We present data and argument to show that in Tetris-a real-time, interactive video game-certain cognitive and perceptual problems ore more quicktv, easily, and reliably solved by performing actions in the world than by performing com-putational actions in the head atone. We have found that some of the translations and rotations made by players of this video game are best understood as actions that use the world to improve cognition. These actions are not used to implement a plan, or to implement a reaction; they are used to change the world in order to simplify the problem-solving task. Thus, we distinguish pragmatic octions--actions performed to bring one physically closer to a goal-from epistemic actions-actions performed to uncover informatioan that is hidden or hard to compute mentally. To illustrate the need for epistemic actions, we first develop a standard information-processing model of Tetris cognition and show that it cannot explain performance data from human players of the game-even when we relax the assumption of fully sequential processing. Standard models disregard many actions taken by players because they appear unmotivated or superfluous. How-ever, we show that such actions are actually far from superfluous; they play a valuable role in improving human performance. We argue that traditional accounts are limited because they regard action as having o single function: to change the world. By recognizing a second function of action-an epistemic func-tion-we can explain many of the actions that a traditional model cannot. Al-though our argument is supported by numerous examples specifically from Tetris, we outline how the new category of epistemic action can be incorporated into theories of action more generally. In this article, we introduce the general idea of an epistemic action and discuss its role in Tetris, a real-time, interactive video game. Epistemic actions-physical actions that make mental computation easier, faster, or more We thank Steve Haehnichen for his work on the initial implementations of Tetris 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...

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SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUS-TAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into

We describe a method of learning generative models of objects from a set of images of the object under different, and unknown, illumination. Such a model allows us to approximate the objects’ appearance under a range of lighting conditions. This work is closely related to photometric stereo with unknown light sources and, in particular, to the use of Singular Value Decomposition (SVD) to estimate shape and albedo from multiple images up to a linear transformation [15]. Firstly we analyze and extend the SVD approach to this problem. We demonstrate that it applies to objects for which the dominant imaging effects are Lambertian reflectance with a distant light source and a background ambient term. To determine that this is a reasonable approximation we calculate the eigenvectors of the SVD on a set of real objects, under varying lighting conditions, and demonstrate that the first few eigenvectors account for most of the data in agreement with our predictions. We then analyze the linear ambiguities in the SVD approach and demonstrate that previous methods proposed to resolve them [15] are only valid under certain conditions. We discuss alternative possibilities and, in particular, demonstrate that knowledge of the object class is sufficient to resolve this problem. Secondly, we describe the use of surface consistency for putting constraints on the possible solutions. We prove that this constraint reduces the ambiguities to a subspace called the generalized bas relief ambiguity (GBR) which is inherent in the Lambertian reflectance function (and which can be shown to exist even if attached and cast shadows are present [3]). We demonstrate the use of surface consistency to solve for the shape and albedo up to a GBR and describe, and implement, a variety of additional assumptions to resolve the GBR. Thirdly, we demonstrate an iterative algorithm that can detect and remove some attached shadows from the objects thereby increasing the accuracy of the reconstructed shape and albedo. 1

Neurophysiological evidence is described, showing that some neurons in the macaque temporal cortical visual areas have responses that are invariant with respect to the position, size and view of faces and objects, and that these neurons show rapid processing and rapid learning. A theory is then described of how such invariant representations may be produced in a hierarchically organized set of visual cortical areas with convergent connectivity. The theory proposes that neurons in these visual areas use a modified Hebb synaptic modification rule with a short-term memory trace to capture whatever can be captured at each stage that is invariant about objects as the object changes in retinal position, size, rotation and view. Simulations are then described which explore the operation of the architecture. The simulations show that such a processing system can build invariant representations of objects.

We explore representation of 3D objects in which several distinct 2D views are stored for each object. We demonstrate the ability of a two-layer network of thresholded summation units to support such representations. Using unsupervised Hebbian relaxation, the network learned to recognize ten objects from different viewpoints. The training process led to the emergence of compact representations of the specific input views. When tested on novel views of the same objects, the network exhibited a substantial generalization capa- bility. In simulated psychophysical experiments, the network's behavior was qualitatively similar to that of human subjects.

Much indirect evidence supports the hypothesis that transformations of mental images are

We investigated preferred or canonical views for familiar and novel three-dimensional objects using computer-graphics psychophysics. We assessed the canonical views for objects by allowing participants to actively rotate realistically shaded three-dimensional models in realtime. Objects were viewed on a Silicon Graphics Workstation and manipulated in virtual space using a three degree-of-freedom input device. In the first experiment, participants adjusted each object to the viewpoint from which they would take a photograph if they planned to use the object to illustrate a brochure. In the second experiment, participants mentally imaged each object based on the name and then adjusted the object to the viewpoint from which they imagined it. In both experiments, there was a large degree of consistency across participants in terms of the preferred view for a given object. Our results provide new insights on the geometrical, experiential, and functional attributes that determine canonical v...

Theories of visual object recognition must solve the problem of recognizing 3D objects given that perceivers only receive 2D patterns of light on their retinae. Recent findings from human psychophysics, neurophysiology and machine vision provide converging evidence for `image-based' models in which objects are represented as collections of viewpoint-specific local features. This approach is contrasted with `structural-description' models in which objects are represented as configurations of 3D volumes or parts. We then review recent behavioral results that address the biological plausibility of both approaches, as well as some of their computational advantages and limitations. We conclude that, although the image-based approach holds great promise, it has potential pitfalls that may be best overcome by including structural information. Thus, the most viable model of object recognition may be one that incorporates the most appealing aspects of both image-based and structural-description theories. 1998 Elsevier Science B.V. All rights reserved Keywords: Object recognition; Image-based model; Structural description 1.