Hadeel Haj-Ali

Hadeel Haj-Ali
University College London | UCL · Experimental Psychology

Master of Arts

About

7
Publications
26,444
Reads
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63
Citations
Introduction
I investigate the psychological and neural mechanisms of risk-taking behavior. I'm also interested in the structure of Affect and the neural foundation of the conscious emotional experience.
Additional affiliations
May 2015 - May 2023
The Institute of Information Processing and Decision Making
Position
  • Researcher
September 2022 - September 2026
University College London
Position
  • PhD Student
Description
  • My Ph.D. is supervised by Professor Tali Sharot and funded by the Economic and Social Research Council (ESRC).
October 2018 - September 2022
University of Haifa
Position
  • Teaching fellow
Description
  • I previously held the position of Head Teaching Assistant at the University of Haifa, where I taught statistics to undergraduate students.
Education
September 2022 - September 2026
University College London
Field of study
  • Experimental Psychology
October 2018 - August 2020
University of Haifa
Field of study
  • Social Sciences
September 2014 - September 2018
University of Haifa
Field of study
  • Psychology

Publications

Publications (7)
Article
Full-text available
The bipolar valence-arousal model is assumed by many to be an underlying structure of conscious experience of core affect and emotion. In this work, we compare three versions of the bipolar valence-arousal model at the neural domain, using functional magnetic resonance imaging (fMRI). Specifically, we systematically contrast three models of arousal...
Article
Full-text available
We examined the possible dissociation between two modes of valence: affective valence - valence of the emotional response, and semantic valence - stored knowledge about valence of an object or event. In Experiment 1, fifty participants viewed affective pictures that were repeatedly presented while their facial EMG activation and heart rate response...
Poster
Habituation is perhaps the most pervasive and evolutionary ancient form of learning, defined as attenuation of response following repeated exposure to a stimulus. The ability to habituate to affective information is especially important, as constant activation of a strong emotional response can be maladaptive in most everyday situations. Surprising...

Questions

Questions (5)
Question
I'm looking for the best source to self-learn Meta-analysis on fMRI data. There are sources available for Meta-analysis, however, I'm not sure if it can be highly relevant for fMRI data.
Thank you in advance,
Question
I'm doing a fMRI analysis using parametric modulation. In my case, I'm not interested in comparing two conditions. I have one condition which is parametric modulated and I would like to do a simple t test for this specific regressor to see its contribution to the data.  what I first did was giving it a value of 1 and the rest of zero, but since there are multiple runs for each subject ( 4 in my case ) , do you  suggest that each regressor for each run gets a value of 1 OR a value of (1/number of sessions) OR does it matter at all ?-( all the subjects have the same number of sessions. ) 
A second question would be, should I use each contrast image from the 4 runs for each subject( from the first level analysis ) as a separate contrast image or Should I do a sort of averaging a contrast images ( using ImCalc) ? In other words, at the end I should look at the 4 contrast images for each subject(from the four sessions)  as a one contrast image  for the second level  ? 
Thank you in advance
Hadeel
Question
I'm working on a fMRI project and I want to understand how important it is to model Temporal Derivatives and what indicators does this modeling give us ? 
I get that  the condition regressor explains variance first,  then the temporal derivative explains any additional unexplained variance. 
But how does the detection of a small differences in the latency of the peak response(Temporal derivative function ) help us to explain additional variance? And can we just use the model derivative regressor and use only it for our analysis? since it contains information about the condition and onsets etc.. 
Thank you in advance 
Question
I'm doing an fMRI analysis using SPM and I have a question regarding the first level analysis .Could you please explain to me why it is important to separate the sessions in spm 1st level analysis ? I thought about combining the sessions and relate to them as one session, and  to include the "rp" file that I got from realignment to model the variance between the sessions\scans, as a regressor.
Thank you in advance 

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