Further Explorations of Expert Object Recognition

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Further Explorations of Expert Object Recognition

• Assaf Harel
• Department of Psychology,
• Hebrew University, Jerusalem

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Acknowledgements

• Prof. Shlomo Bentin
• Prof. Rafael Malach
• Yulia Golland

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What is Expert Object Recognition?

• Experts have more experience with and are
• more knowledgeable about objects
• in their domain of expertise.
• Theoretical question What role does
• experience play in the object recognition
• system?
• 1) Cognitive substrate
• 2) Neural substrate

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Expertise a downward shift to subordinate
level.But, what type of processing is needed
for subordinate level (expert) identification?
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And Then Came Faces The Face Expertise Model

• Are faces special?
• Object recognition is domain general. Face and
  object processing should not be functionally
  independent. Faces are an example of stimuli
  which observers have gained natural expertise
  with.
• Therefore, according to this hypothesis experts
  in any kind of object recognition will show
  face-specific effects.

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Neuroimaging Findings

• Gauthier et al. (2000) suggested that FFA, a
  region which shows preferential activation for
  faces, can also be activated while bird and car
  experts viewed objects in their domain of
  expertise.
• FFA activation was also found for laboratory
  created expertise with Greebles (Gauthier et al.,
  1999).

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Focus of Present Research

• Expert object recognition in and of itself,
  independent of the domain specificity/expertise
  debate.
• Research question How is expert object
  recognition expressed in the brain?
• Are there any other expertise areas, except for
  the FFA?
• How early in the visual stream can we find
  expertise effects?
• Expertise-specific areas or a network of
  expertise?

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Selection of Car experts

• 14 car experts (all males, mean age 27) were
  selected based on their performance on a car
  discrimination task.
• Participants had to determine whether two cars
  were of the same model (within maker) varying in
  year, color, and orientation. Their accuracy (d)
  on this task was 1.39 compared with a group of 20
  novices whose accuracy was 0.57 (t(32)7.72,
  plt0.01).
• In a similar recognition task, with a different
  object category (airplanes), experts were as
  accurate as novices (0.67 and 0.43, respectively,
  t(32)1.72, p0.09).

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Methods

• Participants 9 car experts (males, mean age 23)
  and 10 novices (males, mean age 25).
• 1.5-T Signa Horizon LX 8.25 GE scanner of the Tel
  Aviv Sourasky Medical Center.
• fMRI parameters Gradient EPI sequence (TR3000
  ms, TE55 ms, FOV240x240 mm, matrix size 80x80,
  slice thickness4 mm, 1 mm gap, 27 axial slices).
• T1-weighted high resolution (1x1x1 mm) anatomic
  images and a whole-brain SPGR sequence.

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fMRI Experiment

• Three scans
• 2 experimental scans (faces, cars, airplanes)
• One-back memory task
• External localizer (faces, houses, tools,
  geometric patterns)
• One-back covert memory task

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All stimuli were equiluminant
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Experimental Design
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Results Group Activation Maps
Experts
Novices
Faces gt Houses
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Novices
Cars gt Airplanes
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Experts
Cars gt Airplanes
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Experts
Novices
Cars gt Airplanes
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• FFA was activated by faces in experts and
  novices. FFA was not preferentially activated in
  car experts while viewing cars.
• In contrast to faces, the pattern of activation
  elicited by cars was different for experts and
  novices.
• Whereas in novices, activation was limited to
  medio-occipital regions, in experts the
  car-activation was wide-spread, distributed over
  a large portion of the occipital cortex and
  extending to posterior regions of the inferior
  temporal lobe.

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ROI Analysis

• Four ROIs were defined using the external
  localizer
• FFA defined by the contrast Faces vs. Houses.
• LO - defined by the contrast Tools vs. Textures.
• CoS - defined by the contrast Houses vs. Faces.
• Early visual areas - defined by the contrast
  Textures vs. all other object categories.

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ROI Analysis Results


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• The analysis of the pre-defined ROIs revealed no
  difference between car-activation in experts and
  novices neither in the FFA nor in the LO.
• In early visual areas, equivalent activation was
  found across categories in novices, while in
  experts cars elicited significantly higher
  activation than faces and airplanes. A similar
  trend was found in the CoS.

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What Can We Learn About Expert Object Recognition?

• The neural substrates of car expertise are not
  equivalent to those of face expertise.
• Expert object recognition is distributed and not
  restricted to a specific hot spot such as the
  FFA.
• Expert object recognition in different domains
  recruits different brain regions.
• Is face recognition the right model for expert
  object recognition?

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• Alternative explanation the extent of activity
  for objects of expertise (such as cars), which is
  not seen for faces, might indicate general
  alertness/arousal (emotional reaction?)
  superimposed on peculiar perceptual processes.

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Mourao-Miranda et al. (Neuroimage, 2003)
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What Can We Learn About Expert Object Recognition?

• The results suggest a notion of a dedicated
  expert object recognition network, whereby early
  vision is top-down modulated according to
  differential recognition goals.
• Theoretical framework Reverse Hierarchy Theory
  (Ahissar Hochstein, TiCS 2004)

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Hochstein Ahissar, 2002Ahissar Hochstein,
2004
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• In situations requiring better signal-to-noise
  ratio (such as discriminating among similar
  members of the same class) highly-trained
  performers, who have had a great deal of training
  experience, base their performance on low-level
  representations guided by top-down activated
  pathways.
• In the present study, this model manifests in the
  car selective activation found in early visual
  areas of car experts and in the extensive pattern
  of activation for objects of expertise.

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Future Research

• Neuroimaging studies manipulating
  alertness/interest/arousal.
• Behavioral measures manipulating low- level
  processing.
• Temporal dynamics of low-level processing vs.
  high-level processing in experts.

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RHT and Perceptual Learning

• Perceptual Learning practice-induced
  improvement in the ability to perform specific
  perceptual tasks.
• Perceptual learning improvement largely stems
  from a gradual top-down guided increase in
  usability in first high and then lower-level
  task-relevant information.
• This process is subserved by a cascade of
  top-to-bottom level modifications that enhance
  task-relevant, and prune irrelevant, information.

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