19 found
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  1.  25
    Emotion Regulation in Depression: Relation to Cognitive Inhibition.Jutta Joormann & Ian H. Gotlib - 2010 - Cognition and Emotion 24 (2):281-298.
  2.  11
    Updating Positive and Negative Stimuli in Working Memory in Depression.Sara M. Levens & Ian H. Gotlib - 2010 - Journal of Experimental Psychology: General 139 (4):654-664.
  3.  21
    The Everyday Dynamics of Rumination and Worry: Precipitant Events and Affective Consequences.Katharina Kircanski, Renee J. Thompson, James Sorenson, Lindsey Sherdell & Ian H. Gotlib - 2017 - Cognition and Emotion 32 (7):1424-1436.
    ABSTRACTRumination and worry are two perseverative, negatively valenced thought processes that characterise depressive and anxiety disorders. Despite significant research interest, little is known about the everyday precipitants and consequences of rumination and worry. Using an experience sampling methodology, we examined and compared rumination and worry with respect to their relations to daily events and affective experience. Participants diagnosed with Major Depressive Disorder, Generalized Anxiety Disorder, co-occurring MDD–GAD, or no diagnosis carried an electronic device for one week and reported on rumination, (...)
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  4.  11
    Emotional Variability and Clarity in Depression and Social Anxiety.Renee J. Thompson, Matthew Tyler Boden & Ian H. Gotlib - 2017 - Cognition and Emotion 31 (1):98-108.
  5.  32
    Biases in Visual Attention in Depressed and Nondepressed Individuals.Ian H. Gotlib, Anne L. McLachlan & Albert N. Katz - 1988 - Cognition and Emotion 2 (3):185-200.
  6.  12
    Risk for Psychopathology in the Children of Depressed Mothers: A Developmental Model for Understanding Mechanisms of Transmission.Sherryl H. Goodman & Ian H. Gotlib - 1999 - Psychological Review 106 (3):458-490.
  7.  10
    EEG Alpha Asymmetry, Depression, and Cognitive Functioning.Ian H. Gotlib - 1998 - Cognition and Emotion 12 (3):449-478.
  8.  25
    Sensitivity to Reward and Punishment in Major Depressive Disorder: Effects of Rumination and of Single Versus Multiple Experiences.Anson J. Whitmer, Michael J. Frank & Ian H. Gotlib - 2012 - Cognition and Emotion 26 (8):1475-1485.
  9.  17
    The Effects of Optimism and Pessimism on Updating Emotional Information in Working Memory.Sara M. Levens & Ian H. Gotlib - 2012 - Cognition and Emotion 26 (2):341-350.
  10.  10
    Cognitive Biases and Affect Persistence in Previously Dysphoric and Never-Dysphoric Individuals.Eva Gilboa & Ian H. Gotlib - 1997 - Cognition and Emotion 11 (5-6):517-538.
  11.  13
    Memory for Affectively Valenced and Neutral Stimuli in Depression: Evidence From a Novel Matching Task.Ian H. Gotlib, John Jonides, Martin Buschkuehl & Jutta Joormann - 2011 - Cognition and Emotion 25 (7):1246-1254.
  12.  11
    Cognition and Depression: Issues and Future Directions.Ian H. Gotlib, Howard S. Kurtzman & Mary C. Blehar - 1997 - Cognition and Emotion 11 (5-6):663-673.
  13.  43
    The Effects of Induced and Naturally Occurring Dysphoric Mood on Biases in Self-Evaluation and Memory.Eva Gilboa, John E. Roberts & Ian H. Gotlib - 1997 - Cognition and Emotion 11 (1):65-82.
  14.  13
    The Cognitive Psychology of Depression: Introduction to the Special Issue.Ian H. Gotlib, Howard S. Kurtzman & Mary C. Blehar - 1997 - Cognition and Emotion 11 (5-6):497-500.
  15.  10
    Attentional Bias in Euthymic Bipolar I Disorder.Andrew D. Peckham, Sheri L. Johnson & Ian H. Gotlib - 2016 - Cognition and Emotion 30 (3):472-487.
  16.  10
    Memory for Novel Positive Information in Major Depressive Disorder.James E. Sorenson, Daniella J. Furman & Ian H. Gotlib - 2014 - Cognition and Emotion 28 (6):1090-1099.
  17.  8
    An Information Processing Analysis of the Emotional Disorders.Ian H. Gotlib - 1990 - Cognition and Emotion 4 (1):53-60.
  18.  3
    Defining Emotion: A Clinical Perspective.Ian H. Gotlib - 2007 - Social Science Information 46 (3):387-391.
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  19. Support Vector Machines and Affective Science.Chris H. Miller, Matthew D. Sacchet & Ian H. Gotlib - forthcoming - Emotion Review:175407392093078.
    Support vector machines are being used increasingly in affective science as a data-driven classification method and feature reduction technique. Whereas traditional statistical methods typically compare group averages on selected variables, SVMs use a predictive algorithm to learn multivariate patterns that optimally discriminate between groups. In this review, we provide a framework for understanding the methods of SVM-based analyses and summarize the findings of seminal studies that use SVMs for classification or data reduction in the behavioral and neural study of emotion (...)
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