Interventionism is a theory of causal explanation developed by Woodward and Hitchcock. I defend an interventionist perspective on the causal explanations offered within scientific psychology. The basic idea is that psychology causally explains mental and behavioral outcomes by specifying how those outcomes would have been different had an intervention altered various factors, including relevant psychological states. I elaborate this viewpoint with examples drawn from cognitive science practice, especially Bayesian perceptual psychology. I favorably compare my interventionist approach with well-known nomological and (...) mechanistic theories of psychological explanation. (shrink)

Teleological accounts of sensory normativity treat normal functioning for a species as a standard: sensory error involves departure from normal functioning for the species, i.e. sensory malfunction. Straightforward reflection on sensory trade-offs reveals that normal functioning for a species can exhibit failures of accuracy. Acknowledging these failures of accuracy is central to understanding the adaptations of a species. To make room for these errors we have to go beyond the teleological framework and invoke the notion of an ideal observer from (...) vision science. The notion of an ideal observer also sheds light on the important distinction between sensory malfunction and sensory limitation. (shrink)

The goal of perceptual systems is to allow organisms to adaptively respond to ecologically relevant stimuli. Because all perceptual inputs are ambiguous, perception needs to rely on prior knowledge accumulated over evolutionary and developmental time to turn sensory energy into information useful for guiding behavior. It remains controversial whether the guidance of perception extends to cognitive states or is locked up in a “cognitively impenetrable” part of perception. I argue that expectations, knowledge, and task demands can shape perception at multiple (...) levels, leaving no part untouched. The position advocated here is broadly consistent with the notion that perceptual systems strive to minimize prediction error en route to globally optimal solutions :181–204, 2013). On this view, penetrability should be expected whenever constraining lower-level processes by higher level knowledge is minimizes global prediction error. Just as Fodor feared cognitive penetration of perception threatens theory-neutral observation and the distinction between observation and inference. However, because theories themselves are constrained by the task of minimizing prediction error, theory-laden observation turns out to be superior to theory-free observation in turning sensory energy into useful information. (shrink)

In their exhaustive study of the cognitive operation of analogy, Hofstadter and Sander arrive at a paradox: the creative and inexhaustible production of analogies in our thought must derive from a “reminding” operation based upon the availability of the detailed totality of our experience. Yet the authors see no way that our experience can be stored in the brain in such detail nor do they see how such detail could be accessed or retrieved such that the innumerable analogical remindings we (...) experience can occur. Analogy creation, then, should not be possible. The intent here is to sharpen and deepen our understanding of the paradox, emphasizing its criticality. It will be shown that the retrieval problem has its origins in the failure of memory theory to recognize the actual dynamic structure of events. This structure is comprised of invariance laws as per J. J. Gibson, and this event “invariance structure” is exactly what supports Hofstadter and Sander’s missing mechanism for analogical reminding. Yet these structures of invariants, existing only over optical flows, auditory flows, haptic flows, etc., are equally difficult to imagine being stored in a static memory, and thus only exacerbate the problem of the storage of experience in the brain. A possible route to the solution of this dilemma, based in the radical model of Bergson, is also sketched. (shrink)

Given the ubiquity and centrality of social and relational influences to the human experience, our conception of self-governance must adequately account for these external influences. The inclusion of socio-historical, externalist considerations into more traditional internalist accounts of autonomy has been an important feature of the debate over personal autonomy in recent years. But the relevant socio-temporal dynamics of autonomy are not only historical in nature. There are also important, and under-examined, future-oriented questions about how we retain autonomy while incorporating new (...) values into the existing set that guides our interaction with the world. In this paper, we examine these questions from two complementary perspectives: philosophy and neuroscience. After contextualizing the philosophical debate, we show the importance to theories of autonomous agency of the capacity to appropriately adapt our values and beliefs, in light of relevant experiences and evidence, to changing circumstances. We present a plausible philosophical account of this process, which we claim is generally applicable to theories about the nature of autonomy, both internalist and externalist alike. We then evaluate this account by providing a model for how the incorporation of values might occur in the brain; one that is inspired by recent theoretical and empirical advances in our understanding of the neural processes by which our beliefs are updated by new information. Finally, we synthesize these two perspectives and discuss how the neurobiology might inform the philosophical discussion. (shrink)

Recent work in computational and cognitive neuroscience depicts the brain as an ever‐active prediction machine: an inner engine continuously striving to anticipate the incoming sensory barrage. I briefly introduce this class of models before contrasting two ways of understanding the implied vision of mind. One way (Conservative Predictive Processing) depicts the predictive mind as an insulated inner arena populated by representations so rich and reconstructive as to enable the organism to ‘throw away the world’. The other (Radical Predictive Processing) stresses (...) the use of fast and frugal, action‐involving solutions of the kind highlighted by much work in robotics and embodied cognition. But it goes further, by showing how predictive schemes can combine frugal and more knowledge‐intensive strategies, switching between them fluently and continuously as task and context dictate. I end by exploring some parallels with work in enactivism, and by noting a certain ambivalence concerning internal representations and their role in the predictive mind. (shrink)

It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be (...) explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal–mechanical explanation. 1 Introduction2 What a Great Many Phenomena Bayesian Decision Theory Can Model3 The Case of Information Integration4 How Do Bayesian Models Unify?5 Bayesian Unification: What Constraints Are There on Mechanistic Explanation?5.1 Unification constrains mechanism discovery5.2 Unification constrains the identification of relevant mechanistic factors5.3 Unification constrains confirmation of competitive mechanistic models6 ConclusionAppendix. (shrink)

Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to (...) adaptive success. This target article critically examines this approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency. (shrink)

According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) From the use of Bayesian (...) models in theoretical neuroscience, have we learned or can we hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine? From actual practice in theoretical neuroscience, we argue for three claims. First, currently Bayesian models do not provide mechanistic explanations; instead they are useful devices for predicting and systematizing observational statements about people's performances in a variety of perceptual tasks. That is, currently we should have an instrumentalist attitude towards Bayesian models in neuroscience. Second, the inference typically drawn from Bayesian behavioural performance in a variety of perceptual tasks to underlying Bayesian mechanisms should be understood within the three-level framework laid out by David Marr ( [1982] ). Third, we can hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine to the extent that Bayesian models will prove successful in yielding secure and informative predictions of both subjects' perceptual performance and features of the underlying neural mechanisms. (shrink)

The ultimatum game is a simple bargaining situation where the behavior of people frequently contradicts the optimal strategy according to classical game theory. Thus, according to many scholars, the commonly observed behavior should be considered irrational. We argue that this putative irrationality stems from a wrong conception of metanormativity (the study of norms about the establishment of norms). After discussing different metanormative conceptions, we defend a Quinean, naturalistic approach to the evaluation of norms. After reviewing empirical literature on the ultimatum (...) game, we argue that the common behavior in the ultimatum game is rational and justified. We therefore suggest that the norms of economic rationality should be amended. (shrink)

A common approach to explaining the perception of form is through the use of static features. The weakness of this approach points naturally to dynamic definitions of form. Considering dynamical form, however, leads inevitably to the need to explain how events are perceived as time-extended—a problem with primacy over that even of qualia. Optic flow models, energy models, models reliant on a rigidity constraint are examined. The reliance of these models on the instantaneous specification of form at an instant, t, (...) or across a series of such instants forces the consideration of the primary memory supporting both the perception of time-extended events and the time-extension of consciousness. This cannot be reduced to an integration over space and time. The difficulty of defining the basis for this memory is highlighted in considerations of dynamic form in relation to scales of time. Ultimately, the possibility is raised that psychology must follow physics in a more profound approach to time and motion. (shrink)

Perception solves computationally demanding problems at lightning fast speed. It recovers sophisticated representations of the world from degraded inputs, often in a matter of milliseconds. Any theory of perception must be able to explain how this is possible; in other words, it must be able to explain perception’s computational tractability. One of the few attempts to move toward such an explanation is the information encapsulation hypothesis, which posits that perception can be fast because it keeps computational costs low by forgoing (...) access to information stored in cognition. I argue that we have no compelling reason to believe that encapsulation explains (or even contributes to an explanation of) perceptual tractability, and much reason to doubt it. This is because there exist much deeper computational challenges for perception than information access, and these threaten to make the costs of access irrelevant. If this is right, it undermines a core computational motivation for encapsulation and sends us back to the drawing board for explanations of perceptual tractability. (shrink)

A prominent view holds that perception and memory are distinguished at least partly by their temporal orientation: Perception functions to represent the present, while memory functions to represent the past. Call this view perceptual presentism. This chapter critically examines perceptual presentism in light of contemporary perception science. I adduce evidence for three forms of perceptual sensitivity to the past: (i) shaping perception by past stimulus exposure, (ii) recruitment of mnemonic representations in perceptual processing, and (iii) perceptual representation of present objects (...) as possessing past properties. I argue that forms (i) and (ii) are consistent with perceptual presentism, while form (iii) poses a genuine threat to the view. While the empirical case for form (iii) remains inconclusive, I suggest that the most serious challenges to perceptual presentism derive from representations that integrate mnemonic and present-tensed elements in the performance of canonical perceptual functions, such as perceiving object continuity over time. (shrink)

Perceptual processes differ from cognitive, this paper argues, in functioning to be causally controlled by proximal stimuli, and being modular, at least in a modest sense that excludes their being isotropic in Jerry Fodor's sense. This claim agrees with such theorists as Jacob Beck and Ben Phillips that a function of stimulus-control is needed for perceptual status. In support of this necessity claim, I argue, inter alia, that E.J. Green's recent architectural account misclassifies processes deploying knowledge of grammar as perceptual. (...) _Pace_ Beck and Phillips, however, I argue a function of stimulus-control is insufficient for perceptual as opposed to cognitive status. One consideration in favour of such insufficiency, noted but (I argue) not convincingly rebutted by these theorists, concerns perpetually grounded demonstrative thought. Two other considerations trade on the fact that a function of stimulus-control can arise not from blind nature but intentional design or social institutions, where so-functioning processes may but need not be perceptual. I offer two cases where such processes are cognitive, viz. skilful play-by-play announcing of ongoing events, and voluntary visualizing of ongoing events under the guidance of apt play-by-play announcements, dubbed announcement-driven visualizing (ADV). The cognitive status of these three diverse phenomena cannot be explained by an absence of a perception-like representational format or content (for ADV has such) or by a presence of personal-level mental states causally mediating between stimuli and outputs (for perception has such). A bettter explanation invokes, I argue, the non-modular character of the generating process. (shrink)

Ballard and Zhang offer a fascinating review of how computational models of human vision have evolved since David Marr proposed his Tri‐Level Hypothesis, with a focus on the refinement of algorithm descriptions over time.

If there are two dependent positive real variables x1 and x2, and only x1 is known, what is the probability that x2 is larger versus smaller than x1? There is no uniquely correct answer according to “frequentist” and “subjective Bayesian” definitions of probability. Here we derive the answer given the “objective Bayesian” definition developed by Jeffreys, Cox, and Jaynes. We declare the standard distance metric in one dimension, d(A,B)≡|A−B|, and the uniform prior distribution, as axioms. If neither variable is known, (...) P(x2x1)=P(x2>x1). This appears obvious, since the state spaces x2x1 and x2>x1 have equal size. However, if x1 is known and x2 unknown, there are infinitely more numbers in the space x2>x1 than x2x1. Despite this asymmetry, we prove P(x2x1∣x1)=P(x2>x1∣x1), so that x1 is the median of p(x2|x1), and x1 is statistically independent of ratio x2/x1. We present three proofs that apply to all members of a set of distributions. Each member is distinguished by the form of dependence between variables implicit within a statistical model (gamma, Gaussian, etc.), but all exhibit two symmetries in the joint distribution p(x1,x2) that are required in the absence of prior information: exchangeability of variables, and non-informative priors over the marginal distributions p(x1) and p(x2). We relate our conclusion to physical models of prediction and intelligence, where the known ’sample’ could be the present internal energy within a sensor, and the unknown the energy in its external sensory cause or future motor effect. (shrink)

Can perceptual experiences be states of uncertainty? We might expect them to be, if the perceptual processes from which they're generated, as well as the behaviors they help produce, take account of probabilistic information. Yet it has long been presumed that perceptual experiences purport to tell us about our environment, without hedging or qualifying. Against this long-standing view, I argue that perceptual experiences may well occasionally be states of uncertainty, but that they are never probabilistically structured. I criticize a powerful (...) line of reasoning that we should expect perceptual experience to be probabilistic, given their interfaces with unconscious probabilistic information, with behavior responsive to it, and with credences. (shrink)

In this paper we argue that predictive processing (PP) theory cannot account for the phenomenon of affect-biased attention prioritized attention to stimuli that are affectively salient because of their associations with reward or punishment. Specifically, the PP hypothesis that selective attention can be analyzed in terms of the optimization of precision expectations cannot accommodate affect-biased attention; affectively salient stimuli can capture our attention even when precision expectations are low. We review the prospects of three recent attempts to accommodate affect with (...) tools internal to PP theory: Miller and Clark’s (2018) embodied inference; Seth’s (2013) interoceptive inference; and Joffily and Coricelli’s (2013) rate of change of free energy. In each case we argue that the account does not resolve the challenge from affect-biased attention. For this reason, we conclude that prediction error minimization is not sufficient to explain all mental phenomena, contrary to the claim that the PP framework provides a unified theory of all mental phenomena or the brain ‘s cognitive functioning. Nevertheless, we suggest that empirical investigation of the interaction between affective salience and precision expectations should prove helpful in understanding the limits of PP theory, and may provide new directions for the application of a Bayesian perspective to perception. (shrink)

In discussion on consciousness and the hard problem, there is an unquestioned background assumption, namely, our experience is stored in the brain. Yet Bergson argued that this very question, “Is experience stored in the brain?” is the critical issue in the problem of consciousness. His examination of then-current memory research led him, save for motor or procedural memory, to a “no” answer. Others, for example Sheldrake, have continued this negative assessment of the research findings. So, has this assumption actually been (...) proven since Bergson? Do we know how experience is stored? Or that it is stored? Here, a recent review and model of memory is examined to see where this assumption actually stands. Again, the assessment will be that nothing has changed. The core of the problem, it will be argued, lies in two things: Firstly, the search for how/where experience is stored is motivated—rephrasing Bergson—in the classic metaphysic, a framework on space and time whose logic cannot be coherently, logically adhered to in attempting to explain how experience is stored. Secondly, the search generally assumes an inadequate theory of perception that is implicitly based in this classic metaphysic. If framed within Bergson’s model of perception and his temporal metaphysic, conjoined with J. J. Gibson’s model, the storage-search appears misguided from the start. (shrink)

I offer an account of perceptual warrant according to which one’s basic perceptual beliefs are immediately and defeasibly warranted if they are formed on the basis of experiences produced by a competent perceptual system. I claim that sub-personal features of one’s perceptual systems can render one competent to perceptually represent a particular environment. When these conditions are met, one is warranted in forming beliefs on the basis of one’s perceptual experiences. I develop my account of perceptual warrant in the context (...) of a Bayesian theory of perceptual processing. (shrink)

Nick Shea’s Representation in Cognitive Science commits him to representations in perceptual processing that are about probabilities. This commentary concerns how to adjudicate between this view and an alternative that locates the probabilities rather in the representational states’ associated “attitudes”. As background and motivation, evidence for probabilistic representations in perceptual processing is adduced, and it is shown how, on either conception, one can address a specific challenge Ned Block has raised to this evidence.

A popular view presents explanations in the cognitive sciences as causal or mechanistic and argues that an important feature of such explanations is that they allow us to manipulate and control the explanandum phenomena. Nonetheless, whether there can be explanations in the cognitive sciences that are neither causal nor mechanistic is still under debate. Another prominent view suggests that both causal and non-causal relations of counterfactual dependence can be explanatory, but this view is open to the criticism that it is (...) not clear how to distinguish explanatory from non-explanatory relations. In this paper, I draw from both views and suggest that, in the cognitive sciences, relations of counterfactual dependence that allow manipulation and control can be explanatory even when they are neither causal nor mechanistic. Furthermore, the ability to allow manipulation can determine whether non-causal counterfactual dependence relations are explanatory. I present a preliminary framework for manipulation relations that includes some non-causal relations and use two examples from the cognitive sciences to show how this framework distinguishes between explanatory and non-explanatory, non-causal relations. The proposed framework suggests that, in the cognitive sciences, causal and non-causal relations have the same criterion for explanatory value, namely, whether or not they allow manipulation and control. (shrink)

A Bayesian mind is, at its core, a rational mind. Bayesianism is thus well-suited to predict and explain mental processes that best exemplify our ability to be rational. However, evidence from belief acquisition and change appears to show that we do not acquire and update information in a Bayesian way. Instead, the principles of belief acquisition and updating seem grounded in maintaining a psychological immune system rather than in approximating a Bayesian processor.

We investigated people's ability to infer others’ mental states from their emotional reactions, manipulating whether agents wanted, expected, and caused an outcome. Participants recovered agents’ desires throughout. When the agent observed, but did not cause the outcome, participants’ ability to recover the agent's beliefs depended on the evidence they got. When the agent caused the event, participants’ judgments also depended on the probability of the action ; when actions were improbable given the mental states, people failed to recover the agent's (...) beliefs even when they saw her react to both the anticipated and actual outcomes. A Bayesian model captured human performance throughout, consistent with the proposal that people rationally integrate information about others’ actions and emotional reactions to infer their unobservable mental states. (shrink)

Perception purports to help you gain knowledge of the world even if the world is not the way you expected it to be. Perception also purports to be an independent tribunal against which you can test your beliefs. It is natural to think that in order to serve these and other central functions, perceptual representations must not causally depend on your prior beliefs and expectations. In this paper, I clarify and then argue against the natural thought above. All perceptual systems (...) must solve an under-determination problem: the sensory data they receive could be caused by indefinitely many arrangements of distal objects and properties. Using a Bayesian approach to perceptual processing, I argue that in order to solve the under-determination problem, perceptual capacities must rely on prior beliefs or expectations of some kind. I then argue that perceptual states or processes can help ground knowledge of the world whether the ‘beliefs’ necessary for perceptual processing are encoded as sub-personal states within a perceptual system or cognitive states, such as person-level beliefs. My argument has two main parts. First, I give a preliminary argument that cognitive influence on perception can be appropriate, and I respond to three lines of objection. Second, I argue that cognitively influenced perceptual states can be instances of seeing that p, which makes the relevant states well suited to help ground knowledge that p. I conclude that a cognitively penetrated perceptual state or process can help ground knowledge under some circumstances. (shrink)

We argue that Bayesian models are best categorized as methodological or theoretical. That is, models are used as tools to constrain theories, with no commitment to the processes that mediate cognition, or models are intended to approximate the underlying algorithmic solutions. We argue that both approaches are flawed, and that the Enlightened Bayesian approach is unlikely to help.

Judea Pearl has argued that counterfactuals and causality are central to intelligence, whether natural or artificial, and has helped create a rich mathematical and computational framework for formally analyzing causality. Here, we draw out connections between these notions and various current issues in cognitive science, including the nature of mental “programs” and mental representation. We argue that programs (consisting of algorithms and data structures) have a causal (counterfactual-supporting) structure; these counterfactuals can reveal the nature of mental representations. Programs can also (...) provide a causal model of the external world. Such models are, we suggest, ubiquitous in perception, cognition, and language processing. (shrink)

Philosophical Psychology, Volume 0, Issue 0, Page 1-6, Ahead of Print.

We often feel that our perceptual experience is richer than what we can express. For instance, when flashed with a large set of letters, we feel that we can see them all, while we can report only a few. However, the nature of this subjective impression remains highly debated: while many favour a dissociation between two forms of consciousness , others contend that the richness of phenomenal experience is a mere illusion. Here we addressed this question with a classical partial-report (...) paradigm now modified to include unexpected items in the unreported parts of the stimuli. We show that even in the presence of unexpected pseudo-letters, participants still felt that there were only letters. Additionally, we show that this feeling reflects an illusion whereby participants reconstruct letters using partial letter-like information. We propose that the feeling of seeing emerges from the interplay between partially accessible information and expectations. (shrink)

When we perceive the external world, our brain has to deal with the incompleteness and uncertainty associated with sensory inputs, memory and prior knowledge. In theoretical neuroscience probabilistic approaches have received a growing interest recently, as they account for the ability to reason with incomplete knowledge and to efficiently describe perceptive and behavioral tasks. How can the probability distributions that need to be estimated in these models be represented and processed in the brain, in particular at the single cell level? (...) We consider the basic function carried out by photoreceptor cells which consists in detecting the presence or absence of light. We give a system-level understanding of the process of phototransduction based on a bayesian formalism: we show that the process of phototransduction is equivalent to a temporal probabilistic inference in a Hidden Markov Model (HMM), for estimating the presence or absence of light. Thus, the biochemical mechanisms of phototransduction underlie the estimation of the current state probability distribution of the presence of light. A classical descriptive model describes the interactions between the different molecular messengers, ions, enzymes and channel proteins occurring within the photoreceptor by a set of nonlinear coupled differential equations. In contrast, the probabilistic HMM model is described by a discrete recurrence equation. It appears that the binary HMM has a general solution in the case of constant input. This allows a detailed analysis of the dynamics of the system. The biochemical system and the HMM behave similarly under steady-state conditions. Consequently a formal equivalence can be found between the biochemical system and the HMM. Numerical simulations further extend the results to the dynamic case and to noisy input. All in all, we have derived a probabilistic model equivalent to a classical descriptive model of phototransduction, which has the additional advantage of assigning a function to phototransduction. The example of phototransduction shows how simple biochemical interactions underlie simple probabilistic inferences. (shrink)

How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common (...) Bayesian formalism. The concepts we review are ambiguities, fusion, multimodality, conflicts, modularity, hierarchies and loops. First, each of these concepts is introduced briefly using some examples from the neuroscience, psychophysics or robotics literature. Then, the concept is formalized using a template Bayesian model. The assumptions and common features of these models, as well as their major differences, are outlined and discussed. (shrink)

Early normative studies of human behavior revealed a gap between the norms of practical rationality (what humans ought to do) and the actual human behavior (what they do). It has been suggested that, to close the gap between the descriptive and the normative, one has to revise norms of practical rationality according to the Quinean, engineering view of normativity. On this view, the norms must be designed such that they effectively account for behavior. I review recent studies of human perception (...) which pursued normative modeling and which found good agreement between the normative prescriptions and the actual behavior. I make the case that the goals and methods of this work have been incompatible with those of the engineering approach. I argue that norms of perception and action are observer-independent properties of biological agents; the norms are discovered using methods of natural sciences rather than the norms are designed to fit the observed behavior. (shrink)

Bergson’s model of time (1889) is perhaps the proto-phenomenological theory. It is part of a larger model of mind (1896) which can be seen in modern light as describing the brain as supporting a modulated wave within a holographic field, specifying the external image of the world, and wherein subject and object are differentiated not in terms of space, but of time. Bergson’s very concrete model is developed and deepened with Gibson’s ecological model of perception. It is applied to the (...) problems of consciousness, direct realism, qualia and illusions. The model implies an entirely different basis for memory and cognition, and a brief overview is given for the basis of direct memory, compositionality and systematicity. (shrink)

Physical reality as an explanatory model is an abstraction of the mind. Every perceptual system is a user interface, like the dashboard of an aeroplane or the desktop of a computer. We do not see or otherwise perceive reality but only interface with reality. The user interface concept is a starting point for a critical dialogue with those epistemic theories that present themselves as veridical and take explanatory abstractions as ontological primitives. At the heart of any scientific model are assumptions (...) about which things exist, how they are related, and how we can know them. Scientific models take our knowledge beyond ordinary experience toward explanatory abstractions. The main problem with veridical models lies in why we cannot express our theories and the explanatory abstractions associated with them other than through classical perceptual interface symbols. This study analyses the limits, possibilities and constraints of explanatory abstractions. (shrink)