Study the influence of (eye) motor control on selective attention
Develop a method to extract motor control parameters during visual search
Develop a method to extract selective attention features during visual search
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Giacomo Veneri 2012 phd dissertation
1. Feature-Based Information Processing
of Selective Attention through Entropy
Analysis system
Giacomo Veneri
November 2012
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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2. Objectives
• Study the influence of (eye) motor control on
selective attention
• Develop a method to extract motor control
parameters during visual search
• Develop a method to extract selective attention
features during visual search
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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Methods Results
Attention
FE
Motor
Control
FE
TMT
ET
Healthy
Subjects
Patients
SCA2,NDC
Psychological Test
3. Selective Attention
• Selective attention ( Posner,
1980) is the process to select
some region of the scene to
be processed in detail; then,
selective attention works as
filter.
• Top-Down: attentional process
that influences sensory
processing in an automatic
and persistent manner
• Bottom-Up: influence on the
nervous system due to
extrinsic properties of the
stimuli
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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4. Motor Control and Cerebellum
• The neuronal circuitry of the
cerebellum is thought to
encode internal models that
reproduce the dynamic
properties of body parts
(Kelly2003,Ito2005,Ito2006a).
• These models control the
movement allowing the brain
to precisely control the
movement without the need
for sensory feedback
(Barlow2002,Ito2008,King2011
)
• SCA2 and NDC Patients
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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5. Attention and Motor control
(Corbetta2001, Osborne2011)
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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6. Methods
1. Veneri, G., Federighi, P., Rosini, F., Federico, A., & Rufa, A. (2010). Influences of data filtering on human-computer interaction by gaze-contingent
display and eye-tracking applications. Computers in Human Behavior , 26 (6), 1555 - 1563. doi: 10.1016/j.chb.2010.05.030 [SCOPUS, ACM]
2. Veneri, G., Federighi, P., Rosini, F., Federico, A., & Rufa, A. (2011, Mar). Spike removal through multiscale wavelet and entropy
analysis of ocular motor noise: A case study in patients with cerebellar disease. Journal of Neuroscience Methods , 196 (2), 318–326.
doi: 10.1016/j.jneumeth.2011.01.006 [MEDLINE, SCOPUS]
3. Veneri, G., Piu, P., Rosini, F., Federighi, P., Federico, A., & Rufa, A. (2011). Automatic eye fixations identification based on analysis of variance and
covariance. Pattern Recognition Letters , 32 (13), 1588 - 1593. doi: 10.1016/j.patrec.2011.06.012 [SCOPUS]
4. Veneri, G., Pretegiani, E., Rosini, F., Federighi, P., Federico, A., & Rufa, A. (2011, Mar). Evaluating the human ongoing visual search performance by
eye tracking application and se-quencing tests. Comput Methods Programs Biomed . Retrieved from http://dx.doi.org/10.1016/j.cmpb.2011.02.006
doi:10.1016/j.cmpb.2011.02.006 [SCOPUS. MEDLINE, ACM]
5. Veneri, G., Rosini, F., Federighi, P., Federico, A., & Rufa, A.(2012, Feb). Evaluating gaze control on a multi-target sequenc-ing task:
The distribution of fixations is evidence of exploration optimisation. Comput Biol Med , 42 (2), 235–244. Retrieved from
http://dx.doi.org/10.1016/j.compbiomed.2011.11.013 doi: 10.1016/j.compbiomed.2011.11.013 [SCOPUS. MEDLINE, ACM]
InProceedings
1. Veneri, G., Federighi, P., Pretegiani, E., Rosini, F., Federico, A., & Rufa, A. (2009). Eye tracking - stimulus integrated semi automatic case base system.
In Proceeding of the 13th world multi-conference on systemics, cybernetics and informatics.
2. Veneri, G., Pretegiani, E., Federighi, P., Rosini, F., & Rufa, A. (2010). Evaluating human visual search performance by monte carlo methods and
heuristic model. In IEEE (Ed.), 10th ieee international conference on information technology and applications in biomedicine (itab 2010). [SCOPUS,
IEEE]
3. Veneri, G., Piu, P., Federighi, P., Rosini, F., Federico, A., & Rufa, A. (2010, jun.). Eye fixations identification based on statistical analysis - case study. In
Cognitive information processing (cip), 2010 2nd international workshop on (p. 446 -451). IEEE. doi: 10.1109/CIP.2010.5604221 [SCOPUS, IEEE]
Others (posters)
1. Veneri, G., Federighi, P., Rosini, F., Pretegiani, E., Federico, A., & Rufa, A. (2009). The role of latest fixations on ongoing visual search: a model to
evaluate the selection mechanism. In Rovereto workshop of attention.
2. Veneri, G., Olivetti, E., Avesani, P., Federico, A., & Rufa, A. (2011). Bayesian hypothesis on selective attention. In Rovereto visual attention congress.
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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7. PSYCHOLOGICAL TEST
Eye Tracking, TMT, ET
Methods Results
Attention
FE
Motor
Control
FE
TMT
ET
Healthy
Subjects
Patients
SCA2,NDC
Psychological Test
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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8. Eye Tracking
• Eye tracking is the
process of measuring
either the point of gaze
(where one is looking)
or the motion of an eye
relative to the head.
• ASL 3000 (240Hz)
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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9. Visual (conjunction) Search Test
E Search (Wolfe, 1994) Sequencing (Reitan, 1958)
... and others (Veneri 2010, Veneri 2012)Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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10. SELECTIVE ATTENTION FEATURES
EXTRACT
Psycological Test, Mathematical Method
Methods Results
Attention
FE
Motor
Control
FE
TMT
ET
Healthy
Subjects
Patients
SCA2,NDC
Psychological Test
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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11. Attention Features Extraction 1/2
Common Method
• Visited ROI
• Reaction Time
Our geometric Method (Veneri,
Rosini 2012)
• Distance to nearest Target
• Distance to Nearest ROI
• Sequencing
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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DN
DT
12. Sequencing (2/2)
• Look for the best path (Veneri, Rosini 2012)
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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13. MOTOR CONTROL FEATURES
EXTRACTION
Wavelet Entropy
Methods Results
Attention
FE
Motor
Control
FE
TMT
ET
Healthy
Subjects
Patients
SCA2,NDC
Psychological Test
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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14. Motor Control Noise Evaluation
• (Beers2007, Veneri2011)
gaze noise may be additive
with or multiplicative of the
eye movement, and is lost
in recording noise (RN) due
to blinks or signal loss;
• noise = PN + RN = SDN
(signal) + ADN + RN where
SDN is physiological signal
dependent noise and ADN
physiological additive noise.
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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15. Frequency Analysis
Fourier analysis
• A signal is a «sum» of a sine
curve
ECG Example
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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17. Decomposed Eye Signal
Original signal
Noise?
Main componet
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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18. Wavelet Entropy
The idea (Veneri 2011)
• After decomposition
• We removed spikes
• We evaluated Entropy
• Entropy is the measure of
the chaos on a system
Algorithm
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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19. RESULTS
Healthy Subjects and Patients
Methods Results
Attention
FE
Motor
Control
FE
TMT
ET
Healthy
Subjects
Patients
SCA2,NDC
Psychological Test
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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24. Entropy levels
All levels Last level
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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25. Variance
Signal Signal on fixations
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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26. Before conclusions
• Proposed Wavelet
Entropy Implementation
is NOT noise on fixations
or noise of global signal
• Proposed Wavelet
Entropy Implementation
«catches» motor noise
topical featurese of each
subject (colored noise)
• Wavelet Type or levels are
critical
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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27. Selective attention
• DT provided a indicator to under-
stand the ability of humans to
converge to the target.
• ANOVA reported significant
difference among groups (F
(2, 35) = 9.476, p < 0.01)
• post-hoc Sidak procedure
confirmed significant
difference between
– CTRL-SCA2 (p CTRL−SCA2 < 0.01),
– CTRL-NDC (p NDC−SCA2 ≤ 0.01);
– no significant dif-ference was
found between SCA2-NDC (p
SCA2−NDC = 0.622).
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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28. Correlation DT-E
• Pearson and Spearman test reported correlation between E and DT
for NDC patients (p < 0.05, ρ = 0.892, A), and correlation for SCA2
patients (p < 0.05, ρ = 0.736, B) not confirmed by Spearman (p =
0.18). No correlation was found for CTRL subjects (p = 0.43).
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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30. Summary
• In the current work two methods have been developed:
• Selective attention evaluation
• Entropy analysis through wavelet decomposition.
• Both methods are based on eye tracking
• Subjects and patients cannot control eye movements or
fixations perfectly, then, analysing eye motor entropy it is
possible to extract some important features and conclusions.
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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31. Tool
1. Import Eye gaze data
2. Export Eye gaze data
3. Fixations recognition
(Veneri, Piu, et al., 2010,
2011; Salvucci & Gold-
berg, 2000)
4. Saccades recognition
(Fischer et al., 1993)
5. TMT sequencing analysis
6. Transition Matrix analysis
7. ROI Analysis
8. Experiment segmentation
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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32. Study the influence
• Does the motor control (cerebellum) influence
selective attention?
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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33. Cerebellum could influence selective
attention (Top-Down) sending
afferent information of noise in order
to minimize the functional
cost of energy.
Our hypothesis is systematically
supported by recent application of
opti-mal control theory; (Najemnik &
Geisler, 2005), (Beers, 2007) and
(Osborne, 2011) argued that humans’
vision is an optimal mechanism
minimizing the
effect of motor or cognitive noise. Our
findings are compatible with this
hypothesis: patients preferred sparser
fixations avoiding saccade directed to
the
target. The non correlation of DN with
WS suggested that this mechanism
was a strategy to minimize the effort
to control saccade rather than a direct
influence on visual search.
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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34. THANKS
Feature-Based Information Processing of Selective Attention through
Entropy Analysis system
Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI
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