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SUMMARY:Using Computational Models to Characterise and Engage and Putative
  Treatment Targets for Depression - Professor Michael Browning - Computati
 onal Psychiatry Lab\, Dept. of Psychiatry\, Univ. of Oxford
DTSTART:20180924T120000Z
DTEND:20180924T130000Z
UID:TALK109960@talks.cam.ac.uk
CONTACT:Lorraine Coulson
DESCRIPTION:Negative affective bias\, the tendency to prioritise the proce
 ssing of negative relative to positive events\, is commonly observed in de
 pressed patients and is believed to be causally related to symptom develop
 ment. However\, why such biases develop is not known. Using an information
  theoretic framework\, we investigated whether affective biases may reflec
 t individuals’ estimates of the information content of negative relative
  to positive events.\n\nIn a series of studies\, the information content o
 f positive and negative task outcomes was manipulated independently by var
 ying the volatility (unexpected uncertainty) or the noise (expected uncert
 ainty) of their occurrence. Human participants altered the learning rates 
 used for the outcomes selectively\, preferentially learning from more vola
 tile outcomes\, but not adapting to the noise of the outcome. The volatili
 ty effect was associated with activity of the central norepinephrine syste
 m\, estimated using pupilometry\, for loss outcomes. In contrast\, analysi
 s using a lesioned Bayesian optimal model suggested that the apparent nois
 e-blindness was due to an inability to track variations in the noise of ou
 tcomes across the task.   \n\nHumans maintain independent but imperfect es
 timates of the information content of distinct positive and negative outco
 mes which may bias their processing of affective events. Normalising affec
 tive biases using computationally inspired interventions may represent a n
 ovel approach to treatment for depression.
LOCATION:Kenneth Craik Room\, Craik Marshall Building\, Downing Site\, Cam
 bridge
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