scholarly journals The Effect of Mineralocorticoid and Glucocorticoid Receptor Antagonism on Autobiographical Memory Recall and Amygdala Response to Implicit Emotional Stimuli

2016 ◽  
Vol 19 (9) ◽  
pp. pyw036 ◽  
Author(s):  
Kymberly D. Young ◽  
Sheldon H. Preskorn ◽  
Teresa Victor ◽  
Masaya Misaki ◽  
Jerzy Bodurka ◽  
...  
Keyword(s):  
Glucocorticoid Receptor ◽  
Autobiographical Memory ◽  
Memory Recall ◽  
Emotional Stimuli ◽  
Receptor Antagonism ◽  
Amygdala Response

The Malleability of Narrative Content and Structure in Autobiographical Memory Recall

2008 ◽  
Author(s):  
Alisha C. Holland ◽  
Maya Tamir ◽  
Elizabeth A. Kensinger
Keyword(s):  
Autobiographical Memory ◽  
Memory Recall

Field visual perspective during autobiographical memory recall is less frequent among patients with schizophrenia

2013 ◽  
Vol 150 (1) ◽  
pp. 88-92 ◽  
Author(s):  
Jevita Potheegadoo ◽  
Fabrice Berna ◽  
Christine Cuervo-Lombard ◽  
Jean-Marie Danion
Keyword(s):  
Autobiographical Memory ◽  
Visual Perspective ◽  
Memory Recall

Liver-selective glucocorticoid receptor antagonism decreases glucose production and increases glucose disposal, ameliorating insulin resistance

Metabolism ◽  
2007 ◽  
Vol 56 (3) ◽  
pp. 380-387 ◽  
Author(s):  
Bradley Zinker ◽  
Amanda Mika ◽  
Phong Nguyen ◽  
Denise Wilcox ◽  
Lars Öhman ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Glucocorticoid Receptor ◽  
Glucose Production ◽  
Glucose Disposal ◽  
Receptor Antagonism

scholarly journals Modelling Autobiographical Memory Loss across Life Span

2019 ◽  
Vol 33 ◽  
pp. 1368-1375 ◽  
Author(s):  
Di Wang ◽  
Ah-Hwee Tan ◽  
Chunyan Miao ◽  
Ahmed A. Moustafa
Keyword(s):  
Life Span ◽  
Autobiographical Memory ◽  
Recall Performance ◽  
Research Work ◽  
Age Groups ◽  
Memory Loss ◽  
Human Memory ◽  
Long Term Memory ◽  
Model Parameters ◽  
Memory Recall

Neurocomputational modelling of long-term memory is a core topic in computational cognitive neuroscience, which is essential towards self-regulating brain-like AI systems. In this paper, we study how people generally lose their memories and emulate various memory loss phenomena using a neurocomputational autobiographical memory model. Specifically, based on prior neurocognitive and neuropsychology studies, we identify three neural processes, namely overload, decay and inhibition, which lead to memory loss in memory formation, storage and retrieval, respectively. For model validation, we collect a memory dataset comprising more than one thousand life events and emulate the three key memory loss processes with model parameters learnt from memory recall behavioural patterns found in human subjects of different age groups. The emulation results show high correlation with human memory recall performance across their life span, even with another population not being used for learning. To the best of our knowledge, this paper is the first research work on quantitative evaluations of autobiographical memory loss using a neurocomputational model.

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