141212 Paradigms mschneebeli€¦ · Decision paradigms ! Diaconescu et al. 2014 ! Iglesias et al....

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






}  Discussion


Brainstorming






}  Discussion


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?

?


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


Comorbidities }  Krueger & Markon 2006

A B

2 1

A


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


Age }  Many developmental effects }  Findings are often in children with autism }  Example:

Dream Tired

Wake up Snore Bed

Alarm clock Pyjama Night

...


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


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






}  Discussion


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


Two possible mechanisms

}  Neural mechanism for precision directly affected in ASD

}  Meta-learning prior to the setting of precision may be deficient


Where to place that?

Friston et al. 2010 Friston et al. 2013


How is precision updated?

ASD Does the model differ how precision is updated or is it a matter of parameters?


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


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”


Two possible mechanisms

}  Neural mechanism for precision directly affected in ASD

}  Meta-learning prior to the setting of precision may be deficient


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)





}  Neurophysiological measures }  Mismatch negativity }  Active information storage }  Activity in salience networks (insula)


Modelling Predictive Coding }  Diaconescu et al. 2014 }  Supports HGF with volatility

}  Possible Problems: Too many factors and social demands?


PE during sensory learning }  Iglesias 2013


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.





}  Neurophysiological measures }  Mismatch negativity (Gomot 2011, Maekawa 2011) }  Active information storage (Gomez 2014) }  Activity in salience networks (insula) (Uddin 2014)


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.


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?





}  Neurophysiological measures }  Mismatch negativity (Gomot 2011, Maekawa 2011) }  Active information storage (Gomez 2014) }  Activity in salience networks (insula) (Uddin, 2014)


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


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


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


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?


Thank you for your attention

}  Questions?

}  Discussion is opened J


141212 Paradigms mschneebeli€¦ · Decision paradigms ! Diaconescu et al. 2014 ! Iglesias et al....

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