Title |
The influence of emotions on cognitive control: feelings and beliefs—where do they meet?
|
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Published in |
Frontiers in Human Neuroscience, January 2013
|
DOI | 10.3389/fnhum.2013.00508 |
Pubmed ID | |
Authors |
Katia M. Harlé, Pradeep Shenoy, Martin P. Paulus |
Abstract |
The influence of emotion on higher-order cognitive functions, such as attention allocation, planning, and decision-making, is a growing area of research with important clinical applications. In this review, we provide a computational framework to conceptualize emotional influences on inhibitory control, an important building block of executive functioning. We first summarize current neuro-cognitive models of inhibitory control and show how Bayesian ideal observer models can help reframe inhibitory control as a dynamic decision-making process. Finally, we propose a Bayesian framework to study emotional influences on inhibitory control, providing several hypotheses that may be useful to conceptualize inhibitory control biases in mental illness such as depression and anxiety. To do so, we consider the neurocognitive literature pertaining to how affective states can bias inhibitory control, with particular attention to how valence and arousal may independently impact inhibitory control by biasing probabilistic representations of information (i.e., beliefs) and valuation processes (e.g., speed-error tradeoffs). |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 38% |
United Kingdom | 1 | 13% |
Unknown | 4 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 63% |
Practitioners (doctors, other healthcare professionals) | 2 | 25% |
Scientists | 1 | 13% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 3% |
United Kingdom | 2 | <1% |
Italy | 1 | <1% |
Sweden | 1 | <1% |
France | 1 | <1% |
Canada | 1 | <1% |
Netherlands | 1 | <1% |
Japan | 1 | <1% |
Spain | 1 | <1% |
Other | 0 | 0% |
Unknown | 210 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 54 | 24% |
Student > Master | 34 | 15% |
Researcher | 31 | 14% |
Student > Bachelor | 23 | 10% |
Student > Doctoral Student | 15 | 7% |
Other | 41 | 18% |
Unknown | 27 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 115 | 51% |
Neuroscience | 17 | 8% |
Agricultural and Biological Sciences | 12 | 5% |
Medicine and Dentistry | 10 | 4% |
Business, Management and Accounting | 8 | 4% |
Other | 28 | 12% |
Unknown | 35 | 16% |