Zenon Pylyshyn

��In four experiments we address the question whether several visual objects can be selected voluntarily (exogenously) and then tracked in a Multiple Object Tracking paradigm and, if so, whether the selection involves a different process. Experiment 1 showed that items can indeed be selected based on their labels. Experiment 2 showed that to select the complement set to a set that is automatically (exogenously) selected — e.g. to select all objects not flashed — observers require additional time and that given 1080 ms they were able to select and track them as well as those selected automatically. Experiment 3 showed that the additional time needed in the previous experiment cannot be attributed solely to time required to disengage attention from the initially automatic selections. Experiment 4 showed that the added time provides a monotonically greater benefit when there are more targets, suggesting a serial process. These results are discussed in relation to the Visual Index (FINST) theory which assumes that visual indexes are captured by a data-driven process. It is suggested that voluntarily allocated attention can be used to facilitate the automatic attention capture by objects of interest.

The computational view of mind rests on certain intuitions regarding the fundamental similarity between computation and cognition. We examine some of these intuitions and suggest that they derive from the fact that computers and human organisms are both physical systems whose behavior is correctly described as being governed by rules acting on symbolic representations. Some of the implications of this view are discussed. It is suggested that a fundamental hypothesis of this approach is that there is a natural domain of human functioning that can be addressed exclusively in terms of a formal symbolic or algorithmic vocabulary or level of analysis. Much of the paper elaborates various conditions that need to be met if a literal view of mental activity as computation is to serve as the basis for explanatory theories. The coherence of such a view depends on there being a principled distinction between functions whose explanation requires that we posit internal representations and those that we can appropriately describe as merely instantiating causal physical or biological laws. In this paper the distinction is empirically grounded in a methodological criterion called the " cognitive impenetrability condition." Functions are said to be cognitively impenetrable if they cannot be influenced by such purely cognitive factors as goals, beliefs, inferences, tacit knowledge, and so on. Such a criterion makes it possible to empirically separate the fixed capacities of mind from the particular representations and algorithms used on specific occasions. In order for computational theories to avoid being ad hoc, they must deal effectively with the "degrees of freedom" problem by constraining the extent to which they can be arbitrarily adjusted post hoc to fit some particular set of observations. This in turn requires that the fixed architectural function and the algorithms be independently validated. It is argued that the architectural assumptions implicit in many contemporary models run afoul of the cognitive impenetrability condition, since the required fixed functions are demonstrably sensitive to tacit knowledge and goals. The paper concludes with some tactical suggestions for the development of computational cognitive theories

I recently discovered that work I was doing in the laboratory and in theoretical writings was implicitly taking a position on a set of questions that philosophers had been worrying about for much of the past 30 or more years. My clandestine involvement in philosophical issues began when a computer science colleague and I were trying to build a model of geometrical reasoning that would draw a diagram and notice things in the diagram as it drew it (Pylyshyn, Elcock, Marmor, & Sander, 1978). One problem we found we had to face was that if the system discovered a right angle it had no way to tell whether this was the intersection of certain lines it had drawn earlier while constructing a certain figure, and if so which particular lines they were. Moreover, the model had no way of telling whether this particular right angle was identical to some bit of drawing it had earlier encountered and represented as, say, the base of a particular triangle. There was, in other words, no way to determined the identity of an element (I use the term “element” when referring to a graphical unit such as used in experiments. Otherwise when speaking informally I use the term “thing” on the grounds that nobody would mistake that term for a technical theoretical construct. Eventually I end up calling them “Visual Objects” to conform to usage in psychology) at two different times if it was represented differently at those times. This led to some speculation about the need for what we called a “finger” that could be placed at a particular element of interest and that could be used to identify it as particular token thing (the way you might identify a particular feature on paper by labeling it). In general we needed something like a finger that would stay attached to a particular element and could be used to maintain a correspondence between the individual element that was just noticed now and one that had been represented in some fashion at an earlier time. The idea of such fingers (which....

Each of the chapters in this volume devotes considerable attention to defining and elaborating the notion of the frame problem-one of the hard problems of artificial intelligence. Not only do the chapters clarify the problems at hand, they shed light on the different approaches taken by those in artificial intelligence and by certain philosophers who have been concerned with related problems in their field. The book should therefore not be read merely as a discussion of the frame problem narrowly conceived, but also as a general analysis of what could be a major challenge to the design of computer systems exhibiting general intelligence.

We present three studies examining whether multiple-object tracking (MOT) benefits from the active inhibition of nontargets, as proposed in (Pylyshyn, 2004). Using a probedot technique, the first study showed poorer probe detection on nontargets than on either the targets being tracked or in the empty space between objects. The second study used a matching nontracking task to control for possible masking of probes, independent of target tracking. The third study examined how localized the inhibition is to individual nontargets. The result of these three studies led to the conclusion that nontargets are subject to a highly localized object-based inhibition. Implications of this finding for the FINST visual index theory are discussed. We suggest that we need to distinguish between the differentiation (or individuation) of enduring token objects and the process of making the objects accessible through indexes, with only the latter being limited to 4 or 5 objects

I’m one of those who is awed and impressed by the potential of this field and have devoted some part of my energy to persuading people that it is a positive force. I have done so largely on the grounds of its economic benefits and it potential for making the fruits of computer technology more generally available to the public — for example, to help the overworked physician; to search for oil and minerals and help manage our valuable resources; to explore, mine, and experimentindangerousenvironments;toallowthenon-computingpublicaccesstovast libraries of important information and even advice, and in the process give real meaning to the..

This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h e m a j o r d i s t i n c t i o n i s t h a t , w h i l e b o t h Connectionist and Classical architectures postulate representational mental states, the latter but not the former are committed to a symbol-level of representation, or to a ‘language of thought’: i.e., to representational states that have combinatorial syntactic and semantic structure. Several arguments for combinatorial structure in mental representations are then reviewed. These include arguments based on the ‘systematicity’ of mental representation: i.e., on the fact that cognitive capacities always exhibit certain symmetries, so that the ability to entertain a given thought implies the ability to entertain thoughts with semantically related contents. We claim that such arguments make a powerful case that mind/brain architecture is not Connectionist at the cognitive level. We then consider the possibility that Connectionism may provide an account of the neural (or ‘abstract neurological’) structures in which Classical cognitive architecture is implemented. We survey a n u m b e r o f t h e s t a n d a r d a r g u m e n t s t h a t h a v e b e e n o f f e r e d i n f a v o r o f Connectionism, and conclude that they are coherent only on this interpretation.

It is generally accepted that there is something special about reasoning by using mental images. The question of how it is special, however, has never been satisfactorily spelled out, despite more than thirty years of research in the post-behaviorist tradition. This article considers some of the general motivation for the assumption that entertaining mental images involves inspecting a picture-like object. It sets out a distinction between phenomena attributable to the nature of mind to what is called the cognitive architecture, and ones that are attributable to tacit knowledge used to simulate what would happen in a visual situation. With this distinction in mind, the paper then considers in detail the widely held assumption that in some important sense images are spatially displayed or are depictive, and that examining images uses the same mechanisms that are deployed in visual perception. I argue that the assumption of the spatial or depictive nature of images is only explanatory if taken literally, as a claim about how images are physically instantiated in the brain, and that the literal view fails for a number of empirical reasons – for example, because of the cognitive penetrability of the phenomena cited in its favor. Similarly, while it is arguably the case that imagery and vision involve some of the same mechanisms, this tells us very little about the nature of mental imagery and does not support claims about the pictorial nature of mental images. Finally, I consider whether recent neuroscience evidence clarifies the debate over the nature of mental images. I claim that when such questions as whether images are depictive or spatial are formulated more clearly, the evidence does not provide support for the picture-theory over a symbol-structure theory of mental imagery. Even if all the empirical claims were true, they do not warrant the conclusion that many people have drawn from them: that mental images are depictive or are displayed in some (possibly cortical) space. Such a conclusion is incompatible with what is known about how images function in thought. We are then left with the provisional counterintuitive conclusion that the available evidence does not support rejection of what I call the “null hypothesis”; namely, that reasoning with mental images involves the same form of representation and the same processes as that of reasoning in general, except that the content or subject matter of thoughts experienced as images includes information about how things would look