The literature on interference in the Stroop Color-Word Task, covering over 50 years and some 400 studies, is organized and reviewed. In so doing, a set ofl 8 reliable empirical findings is isolated that must be captured by any successful theory of the Stroop effect. Existing theoretical positions are summarized and evaluated in view of this critical evidence and the 2 major candidate theories--relative speed of processing and automaticity of reading--are found to be wanting. It is concluded that recent theories placing the explanatory weight on parallel processing of the irrelevant and the relevant dimensions are likely to be more successful than are earlier theories attempting to locate a single bottleneck in attention. In 1935, J. R. Stroop published his landmark article on attention and interference, an article more influential now than it was then. Why has the Stroop task continued to fascinate us? Perhaps the task is seen as tapping into the primitive operations of cognition, offering clues to the fundamental process of attention. Perhaps the robustness of the phenomenon provides a special challenge to decipher. Together these are powerful attractions

To investigate the relations between structure and function in both artificial and natural neural networks, we present a series of simulations and analyses with modular neural networks. We suggest a number of design principles in the form of explicit ways in which neural modules can cooperate in recognition tasks. These results may supplement recent accounts of the relation between structure and function in the brain. The networks used consist out of several modules, standard subnetworks that serve as higher-order units with a distinct structure and function. The simulations rely on a particular network module called CALM (Murre, Phaf, and Wolters, 1989, 1992). This module, developed mainly for unsupervised categorization and learning, is able to adjust its local learning dynamics. The way in which modules are interconnected is an important determinant of the learning and categorization behaviour of the network as a whole. Based on arguments derived from neuroscience, psychology, compu...

This article presents a theory that inte~ates space-based and object-based approaches to visual attention. The theory puts together M. P. van Oeffelen and P. G. Vos's ( 1982, 1983) COntour DEtector (CODE) theory of perceptual grouping by proximity with C. Bundesen's (1990) theory of visual attention (TVA). CODE provides input to TVA, accounting for spatially based between-object selection, and TVA converts the input to output, accounting for feature- and category-based withinobject selection. CODE clusters nearby items into perceptual groups that are both perceptual objects and regions of space, thereby integrating object-based and space-based approaches to attention. The combined theory provides a quantitative account of the effects of grouping by proximity and dis~nce between items on reaction time and accuracy data in 7 empirical situations that shaped the current literature on visual spatial attention. For the last decade the attention literature has been embroiled in a debate over the nature of visual spatial attention that focuses on the "thing " that attention selects (e.g., Baylis &

This book chapter examines the role of spatial attention from a computational perspective. It is intended as an overview for cognitive scientists interested in computational modeling of attentional phenomena. Because the function of attention can be understood only in its relation to visual information processing, we model not only the attentional system itself, but also the process of object recognition. We begin by presenting a basic model of object recognition, showing that interference prevents the system from reliably processing multiple, complex stimuli, and then we show how a simple mechanism of attentional selection can reduce this interference. Our first goal is to present a model that is computationally adequate, that is, a model that has the computational power to perform the sort of visual information processing tasks that people do. We then turn to simulations showing that the model can account for diverse experimental data, including: the benefit of attentional precuing, the time course of attention shifts, the effect of spatial uncertainty, the effect of irrelevant stimuli, the relation of object-based and location-based selection, and visual search. We conclude with a discussion of basic questions about computation modeling, including: Why build computational models? What makes a model compelling? When is a model right or wrong? Should one opt for depth or breadth in model coverage?

When subjects switch between a pair of stimulus–response tasks, reaction time is slower on trial N if a different task was performed on trial N � 1. We present a parallel distributed processing (PDP) model that simulates this effect when subjects switch between word reading and color naming in response to Stroop stimuli. Reaction time on ‘‘switch trials’ ’ can be slowed by an extended response selection process which results from (a) persisting, inappropriate states of activation and inhibition of task-controlling representations; and (b) associative learning, which allows stimuli to evoke tasks sets with which they have recently been associated (as proposed by Allport & Wylie, 2000). The model provides a good fit to a large body of empirical data, including findings which have been seen as problematic for this explanation of switch costs, and shows similar behavior when the parameters are set to random values, supporting Allport and Wylie’s proposal. © 2001 Elsevier Science Key Words: task switching; task set; Stroop effect; parallel distributed processing; executive functions. Atkinson and Shiffrin (1968) proposed a distinction between relatively permanent cognitive structures, such as short- and long-term memory, and control processes which harness those fixed structures in order to attain specific goals. This distinction was elaborated in the following years (e.g.,

Two experiments studied the emergence of bindings between stimulus features �object files) and between stimulus and response features �event files) over time. Choice responses �R2) were signalled by the shape of a stimulus �S2) that followed another stimulus �S1) of the same or different shape, location, and colour. S1 did not require a response �Experiment 1) or trigger a precued simple response �R1) that was or was not repeated by R2 �Experiment 2). Results demonstrate that the mere cooccurrence of stimulus features, and of stimuli and responses, is sufficient to bind their codes. Bindings emerge quickly and remain intact for at least four seconds. Which features are considered depends on their task-relevance; hence, integration reflects the current attentional set. There was no consistent trend toward higher order interactions as a function of time or of the amount of attention devoted to S1, suggesting that features are not integrated into a single, global superstructure, but enter independent local bindings presumably subserving different functions. OBJECT AND EVENT FILES

Traditional bottom-up models of visual processing assume that figure-ground organization precedes object recognition. This assumption seems logically necessary: How can object recognition occur before a region is labeled as figure? However, some behavioral studies find that familiar regions are more likely to be labeled figure than less familiar regions, a-problematic finding for bottom-up models. An interactive account is proposed in which figure-ground processes receive top-down input from object representations in a hierarchical system. A graded, interactive computational model is presented that accounts for behavioral results in which familiarity effects are found. The interactive model offers an alternative conception of visual processing to bottom-up models. In a typical visual scene multiple objects partially occlude one another, which makes object recognition a computation-ally complex task. Traditional information-processing theo-ries of visual perception have suggested that prior to object representation and recognition, an earlier stage of perceptual organization occurs to determine which features, locations, or surfaces most likely belong together (for examples, see

Most accounts of the Stroop effect (Stroop, 1935) emphasize its negative aspect, namely, that in particular situations, processing of an irrelevant stimulus dimension interferes with participants ’ performance of the instructed task. In contrast, this paper emphasizes the fact that, even with that interference, participants actually can (and usually do) exert enough control to perform the instructed task. An Adaptive Control of Thought–Rational (ACT–R) model of the Stroop task interprets this as a kind of learned strategic control. Specifically, the concept of utility is applied to the two processes that compete in the Stroop task, and a utility-learning mechanism serves to update the corresponding utility values according to experience and hence influence the competition. This model both accounts for various extant Stroop results and makes novel predictions about when people can reduce their susceptibility to Stroop interference. These predictions are tested in three experiments that involve a double-response variant of the Stroop task.

The present research examines the nature of the interference effects in a number of selective attention tasks. All of these tasks result in interference in performance by presenting information that is irrelevant to task performance but competes for selection. The interference from this competing information slows the response time (RT) of participants relative to a condition where the competition is minimized. We use a convolution of an exponential and a Gaussian (ex-Gaussian) distribution to examine the influence of interference on the characteristics of RT distributions. Consistent with previous research, we show that interference in the Stroop task is reflected by both the Gaussian and exponential portions of the exGaussian. In contrast, in four experiments we show that several other interference tasks evidence interference that is reflected only in the Gaussian portion of the ex-Gaussian distribution. We suggest that these difference reflect the operation of different selection me...

We develop a model of scan path generation based on the output of low level filters. The highest variance of Gabor jet filters computed over orientations are used as the object of attention. These points are held in a feature map which is inhibited as attention points are visited, creating a new attention point elsewhere. Scan paths generated this way can be used for recognition purposes where “single-shot ” methods, such as PCA, would fail because the image is not registered.