Dissertation available on attention in depression

[Greg Siegle]

Dear Connectionists, 

The following dissertation is now available on-line at 
   http://www.sci.sdsu.edu/CAL/greg/dissert/

Cognitive and Physiological Aspects of Attention to
Personally Relevant Negative Information in Depression

	   by

       Greg Jeremy Siegle

Abstract

Evidence suggests depressed individuals pay excessive attention to negative
information. The current research investigates the nature and clinical
implications of such attention biases. A computational neural network,
reflecting interacting brain systems that identify emotional and nonemotional
aspects of information, is described in which depression is identified
with strongly learning certain negative information. The model's behavior
suggested that depressed people are reminded of, and attend to personally
relevant negative information in response to many stimuli.

Predictions for depressed and nondepressed individuals' reaction times,
signal detection rates, and the time course of cognitive load in response
to emotional stimuli were derived from the computational model. To evaluate
these predictions, pupil dilations and reaction times were collected from
24 unmedicated depressed and 25 nondepressed adults in response to emotional
lexical decision and valence identification tasks. Pupil dilation was used
to index cognitive load.

Mixed ANOVA planned contrasts were employed to evaluate predictions.
In support of model derived predictions, depressed individuals rated many
stimuli as negative more than nondepressed individuals. The network's behavior
suggested that depressed individuals would be quicker to say that negative
words were negative, than positive words were positive, and that this difference 
would be reduced in nondepressed individuals. This prediction was supported
empirically.

Principal components analysis of pupil dilations revealed early attentional
components (at or before reaction times) and late, possibly ruminative,
components (peaking 2 and 4 seconds after reaction times). The computational
model suggested cognitive load, indexed by pupil dilation, would be highest
for nondepressed individuals during early stages of attention but highest
for depressed individuals during later stages of attention. This prediction
was supported. Contrary to predictions, differences in depressed individuals'
dilations to positive and negative stimuli were not detected.

These data suggest depressed individuals may not initially attend to
the content of presented information, but may quickly associate any incoming
information with whatever made them depressed. Sustained attention to personally 
relevant negative information may characterize depressive attention biases.
Targeting implicated cognitive and brain processes may improve interventions
for depression.


-----------------------------------------
Greg Siegle, Ph.D.
San Diego State University / University of California, San Diego /
  University of Toronto
www.sci.sdsu.edu/CAL/greg.html    416-979-4747 x2376
The Clarke Institute, 250 College St., Toronto, ON M5T 1R8 Canada
Visit the Connectionist Models of Cognitive, Affective,
   Brain, and Behvioral Disorders website at
   www.sci.sdsu.edu/CAL/connectionist.models