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Paradigms – Conclusion and outlook
Maya Schneebeli
12.12.2014 Computational Psychiatry Seminar - Autism 1
Content } Brainstorming
} General considerations } Research Questions } Participants } Experimental settings
} Precision in autism } Description and Models } Some evidence } Measurements
} Behavioral } Neurophysiological
} Discussion
12.12.2014 Computational Psychiatry Seminar - Autism 2
Content } Brainstorming
} General considerations } Research Questions } Participants } Experimental settings
} Precision in autism } Description and Models } Some evidence } Measurements
} Behavioral } Neurophysiological
} Discussion
12.12.2014 Computational Psychiatry Seminar - Autism 3
Content } Brainstorming
} General considerations } Research Questions } Participants } Experimental settings
} Precision in autism } Description and Models } Some evidence } Measurements
} Behavioral } Neurophysiological
} Discussion
12.12.2014 Computational Psychiatry Seminar - Autism 5
What are the questions we want to ask? } Is there a common denominator? } Are there identificable subtypes? And in which way do
they differ? } How does the diagnose (symptoms, physiology) develop
from early childhood to adulthood?
?
12.12.2014 Computational Psychiatry Seminar - Autism 6
Which subjects do we include? } Comorbidities
} Anxiety } Depression } OCD } Attention deficits } Motor problems } Etc.
} Age } Individual problems but also coping and compensation plays a
greater role with increasing age.
} Spectrum
12.12.2014 Computational Psychiatry Seminar - Autism 7
Comorbidities } Krueger & Markon 2006
A B
2 1
A
12.12.2014 Computational Psychiatry Seminar - Autism 8
Comorbidities } Proposal of London (2014)
} Problem: Associated symptoms are often excluded in autism studies
} Proposal: Diagnostic sceme } 1st axis: Developmental disorders } 2nd axis: Symptoms like sensory over-responsiveness or intelligence
disability
} But: Syndromes consist of a collection of co-occurring symptoms. If they are abolished, one could loose information and a better global understanding.
} Possible solution: Refinement of diagnose, maybe with probability associated symptoms
12.12.2014 Computational Psychiatry Seminar - Autism 9
Age } Many developmental effects } Findings are often in children with autism } Example:
Dream Tired
Wake up Snore Bed
Alarm clock Pyjama Night
...
12.12.2014 Computational Psychiatry Seminar - Autism 10
Spectrum } Great heterogeneity } How to categorize cases? } Example: Autism Quotient
} 𝑃𝐴𝑆𝐷 𝐴𝑄≥32 = 𝑃𝐴𝑄≥32𝐴𝑆𝐷 𝑃(𝐴𝑆𝐷)/𝑃(𝐴𝑄≥32) } 𝑃𝐴𝑄≥32𝐴𝑆𝐷 =.793 (Baron-Cohen et al, 2001)
} 𝑃(𝐴𝑆𝐷)= .01 (Baron-Cohen et al., 2009)
} 𝑃(𝐴𝑄 ≥32)= .023 (Baron-Cohen et al., 2001) } 𝑃𝐴𝑆𝐷 𝐴𝑄≥32 = 0.793∙0.01/0.023 =.34
} Important: Careful assessment of symptoms
12.12.2014 Computational Psychiatry Seminar - Autism 11
Experimental setting } Reward (Dichter et al. 2012) } Labor vs. real world (Kenworthy et al. 2008; Geurts et al. 2009)
} Voluntary vs. Forced tasks (Poljac and Bekkering, 2012)
} Flexibility across modalities (Poljac and Bekkering, 2012)
} Further possible problems } Social demands } Instructions } Distractors
12.12.2014 Computational Psychiatry Seminar - Autism 12
Content } Brainstorming
} General considerations } Research Questions } Participants } Experimental settings
} Precision in autism } Description and Models } Some evidence } Measurements
} Behavioral } Neurophysiological
} Discussion
12.12.2014 Computational Psychiatry Seminar - Autism 13
What do we want to test? } Van de Cruys 2014: HIPPEA
} High, inflexible precision of prediction errors in autism } Problem in meta-learning
} Learning what is learnable } Estimating predictability of new contingencies
} Leads to } New learning for every new event and overfitting } Because it is a very basic way how the brain works, it leads to
peculiarities in many domains: perception, attention, learning and executive functioning
12.12.2014 Computational Psychiatry Seminar - Autism 14
Two possible mechanisms
} Neural mechanism for precision directly affected in ASD
} Meta-learning prior to the setting of precision may be deficient
12.12.2014 Computational Psychiatry Seminar - Autism 15
Where to place that?
Friston et al. 2010 Friston et al. 2013
12.12.2014 Computational Psychiatry Seminar - Autism 16
How is precision updated?
ASD Does the model differ how precision is updated or is it a matter of parameters?
12.12.2014 Computational Psychiatry Seminar - Autism 17
Some evidence... } Supporting increased sensory precision
} Visual illusions in Happé, 1996 } Better performance in visual search, O’Riordan et al. 2001
} Hypopriors } Revearsal learning in D’Cruz et al. 2013
12.12.2014 Computational Psychiatry Seminar - Autism 18
Revearsal learning } 80:20 left correct } After multiple trials 20:80 left correct
} Result: More regressive errors in the ASD group
“Choose the animal that is usually in the correct location. After a while the correct location may change”
12.12.2014 Computational Psychiatry Seminar - Autism 19
Two possible mechanisms
} Neural mechanism for precision directly affected in ASD
} Meta-learning prior to the setting of precision may be deficient
12.12.2014 Computational Psychiatry Seminar - Autism 20
How to test precision? } Behavioral measures
} Decision paradigms ¨ Diaconescu et al. 2014 ¨ Iglesias et al. 2012
} Attention ¨ Vossels et al. 2014 ¨ Ebbinghaus illusion, current project
} Neurophysiological measures } Mismatch negativity } Active information storage } Activity in salience networks (insula)
12.12.2014 Computational Psychiatry Seminar - Autism 21
How to test precision? } Behavioral measures
} Decision paradigms ¨ Diaconescu et al. 2014 ¨ Iglesias et al. 2013
} Attention ¨ Vossels et al. 2014 ¨ Ebbinghaus illusion, current project
} Neurophysiological measures } Mismatch negativity } Active information storage } Activity in salience networks (insula)
12.12.2014 Computational Psychiatry Seminar - Autism 22
Modelling Predictive Coding } Diaconescu et al. 2014 } Supports HGF with volatility
} Possible Problems: Too many factors and social demands?
12.12.2014 Computational Psychiatry Seminar - Autism 23
PE during sensory learning } Iglesias 2013
} Three layer HGF compared against 5 models
} No reward
} Low-level precision-weighted PEs } about visual outcome } dopaminoceptive regions like DLPFC, ACC, and insula } dopaminergic VTA/SN (midbrain)
} High-level precision-weighted PEs } about cue-outcome contingencies (conditional probabilities of the
visual outcome given the auditory cue) } activity in the cholinergic basal forebrain.
12.12.2014 Computational Psychiatry Seminar - Autism 25
How to test precision? } Behavioral measures
} Decision paradigms ¨ Diaconescu et al. 2014 ¨ Iglesias et al. 2013
} Attention ¨ Vossels et al. 2014 ¨ Ebbinghaus illusion, current project
} Neurophysiological measures } Mismatch negativity (Gomot 2011, Maekawa 2011) } Active information storage (Gomez 2014) } Activity in salience networks (insula) (Uddin 2014)
12.12.2014 Computational Psychiatry Seminar - Autism 26
Adapted Posner paradigm } Vossels et al. 2013
} Posner paradigm and saccadic eye movement
} Model comparison of 11 information processing models
} Precision model: } individualized Bayes optimality } subject-specific values for ω
(determining subject-specific log-volatility)
} and ϑ (subject-specific meta-volatility).
Could be used to identify the neural and neurochemical basis of attentional selection and saccadic eye movements, in relation to probabilistic expectancies.
12.12.2014 Computational Psychiatry Seminar - Autism 27
Ebbinghaus illusion } E. Aponte } Model:
} 𝑝𝑇↓1 > 𝑇↓2 𝛼 =𝑝(𝑅>0) } 𝑅= log [(𝑇↓1 /𝑀↓1 )(𝑀↓1 /𝑀↓2 )↑𝛽 (𝑀↓2 /𝑇↓2 )]
} Question } How much is the context integrated under certainty and
uncertainty? } Reflected in eye movement?
12.12.2014 Computational Psychiatry Seminar - Autism 28
How to test precision? } Behavioral measures
} Decision paradigms ¨ Diaconescu et al. 2014 ¨ Iglesias et al. 2012
} Attention ¨ Vossels et al. 2014 ¨ Ebbinghaus illusion, current project
} Neurophysiological measures } Mismatch negativity (Gomot 2011, Maekawa 2011) } Active information storage (Gomez 2014) } Activity in salience networks (insula) (Uddin, 2014)
12.12.2014 Computational Psychiatry Seminar - Autism 29
Mismatch negativity } MMN
} Occurs in sensory oddball events } 150-200ms after deviant stimulus
} Gomot 2011 } Silent movie, listening to standard and deviant (p=.15) tones } Autistic children showed
} shorter MMN latencies } Larger P3a (reflects attention shift towards new stimulus)
} But Maekawa 2011 } Visual MMN in adults with autism } Results
} behavioral target detection was significantly faster } the P1 response (80–120 ms) to standard and deviant stimuli was significantly smaller } the P300 latency (300–500 ms) was prolonged and its amplitude was decreased } both the mean amplitude and latency of vMMN (150–300 ms) were within the normal
range
12.12.2014 Computational Psychiatry Seminar - Autism 30
Active information storage } Gomez 2014 } Active information storage
} Used in self organizing networks } Measured with mutual information of a random variable at a
certain time point and preceding random variable } High in rich, but predictible dynamics } Measure for predicted information
} Paradigm } Black-white faces and scrambled «faces»
} Result: Decrease in AIS in hippocampus in ASD
12.12.2014 Computational Psychiatry Seminar - Autism 31
Challenges } How complex should an experiment be?
} Multisensory } Social components } Levels of forward and backpropagation in the model?
} Model } Based on HGF } with a mechanisms for HIPPEA
} Close relatedness of precision and } Attention } Salience
12.12.2014 Computational Psychiatry Seminar - Autism 32
Another thought...
} What is the drive to learn completely new things, expose oneself to novel or even risky situations?
} Can it be explained by means of precision?
12.12.2014 Computational Psychiatry Seminar - Autism 33