Analysis on Factors of Forgetting and Three Ways for Effective Memory

Psychologists often attribute the term 'memory' to the retention of information like knowledge or experience. This paper focuses on the reason why not only older people but also the younger generation have been reported that they often forget about certain things that happened before or not happened yet. This paper includes the methods of comparing several data from different studies and citing many conclusions in other studies. This paper presents current knowledge about how and why people usually forget. The conclusion I draw from this whole process of writing is that the factors such as the aging, stress and emotion, encoding failure, storage failure, and the retrieval failure can influence differently on the retain of the memory. But, still, we have some solutions to do our utmost in reducing their effects.

Real-Time Multi-Modal Estimation of Dynamically Evoked Emotions Using EEG, Heart Rate and Galvanic Skin Response

Emotion estimation systems based on brain and physiological signals such as electro encephalography (EEG), blood-volume pressure (BVP), and galvanic skin response (GSR) are gaining special attention in recent years due to the possibilities they offer. The field of human–robot interactions (HRIs) could benefit from a broadened understanding of the brain and physiological emotion encoding, together with the use of lightweight software and cheap wearable devices, and thus improve the capabilities of robots to fully engage with the users emotional reactions. In this paper, a previously developed methodology for real-time emotion estimation aimed for its use in the field of HRI is tested under realistic circumstances using a self-generated database created using dynamically evoked emotions. Other state-of-the-art, real-time approaches address emotion estimation using constant stimuli to facilitate the analysis of the evoked responses, remaining far from real scenarios since emotions are dynamically evoked. The proposed approach studies the feasibility of the emotion estimation methodology previously developed, under an experimentation paradigm that imitates a more realistic scenario involving dynamically evoked emotions by using a dramatic film as the experimental paradigm. The emotion estimation methodology has proved to perform on real-time constraints while maintaining high accuracy on emotion estimation when using the self-produced dynamically evoked emotions multi-signal database.

Neural Biases to Covert and Overt Signals of Fear: Dissociation by Trait Anxiety and Depression

Although biases toward signals of fear may be an evolutionary adaptation necessary for survival, heightened biases may be maladaptive and associated with anxiety or depression. In this study, event-related potentials (ERPs) were used to examine the time course of neural responses to facial fear stimuli (versus neutral) presented overtly (for 500 msec with conscious attention) and covertly (for 10 msec with immediate masking to preclude conscious awareness) in 257 nonclinical subjects. We also examined the impact of trait anxiety and depression, assessed using psychometric ratings, on the time course of ERPs. In the total subject group, controlled biases to overtly processed fear were reflected in an enhancement of ERPs associated with structural encoding (120–220 msec) and sustained evaluation persisting from 250 msec and beyond, following a temporo-occipital to frontal topography. By contrast, covert fear processing elicited automatic biases, reflected in an enhancement of ERPs prior to structural encoding (80–180 msec) and again in the period associated with automatic orienting and emotion encoding (230–330 msec), which followed the reverse frontal to temporo-occipital topography. Higher levels of trait anxiety (in the clinical range) were distinguished by a heightened bias to covert fear (speeding of early ERPs), compared to higher depression which was associated with an opposing bias to overt fear (slowing of later ERPs). Anxiety also heightened early responses to covert fear, and depression to overt fear, with subsequent deficits in emotion encoding in each case. These findings are consistent with neural biases to signals of fear which operate automatically and during controlled processing, feasibly supported by parallel networks. Heightened automatic biases in anxiety may contribute to a cycle of hypervigilance and anxious thoughts, whereas depression may represent a “burnt out” emotional state in which evaluation of fear stimuli is prolonged only when conscious attention is allocated.