Critical Features for Recognition of Biological Motion: a presentation of Casile & Giese (2005) paper

1. Critical features for the recognition of
biological motion
Casile & Giese (2005)
Your Name
Your Title
Your Organization (Line #1)
2005-12-31 Your Organization (Line #2)

2. Introduction
Point-light stimuli experiments
Perception of complex biological movements
(Johansson, 1973)
Not impaired
By adding noise
(Cutting et al. 1988)
By changing the contrast polarity of the dots
Ahlström et al.1997)
If only a subset of dots is visible
If the dots are displaced on the skeleton in every frame
Stimulus Perception
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3. Introduction
Different Hypotheses
Hypothesis 1: Computational mechanisms reconstruct
the missing information from impoverished stimuli by
fitting a skeleton model to the stimuli (dots)
most of the existing algorithms are computationally
expensive and have no obvious neural implementation
Hypothesis 2: Generalization from normal to point-light
stimuli is based on specific features that are shared by
both stimulus classes.
The nature of such features is largely unknown
It has been discussed whether they are based on
form or motion information Motion Form
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4. Method
Two movies
Stickman walking Moving dots
Stimulus
Optic Flow
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5. Analysis and Results
Two movies
Stickman walking Moving dots
Stimulus
r=0.09
PCA
Optic Flow
r=0.93
The dominant motion features are very similar for both stimuli, but the
dominant form features are different.
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6. Psychophysical experiments
CFS Stimulus
Object 1
Direction
Motion Information
Random Dots
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7. Motion Information
Experiment 1
Random Dots
1A Arrangement
Asymmetric CFS Stimulus
Written report about their perceptual impression
13/17: “Human walking”
4/17: “jumping dots” or “nothing” direction
1B
Symmetric CFS Stimulus
2/9: “Human Walking”
4/9: “Human performing actions”
3/9 “Nothing” or “The Number 8”
1C
Random dots
2/10: “Human Performing actions”
8/10: “Nothing”
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8. Experiment 1
Results
Opponent motion seems to be critical for generating the impression of a
walking human. The presence of moving dots within the same four
regions is not sufficient.
Skeleton model hypothesis seems to be wrong
Coarse position information is not sufficient to fit
this model
The random dots' positions do not comply with
the kinematics of a moving human body
Alternative hypothesis: We use fuzzy templates for the human body
shape that fit the CFS in a sub-optimal way
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9. Experiment 2
Method
If reconstruction of the human body shape from point positions is critical for the recognition of
point-light walkers, then a stimulus that complies with kinematics should be easier recognized
than the CFS stimulus
SPS CFS
(Sequential Position Stimulus) (Critical Features Stimulus)
.... ...
.. ..
..
... ..
. VS
.. . .
Frame 1 Frame 2 Frame 3
t
SPS does not affect body shape and matches exactly the human body kinematics
1,2,4 dots 1 frame
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10. Experiment 2
Results
SPS CFS
..
. . VS
.....
7 Subjects
Task: Recognition of direction of walking
No differences between the two stimuli
No precise information about the body shape is needed
Both stimuli might be processed by a common mechanism
Asymmetry of the stimulus seems to be an important factor
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11. Neural Model
I. Local Motion Detectors (LMD): small receptive fields, direction preference
II. Opponent motion detectors: Respond if LMD -within two adjacent subfields- with oposite
direction preference are active
III. Detectors for complex global optic flow patterns: Larger receptive fields than the whole
point-light stimulus, selectivity established by training, each frame has an optic flow pattern that
is encoded by a radial basis function
IV. Motion Pattern Neurons: Sum and temporally smooth the activities of optic flow pattern
detectors that belong to the same human action
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12. Neural Model
Psychophysical experiment
Results
Recognition performances for both types of stimuli are very similar.
Recognition performance increases with the number of dots in the stimulus
Recognition rates for 8 and 4 dots are close to the values obtained in the psychophysical experiment
The recognition rates for 2 dots are lower than human performance
This model is not able to analyze stimuli with a single dot
No strong increase of performance with the lifetime of dots
High recognition rates can be accomplished solely based on the proposed critical
motion feature
High performance rates for degraded stimuli can be accomplished without complex
computational mechanisms
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13. Discussion
Normal and point-light stimuli share very similar dominant mid-level optic flow features
r=0.93
The appropriate spatial arrangement of these features induces the percept of a person
walking, even though the stimuli do not comply with the kinematics of the human body
The detailed form information provided by the SPS does not seem to improve the recognition of
walking direction
A neural model that exploits these critical features achieves substantial recognition rates, even for
degraded point-light stimuli
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14. Discussion
Physiological studies support this computational model (neural detectors for opponent motion)
Simple neural circuit. Not complex computational mechanism.
The local motion information can be used for
other discrimination tasks (e.g. identification of gait)
Fast
Slow
http://www.biomotionlab.ca/Demos/BMLwalker.html
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15. Discussion
Physiological studies support this computational model (neural detectors for opponent motion)
Simple neural circuit. Not complex computational mechanism.
The local motion information can be used for
other discrimination tasks (e.g. identification of gait)
For more difficult tasks, more information might be required.
Female
Male
http://www.biomotionlab.ca/Demos/BMLwalker.html
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16. Thank you!
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