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Record W7066551974

Is human object recognition invariant to depth cues?

2014· dissertation· en· W7066551974 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2014
Typedissertation
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsMcGill University
Fundersnot available
KeywordsCognitive neuroscience of visual object recognitionDepth perceptionInvariant (physics)PerceptionFacial recognition systemObject (grammar)Face perceptionPattern recognition (psychology)Adaptation (eye)
DOInot available

Abstract

fetched live from OpenAlex

Background. The human visual system can represent complex 3-D surfaces from multiple depth cues, but individual depth cues are processed by different cortical networks. For example, different cortical areas, scattered in the dorsal and ventral visual pathways, process depth from motion vs. shaded cues to extract 3-D surface information that may ultimately be used for object recognition. It is not known whether there is only one underlying mechanism for object recognition which combines multiple depth cues or whether there are multiple cue-specific representations of objects. Assuming efficiency in the object recognition mechanism, a single object representation is predicted to be formed in a specific brain region in the ventral visual pathway from multiple depth cues that are processed by different cortical areas. Thus, we were interested to investigate whether a complex object representation, such as a face representation, is achieved by a single depth cue and whether this representation is depth cue invariant by the level of the occipitotemporal cortex.Methods. We utilized the face identity aftereffect and the MEG adaptation paradigm to investigate the depth cue invariance. Face identity aftereffect is the transitory distortion in the perception of facial identity following extended periods of adaptation to a configurally distorted face called an anti-face. We measured face identification thresholds for each depth cue and across different depth cues in four subjects using a four-alternative forced-choice task with 3 conditions: matched anti-face adaptor, non-matched anti-face adaptor, as well as without adaptation. Using the MEG adaptation paradigm, we were interested to examine the adaptation of the face-selective M170 component, arising from the inferior occipitotemporal sources. We specifically asked whether this component showed reduced amplitude in response to a shaded face when it was preceded by a face, regardless of the depth cue defining the surface. We particularly looked at the adaptation of the face-selective M170 component across fourteen subjects and compared its amplitude and latency across different conditions.Results. We found robust face identity aftereffect not only from individual depth cues but also across different depth cues. The aftereffect was strongest in the shaded condition, weakest in the structure-from-motion condition, and comparable across texture and stereo disparity. Using the MEG adaptation paradigm, we found that part of the lateral occipital complex, particularly the right posterior inferior temporal gyrus, is tolerant to different depth cues. Conclusion. Despite the fact that our perception of the 3-D world is empowered by many depth cues, how depth cues contribute to object recognition is very poorly understood. Using the face identity aftereffect and the MEG adaptation approach we provided evidence for the existence of the depth cue invariance of complex object representations. These results are of fundamental importance to cortical organization of vision, but also valuable for developing object recognition algorithms for automated assessment based on 3-D information.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.305
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2014
Admission routes1
Has abstractyes

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