A letter in the peripheral visual field is much harder to identify in the presence of nearby letters. This is called "crowding". In general, masking is a procedure: introducing any "mask" pattern that affects discriminability of the signal. Crowding conforms to the masking paradigm, but the crowding effect is unlike ordinary masking. Here we characterize crowding, and present diagnostic tests that distinguish it from ordinary masking. In ordinary masking, the signal disappears. In crowding, it remains visible, but is ambiguous, confounded with its neighbors. Masks are usually effective only if they overlap the signal, but the crowding effect extends over a large region. The width of that region is proportional to signal eccentricity from the fovea and independent of signal size, mask size, signal and mask font, and number of masks. At 4 deg eccentricity, the threshold contrast for identification of a 0.32 deg signal letter is elevated (up to six-fold) by mask letters anywhere in a 2.3 deg region, seven times wider than the signal. In ordinary masking, threshold contrast rises as a power function of mask contrast, with a shallow log-log slope of 0.5 to 1, while in crowding, threshold is a sigmoidal function of mask contrast, with a steep log-log slope of 2 at close spacing. Most remarkably, although the threshold elevation decreases exponentially with spacing, the threshold and saturation contrasts of crowding are independent of spacing. Finally, ordinary masking is similar for detection and identification, but crowding occurs only for identification, not detection. More precisely, crowding occurs only in tasks that cannot be done based on a single detection by coarsely coded feature detectors. These results (and observers' introspections) suggest that ordinary masking b...

A host of experimental data has been accumulating on the properties and mechanisms of object recognition in cortex. We review the main findings, and summarize them using a quantitative, biologically plausible, Standard Model. The model is a tool to interpret and understand the available data, and generate questions and predictions for new experiments.

Neurophysiological and psychophysical research has provided convincing evidence that the shape of complex stationary objects is neurally encoded in representations that are based on learned two-dimensional prototypical views. A number of neural models have been proposed that account for this fact, and which also model correctly the invariance properties of neurons in the ventral pathway with respect to spatial position and scaling of the recognized objects. We have developed a model for the recognition of biological motion that exploits similar neural principles. The proposed model is compatible with a number of well-known neurophysiological facts and reproduces a variety of psychophysical results. A number of predictions is derived from the model that can be tested psychophysically, neurophysiologically, and in FMRI experiments. 1 Introduction Experimental research has provided insights in the neural basis of the recognition of complex stationary objects, like faces. A cent...

The authors investigated how human adults encode and remember parts of multielement scenes composed of recursively embedded visual shape combinations. The authors found that shape combinations that are parts of larger configurations are less well remembered than shape combinations of the same kind that are not embedded. Combined with basic mechanisms of statistical learning, this embeddedness constraint enables the development of complex new features for acquiring internal representations efficiently without being computationally intractable. The resulting representations also encode parts and wholes by chunking the visual input into components according to the statistical coherence of their constituents. These results suggest that a bootstrapping approach of constrained statistical learning offers a unified framework for investigating the formation of different internal representations in pattern and scene perception.

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There is a great deal of interest in characterizing the representations and processes that support visual word priming and written word identification more generally. On one view, these phenomena are supported by abstract orthographic representations that map together visually dissimilar exemplars of letters and words (e.g., the letters A/a map onto a common abstract letter code a*). On a second view, orthographic codes consist in a collection of episodic representations of words that interact in such a way that it sometimes looks as if there are abstract codes. P.L. Tenpenny (1995) contrasted these general approaches, and concluded by endorsing the episodic account, arguing that no evidence demands that we posit abstract orthographic representations. This review re-considers the evidence, and argues that a variety of priming and non-priming research strongly supports the conclusion that abstract orthographic codes exist and support priming and word identification. On this account, episodic representations are represented separately from abstract orthographic knowledge, and contribute minimally to these functions. In defense of abstractionist theories of repetition priming and word identification There is a great deal of interest in characterizing the representations and processes that support the improved processing of stimuli repeated during an experiment; the so-called repetition priming effect. Indeed, two different types of repetition priming have been intensively studied from two quite different perspectives. On the one hand, researchers interested in memory have tended to focus on long-term repetition priming, in which facilitation can last minutes, hours, and sometimes longer (Sloman, Hayman, Ohta, Law, & Tulving, 1988). For example, participants are generally f...

The gaze movements accompanying target localization were examined via human observers and a computational model (target acquisition model [TAM]). Search contexts ranged from fully realistic scenes to toys in a crib to Os and Qs, and manipulations included set size, target eccentricity, and target–distractor similarity. Observers and the model always previewed the same targets and searched identical displays. Behavioral and simulated eye movements were analyzed for acquisition accuracy, efficiency, and target guidance. TAM’s behavior generally fell within the behavioral mean’s 95% confidence interval for all measures in each experiment/condition. This agreement suggests that a fixed-parameter model using spatiochromatic filters and a simulated retina, when driven by the correct visual routines, can be a good general-purpose predictor of human target acquisition behavior.

this paper, we will argue that despite these failures of explicit change detection, participant's visual memory representation of the scene was nevertheless sensitive to the difference between views. Specifically, consecutive views of the scene were compared, and although the difference between views was not sufficient to yield explicit change detection, it was sufficient to update memory to reflect the changed viewpoint. Before directly examining whether memory was updated to reflect recent views (Experiment 2), it is important to eliminate two alternative explanations for the poor explicit change detection performance in Experiment 1. First, changes may have been missed because participants simply failed to construct or retain a representation from a previous view (O'Regan, 1992). Second, it could be that a representation of the previous view was retained across the masked interval, but it was immediately overwritten by perceptual processing of the next view, without comparison of the two views (Rensink et al., 1997)

We present a biologically motivated architecture for object recognition that is based on a hierarchical feature detection model in combination with a memory architecture that implements short-term and long-term memory for objects. A particular focus is the functional realization of online and incremental learning for the task of appearance-based object recognition of many complex-shaped objects. We propose some modifications of learning vector quantization algorithms that are especially adapted to the task of incremental learning and capable of dealing with the stability-plasticity dilemma of such learning algorithms. Our technical implementation of the neural architecture is capable of online learning of 50 objects within less than three hours.

Understanding how biological visual systems perform object recognition is one of the ultimate goals in computational neuroscience. Among the biological models of recognition the main distinctions are between feedforward and feedback and between object-centered and view-centered. From a computational viewpoint the different recognition tasks --- for instance categorization and identification --- are very similar, representing different trade-offs between specificity and invariance. Thus the different tasks do not strictly require different classes of models. The focus of the review is on feedforward, view-based models that are supported by psychophysical and physiological data.