Zenon Pylyshyn

This commentary argues that the “illusion” to which Wegner refers in The Illusion of Conscious Will is actually the illusion that our conscious experience of mentally causing certain behaviors explains the behavior in question: It is not the subjective experience itself that is illusory, but the implied causal explanation. The experience of “mental effort” is cited as another example of this sort of illusion. Another significant example is the experience that properties of the representation of our mental images are responsible for certain patterns of behavior observed in mental imagery experiments. Examples include the increase in reaction time found when details are reported from smaller images or when attention is switched between different places and features (imagined as further apart than they are) within a single image. These examples illustrate the nature of the “illusion” involved: It is the illusion that certain observed regularities occur because of the content of the experience, as opposed to the converse – that experience has the content it does because of what the person figures out would happen in the imagined situation.

In Multiple Object Tracking (MOT), an observer is able to track 4 – 5 objects in a group of otherwise indistinguishable objects that move independently and unpredictably about a display. According to the Visual Indexing Theory (Pylyshyn, 1989), successful tracking requires that target objects be indexed while they are distinct -- before tracking begins. In the typical MOT task, the target objects are briefly flashed resulting in the automatic assignment of indexes. The question arises whether indexes are only assigned automatically or whether they can be assigned voluntarily in a top-down manner. This study compares several ways of specifying which of 8 items are the targets to be tracked. In the Flash condition the target items were flashed, in the Nonflash condition the targets were the items not flashed, and in the Number condition the targets were specified by number (e.g., items numbered 1-4). The results showed no difference between the three conditions, suggesting that tracking was possible with either voluntary or involuntary indexing. The second experiment tested the hypothesis that voluntary indexing is possible only if the target items are visited serially. The conditions were the same as experiment 1 except that the time available for index assignment was too short to allow targets to be visited serially. In this experiment, targets flashed only once (or, in the Numbers condition, remained visible for about 400 ms). The results showed a decrease in tracking performance for the Number condition, but the Flash and the Nonflash conditions did not differ, suggesting that as long as the designation of targets was done rapidly, the observer did not have to visit each target serially in order to index it. These results suggest that indexing can occur both automatically and voluntarily, and without serially visiting them, so long as the items are successfully specified.

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

The target article proposes that visual experience arises when sensorimotor contingencies are exploited in perception. This novel analysis of visual experience fares no better than the other proposals that the article rightly dismisses, and for the same reasons. Extracting invariants may be needed for recognition, but it is neither necessary nor sufficient for having a visual experience. While the idea that vision involves the active extraction of sensorimotor invariants has merit, it does not replace the need for perceptual representations. Vision is not just for the immediate controlling of action; it is also for finding out about the world, from which inferences may be drawn and beliefs changed.

Few areas of study have led to such close and intense interactions among computer scientists, psychologists, and philosophers as the area now referred to as cognitive science. Within this discipline, few problems have inspired as much debate as the use of notions such as meaning, intentionality, or the semantic content of mental states in explaining human behavior. The set of problems surrounding these notions have been viewed by some observers as threatening the foundations of cognitive science as currently conceived, and by others as providing a new and scientifically sound formulation of certain classical problems in the philosophy of mind. The chapters in this volume help bridge the gap among contributing disciplines-computer science, philosophy, psychology, neuroscience-and discuss the problems posed from various perspectives.

Using the Multiple Object Tracking (MOT) task, Pylyshyn & Leonard (VSS03) showed that a small brief probe dot was detected more poorly when it occurred on a nontarget than when it occurred either on a target or in the space between items, suggesting that moving nontarget items were inhibited. Here we generalize this finding by comparing probe detection performance against a baseline condition in which no tracking was required. We examined both a baseline condition in which objects did not move and one in which they moved exactly as in the probe detection task, but without tracking. Detection in the nonmoving control was essentially error-free, but probe detection performance in the moving (non-tracking) control task did depend on the probe location. Nonetheless, the findings reported earlier (worse detection on nontargets than anywhere else) remained after we took account of the baseline performance (using several different baseline comparison methods). Using this new baseline-control method we also showed that inhibition does not spread more than about 1.3 degrees of visual angle from the nontargets as the latter move during a tracking trial. In the present report we also describe some preliminary studies of conditions under which enhancement of targets, as well as inhibition of nontargets, may be obtained. Increasing the number and type of nontargets appeared to improve the detection of probes on targets, relative to baseline. However, we failed to find evidence that potentially distracting nontarget objects were inhibited more than clearly task-irrelevant objects, as would be expected if inhibition were a top-down process applied in order to improve performance on the main tracking task. For example, using the comparison with its matching baseline, we showed that task-irrelevant stationary items (which are never confused with targets in the tracking task) appeared to be inhibited even more than nontargets.

It is argued that the traditional distinction between artificial intelligence and cognitive simulation amounts to little more than a difference in style of research - a different ordering in goal priorities and different methodological allegiances. Both enterprises are constrained by empirical considerations and both are directed at understanding classes of tasks that are defined by essentially psychological criteria. Because of the different ordering of priorities, however, they occasionally take somewhat different stands on such issues as the power/generality trade-off and on the relevance of the sort of data collected in experimental psychology laboratories