High-Level Visual Object Representations Are Constrained by Position

AbstractVisual object representations are commonly thought to emerge rapidly, yet it has remained unclear to what extent early brain responses reflect purely low-level visual features of these objects and how strongly those features contribute to later categorical or conceptual representations. Here, we aimed to estimate a lower temporal bound for the emergence of conceptual representations by defining two criteria that characterize such representations: 1) conceptual object representations should generalize across different exemplars of the same object, and 2) these representations should reflect high-level behavioral judgments. To test these criteria, we compared magnetoencephalography (MEG) recordings between two groups of participants (n = 16 per group) exposed to different exemplar images of the same object concepts. Further, we disentangled low-level from high-level MEG responses by estimating the unique and shared contribution of models of behavioral judgments, semantics, and different layers of deep neural networks of visual object processing. We find that 1) both generalization across exemplars as well as generalization of object-related signals across time increase after 150 ms, peaking around 230 ms; 2) behavioral judgments explain the most unique variance in the response after 150 ms. Collectively, these results suggest a lower bound for the emergence of conceptual object representations around 150 ms following stimulus onset.

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Representational content of oscillatory brain activity during object recognition

10.1101/2020.09.02.279216 ◽

2020 ◽

Author(s):

Leila Reddy ◽

Radoslaw Martin Cichy ◽

Rufin VanRullen

Keyword(s):

Brain Activity ◽

Sensory Information ◽

Representational Content ◽

Stimulus Presentation ◽

Visual Object ◽

Object Representations ◽

Beta Band ◽

Whole Brain ◽

Frequency Bands ◽

High Level

AbstractNumerous theories propose a key role for brain oscillations in visual perception. Most of these theories postulate that sensory information is encoded in specific oscillatory components (e.g., power or phase) of specific frequency bands. These theories are often tested with whole-brain recording methods of low spatial resolution (EEG or MEG), or depth recordings that provide a local, incomplete view of the brain. Opportunities to bridge the gap between local neural populations and whole-brain signals are rare. Here, using representational similarity analysis we ask which MEG oscillatory components (power and phase, across various frequency bands) correspond to low or high-level visual object representations, using brain representations from fMRI, or layer-wise representations in Deep Neural Networks (DNNs) as a template for low/high-level object representations. The results showed that around stimulus onset and offset, most transient oscillatory signals correlated with low-level brain patterns (V1). During stimulus presentation, sustained beta (∼20Hz) and gamma (>60Hz) power best correlated with V1, while oscillatory phase components correlated with IT representations. Surprisingly, this pattern of results did not always correspond to low- or high-level DNN layer activity. In particular, sustained beta-band oscillatory power reflected high-level DNN layers, suggestive of a feed-back component. These results begin to bridge the gap between whole-brain oscillatory signals and object representations supported by local neuronal activations.

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Surprise-driven acquisition of visual object representations for cognitive mobile robots

2011 IEEE International Conference on Robotics and Automation ◽

10.1109/icra.2011.5979718 ◽

2011 ◽

Cited By ~ 3

Author(s):

Werner Maier ◽

Eckehard Steinbach

Keyword(s):

Mobile Robots ◽

Visual Object ◽

Object Representations

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Visual search for object categories is predicted by the representational architecture of high-level visual cortex

Journal of Neurophysiology ◽

10.1152/jn.00569.2016 ◽

2017 ◽

Vol 117 (1) ◽

pp. 388-402 ◽

Cited By ~ 14

Author(s):

Michael A. Cohen ◽

George A. Alvarez ◽

Ken Nakayama ◽

Talia Konkle

Keyword(s):

Visual Cortex ◽

Visual Search ◽

Visual System ◽

Visual Processing ◽

Search Task ◽

Visual Search Task ◽

Visual Object ◽

Neural Responses ◽

Object Categories ◽

High Level

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing.

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Knowledge-driven perceptual organization reshapes information sampling via eye movements

10.1101/2021.09.24.461220 ◽

2021 ◽

Author(s):

Marek A. Pedziwiatr ◽

Elisabeth von dem Hagen ◽

Christoph Teufel

Keyword(s):

Eye Movements ◽

Feature Space ◽

Object Perception ◽

Visual Features ◽

Object Representations ◽

Object Knowledge ◽

Recent Developments ◽

Gaze Behaviour ◽

Information Sampling ◽

High Level

Humans constantly move their eyes to explore the environment and obtain information. Competing theories of gaze guidance consider the factors driving eye movements within a dichotomy between low-level visual features and high-level object representations. However, recent developments in object perception indicate a complex and intricate relationship between features and objects. Specifically, image-independent object-knowledge can generate objecthood by dynamically reconfiguring how feature space is carved up by the visual system. Here, we adopt this emerging perspective of object perception, moving away from the simplifying dichotomy between features and objects in explanations of gaze guidance. We recorded eye movements in response to stimuli that appear as meaningless patches on initial viewing but are experienced as coherent objects once relevant object-knowledge has been acquired. We demonstrate that gaze guidance differs substantially depending on whether observers experienced the same stimuli as meaningless patches or organised them into object representations. In particular, fixations on identical images became object-centred, less dispersed, and more consistent across observers once exposed to relevant prior object-knowledge. Observers' gaze behaviour also indicated a shift from exploratory information-sampling to a strategy of extracting information mainly from selected, object-related image areas. These effects were evident from the first fixations on the image. Importantly, however, eye-movements were not fully determined by object representations but were best explained by a simple model that integrates image-computable features and high-level, knowledge-dependent object representations. Overall, the results show how information sampling via eye-movements in humans is guided by a dynamic interaction between image-computable features and knowledge-driven perceptual organisation.

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Marmoset invariant object recognition behavior mirrors that of humans and macaques at fine scale

10.1101/2020.10.19.345561 ◽

2020 ◽

Author(s):

Alexander J.E. Kell ◽

Sophie L. Bokor ◽

You-Nah Jeon ◽

Tahereh Toosi ◽

Elias B. Issa

Keyword(s):

Object Recognition ◽

Model Organism ◽

High Standard ◽

Visual Object ◽

Visual Object Recognition ◽

Visual Neuroscience ◽

Brain Functions ◽

Invariant Object Recognition ◽

Small Model ◽

High Level

The marmoset—a small monkey with a flat cortex—offers powerful techniques for studying neural circuits in a primate. However, it remains unclear whether brain functions typically studied in larger primates can be studied in the marmoset. Here, we asked whether the 300-gram marmosets’ perceptual and cognitive repertoire approaches human levels or is instead closer to rodents’. Using high-level visual object recognition as a testbed, we found that on the same task marmosets substantially outperformed rats and generalized far more robustly across images, all while performing ∼1000 trials/day. We then compared marmosets against the high standard of human behavior. Across the same 400 images, marmosets’ image-by-image recognition behavior was strikingly human-like—essentially as human-like as macaques’. These results demonstrate that marmosets have been substantially underestimated and that high-level abilities have been conserved across simian primates. Consequently, marmosets are a potent small model organism for visual neuroscience, and perhaps beyond.

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Capacity for movement is an organisational principle in object representations

10.31234/osf.io/3x2qh ◽

2021 ◽

Author(s):

Sophia Shatek ◽

Amanda K Robinson ◽

Tijl Grootswagers ◽

Thomas A. Carlson

Keyword(s):

Moving Objects ◽

Time Pressure ◽

Visual Object ◽

Object Representations ◽

Directed Movement ◽

Neural Data ◽

Eeg Data ◽

Passive Viewing ◽

Static Images ◽

The Relationship

The ability to perceive moving objects is crucial for survival and threat identification. The association between the ability to move and being alive is learned early in childhood, yet not all moving objects are alive. Natural, non-agentive movement (e.g., clouds, fire) causes confusion in children and adults under time pressure. Recent neuroimaging evidence has shown that the visual system processes objects on a spectrum according to their ability to engage in self-propelled, goal-directed movement. Most prior work has used only moving stimuli that are also animate, so it is difficult to disentangle the effect of movement from aliveness or animacy in representational categorisation. In the current study, we investigated the relationship between movement and aliveness using both behavioural and neural measures. We examined electroencephalographic (EEG) data recorded while participants viewed static images of moving or non-moving objects that were either natural or artificial. Participants classified the images according to aliveness, or according to capacity for movement. Behavioural classification showed two key categorisation biases: moving natural things were often mistaken to be alive, and often classified as not moving. Movement explained significant variance in the neural data, during both a classification task and passive viewing. These results show that capacity for movement is an important dimension in the structure of human visual object representations.

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