Developing a neurocomputational model of change detection
Despite the large number of empirical investigations devoted to the topic of change blindness (Rensink, 2002), there existed no computationally explicit theory of this phenomenon. Researchers have instead relied on relatively vague appeals to attentional and representational limitations to explain their empirical findings. For example, one popular explanation for change blindness assumes a very limited ability to represent detailed Visual information, with an incomplete or sparse object representation of a scene resulting if this limit is exceeded (Rensink et al., 1997). According to this scheme, detection failures occur when the object change involves one of these unrepresented patterns. Following Zelinsky (2001b), the current project focused instead on constraints within the comparison operation, proposing that change detection failure may vary with the Visual similarity between the changing objects (Zelinsky, 2003c). A change involving two very dissimilar objects might be more easily detected than a change involving two more similar objects, not because of a sparse representation, but rather because the change signal would be weaker in the high-similarity condition. To make explicit this assumption, the real-world objects used in Zelinsky (1999d) were quantified in terms of high-dimensional vectors of linear filter responses (Zelinsky, 2000b), then compared using a simple Euclidean distance metric. Repeating this comparison operation for each object pair produced a table estimating the Visual similarity of each object to every other object. A behavioral experiment was then conducted to determine if the object pairs estimated by the model to be the most Visually similar were the same object pairs resulting in the greatest incidence of change detection failure. Analysis of the data supported this hypothesis, with the model well describing the observed change detection behavior. The findings from this study suggest that sparse representation and attentional limitations cannot simply be assumed from the change blindness phenomenon. As demonstrated by this implementation proof, even a dense representation might produce change detection failure if the patterns undergoing change are Visually similar.
Research Philosophy
Each time we engage in a moderately complex task, we likely enlist the help of an untold number of simpler visuo-motor operations that exist largely outside of our conscious awareness. Consider for instance the steps involved in preparing a cup of coffee. For the sake of simplicity, assume that the coffee has already been brewed and is waiting in the pot, and that all of the essential accessories, an empty cup, a spoon, a carton of
cream, and a tin of sugar, are sitting on a countertop in front of you. What is your first step toward accomplishing this goal? The very first thing that you might do is to move your eyes to the handle of the coffee pot, followed shortly thereafter by the much slower movement of your preferred hand to the same target. Because the coffee pot is hot and the handle is relatively small, this change in fixation is needed to guide your hand to a safe and useful place in which to grasp the object. After lifting the pot, your eye may then dart over to the cup. This action is needed, not only to again guide the pot to a very specific point in space directly over the cup, but also to provide feedback to the pouring operation so as to avoid a spill. After sitting the pot back on the counter (an act that may or may not require another eye movement), your gaze will likely shift to the spoon. Lagging shortly behind this behavior may be simultaneous movements of your hands, with your dominant hand moving toward the sugar tin and your non-preferred hand moving to the spoon. The spoon is a relatively small and slender object that again requires assistance from foveal vision for grasping; the tin is a rather bulky and indelicate object that does not require precise Visual information to inform the grasping operation. Once the spoon is in hand and the lid to the tin is lifted, gaze can then be directed to the tin in order to help scoop out the correct measure of sugar. To ensure that the spoon is kept level, a tracking operation may be used to keep your gaze on the loaded spoon as it moves slowly to the cup. After receiving the sugar, and following a few quick turns of the spoon, your coffee would finally be ready to drink (see Land et al., 1998, for a similarly framed example).
eye movements and visual cognition