We offer an ecological (Gibsonian) alternative to cognitive (im)penetrability. Whereas Pylyshyn explains cognitive (im)penetrability by focusing solely on computations carried out by the nervous system, according to the ecological approach the perceiver as a knowing agent influences the entire animal-environmental system: in the determination of what constitutes the environment (affordances), what constitutes information, what information is detected and, thus, what is perceived.

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)

Everybody knows the phenomena summarized with the term attention: concentration, focalization, limitation, selection, and intensification . The explanation of these phenomena is, however, a different matter. Problems easily arise with regard towhathas to be explained and with regard to thestyleof explanation. A problem of the first kind is the “methodology of ‘bad focus’”: the explanation starts with and is fixated on an intuitively striking but nonessential behavioral feature or cognitive achievement. A problem of the second kind is a “virtus dormitiva” (...) explanation: the explanation starts with emphasizing one aspect of the observed phenomena, the emphasized aspect receives an interesting and suggestive name, and that name with its connotations is used as a concept in the explanation. At the start of contemporary, behavior-based, information processing psychology, avirtus dormitivaexplanation infiltrated the functional accounts of the phenomena of attention; the empirical observation that people show performance limitations was translated into the theoretical concept of a communication channel with a limited capacity. That limited capacity notion became the core concept in what can be called the standard theory of attention. This standard theory of attention faced severe difficulties in explaining the guidance of attention by the information processor's goals and intentions. Subsequent modifications, concerned with removing these difficulties, revealed that selection, guided by goals and intentions, is the essential behavioral feature and that the observed performance limitations are a result of this selection. So, the limited capacity theorizing was not only plagued by avirtus dormitivaexplanation, it also suffered from the methodology of bad focus. (shrink)

While some studies suggest cultural differences in visual processing, others do not, possibly because the complexity of their tasks draws upon high-level factors that could obscure such effects. To control for this, we examined cultural differences in visual search for geometric figures, a relatively simple task for which the underlying mechanisms are reasonably well known. We replicated earlier results showing that North Americans had a reliable search asymmetry for line length: Search for long among short lines was faster than vice (...) versa. In contrast, Japanese participants showed no asymmetry. This difference did not appear to be affected by stimulus density. Other kinds of stimuli resulted in other patterns of asymmetry differences, suggesting that these are not due to factors such as analytic/holistic processing but are based instead on the target-detection process. In particular, our results indicate that at least some cultural differences reflect different ways of processing early-level features, possibly in response to environmental factors. (shrink)

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. (shrink)

Different explanations of color vision favor different philosophical positions: Computational vision is more compatible with objectivism (the color is in the object), psychophysics and neurophysiology with subjectivism (the color is in the head). Comparative research suggests that an explanation of color must be both experientialist (unlike objectivism) and ecological (unlike subjectivism). Computational vision's emphasis on optimally prespecified features of the environment (i.e., distal properties, independent of the sensory-motor capacities of the animal) is unsatisfactory. Conceiving of visual perception instead as the (...) visual guidance of activity in an environment that is determined largely by that very activity suggests new directions for research. (shrink)

Some things look more complex than others. For example, a crenulate and richly organized leaf may seem more complex than a plain stone. What is the nature of this experience—and why do we have it in the first place? Here, we explore how object complexity serves as an efficiently extracted visual signal that the object merits further exploration. We algorithmically generated a library of geometric shapes and determined their complexity by computing the cumulative surprisal of their internal skeletons—essentially quantifying the (...) “amount of information” within each shape—and then used this approach to ask new questions about the perception of complexity. Experiments 1–3 asked what kind of mental process extracts visual complexity: a slow, deliberate, reflective process (as when we decide that an object is expensive or popular) or a fast, effortless, and automatic process (as when we see that an object is big or blue)? We placed simple and complex objects in visual search arrays and discovered that complex objects were easier to find among simple distractors than simple objects are among complex distractors—a classic search asymmetry indicating that complexity is prioritized in visual processing. Next, we explored the function of complexity: Why do we represent object complexity in the first place? Experiments 4–5 asked subjects to study serially presented objects in a self‐paced manner (for a later memory test); subjects dwelled longer on complex objects than simple objects—even when object shape was completely task‐irrelevant—suggesting a connection between visual complexity and exploratory engagement. Finally, Experiment 6 connected these implicit measures of complexity to explicit judgments. Collectively, these findings suggest that visual complexity is extracted efficiently and automatically, and even arouses a kind of “perceptual curiosity” about objects that encourages subsequent attentional engagement. (shrink)

Purely parallel neural networks can model object recognition in brief displays – the same conditions under which illusory conjunctions have been demonstrated empirically. Correcting errors of illusory conjunction is the “tag-assignment” problem for a purely parallel processor: the problem of assigning a spatial tag to nonspatial features, feature combinations, and objects. This problem must be solved to model human object recognition over a longer time scale. Our model simulates both the parallel processes that may underlie illusory conjunctions and the serial (...) processes that may solve the tag-assignment problem in normal perception. One component of the model extracts pooled features and another provides attentional tags that correct illusory conjunctions. Our approach addresses two questions: How can objects be identified from simultaneously attended features in a parallel, distributed representation? How can the spatial selectional requirements of such an attentional process be met by a separation of pathways for spatial and nonspatial processing? Our analysis of these questions yields a neurally plausible simulation of tag assignment based on synchronizing feature processing activity in a spatial focus of attention. (shrink)