Object-Based Attention

Cavanagh, Patrick; Caplovitz, Gideon P.; Lytchenko, Taissa K.; Maechler, Marvin; Tse, Peter U.; Sheinberg, David L.

doi:10.31234/osf.io/2bsn7

Cited by 4 publications

(14 citation statements)

References 150 publications

(258 reference statements)

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“…Our main interest was to use this computational framework to assess the importance of object-level attentional units for the guidance of human eye movements. Experimental research on object-based attention has repeatedly shown that objects play a central role in attentional selection (Scholl, 2001; Cavanagh et al, 2022) (cf. Sec.…”

Section: Discussionmentioning

confidence: 99%

“…How exactly these object representations are formed in the brain is still an open question (Cichy, Pantazis, and Oliva, 2014; Bonner and Epstein, 2021). We follow the argumentation of Cavanagh et al (2022) and assume that object-based attention can act on fully evolved object representations already before they are selected as saccade targets. An interesting alternative to providing predefined object segmentation masks would be an implementation where object representations and attentional selection inform and influence each other.…”

Section: Discussionmentioning

confidence: 99%

“…Deeprose and Andrade (2006), Tanaka et al (1991), and Ansorge, Kunde, and Kiefer (2014)) and unconscious object-based attention (cf. Chou and Yeh (2012) and Huang, Yeh, et al (2015)), however, suggests that the pre-attentive targets of object-based attention can be fully developed object representations with boundaries that do not need to be refined when attended (Cavanagh et al, 2022). Neri (2017) shows that the global structure of a scene, defined by meaningful object boundaries, can be inferred within the first 100 ms and suggests that perception is quickly organized around the inferred scene layout.…”

Section: Methodsmentioning

confidence: 99%

“…Segmented objects were tracked across frames using DeepSort (Wojke, Bewley, and Paulus, 2017) and manually controlled for obvious segmentation or tracking mistakes. As argued for in Cavanagh et al (2022), we assumed that object masks are fully evolved before being attended and do not depend on any visual information which could have been acquired during the scanpath. Future versions of ScanDy may, for comparison, have the option to start using coarse object representations, which will then be refined as visual information is gathered during scene exploration.…”

Section: Methodsmentioning

confidence: 99%

“…However, there is growing evidence that objects also play an important role in the selection process (Nuthmann and Henderson, 2010; Pajak and Nuthmann, 2013; Einhäuser, Spain, and Perona, 2008). It has been established in a wide range of psychophysical experiments that objects influence visual attention in multiple ways (e.g., Egly, Driver, and Rafal (1994), Kahneman, Treisman, and Gibbs (1992), and O’Craven, Downing, and Kanwisher (1999), see Scholl (2001)and Cavanagh et al (2022) for reviews, and Sec. Objects in Methods: ScanDy for more details).…”

Section: Introductionmentioning

confidence: 99%

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Objects guide human gaze behavior in dynamic real-world scenes

Roth

Rolfs

Hellwich

et al. 2023

Preprint

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The complexity of natural scenes makes it challenging to experimentally study the mechanisms behind human gaze behavior when viewing dynamic environments. Historically, eye movements were believed to be driven primarily by bottom-up saliency, but increasing evidence suggests that objects also play a significant role in guiding attention. We present a new computational framework to investigate the importance of objects for attentional guidance. This framework is designed to simulate realistic scanpaths for dynamic real-world scenes, including saccade timing and smooth pursuit behavior. Individual model components are based on psychophysically uncovered mechanisms of visual attention and saccadic decision-making. All mechanisms are implemented in a modular fashion with a small number of well-interpretable parameters. To systematically analyze the importance of objects in guiding gaze behavior, we implemented four different models within this framework: two purely location-based models, where one is based on low-level saliency and one on high-level saliency, and two object-based models, with one incorporating low-level saliency for each object and the other one not using any saliency information. We optimized each model's parameters to reproduce the saccade amplitude and fixation duration distributions of human scanpaths using evolutionary algorithms. We compared model performance with respect to spatial and temporal fixation behavior, including the proportion of fixations exploring the background, as well as detecting, inspecting, and revisiting objects. A model with object-based attention and inhibition, which uses saliency information to prioritize between objects for saccadic selection, leads to scanpath statistics with the highest similarity to the human data. This demonstrates that scanpath models benefit from object-based attention and selection, suggesting that object-level attentional units play an important role in guiding attentional processing.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Objects guide human gaze behavior in dynamic real-world scenes

Roth

Rolfs

Hellwich

et al. 2023

Preprint

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Thalamo-Cortical Interaction for Incremental Binding in Mental Contour-Tracing

Schmid,

Neumann

2023

Preprint

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Visual object-based attention marks a key process of mammalian perception. By which mechanisms this process is implemented and how it can be interacted with by means of attentional control is not completely understood yet. Incremental binding is a mechanism required in more demanding scenarios of object-based attention and is likewise experimentally investigated quite well. Attention spreads across a representation of the visual object and labels bound elements by constant up-modulation of neural activity. The speed of incremental binding was found to be dependent on the spatial arrangement of distracting elements in the scene and to be scale invariant giving rise to the growth-cone hypothesis. In this work, we propose a neural dynamical model of incremental binding that provides a mechanistic account for these findings. Through simulations, we investigate the model properties and demonstrate how an attentional spreading mechanism tags neurons that participate in the object binding process. They utilize Gestalt properties and eventually show growth-cone characteristics labeling perceptual items by delayed activity enhancement of neuronal firing rates. We discuss the algorithmic process underlying incremental binding and relate it to the model’s computation. This theoretical investigation encompasses complexity considerations and finds the model to be not only of explanatory value in terms of neurohpysiological evidence, but also to be an efficient implementation of incremental binding striving to establish a normative account. By relating the connectivity motifs of the model to neuroanatomical evidence, we suggest thalamo-cortical interactions to be a likely candidate for the flexible and efficient realization suggested by the model. There, pyramidal cells are proposed to serve as the processors of incremental grouping information. Local bottom-up evidence about stimulus features is integrated via basal dendritic sites. It is combined with an apical signal consisting of contextual grouping information which is gated by attentional task-relevance selection mediated via higher-order thalamic representations.Author SummaryUnderstanding a visual scene requires us to tell apart visual objects from one another. Object-based attention is the process by which mammals achieve this. Mental processing of object components determines whether they are compatible to the overall object and, thus, should be grouped together to be perceived as a whole or not. For complicated objects, this processing needs to happen serially, determining the compatibility step by step. In this work, we propose a neural model of this process and try to answer the question of how it might be implemented in the brain. We test the model on a case of object-based attention for grouping elongated lines and compare it to the available experimental evidence. We additionally show that the model not only explains this evidence, but it does so also by spending neurons and connections efficiently — a property likewise desirable for brains and machines. Together, these findings suggest which brain areas might be involved in realizing this process and how to reason about the complexity of this computation.

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A shared code for perceiving and imagining objects in human ventral temporal cortex

Wadia,

Reed,

Chung

et al. 2024

Preprint

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Mental imagery is a remarkable phenomenon that allows us to remember previous experiences and imagine new ones. Animal studies have yielded rich insight into mechanisms for visual perception, but the neural mechanisms for visual imagery remain poorly understood. Here, we first determined that ~80% of visually responsive single neurons in human ventral temporal cortex (VTC) used a distributed axis code to represent objects. We then used that code to reconstruct objects and generate maximally effective synthetic stimuli. Finally, we recorded responses from the same neural population while subjects imagined specific objects and found that ~40% of axis-tuned VTC neurons recapitulated the visual code. Our findings reveal that visual imagery is supported by reactivation of the same neurons involved in perception, providing single neuron evidence for existence of a generative model in human VTC.

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Object-Based Attention

Cited by 4 publications

References 150 publications

Objects guide human gaze behavior in dynamic real-world scenes

Objects guide human gaze behavior in dynamic real-world scenes

Thalamo-Cortical Interaction for Incremental Binding in Mental Contour-Tracing

A shared code for perceiving and imagining objects in human ventral temporal cortex

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