Positive Arousal Enhances the Consolidation of Item Memory

2015 ◽  
Vol 74 (2) ◽  
pp. 91-104 ◽  
Author(s):  
Bo Wang
Keyword(s):  
Episodic Memory ◽  
Memory Consolidation ◽  
Source Memory ◽  
Emotional Arousal ◽  
Theoretical Models ◽  
Memory Test ◽  
Learning Phase ◽  
Item Memory ◽  
Memory Tests ◽  
Immediate Memory

Emotional arousal induced after learning has been shown to modulate memory consolidation. However, it is unclear whether the effect of postlearning arousal can extend to different aspects of memory. This study examined the effect of postlearning positive arousal on both item memory and source memory. Participants learned a list of neutral words and took an immediate memory test. Then they watched a positive or a neutral videoclip and took delayed memory tests after either 25 minutes or 1 week had elapsed after the learning phase. In both delay conditions, positive arousal enhanced consolidation of item memory as measured by overall recognition. Furthermore, positive arousal enhanced consolidation of familiarity but not recollection. However, positive arousal appeared to have no effect on consolidation of source memory. These findings have implications for building theoretical models of the effect of emotional arousal on consolidation of episodic memory and for applying postlearning emotional arousal as a technique of memory intervention.

I Like Her, Because I Like Myself: Self-Evaluation as a Source of Interpersonal Attitudes

2003 ◽  
Vol 50 (4) ◽  
pp. 239-246 ◽  
Author(s):  
Eva Walther ◽  
Claudia Trasselli
Keyword(s):  
Opposite Effect ◽  
Learning Phase ◽  
The Self ◽  
Attitude Formation ◽  
Formation Processes ◽  
False Feedback ◽  
Self Evaluation ◽  
Evaluation Group ◽  
Group A ◽  
The Difference

Abstract. Two experiments tested the hypothesis that self-evaluation can serve as a source of interpersonal attitudes. In the first study, self-evaluation was manipulated by means of false feedback. A subsequent learning phase demonstrated that the co-occurrence of the self with another individual influenced the evaluation of this previously neutral target. Whereas evaluative self-target similarity increased under conditions of negative self-evaluation, an opposite effect emerged in the positive self-evaluation group. A second study replicated these findings and showed that the difference between positive and negative self-evaluation conditions disappeared when a load manipulation was applied. The implications of self-evaluation for attitude formation processes are discussed.

scholarly journals EXPRESS: Standing out from the crowd: Both cue numerosity and social information affect attention in multi-agent contexts

2021 ◽  
pp. 174702182110130
Author(s):  
Francesca Capozzi ◽  
Andrew Paul Bayliss ◽  
Jelena Ristic
Keyword(s):  
Social Interactions ◽  
Social Information ◽  
Test Phase ◽  
Learning Phase ◽  
Complex Interplay ◽  
Two Phase ◽  
Attentional Allocation ◽  
Social Cue ◽  
Gaze Cuing ◽  
Multi Agent

Groups of people offer abundant opportunities for social interactions. We used a two-phase task to investigate how social cue numerosity and social information about an individual affected attentional allocation in such multi-agent settings. The learning phase was a standard gaze-cuing procedure in which a stimulus face could be either uninformative or informative about the upcoming target. The test phase was a group-cuing procedure in which the stimulus faces from the learning phase were presented in groups of three. The target could either be cued by the group minority (i.e., one face) or majority (i.e., two faces) or by uninformative or informative stimulus faces. Results showed an effect of cue numerosity, whereby responses were faster to targets cued by the group majority than the group minority. However, responses to targets cued by informative identities included in the group minority were as fast as responses to targets cued by the group majority. Thus, previously learned social information about an individual was able to offset the general enhancement of cue numerosity, revealing a complex interplay between cue numerosity and social information in guiding attention in multi-agent settings.

Machine learning phase space quantum dynamics approaches

2021 ◽  
Vol 154 (18) ◽  
pp. 184104
Author(s):  
Xinzijian Liu ◽  
Linfeng Zhang ◽  
Jian Liu
Keyword(s):  
Machine Learning ◽  
Phase Space ◽  
Quantum Dynamics ◽  
Learning Phase

scholarly journals Novel Hopfield Neural Network Model with Election Algorithm for Random 3 Satisfiability

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1292
Author(s):  
Muna Mohammed Bazuhair ◽  
Siti Zulaikha Mohd Jamaludin ◽  
Nur Ezlin Zamri ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Mohd. Asyraf Mansor ◽  
...  
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Hopfield Neural Network ◽  
Higher Order ◽  
Research Field ◽  
Learning Phase ◽  
Logical Rule ◽  
Election Algorithm ◽  
Logical Rules ◽  
Retrieval Phase

One of the influential models in the artificial neural network (ANN) research field for addressing the issue of knowledge in the non-systematic logical rule is Random k Satisfiability. In this context, knowledge structure representation is also the potential application of Random k Satisfiability. Despite many attempts to represent logical rules in a non-systematic structure, previous studies have failed to consider higher-order logical rules. As the amount of information in the logical rule increases, the proposed network is unable to proceed to the retrieval phase, where the behavior of the Random Satisfiability can be observed. This study approaches these issues by proposing higher-order Random k Satisfiability for k ≤ 3 in the Hopfield Neural Network (HNN). In this regard, introducing the 3 Satisfiability logical rule to the existing network increases the synaptic weight dimensions in Lyapunov’s energy function and local field. In this study, we proposed an Election Algorithm (EA) to optimize the learning phase of HNN to compensate for the high computational complexity during the learning phase. This research extensively evaluates the proposed model using various performance metrics. The main findings of this research indicated the compatibility and performance of Random 3 Satisfiability logical representation during the learning and retrieval phase via EA with HNN in terms of error evaluations, energy analysis, similarity indices, and variability measures. The results also emphasized that the proposed Random 3 Satisfiability representation incorporates with EA in HNN is capable to optimize the learning and retrieval phase as compared to the conventional model, which deployed Exhaustive Search (ES).

scholarly journals Log Posterior Approach in Learning Rules Generated using N-Gram based Edit distance for Keyword Search

2018 ◽  
Vol 27 (4) ◽  
pp. 555-563
Author(s):  
M. Priya ◽  
R. Kalpana
Keyword(s):  
Edit Distance ◽  
Keyword Search ◽  
Probabilistic Approach ◽  
Likelihood Estimation ◽  
Learning Phase ◽  
Rule Generation ◽  
Machine Model ◽  
Approximate Search ◽  
N Gram ◽  
Rule Based Approach

Abstract Challenging searching mechanisms are required to cater to the needs of search engine users in probing the voluminous web database. Searching the query matching keyword based on a probabilistic approach is attractive in most of the application areas, viz. spell checking and data cleaning, because it allows approximate search. A probabilistic approach with maximum likelihood estimation is used to handle real-world problems; however, it suffers from overfitting data. In this paper, a rule-based approach is presented for keyword searching. The process consists of two phases called the rule generation phase and the learning phase. The rule generation phase uses a new technique called N-Gram based Edit distance (NGE) to generate the rule dictionary. The Turing machine model is implemented to describe the rule generation using the NGE technique. In the learning phase, a log model with maximum-a-posterior estimation is used to select the best rule. When evaluated in real time, our system produces the best result in terms of efficiency and accuracy.

scholarly journals Politik Identitas Islam dan Pengaruhnya Terhadap Demokrasi di Indonesia

POLITEA ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 181
Author(s):  
Haikal Fadhil Anam
Keyword(s):  
Political Identity ◽  
Political Influence ◽  
Learning Phase ◽  
The Political ◽  
Islamic State ◽  
Political Will ◽  
Large Wave ◽  
The Future ◽  
Islamic Identity ◽  
Rule Out

<p>The political identity of Islam emerged in a very large wave after the mobilization of time at the Jakarta elections in 2016. This has many implications for various aspects of State life, including the current democracy in Indonesia. In this case, Indonesia is a country that is still in the learning phase of democracy. The political influence of Islamic identity on democracy will make the nation split. This is backed by the strong narrative of the Political Islamic Group which at the end of the goal, wanted to establish the Islamic State. The future is political, will further heed and mobilize Muslims, as a majority, and rule out other religions.</p><p> </p>

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