Attention in hierarchical models of object recognition

Walther, Dirk; Koch, Christof

doi:10.1016/s0079-6123(06)65005-x

Cited by 57 publications

(34 citation statements)

References 99 publications

(136 reference statements)

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“…In other words, an attention map could multiplicatively gate only features that are positioned at attended locations. This is consistent with recent findings in the computational modeling of object recognition, which have shown that attention reduces interference and increases performance in object recognition in multiobject visual images (Fazl, Grossberg, & Mingolla, 2009;Walther & Koch, 2007). It should be noted that the attention map might also serve to read out information from the inferior and the superior IPS during retrieval.…”

Section: Computation In a Cortical Microcircuitsupporting

confidence: 91%

A computational model of fMRI activity in the intraparietal sulcus that supports visual working memory

Domijan

2011

Cogn Affect Behav Neurosci

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A computational model was developed to explain a pattern of results of fMRI activation in the intraparietal sulcus (IPS) supporting visual working memory for multiobject scenes. The model is based on the hypothesis that dendrites of excitatory neurons are major computational elements in the cortical circuit. Dendrites enable formation of a competitive queue that exhibits a gradient of activity values for nodes encoding different objects, and this pattern is stored in working memory. In the model, brain imaging data are interpreted as a consequence of blood flow arising from dendritic processing. Computer simulations showed that the model successfully simulates data showing the involvement of inferior IPS in object individuation and spatial grouping through representation of objects' locations in space, along with the involvement of superior IPS in object identification through representation of a set of objects' features. The model exhibits a capacity limit due to the limited dynamic range for nodes and the operation of lateral inhibition among them. The capacity limit is fixed in the inferior IPS regardless of the objects' complexity, due to the normalization of lateral inhibition, and variable in the superior IPS, due to the different encoding demands for simple and complex shapes. Systematic variation in the strength of self-excitation enables an understanding of the individual differences in working memory capacity. The model offers several testable predictions regarding the neural basis of visual working memory.

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Section: Computation In a Cortical Microcircuitsupporting

confidence: 91%

A computational model of fMRI activity in the intraparietal sulcus that supports visual working memory

Domijan

2011

Cogn Affect Behav Neurosci

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“…Specifically, IPS may represent a computational hub that integrates auditory input from the auditory parabelt (Pandya and Kuypers 1969; Divac et al 1977; Hyvarinen 1982) and forms a relay station between the sensory and prefrontal cortex, which associates sensory signals with behavioral meaning (Petrides and Pandya 1984; Fritz et al 2010). Similar computational operations have been attributed to the parietal cortex in saliency map models of visual feature search (Gottlieb et al 1998; Itti and Koch 2001; Walther and Koch 2007; Geng and Mangun 2009). Overall our results suggest that IPS plays an automatic, bottom-up role in auditory figure-ground processing, and call for a re-examination of the prevailing assumptions regarding the neural computations and circuits that mediate auditory scene analysis.…”

Section: Evoked Transition Responsesmentioning

confidence: 64%

Neural Correlates of Auditory Figure-Ground Segregation Based on Temporal Coherence

et al. 2016

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To make sense of natural acoustic environments, listeners must parse complex mixtures of sounds that vary in frequency, space, and time. Emerging work suggests that, in addition to the well-studied spectral cues for segregation, sensitivity to temporal coherence—the coincidence of sound elements in and across time—is also critical for the perceptual organization of acoustic scenes. Here, we examine pre-attentive, stimulus-driven neural processes underlying auditory figure-ground segregation using stimuli that capture the challenges of listening in complex scenes where segregation cannot be achieved based on spectral cues alone. Signals (“stochastic figure-ground”: SFG) comprised a sequence of brief broadband chords containing random pure tone components that vary from 1 chord to another. Occasional tone repetitions across chords are perceived as “figures” popping out of a stochastic “ground.” Magnetoencephalography (MEG) measurement in naïve, distracted, human subjects revealed robust evoked responses, commencing from about 150 ms after figure onset that reflect the emergence of the “figure” from the randomly varying “ground.” Neural sources underlying this bottom-up driven figure-ground segregation were localized to planum temporale, and the intraparietal sulcus, demonstrating that this area, outside the “classic” auditory system, is also involved in the early stages of auditory scene analysis.”

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“…However, search for parameters to include the high-level mechanisms into model gaze attraction function is unsolved task up to now (Walther & Koch, 2007). One of the approaches to this goal can consist in detailed model-based investigation of low-level parameters contribution in scan path formation.…”

Section: Dynamics Of Model Scan Path While Changing Input Window Strumentioning

confidence: 99%

“…The search for eye movement parameters which allow us to estimate the visual task to be solved during the current stage of dynamical image processing, and evaluation of the contribution of dominating components of visual attention is to be unsolved objectives up to now in both experimental and modeling studies (Carrasco, 2011;Lupianez, Klein, & Bartolomeo, 2006;Navalpakkam & Itti, 2005;Walther & Koch, 2007;Wang & Theeuwes, 2012;Wolfe, Birnkrant, Kunar, & Horowitz, 2005;Zelinsky, 2005).…”

Section: Introductionmentioning

confidence: 99%

Scanpaths of Complex Image Viewing: Insights From Experimental and Modeling Studies

et al. 2015

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From the first works of Buswell, Yarbus, and Noton and Stark, the scan path for viewing complex images has been considered as a possible key to objective estimation of cognitive processes and their dynamics. However, evidences both pro and con were revealed in the modern research. In this article, the results supporting the Yarbus-Stark concept are presented. In psychophysical tests, two types of images (three paintings from Yarbus` works and four textures) were used with two instructions, namely, "free viewing" and "search for modified image regions." The focus of the analysis of experimental results and modeling has been given to local elements of the scan path. It was shown that each parameter used (square of viewing area, S; distance between center of mass of viewing area and image center, R; parameter Xi, based on duration of the current fixation and angle between preceding and following saccades), reflects the specificity of both visual task and image properties. Additionally, the return gaze fixations which have a set of specific properties and mainly address to the areas of interest on image were revealed. Evidently these facts can be formalized in an advanced mathematical model as additional instrument to study the mechanisms of complex image viewing.

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Attention in hierarchical models of object recognition

Cited by 57 publications

References 99 publications

A computational model of fMRI activity in the intraparietal sulcus that supports visual working memory

A computational model of fMRI activity in the intraparietal sulcus that supports visual working memory

Neural Correlates of Auditory Figure-Ground Segregation Based on Temporal Coherence

Scanpaths of Complex Image Viewing: Insights From Experimental and Modeling Studies

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