Little, Brown, 1992 Review by Glenn Branch on Jul 5th 1999 Volume: 3, Number: 27.

Drawing on work in psychophysics, psychometrics, and sensory neurophysiology, Clark analyzes the character and defends the integrity of psychophysical explanations of qualitative facts, arguing that the structure of such explanations is sound and potentially successful.

As Ruben notes, the macrostrategy can allow that the distinction may also be drawn at some micro level, but it insists that descent to the micro level is ...

Investigations of the function of consciousness in human information processing have focused mainly on two questions: (1) where does consciousness enter into the information processing sequence and (2) how does conscious processing differ from preconscious and unconscious processing. Input analysis is thought to be initially "preconscious," "pre-attentive," fast, involuntary, and automatic. This is followed by "conscious," "focal-attentive" analysis which is relatively slow, voluntary, and flexible. It is thought that simple, familiar stimuli can be identified preconsciously, but conscious processing is needed (...) to identify complex, novel stimuli. Conscious processing has also been thought to be necessary for choice, learning and memory, and the organization of complex, novel responses, particularly those requiring planning, reflection, or creativity. (shrink)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. (shrink)

Having, in previous papers, distinguished at least three forms of consciousness , I now further examine their differences. This examination has some surprising results. Having argued that neither C1 nor C2 is a phenomenological state?and so different from CN?I now show that CN itself is best thought of as a subclass of a larger state . CS is the set of image?representation states. CN is that set of CS states that we are also C2 about. I argue that CN states (...) should be considered a subset of CS states because they share with other CS states imagistic representational powers as opposed to the sentential ones of C1 and C2. And I argue that there are good reasons to consider any state which utilises imagistic representation as a sensation. I then show that the notion of an unconscious sensation makes sense if we think of it as a CS state that is not C2. More radically, I conclude that either it makes sense to say that there are unfelt sensations or there are felt sensations we are not conscious about. While there are good reasons for saying one or the other of these surprising things, I argue that the second, more radical sounding, alternative is actually better motivated?and not quite so surprising as it sounds. (shrink)

Drawing on philosophy, neuroscience, and artificial intelligence, Simple Minds explores the construction of the mind from the matter of the brain.

Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: (...) the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands: indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context‐dependent, while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction. (shrink)

Many philosophers doubt that one can provide any successful explanation of sensory qualities - of how things look, feel, or seem to a perceiving subject. To provide such an explanation, one would need to explain qualitative facts in non-qualitative terms. Attempts to construct such explanations have seemed, in principle, doomed. Austen Clark examines the strategy used in psychophysics, psychometrics, and sensory neurophysiology to explain qualitative facts. He argues that this strategy could succeed: its structure is sound, and it can answer (...) the various philosophical objections lodged against it. On this basis Professor Clark presents an analysis of senosry qualities that offers the possibility of explaining at least some qualia, and he sketches how this scheme might eventually reduce to neurophysiology. If he is correct, we are not doomed to an eternity of mere acquaintance with our qualia. (shrink)

Connectionism—also known as parallel distributed processing, or neural network modeling—offers promise as a framework to unite clinical and cognitive psychology, and as a tool for studying conscious and unconscious mental activity. This paper describes a neural network model of the case study of Lucy R., from Freud and Breuer's Studies on Hysteria. Though very simple in architecture, the network spontaneously displays analogues of repression and hallucination, corresponding to Lucy R.'s symptoms. Salient elements of Lucy's conscious experience are represented in the (...) model by the activations of neuronlike processors in a fully interconnected network, without hidden units. The model learns to associate elements of experience that were associated in Lucy's case history. Some of these configurations of elements were traumatic for Lucy; trauma is modeled in the network by "emphatic learning," learning accomplished at an abnormally high learning rate. The model suggests that changing associations among conscious elements are sufficient to generate the symptoms Freud observed: apparent repression, hallucination, and recovery through therapy. In the case of Lucy R., Freud's theoretical inference regarding active but unconscious thought is not required by his data. Instead, the unconscious can be understood as a set of complex dispositions embodied in connections between elements of conscious experience. (shrink)