Within-trial effects of stimulus-reward associations

While a globally energizing influence of motivation has long been appreciated in psychological research, a series of more recent studies has described motivational influences on specific cognitive operations ranging from visual attention, to cognitive control, to memory formation. In the majority of these studies, a cue predicts the potential to win money in a subsequent task, thus allowing for modulations of proactive task preparation. Here we describe some recent studies using tasks that communicate reward availability without such cues by directly associating specific task features with reward. Despite abolishing the cue-based preparation phase, these studies show similar performance benefits. Given the clear difference in temporal structure, a central question is how these behavioral effects are brought about, and in particular whether control processes can rapidly be enhanced reactively. We present some evidence in favor of this notion. Although additional influences, for example sensory prioritization of reward-related features, could contribute to the reward-related performance benefits, those benefits seem to strongly rely on enhancements of control processes during task execution. Still, for a better mechanistic understanding of reward benefits in these two principal paradigms (cues vs. no cues), more work is needed that directly compares the underlying processes. We anticipate that reward benefits can be brought about in a very flexible fashion depending on the exact nature of the reward manipulation and task, and that a better understanding of these processes will not only be relevant for basic motivation research, but that it can also be valuable for educational and psychopathological contexts

연합기억에서의 해마의 역할: 절제 연구와 뇌파 연결성 연구로부터의 통찰

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 뇌인지과학과, 2023. 8. 정천기.연합 기억은 서로 관련없는 항목들의 관계에 대한 기억으로 정의됩니다. 해마는 연합기억에서 대체할 수 없는 중요한 역할을 하는 것으로 알려져 있습니다. 그러나, 해마가 단독으로 작용하여 연합 기억을 수행하는 것은 아니라는 점에 유의하는 것은 중요합니다. 연합 기억은 뇌의 여러 영역이 상호 작용하여 작동합니다. 따라서 연합 기억을 수행할 때 단순히 특정 영역이 활성화 되는 것 보다 해마와 기억 관련 네트워크 간의 기능적 연결이 더 중요할 수 있습니다. 먼저 해마가 연합 기억에 어떤 기여를 하는지 알아보기 위해 내측 측두엽 뇌전증으로 수술을 받은 환자를 대상으로 해마의 절제 여부와 수술 후 다양한 기억력 검사에서 나타난 기억력 변화 사이의 관계를 조사했습니다. 절제 영역과 위치의 개인차를 반영하는 복셀 기반 분석을 통해 해마의 절제가 항목 기억보다는 연합 기억의 저하와 관련이 있음을 발견했습니다. 이러한 이해를 바탕으로 저는 기억의 성공과 실패를 예측하기 위해 해마와 기억 관련 대뇌 피질 네트워크 영역 사이의 단일 시행 뇌파 연결성을 활용했습니다. 그 결과, 기억의 수행도를 예측할 때 평균 90% 이상의 정확도를 달성했습니다. 이 정확도는 특정 영역의 뇌 활동만을 예측에 사용하는 것과 비교했을 때 현저히 높은 수치입니다. 요약하자면, 이 논문은 연합 기억에서 해마와 해마의 연결성의 중요한 역할을 강조합니다. 이 연구는 연합 기억 과정을 이해하는 데 있어 특정 뇌 영역에만 초점을 맞추는 것이 아니라 대규모 기억 네트워크의 역할을 이해하는 것이 중요하다는 점을 강조합니다.Associative memory refers to the ability to remember the relationships between unrelated items. The hippocampus (HC) is known to play a critical and irreplaceable role in associative memory. However, it is important to note that the HC does not operate in isolation when it comes to performing associative memory; instead, it interacts with various regions of the brain. Therefore, in the context of associative memory, the functional connectivity between the HC and memory-related networks may be more important than the mere activation of specific regions. To investigate the specific contribution of the HC to associative memory, I examined the relationship between hippocampal resection and postoperative memory changes on various memory tests in patients who underwent surgery for medial temporal lobe epilepsy (MTLE). Through a voxel-based analysis that accounts for individual differences in the resection, it was found that resection of the HC was associated with a decline in associative memory rather than item memory. This finding emphasizes the specific involvement of the HC in associative memory processes. Expanding upon this understanding, I utilized single-trial EEG connectivity between the HC and neocortical regions to predict memory success and failure. The results achieved an average accuracy of over 90% in predicting subsequent memory performance. Notably, this level of accuracy was higher compared to utilizing brain activity in specific regions. In summary, this thesis highlights the significant role of the HC and its connectivity in associative memory. It underscores the significance of hippocampal communication with large-scale brain networks, rather than solely focusing on specific brain regions, in understanding memory processes.Abstract i Contents iii List of Figures v List of Tables vi List of Abbreviations vii I. INTRODUCTION 1 1.1 Associative Memory and the Hippocampus 1 1.2 Associative Memory beyond the MTL 5 1.2.1 Successful Memory Encoding and the Default Mode Network 5 1.2.2 Subsequent Memory Effects 9 1.3 Purpose of the Present Study 13 II. METHODS 14 2.1 Participants 14 2.1.1 Experiment 1. Medial Temporal Lobe Epilepsy Patients 14 2.1.2 Experiment 2. EEG Study Participants 18 2.2 Experimental Design 19 2.2.1 Experiment 1. Pre- and Post-operative Memory Test 19 2.2.2 Expereiment 2. EEG Experimental Paradigm 20 2.3 Data Analysis 22 2.3.1 Experiment 1. MRI Image and Statistical Analysis 22 2.3.2 Experiment 2. EEG Connectivity Analysis for Memory Performance Prediction 25 III. RESULTS 30 3.1 Experiment 1. Postoperative Memory Change Analysis Results 30 3.1.2 Neuropsychological Outcome 30 3.1.3 Voxel-based Analysis 32 3.2 Experiment 2. Memory Performance Prediction Results 35 3.2.1 Behavioral Results 35 3.2.2 Differences in Connectivity Features 35 3.2.3 Classification Accuracy 35 IV. DISCUSSION 40 4.1 Summary 40 4.2 Experiment 1. Associative Memory and Hippocampal Resection 41 4.3 Experiment 2. Prediction of Associative Memory Performance Using Hippocampal Connectivity 44 4.4 Conclusion 50 V. BIBLIOGRAPHY 51 Abstract in Korean 66박

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

A Novel Analysis of Performance Classification and Workload Prediction Using Electroencephalography (EEG) Frequency Data

Across the DOD each task an operator is presented with has some level of difficulty associated with it. This level of difficulty over the course of the task is also known as workload, where the operator is faced with varying levels of workload as he or she attempts to complete the task. The focus of the research presented in this thesis is to determine if those changes in workload can be predicted and to determine if individuals can be classified based on performance in order to prevent an increase in workload that would cause a decline in performance in a given task. Despite many efforts to predict workload and classify individuals with machine learning, the classification and predictive ability of Electroencephalography (EEG) frequency data has not been explored at the individual EEG Frequency band level. In a 711th HPW/RCHP Human Universal Measurement and Assessment Network (HUMAN) Lab study, 14 Subjects were asked to complete two tasks over 16 scenarios, while their physiological data, including EEG frequency data, was recorded to capture the physiological changes their body went through over the course of the experiment. The research presented in this thesis focuses on EEG frequency data, and its ability to predict task performance and changes in workload. Several machine learning techniques are explored in this thesis before a final technique was chosen. This thesis contributes research to the medical and machine learning fields regarding the classification and workload prediction efficacy of EEG frequency data. Specifically, it presents a novel investigation of five EEG frequencies and their individual abilities to predict task performance and workload. It was discovered that using the Gamma EEG frequency and all EEG frequencies combined to predict task performance resulted in average classification accuracies of greater than 90%

Alpha And Gamma Oscillation Amplitudes Synergistically Predict The Perception Of Forthcoming Nociceptive Stimuli

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