The transverse occipital sulcus and intraparietal sulcus show neural selectivity to object-scene size relationships

Welbourne, Lauren E.; Jonnalagadda, Aditya; Giesbrecht, Barry; Eckstein, Miguel P.

doi:10.1038/s42003-021-02294-9

Cited by 12 publications

(10 citation statements)

References 81 publications

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“…The current results illustrating the CNN's ability to learn cues/context contrast with previous findings showing that CNNs do not learn contextual relationships in more complex real-world scenes (47). For example, humans use size relationships between objects and the surrounding scene to visual search (47), and human brain responses in scenes and attention-related areas are influenced by such associations (48,49). Many CNN-based object detectors are not sensitive to such object/scene size relationships (47).…”

Section: Discussioncontrasting

confidence: 98%

A Feedforward Network with a Few Million Neurons Learns from Images to Covertly Attend to Contextual Cues like Human and Bayesian Ideal Observers

Srivast

Wang

Eckstein

2022

Preprint

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Human behavioral experiments with predictive cues and contexts have led to influential conceptualizations of visual attention, such as limited resource mechanisms or weighting by Bayesian priors. How brains might learn to manifest cue/context effects with simple neural architectures is not known. We show that a feedforward convolutional neural network (CNN) with a few million neurons trained on noisy images to detect targets learns to utilize predictive cues and context and predicts human performance for the three most prominent behavioral signatures of covert attention: Posner cueing, set-size effects in search, and contextual cueing. The CNN also approximates a Bayesian ideal observer that has all prior statistical representations of the noise, targets, cues, and context. A larger network pre-trained on natural images and transfer learning can also account for human performance. The findings help understand the neurobiological requirements, computations, and simple neural architectures that lead to covert attention's three landmark behavioral effects.

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Section: Discussioncontrasting

confidence: 98%

A Feedforward Network with a Few Million Neurons Learns from Images to Covertly Attend to Contextual Cues like Human and Bayesian Ideal Observers

Srivast

Wang

Eckstein

2022

Preprint

Self Cite

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“…The context of an object includes the regularities of the scene in which it is found, the cluster of other objects it is typically found with, and the spatial relationships between all of these components. These contextual relationships have repeatedly been shown to facilitate human cognition and perception (Biederman et al, 1982;Davenport & Potter, 2004;Koehler & Eckstein, 2015;Lauer et al, 2020;Mudrik et al, 2010;Welbourne et al, 2021). For example, faster reaction times and more accurate responses in recognizing an object are found when the object is either primed by a contextual association (e.g., contextually related scene; Palmer, 1975), or when it is embedded in a congruent context compared with an incongruent context (Biederman et al, 1982;Davenport & Potter, 2004).…”

Section: Introductionmentioning

confidence: 98%

Contextual associations represented both in neural networks and human behavior

Aminoff

Baror

Roginek

et al. 2022

Preprint

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Contextual associations facilitate object recognition in human vision. However, the role of context in artificial vision remains elusive as does the characteristics that humans use to define context. We investigated whether contextually related objects (bicycle-helmet) are represented more similarly in convolutional neural networks (CNNs) used for image understanding than unrelated objects (bicycle-fork). Stimuli were of objects against a white background and consisted of a diverse set of contexts (N=73). CNN representations of contextually related objects were more similar to one another than to unrelated objects across all CNN layers. Critically, the similarity found in CNNs correlated with human behavior across three experiments assessing contextual relatedness, emerging significant only in the later layers. The results demonstrate that context is inherently represented in CNNs as a result of object recognition training, and that the representation in the later layers of the network tap into the contextual regularities that predict human behavior.

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“…We conclude that disruption of the right IPS in the healthy brain should cause impairment in view normalization for objects shown at incongruent sizes. IPS has been previously associated with the processing of magnitudes such as numerosity, object size, and illusionary size changes [ 30 , 31 , 32 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

View Normalization of Object Size in the Right Parietal Cortex

Hoba

Fink

Zeng

et al. 2022

Vision

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Prior knowledge alters perception already on early levels of processing. For instance, judging the display size of an object is affected by its familiar size. Using functional magnetic resonance imaging, we investigated the neural processes involved in resolving ambiguities between familiar object size and physical object size in 33 healthy human subjects. The familiar size was either small or large, and the object was displayed as either small or large. Thus, the size of the displayed object was either congruent or incongruent with its internally stored canonical size representation. Subjects were asked to indicate where the stimuli appeared on the screen as quickly and accurately as possible, thereby ensuring that differential activations cannot be ascribed to explicit object size judgments. Incongruent (relative to congruent) object displays were associated with enhanced activation of the right intraparietal sulcus (IPS). These data are consistent with but extend previous patient studies, which found the right parietal cortex involved in matching visual objects presented atypically to prototypical object representations, suggesting that the right IPS supports view normalization of objects. In a second experiment, using a parametric design, a region-of-interest analysis supported this notion by showing that increases in size mismatch between the displayed size of an object and its familiar viewing size were associated with an increased right IPS activation. We conclude that the right IPS performs view normalization of mismatched information about the internally stored prototypical size and the current viewing size of an object.

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The transverse occipital sulcus and intraparietal sulcus show neural selectivity to object-scene size relationships

Cited by 12 publications

References 81 publications

A Feedforward Network with a Few Million Neurons Learns from Images to Covertly Attend to Contextual Cues like Human and Bayesian Ideal Observers

A Feedforward Network with a Few Million Neurons Learns from Images to Covertly Attend to Contextual Cues like Human and Bayesian Ideal Observers

Contextual associations represented both in neural networks and human behavior

View Normalization of Object Size in the Right Parietal Cortex

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