Occlusion, symmetry, and object-based attention: reply to Saiki (2000).

J. Saiki (2000) argued that, because the stimuli used by M. Behrmann, R. S. Zemel, and M. C. Mozer (1998) were confounded by symmetry, conclusions about whether amodally completed objects can benefit from object-based attention are unwarranted. Here, the authors examine J. Saiki's claim further and expand on their view of the mechanisms underlying object-based attention, suggesting that perceptual organization is the process whereby features from a single object are selectively attended. In light of this, they claim that heuristics such as symmetry and collinearity play an important role in the facilitation of features from a single object. In support of this claim, they present data from a further experiment using displays that exploit common fate, another grouping heuristic, and show that, under these conditions, the hallmark of object-based attention, a single-object advantage, is obtained for the occluded (amodally completed) shapes.

Object-based attention and occlusion: evidence from normal participants and a computational model.

One way of perceptually organizing a complex visual scene is to attend selectively to information in a particular physical location. Another way of reducing the complexity in the input is to attend selectively to an individual object in the scene and to process its elements preferentially. This latter, object-based attention process was examined, and the predicted superiority for reporting features from 1 relative to 2 objects was replicated in a series of experiments. This object-based process was robust even under conditions of occlusion, although there were some boundary conditions on its operation. Finally, an account of the data is provided via simulations of the findings in a computational model. The claim is that object-based attention arises from a mechanisms that groups together those features based on internal representations developed over perceptual experience and then preferentially gates these features for later, selective processing.

Population coding with motion energy filters: the impact of correlations.

The codes obtained from the responses of large populations of neurons are known as population codes. Several studies have shown that the amount of information conveyed by such codes, and the format of this information, is highly dependent on the pattern of correlations. However, very little is known about the impact of response correlations (as found in actual cortical circuits) on neural coding. To address this problem, we investigated the properties of population codes obtained from motion energy filters, which provide one of the best models for motion selectivity in early visual areas. It is therefore likely that the correlations that arise among energy filters also arise among motion-selective neurons. We adopted an ideal observer approach to analyze filter responses to three sets of images: noisy sine gratings, random dots kinematograms, and images of natural scenes. We report that in our model, the structure of the population code varies with the type of image. We also show that for all sets of images, correlations convey a large fraction of the information: 40% to 90% of the total information. Moreover, ignoring those correlations when decoding leads to considerable information loss-from 50% to 93%, depending on the image type. Finally we show that it is important to consider a large population of motion energy filters in order to see the impact of correlations. Study of pairs of neurons, as is often done experimentally, can underestimate the effect of correlations.

A model for encoding multiple object motions and self-motion in area MST of primate visual cortex.

Many cells in the dorsal part of the medial superior temporal (MST) region of visual cortex respond selectively to specific combinations of expansion/contraction, translation, and rotation motions. Previous investigators have suggested that these cells may respond selectively to the flow fields generated by self-motion of an observer. These patterns can also be generated by the relative motion between an observer and a particular object. We explored a neurally constrained model based on the hypothesis that neurons in MST partially segment the motion fields generated by several independently moving objects. Inputs to the model were generated from sequences of ray-traced images that simulated realistic motion situations, combining observer motion, eye movements, and independent object motions. The input representation was based on the response properties of neurons in the middle temporal area (MT), which provides the primary input to area MST. After applying an unsupervised optimization technique, the units became tuned to patterns signaling coherent motion, matching many of the known properties of MST cells. The results of this model are consistent with recent studies indicating that MST cells primarily encode information concerning the relative three-dimensional motion between objects and the observer.

Localist attractor networks.

Attractor networks, which map an input space to a discrete output space, are useful for pattern completion--cleaning up noisy or missing input features. However, designing a net to have a given set of attractors is notoriously tricky; training procedures are CPU intensive and often produce spurious attractors and ill-conditioned attractor basins. These difficulties occur because each connection in the network participates in the encoding of multiple attractors. We describe an alternative formulation of attractor networks in which the encoding of knowledge is local, not distributed. Although localist attractor networks have similar dynamics to their distributed counterparts, they are much easier to work with and interpret. We propose a statistical formulation of localist attractor net dynamics, which yields a convergence proof and a mathematical interpretation of model parameters. We present simulation experiments that explore the behavior of localist attractor networks, showing that they yield few spurious attractors, and they readily exhibit two desirable properties of psychological and neurobiological models: priming (faster convergence to an attractor if the attractor has been recently visited) and gang effects (in which the presence of an attractor enhances the attractor basins of neighboring attractors).

Probabilistic interpretation of population codes.

We present a general encoding-decoding framework for interpreting the activity of a population of units. A standard population code interpretation method, the Poisson model, starts from a description as to how a single value of an underlying quantity can generate the activities of each unit in the population. In casting it in the encoding-decoding framework, we find that this model is too restrictive to describe fully the activities of units in population codes in higher processing areas, such as the medial temporal area. Under a more powerful model, the population activity can convey information not only about a single value of some quantity but also about its whole distribution, including its variance, and perhaps even the certainty the system has in the actual presence in the world of the entity generating this quantity. We propose a novel method for forming such probabilistic interpretations of population codes and compare it to the existing method.

The Helmholtz machine.

Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially many ways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns. We describe a way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways.

Reliable disparity estimation through selective integration.

A network model of disparity estimation was developed based on disparity-selective neurons, such as those found in the early stages of processing in the visual cortex. The model accurately estimated multiple disparities in regions, which may be caused by transparency or occlusion. The selective integration of reliable local estimates enabled the network to generate accurate disparity estimates on normal and transparent random-dot stereograms. The model was consistent with human psychophysical results on the effects of spatial-frequency filtering on disparity sensitivity. The responses of neurons in macaque area V2 to random-dot stereograms are consistent with the prediction of the model that a subset of neurons responsible for disparity selection should be sensitive to disparity gradients.