There has been a proliferation of proposed mental modules in an attempt to account for different cognitive functions but so far there has been no successful account of their integration. ACT-R (Anderson & Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explains how they are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT-R. These modules are associated with distinct cortical regions. These modules place chunks in buffers where they can be detected by a production system that responds to patterns of information in the buffers. At any point in time a single production rule is selected to respond to the current pattern. Subsymbolic processes serve to guide the selection of rules to fire as well as the internal operations of some modules. Much of learning involves tuning of these subsymbolic processes. Empirical examples are presented that illustrate the predictions of ACT-R’s modules. In addition, two models of complex tasks are described to illustrate how these modules result in strong predictions when they are brought together. One of these models is concerned with complex patterns of behavioral data in a dynamic task and the other is concerned with fMRI data obtained in a study of symbol manipulation.

Active vision systems have the capability of continuously interacting with the environment. The rapidly changing environment of such systems means that it is attractive to replace static representations with visual routines that compute information on demand. Such routines place a premium on image data structures that are easily computed and used. The purpose of this paper is to propose a general active vision architecture based on efficiently computable iconic representations. This architecture employs two primary visual routines, one for identifying the visual image near the fovea (object identification), and another for locating a stored prototype on the retina (object location). This design allows complex visual behaviors to be obtained by composing these two routines with different parameters. The iconic representations are comprised of high-dimensional feature vectors obtained from the responses of an ensemble of Gaussian derivative spatial filters at a number of orientations and...

Is human object recognition viewpoint dependent or viewpointinvariant under #everyday " conditions? Biederman and Gerhardstein #1993# argue that viewpoint-invariant mechanisms are used almost exclusively.However, our analysis indicates that: 1# their conditions for immediate viewpointinvariance lack the generalitytocharacterize a wide range of recognition phenomena; 2# the extensive body of viewpoint-dependent results cannot be dismissed as processing #by-products" or #experimental artifacts"; 3# geon structural descriptions cannot coherently account for category recognition, the domain they are intended to explain. We conclude that the weight of current evidence supports an exemplar-based multiple-views mechanism as an important component of both exemplar-speci#c and categorical recognition. # Many of the ideas in this paper were developed during visits by MJT to the Max#Planck#Institut f#ur biologische Kybernetik in T#ubingen, Germany.We thank Dan Kersten for his insightful comments...

Visual information is processed in posterior parietal cortex for the hypothesized purpose of extracting a variety of affordances for the generation of motor behavior. The term affordance is used to mean that visual cues are mapped directly to parameters that are relevant for motor interaction. In this paper, we present a model of the cortical involvement in grasping, which focuses on the interaction between anterior intra-parietal area (AIP) and premotor area F5. The model represents the role of other intra-parietal areas, working in concert with inferotemporal cortex and F5, to provide AIP with a full range of information from which affordances may be derived. The model also suggests how task information and other constraints may resolve the action opportunities provided by multiple affordances. Our model demonstrates that posterior parietal cortex is not only itself a network of interacting subsystems, but itself functions through a pattern of "cooperative computation" with a multipl...

Abstract--The recognition of three-dimensional ( 3-D) objects from sequences of their two-dimensional ( 2-D) views is modeled by a family of self-organizing neural architectures, called VIEWNET, that use View Information Encoded With NETworks. VIEWNET incorporates a preprocessor that generates a compressed but 2-D invariant representation of an image, a supervised incremental learning system that classifies the preprocessed representations into 2-1) view categories whose outputs are combined into 3-D invariant object categories, and a working memory that makes a 3-D object prediction by accumulating evidence from 3-D object category nodes us multiple 2-D views are experienced. The simplest VIEWNET achieves high recognition scores without the need to explicitly code the temporal order of 2-D views in working memory. Working memories are also discussed that save memory resources by implicitly coding temporal order in terms of the relative activity of 2-D view category nodes, rather than as explicit 2-D view transitions. Variants of the VIEWNET architecture may be used for scene understanding by using a preprocessor and classifier that can determine both what objects are in a scene and where they are located. The present VIEWNET preprocessor includes the CORT-X 2 filter, which discounts the illuminant, regularizes and completes figural boundaries, and suppresses image noise. This boundary segmentation is rendered invariant under 2-D translation, rotation, and dilation by use of a log-polar transform. The invariant spectra undergo Gaassian coarse coding to further reduce noise and 3-D foreshortening effects, and to increase generalization. These compressed codes are input into the

... only one component of more complex "event files" that link information about stimulus and response aspects of an experienced episode.

This article defends the claim that a significant part of visual perception (called "early vision") is impervious to the influence of beliefs, expectations or knowledge. We examine a wide range of empirical evidence that has been cited in support of the continuity of vision and cognition and argue that the evidence either shows withinvision top-down effects, or else the extra-visual effects that are demonstrated occur before the operation of the autonomous early vision system (through the allocation of focal attention) or after the visual system has produced its 3D shape-description (through the intervention of post-visual decision processes).

Abstract not found

Accounts of visually directed actions usually assume that their planning begins with an intention to act. This article describes three experiments that challenged this view through the use of a stimulus-response compatibility paradigm with photographs of common graspable objects as stimuli. Participants had to decide as fast as possible whether each object was upright or inverted. Experiments 1 and 2 examined the effect of the irrelevant dimension of left-fight object orientation on bimanual and tmimanual keypress responses. Experiment 3 examined wrist rotation responses to objects requiring either clockwise or anticlockwise wrist rotations when grasped. The results (a) are consistent with the view that seen objects automatically potentiate components of the actions they afford, (b) show that compatibility effects of an irrelevant stimulus dimension can be obtained across a wide variety of naturally occurring stimuli, and (c) support the view that intentions to act operate on already existing motor representations of the possible actions in a visual scene. The use of vision to control actions has typically been framed as a problem that begins with the intention to act. How we use visual information depends, after all, on the goal of

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 &