Clark and Chalmers propose that the mind extends further than skin and skull. If they are right, then we should expect this to have some effect on our way of knowing our own mental states. If the content of my notebook can be part of my belief system, then looking at the notebook seems to be a way to get to know my own beliefs. However, it is at least not obvious whether self-ascribing a belief by looking at my notebook (...) is a case of introspection the same way that knowing my non-extended beliefs is. Traditionally this sort of introspection is thought to be privileged and special in ways that the extended introspection case seems not to be. There is nothing privileged about looking at my notebook. Anyone could do it. The aim of the paper is to find out how to understand extended introspection and whether there is something privileged and special about knowing one’s own extended beliefs. Moreover, the notebook case has close analogs using twenty-first century technology. It seems possible to know our beliefs that are extended to smartphones, wearable technology or a cloud-based data store. First, I present the case of extended introspection. I then discuss whether it should be understood as ordinary introspection or as mind-reading. Both seem to be bad fits, which finally prompts an original account for extended introspection based on epistemic rules. (shrink)

There is a long and distinguished tradition in philosophy and psychology according to which the mind’s fundamental, foundational connection to the world is made by connecting perceptually to features of objects. On this picture, which we’ll call feature prioritarianism, minds like ours first make contact with the colors, shapes, and sizes of distal items, and then, only on the basis of the representations so obtained, build up representations of the objects that bear these features. The feature priority view maintains, then, (...) that our perception/knowledge of objects asymmetrically depends on our perception/knowledge of simple features. This paper has two aims. First, we will present evidence, drawn from a variety of perceptual effects, that feature prioritarianism cannot be true, since there are cases that speak against the priority of feature representations in perceptual processing. Instead, we claim that the evidence supports an alternative —-and more complex—- no-priority view. Second, we will offer a framework for a no-priority view that both captures the cases we cite and provides a more sensible architecture in which to understand a variety of productive projects in perceptual science, and show how the framework cross-cuts some recent discussions in philosophy of perception. (shrink)

(2013). On the meaning of statements in psychophysics characterizing conditional indeterminacy of percepts. Philosophical Psychology: Vol. 26, No. 2, pp. 234-262. doi: 10.1080/09515089.2012.663715.

Visual modules can be viewed as expressions of a marked analytic attitude in the study of vision. In vision psychology, this attitude is accompanied by hypotheses that characterize how visual modules are thought to operate in perceptual processes. Our thesis here is that there are what we call “intrinsic reasons” for the presence of such hypotheses in a vision theory, that is, reasons of a deductive kind, which are imposed by the partiality of the basic terms in the definition of (...) a module, and by peculiar characteristics of those terms. Specifically, we discuss three hypotheses of functional attributes: successive stages in the action of modules, residual indeterminacy of their effects, and the role of prior constraints. For each of the three, we indicate its occurrence in perceptual psychology, explain corresponding intrinsic reasons, and illustrate such reasons with examples. (shrink)

This is my first publication of the distinction between phenomenal consciousness and access consciousness, though not using quite those terms. It ends with this: "The upshot is this: If Searle is using the access sense of "consciousness," his argument doesn't get to first base. If, as is more likely, he intends the what-it-is-like sense, his argument depends on assumptions about issues that the cognitivist is bound to regard as deeply unsettled empirical questions." Searle replies: "He refers to what he calls (...) an "access sense of consciousness." On my account there is no such sense.". (shrink)

This paper serves to introduce the papers from the symposium by the same title, by describing the sort of work done in philosophy of psychology conceived as a branch of the philosophy of science, distinguishing it from other discussions of psychology in philosophy, and criticizing the claims to set limits on scientific psychology in the largely psychologically uninformed literatures concerning "folk psychology' and "wide" and "narrow" content. Philosophy of psychology as philosophy of science takes seriously and analyzes the explanatory structures, (...) conceptual problems, and evidentiary practices of extant scientific psychology. (shrink)

Cognitive science typically postulates unconscious mental phenomena, computational or otherwise, to explain cognitive capacities. The mental phenomena in question are supposed to be inaccessible in principle to consciousness. I try to show that this is a mistake, because all unconscious intentionality must be accessible in principle to consciousness; we have no notion of intrinsic intentionality except in terms of its accessibility to consciousness. I call this claim the The argument for it proceeds in six steps. The essential point is that (...) intrinsic intentionality has aspectual shape: Our mental representations represent the world under specific aspects, and these aspectual features are essential to a mental state's being the state that it is. (shrink)

It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher order statistical structure of a set of input vectors. One (...) of these generative models, the restricted Boltzmann machine (RBM), has no connections between its hidden units and this makes perceptual inference and learning much simpler. More significantly, after a layer of hidden features has been learned, the activities of these features can be used as training data for another RBM. By applying this idea recursively, it is possible to learn a deep hierarchy of progressively more complicated features without requiring any labeled data. This deep hierarchy can then be treated as a feedforward neural network which can be discriminatively fine-tuned using backpropagation. Using a stack of RBMs to initialize the weights of a feedforward neural network allows backpropagation to work effectively in much deeper networks and it leads to much better generalization. A stack of RBMs can also be used to initialize a deep Boltzmann machine that has many hidden layers. Combining this initialization method with a new method for fine-tuning the weights finally leads to the first efficient way of training Boltzmann machines with many hidden layers and millions of weights. (shrink)

What, in essence, characterizes the mind? According to Searle, the potential to be conscious provides the only definitive criterion. Thus, conscious states are unquestionably "mental"; "shallow unconscious" states are also "mental" by virtue of their capacity to be conscious (at least in principle); but there are no "deep unconscious mental states" - i.e. those rules and procedures without access to consciousness, inferred by cognitive science to characterize the operations of the unconscious mind are not mental at all. Indeed, according to (...) Searle, they have no ontological status - they are simply ways of describing some interesting facets of purely physiological phenomena. (shrink)

There is a broad consensus about the fundamental role of thehippocampal system (hippocampus and its adjacent areas) in theencoding and retrieval of episodic memories. This paper presents afunctional model of this system. Although memory is not asingle-unit cognitive function, we took the view that the wholesystem of the smooth, interrelated memory processes may have acommon basis. That is why we follow the Ockham's razor principleand minimize the size or complexity of our model assumption set.The fundamental assumption is the requirement of (...) solving the socalled ``homunculus fallacy'', which addresses the issue ofinterpreting the input. Generative autoassociators seem to offer aresolution of the paradox. Learning to represent and to recallinformation, in these generative networks, imply maximization ofinformation transfer, sparse representation and noveltyrecognition. A connectionist architecture, which integrates theseaspects as model constraints, is derived. Numerical studiesdemonstrate the novelty recognition and noise filtering propertiesof the architecture. Finally, we conclude that the derivedconnectionist architecture can be related to the neurobiologicalsubstrate. (shrink)

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system that relies on generative models to predict the structure of sensory information. Such a view resonates with a body of work in machine learning that has explored the problem-solving capabilities of hierarchically-organized, multi-layer (i.e., deep) neural networks, many of which acquire and deploy generative models of their training data. The present chapter explores the extent to which the ostensible convergence on a common neurocomputational architecture (...) (centred on predictive processing schemes, hierarchical organization, and generative models) might provide inroads into the problem of digital immortality. In contrast to approaches that seek to recapitulate the connectomic microstructure of the human brain, the present chapter advocates an approach that is rooted in the use of machine learning algorithms. The claim is that a future form of deep learning system could be used to acquire generative models of a given individual or (alternatively) the sensory data that is processed by the brain of a given individual during the course of their biological life. The differences between these two forms of digital immortality are explored, as are some of the options for digital resurrection. (shrink)