Autobiographical memory and depression : an investigation of over general memory processing on recall tasks

Depression is a prevalent diagnosis within psychiatric populations. People with depression have been shown to recall over general autobiographical memories. The current study uses a cognitive stage model of memory to explain the retrieval processes involved in autobiographical memory. Two memory tasks were used: a standard cued recall task and a free recall cued task. Thirty individuals participated in the study: 15 clinically depressed individuals and 15 non-depressed controls. Analysis showed that the depressed group were significantly more over general than the comparison group. The free recall task provided data on the processes involved in retrieving a specific memory. The depressed group were observed to experience an aberration in this process. Further, they were less able to navigate around the autobiographical memory network. Large effect sizes were found between the groups. The implications of these findings in terms of self concept and difficulties problem solving are discussed. In addition, the consequences for assessment, diagnosis, treatment and relapse are explored. Recommendations for future research are made

Memory: looking back and looking forward

This review brings together past and present achievements in memory research, ranging from molecular to psychological discoveries. Despite some false starts, major advances include our growing understanding of learning-related neural plasticity and the characterisation of different classes of memory. One striking example is the ability to reactivate targeted neuronal ensembles so that an animal will seemingly re-experience a particular memory, with the further potential to modify such memories. Meanwhile, human functional imaging studies can distinguish individual episodic memories based on voxel activation patterns. While the hippocampus continues to provide a rich source of information, future progress requires broadening our research to involve other sites. Related challenges include the need to understand better the role of glial-neuron interactions and to look beyond the synapse as the sole site of experience-dependent plasticity. Unmet goals include translating our neuroscientific knowledge in order to optimise learning and memory, especially amongst disadvantaged populations

The Effects of Type of Recall on Memory Accuracy in an Eyewitness Case Vignette

Memory and recall play an essential role in determining convictions in cases of eyewitness testimony. Eyewitnesses can appear confident in their statements, yet one’s memory and recall of a witnessed event can become distorted or manipulated in the process given that it is highly susceptible to various errors, biases, and misleading information such as suggestive questions. The purpose of this study was to further examine how two types of recall (i.e., cued and free) effect eyewitness testimony. In this study, 43 participants read a hypothetical case vignette of a murder crime scene; they were then randomly put into either a cued recall or free recall group. All participants were asked a series of suggestive questions pertaining to the crime vignette in order to measure and compare memory accuracy and confidence ratings between the two recall groups. The findings of this study indicated that while there was no significant difference between the number of details remembered from the crime vignette by the two recall groups, participants in the cued recall group made more mistakes than participants in the free recall group. Participants in the cued recall group were also slightly more likely to say “yes” to the suggestive questions than participants in the free recall group. Overall, the findings of this study indicate that cued recall may not take precedence over free recall when assessing memory in the context of eyewitness testimony, despite a vast amount of literature highlighting the opposite, yet some studies have suggested the same

Factors affecting the efficacy of feedback use during source monitoring

The current study considers how individual differences in working memory capacity (WMC) affect feedback effectiveness. Participants, selected to have high and low WMC, first watched a video of a crime. Subsequently, a post-test questionnaire was administered concerning events taken from the video and additional information suggested to have occurred in the video. After a 10 minute filler task, participants were given a two-part memory test requiring them to identify the source of the information presented in the test statements. During the training portion of the test, half of the participants received feedback as to the accuracy of their source decisions. On the second (assessment) portion of the test, participants did not receive any feedback. Both high and low WMC participants benefited equally from the presentation of feedback; both groups significantly reduced their misattributions of suggested items to the video. There was also a trend toward better source monitoring performance on suggested items in high WMC than low WMC participants, regardless of whether they received feedback. These findings suggest that feedback may be used to improve memory accuracy without requiring substantial executive resources

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Named Entity Recognition for English Language Using Deep Learning Based Bi Directional LSTM-RNN

The NER has been important in different applications like data Retrieval and Extraction, Text Summarization, Machine Translation, Question Answering (Q-A), etc. While several investigations have been carried out for NER in English, a high-accuracy tool still must be designed per the Literature Survey. This paper suggests an English Named Entities Recognition methodology using NLP algorithms called Bi-Directional Long short-term memory-based recurrent neural network (LSTM-RNN). Most English Language NER systems use detailed features and handcrafted algorithms with gazetteers. The proposed model is language-independent and has no domain-specific features or handcrafted algorithms. Also, it depends on semantic knowledge from word vectors realized by an unsupervised learning algorithm on an unannotated corpus. It achieved state-of-the-art performance in English without the use of any morphological research or without using gazetteers of any sort. A little database group of 200 sentences includes 3080 words. The features selection and generations are presented to catch the Name Entity. The proposed work is desired to forecast the Name Entity of the focus words in a sentence with high accuracy with the benefit of practical knowledge acquisition techniques

LifeLogging: personal big data

We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging in order to capture life details of life activities, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses to an information retrieval scientist. This review is a suitable reference for those seeking a information retrieval scientist’s perspective on lifelogging and the quantified self

General Psychology (Fall 2018)

This open textbook represents the version used in several Fall 2018 General Psychology courses at Valparaiso University.https://scholar.valpo.edu/psych_oer/1002/thumbnail.jp