This article examines the role of attention and automaticity in auditory processing as revealed by event-related potential (ERP) research. An ERP component called the mismatch negativity, generated by the brain's automatic response to changes in repetitive auditory input, reveals that physical features of auditory stimuli are fully processed whether or not they are attended. It also suggests that there exist precise neuronal representations of the physical features of recent auditory stimuli, perhaps the traces underlying acoustic sensory (“echoic”) memory. A mechanism of passive attention switching in response to changes in repetitive input is also implicated.

Conscious perception of discrete acoustic stimuli might be mediated by some of the mechanisms underlying another ERP component (NI), one sensitive to stimulus onset and offset. Frequent passive attentional shifts might accountforthe effect cognitive psychologists describe as “the breakthrough of the unattended” (Broadbent 1982), that is, that even unattended stimuli may be semantically processed, without assuming automatic semantic processing or late selection in selective attention.

The processing negativity supports the early-selection theory and may arise from a mechanism for selectively attending to stimuli defined by certain features. This stimulus selection occurs in the form ofa matching process in which each input is compared with the “attentional trace,” a voluntarily maintained representation of the task-relevant features of the stimulus to be attended. The attentional mechanism described might underlie the stimulus-set mode of attention proposed by Broadbent. Finally, a model of automatic and attentional processing in audition is proposed that is based mainly on the aforementioned ERP components and some other physiological measures.

The general problem of visual search can be shown to be computationally intractable in a formal, complexity-theoretic sense, yet visual search is extensively involved in everyday perception, and biological systems manage to perform it remarkably well. Complexity level analysis may resolve this contradiction. Visual search can be reshaped into tractability through approximations and by optimizing the resources devoted to visual processing. Architectural constraints can be derived using the minimum cost principle to rule out a large class of potential solutions. The evidence speaks strongly against bottom-up approaches to vision. In particular, the constraints suggest an attentional mechanism that exploits knowledge of the specific problem being solved. This analysis of visual search performance in terms of attentional influences on visual information processing and complexity satisfaction allows a large body of neurophysiological and psychological evidence to be tied together.

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 “Connection Principle.” 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.

Once we recognize the Connection Principle, we see that it is necessary to perform an inversion on the explanatory models of cognitive science, an inversion analogous to the one evolutionary biology imposes on preDarwinian animistic modes of explanation. In place of the original intentionalistic explanations we have a combination of hardware and functional explanations. This radically alters the structure of explanation, because instead of a mental representation (such as a rule) causing the pattern of behavior it represents (such as rule-governed behavior), there is a neurophysiological cause of a pattern (such as a pattern of behavior), and the pattern plays a functional role in the life of the organism. What we mistakenly thought were descriptions of underlying mental principles in, for example, theories of vision and language were in fact descriptions of functional aspects of systems, which will have to be explained by underlying neurophysiological mechanisms. In such cases, what looks like mentalistic psychology is sometimes better construed as speculative neurophysiology. The moral is that the big mistake in cognitive science is not the overestimation of the computer metaphor (though that is indeed a mistake) but the neglect of consciousness.

To study animal welfare empirically we need an objective basis for deciding when an animal is suffering. Suffering includes a wide range ofunpleasant emotional states such as fear, boredom, pain, and hunger. Suffering has evolved as a mechanism for avoiding sources ofdanger and threats to fitness. Captive animals often suffer in situations in which they are prevented from doing something that they are highly motivated to do. The “price” an animal is prepared to pay to attain or to escape a situation is an index ofhow the animal “feels” about that situation. Withholding conditions or commodities for which an animal shows “inelastic demand” (i.e., for which it continues to work despite increasing costs) is very likely to cause suffering. In designing environments for animals in zoos, farms, and laboratories, priority should be given to features for which animals show inelastic demand. The care ofanimals can thereby be based on an objective, animal-centered assessment of their needs.