Background: Depression is a common illness which is believed to be caused, at least in part, by a tendency to preferentially process negative, at the expense of positive, events. Recent work in computational neuroscience indicates that individuals estimate how informative an event is and are more influenced by those events they judge to be more informative. This suggests a potential mechanism which may lead an individual to develop a negative bias—they may estimate that negative events are more informative than positive events. It also suggests a novel approach to the treatment of depression—interventions which reduce the estimated information content of negative relative to positive events should act to improve mood. A number of factors ca...
Background Following treatment, many depressed patients have significant residual sy...
Cognitive models of depression posit that negatively biased self-referent processing and attention h...
Background and objectives: In cognitive theories of depression, processing biases are assumed to be ...
Background: Depression is a common illness which is believed to be caused, at least in part, by a te...
The past decades have witnessed intense research on valence-specific information processing biases i...
Patients with both depression and anxiety show an increased tendency to deploy attention towards neg...
This thesis is concerned with cognitive biases in depression, with particular focus on attentional a...
Depression has been widely associated with a cognitive deficit leading to the negative interpretatio...
Background: Classic theories posit that depression is driven by a negative learning bias. Most studi...
Cognitive theories of depression posit that after a negative event or mood state, those vulnerable t...
Contains fulltext : 191992.pdf (publisher's version ) (Closed access)Background: O...
Depression disorder is one of the most prevalent mental illnesses in the world. Its societal and eco...
Consistent with the combined cognitive bias hypothesis (Hirsch, Clark, & Mathews, 2006), cognitive b...
Background Following treatment, many depressed patients have significant residual symptoms. However,...
Depression is theorized to be caused in part by biased cognitive processing of emotional information...
Background Following treatment, many depressed patients have significant residual sy...
Cognitive models of depression posit that negatively biased self-referent processing and attention h...
Background and objectives: In cognitive theories of depression, processing biases are assumed to be ...
Background: Depression is a common illness which is believed to be caused, at least in part, by a te...
The past decades have witnessed intense research on valence-specific information processing biases i...
Patients with both depression and anxiety show an increased tendency to deploy attention towards neg...
This thesis is concerned with cognitive biases in depression, with particular focus on attentional a...
Depression has been widely associated with a cognitive deficit leading to the negative interpretatio...
Background: Classic theories posit that depression is driven by a negative learning bias. Most studi...
Cognitive theories of depression posit that after a negative event or mood state, those vulnerable t...
Contains fulltext : 191992.pdf (publisher's version ) (Closed access)Background: O...
Depression disorder is one of the most prevalent mental illnesses in the world. Its societal and eco...
Consistent with the combined cognitive bias hypothesis (Hirsch, Clark, & Mathews, 2006), cognitive b...
Background Following treatment, many depressed patients have significant residual symptoms. However,...
Depression is theorized to be caused in part by biased cognitive processing of emotional information...
Background Following treatment, many depressed patients have significant residual sy...
Cognitive models of depression posit that negatively biased self-referent processing and attention h...
Background and objectives: In cognitive theories of depression, processing biases are assumed to be ...