals. One way to reduce that error is by moving my hand to match my prediction, thus confirming it. Disambiguatory active inference, on the other hand (no pun intended), serves the purpose of gathering evidence in support of a hypothesis about the world (i.e., to augment the precision of one set of predictions). This can raise confidence in one hypothesis or, if there are competing hypotheses, allow the brain to choose between them. Redefining "Cognitive Deficits" To go back to the long multiplication example, one could be prevented from writing the numerals by an external contraption of some sort (much like the case of muscle paralysis preventing the writing of numerals mentioned in section 3.2). In such a situation, the individual trying to accomplish the task would not be able to modify the environment (the paper) to match their predictions (e.g., Redefining "Cognitive Deficits" "there should be a 1 over the 6"). We can also imagine that, for some other reason, the individual may be physically prevented from looking at the written numerals, which would prevent them from disambiguating their memory of the numeral they had previously written. In this example, an external cognitive deficit is generated simply from preventing simple forms of active inferences (i.e., physical action). This example may seem a bit farfetched, but it has been argued [15] that the famous rubber hand illusion is caused by preventing, through experimental set-up, disambigutory active inference and thus that the illusion is much less robust, if not simply absent, when subjects are allowed to move. Redefining "Cognitive Deficits" Although these are simple examples that are far from the systemic and oppressive nature of most disabilities, they nonetheless bring forth the contextual and potentially external (at least, non-essentialist) character of cognitive deficits. Redefining "Cognitive Deficits" To clarify our externalist proposal, it will be useful to compare it to a common conception of disabilities, called the "social model" of disabilities [37] [38] [39] , according to which disabilities are social phenomena suffered by those with (bodily) impairments: impaired individuals are being excluded through social level phenomena such as oppression and prejudice. This conception follows a paradigm change brought about by the field of disability studies, which aims to analyze disabilities in relation to various social, cultural and political factors. Disability studies also aim to enact a shift from approaches that put responsibility on individuals (rehabilitation approaches) to approaches that put a duty on society to adapt its practices to the needs of various individuals [40] . Redefining "Cognitive Deficits" Although the social model is mostly concerned with physical "impairments", Journal of Behavioral and Brain Science we include here neurocognitive variations (i.e., neurodiversity) in how we understand "impairments" in the social model. The reason why we bring attention to this model of disability is to point out its conception of impairment and how our proposed redefinition of cognitive deficits relates and differs from it. The social model makes an externalist move by situating disabilities in the interaction between individuals and their environment, but its internalist roots are very much persistent as is evidenced by its conception of individual impairment: In other words, the social model of disability does not fully consider human (cognitive) variety as a natural phenomenon: it still takes some differences as impairments of the "normal" (i.e., normative) human phenotype. As much as we commend the externalist move, we believe that the social model's internalist roots should be extended: impairments, or in the present case cognitive deficits, can be found outside the individual organism. Autism's "Cognitive Deficits" and Their Source Our externalist conception of cognitive deficits was explained above, and was illustrated with simple examples. We now intend to motivate it and make it relevant to cognitive science. We do so by showing how it affords a better understanding of some of the key traits of autism, whose source, we argue, is similarly external. We will show that most of the cognitive deficits characteristic of autism result from a mismatch (or set of mismatches) between the specific form of neurological functioning typical of autistic people and the environment in which they find themselves. This mismatch is brought about by the fact that current cultural niches that set up the relevant fields of affordances are structured by and for neurotypicals [28] [41]. As we will further argue, this mismatch leads to epistemic injustices [12] that feed back into research on autism and clinical practices [42] , thereby making the deficits appear based on individual shortcomings. Autistic Traits and Environmental Mismatch As we have stated in Section 3.3, traditional explanations of autistic traits have been largely internalist in nature (i.e., resting on strictly internal deficits). Not only does this seem at odds with the increasing tendencies in cognitive science towards 4E approaches, traditional theories of autism, which often emphasize the social aspects of autism, face various problems: for instance, they fail do address traits specific to autism or traits found in all autistic individuals [4] . Autistic Traits and Environmental Mismatch Moreover, these theories face contradicting testimonies from autistic individuals, notably, the social motivation theory of autism, according to which autistic individuals have a deficit in social motivation, is challenged by autistic testimonies claiming that social motivation is present, but its behavioral manifestations can vary from neurotypical behaviors [10] . Alternatively, we can turn to the predictive processing framework for a theory that specifically aims to avoid its predecessor's flaws. In fact, a number of predictive models of autism spectrum disorder have been formulated recently [43] [44], which mostly differ with respect Journal of Behavioral and Brain Science to the phenotypic properties of autism they emphasize [29] . We chose to adopt the HIPPEA model, developed by Van de Cruys et al. [4] [28] [45] [46], according to which autism is explained by high, inflexible precision of prediction errors [4] . Although we believe that HIPPEA and similar theories are on the right track when it comes to the type of neurological functioning that is characteristic of autistic people, its conception of the cognitive deficits typical of autism remains internalist. In this section HIPPEA is not used to characterize autistic cognition as a disorder but to describe a specific human neurodiversity, that is the difference between autistic and neurotypical cognition, which can be viewed as a normally distributed neurocognitive variability in the human population. In fact, to stay true to our externalist deficit model, we should not even speak at this point of autisticvsneurotypical cognition, since we are only describing variation on the brain side of an extended or 4E system. We then use our description of these two types of cognitive functioning to characterize the kind of niche each would prefer (and therefore select or build, presumably). We finally show that autism, as a disorder, results from the fact that people with one type of cognitive functioning must live in an environment built by people with the other. The HIPPEA We noted that some prediction errors reflect inadequate models but, more importantly here, some instead reflect environmental randomness (noise) or volatility (frequent changes in regularities). We also saw that to properly learn and function, predictive systems must be able to distinguish reducible uncertainty from irreducible uncertainty, i.e., signal from noise. To achieve this, the system must learn to identify situations in which its error signals indicate hidden unpredicted environmental causes and when they do not [46] . It must, so to speak, (meta-) learn when to learn. According to HIPPEA, neurotypical cognitive systems (NCS) and autistic cognitive systems (ACS) differ in the way they distinguish between signal and noise, both of which leading to specific ways of dealing with prediction error. NCS are characterized by high differentiation between signal and noise, which leads, in extreme cases, to overemphasizing the context-dependent nature of some prediction errors. The ASC, for its part, is characterized by low differentiation between signal and noise, which leads, in extreme cases, to underemphasizing the context-dependent nature of some prediction errors. Van de Cruys et al. [4] and Lawson et al. [47] The expected high accuracy of the prediction error in the lower levels of the hierarchy has effects on the system's overall functioning, especially on learning. The HIPPEA If HIPPEA is correct, then, given the differential accentuation of prediction error of their respective cognitive system, the lower levels of the ACS's neural hierarchy are much more frequently in a state of learning as opposed to the NCS's neural hierarchy. In the ACS case, learning will try to integrate a high proportion of the new sensory input into its low-level models. These lower level models are therefore closely adjusted to previous input signals, so that every small difference between it and the next one will generate a prediction error signal which, in turn, will lead to new learning, and so on. When the environment is relatively regular and uncertainty is low, this will allow the low-level models to snuggly fit the data: incoming data will be finely modeled (categorized, predicted) allowing for high level perceptual precision. HIPPEA thus explains various traits associated with ACS, including increased perceptual performance such as superior pitch processing or superior contrast sensitivity [4] . The constant accentuation of the prediction error precision in the lower levels of the neural hierarchy has the effect of accentuating each perceptual deviation from the predicted input that is detected. This increased sensitivity to small differences because of the overfitting of predictions may offer a perceptual advantage, as in the example just mentioned. On the other hand, when the environment is particularly noisy or volatile, this can cause overfitting of predictions to sensory data: a given set of data is so closely and precisely modeled that predictions concerning data cannot be generalized to new incoming data from the noisy or volatile environment. The HIPPEA Overfitting also explains the negative aspect of this type of increased attention to detail: hypersensitivity and sensory overload. Since the system is constantly working to reduce prediction error being continually generated at the lower levels of the hierarchy, cognitive resources are constantly solicited and the system as a whole can become overloaded, especially in unstable environments or when facing unpredicted stimuli [1] . The HIPPEA In the second (NCS) case, a lower proportion of the new sensory input will be integrated in low-level models, which may lead to underfitting signals from the environment, and make regular environments perceived less precisely (e.g., details missed or lumped together) and become uninteresting, but at the same time make generalization more efficient in noisy and volatile environments. If the neurological characteristics attributed by HIPPEA to ACS and NCS distribute normally, most individuals reside somewhere around a mean value of noise/signal differentiation: the mean is by definition the NCS mode of learning. The HIPPEA As we move away from the mean towards lower values of noise/signal differentiation, the cognitive traits typical of autism emerge, and get more pronounced the further we are from the mean. As we move away on the other side, other cognitive traits could be expected, though these do not appear to have been assigned psychiatric categories (it would be interesting to see if some do fit the profile of psychiatric categories). Journal of Behavioral and Brain Science In view of the differences between the ACS's and NCS's modes of learning and cognitive processing, due to their different accentuation of prediction error, we can conjecture the type of physical and cultural niche they would prefer, and thus select or build. Because ACS will integrate a higher proportion of signals into its lower level models, which will increase its perception of details in regular environment and, because of overfitting, make noisy or volatile environments difficult to model and thus predict, it seems plausible to surmise that such cognitive systems will be particularly efficient in regular environment and in tasks that make perception of details relevant, but less capable in volatile environments, which will appear puzzling, since change will seem random, uncaused or unmotivated, and even less in noisy environment challenge because ACS will continually attempt to reduce the unreducible. We also saw that NCS will integrate a lower proportion of the new sensory input in low level models, leading to underfitting, and thus less preciseness in regular environments but more efficient processing (generalization) in noisy and volatile environments. It is thus reasonable to conjecture that, since stability, regularity and anticipated stimuli are barely noticed by NCS, people with this type of neurocognitive variation will tend avoid stable and regular environments (fleeing mathematics and science) in favor more volatile environments or environments where the regularities appear at higher levels of processing, such as in politics and gossip. Note that, by definition (i.e., it is "neurotypical"), this is the type of cognitive system that predominates in the current human population (had the other type been predominant, it would have been identified as the neurotypical form), and we can thus believe that its preferred form of environment will be common. We will now explain how autism, as a disorder, comes about because of the mismatch between ACS and the NCS preferred environment. Journal of Behavioral and Brain Science According to HIPPEA, autistic traits (i.e., the behavioral patterns associated with autism) become apparent when there is a mismatch between the organism and the environment, especially when environmental regularities are unstable or volatile. The high and inflexible precision of prediction error alone is thus not sufficient to generate all autistic traits. Recall that the diagnosis of autism spectrum disorder, as understood by the DSM-5 [3] , is established in regards to two axes of observable behaviors: 1) persistent deficits in communication and social interactions in various contexts and 2) restricted and repetitive behaviors, interests and activities. The first axis, which identifies social atypicalities, is explained in the predictive framework by the higher instability of social interactions. The second axis, which identifies behavioral atypicalities, is explained by a preference and tendency to generate environmental stability (i.e., predictability). Perceptual and Behavioral Axis If instability, ambiguity, and unanticipated stimuli require significant investment from ACS, they can be expected to avoid unstable and ambiguous environments. Perceptual and Behavioral Axis The search for stable environments, due to the high precision of prediction error, plays an important explanatory role in HIPPEA. It accounts for an important Journal of Behavioral and Brain Science behavioral aspect in autism: intolerance to change, inflexible adherence to routines and ritualized behaviors. The formation of very precise priors over-adjusted to previous data, as well as the constant upscaling of prediction error, give autistic individuals accurate and rigid models of their environment and of their interactions with it. Changes in this environment or in their routines therefore generate significant prediction error and potentially a form of cognitive overload. According to HIPPEA, the search for stability and predictability in the environment is partly responsible for the so-called "limited interests" of autists. Overfitting and the tendency of predictive systems to search for, or even construct, stability enable autistic individuals to develop increased skills and interests in one or more specific areas. Perceptual and Behavioral Axis Another trait associated with autism is the stereotyped or repetitive character of movements such as self-stimulation. Self-stimulation behaviors, which are often defined as "repetitive movements that do not serve any apparent purpose in the environment" [48] (our translation) in fact seem to serve specific purposes in regards to autistic individuals: "either to provide sensory stimulation or to reduce overstimulation" (our translation) [48] . HIPPEA's explanation of these behaviors rests on active inference: self-stimulation generates stability and reduces prediction error through the confirmation of predictions by self-generated, hence already predicted, sensory signals [29] [43]. Social Axis as an Environmental Mismatch Testimonial and Hermeneutic Injustices One might see in the foregoing a looming threat of some kind of cognitive incommensurability between neurotypicals and neurologically diverse people (notably, autistic individuals), as some studies seem to indicate [49] . We believe however that such a gap between neurologically different groups is the result of existing inequalities in the epistemic contribution to cultural niches and could be bridged were the inequalities corrected (or at least attenuated). Adapting Dotson's concept of epistemic incommensurability, which refers to a phenomenon where epistemic divergences are so important they generate fundamentally incompatible points of view on the world [50] , we speak of cognitive incommensurability to point out the lack of hermeneutical (i.e., interpretative) resources to describe the experience of neurodiverse people, which results in their experience being misunderstood or even ignored by neurotypicals [10] [42]. Testimonial and Hermeneutic Injustices To better understand this phenomenon and its consequences on research and on the creation of cognitive deficits, it will be useful to consider an important conceptual contribution, increasingly present in feminist research, made by Miranda Fricker. Fricker [12] identifies two types of epistemic injustices, namely hermeneutic injustices, which refer to prejudices regarding access to knowledge, and testimonial injustices, which refers to prejudices relating to the credibility of
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