scholarly journals Event boundaries are steppingstones for memory retrieval

2021 ◽  
Author(s):  
Sebastian Michelmann ◽  
Uri Hasson ◽  
Kenneth Norman
Keyword(s):  
Response Times ◽  
Level Structure ◽  
Reaction Times ◽  
Memory Search ◽  
Mental Simulation ◽  
Memory Scanning ◽  
Retrieval Process ◽  
Fine Grained ◽  
Actual Duration ◽  
High Level

Personal experience extends over time. When we recall memories from our lives, we often scan extended episodes to remember, for instance, where we placed our car keys. Here we investigate the hypothesis that high level structure, marked by boundaries between events, guides us through this retrieval process. We present a model of memory search in which humans can replay past experience on a fine-grained temporal scale, but also skip ahead to the beginning of a new event in order to speed up the retrieval process. In a naturalistic interview paradigm, we provide evidence in human response times for such a skipping mechanism: When participants scanned extended segments from a movie in their memory, their reaction times were better explained by a model incorporating the number of event boundaries in the segment and the distance of the target to the previous event boundary (with both predictors explaining significant variance), compared to a model incorporating the actual duration of a segment. This supports the idea that, in scanning their memory, humans can skip to the beginning of a new event if they decide that the target is not present in that event; this has the effect of decoupling their memory scanning reaction times from the actual duration of the segment. This conclusion is further supported by the results of a second study where participants were asked to do detailed mental simulation instead of memory scanning. Participants took substantially longer to perform such mental simulation compared to participants that scanned their memory. Those mental simulation times were still explained better by a model where participants skip, but -- compared to memory scanning -- the threshold for skipping was much higher.

Gender Differences in Criterion Task Set Performance and Subjective Ratings

1988 ◽  
Vol 32 (14) ◽  
pp. 848-852
Author(s):  
Betina Schlegel ◽  
Robert E. Schlegel ◽  
Kirby Gilliland
Keyword(s):  
Gender Differences ◽  
Large Scale ◽  
Response Times ◽  
Memory Search ◽  
False Alarms ◽  
Subjective Ratings ◽  
Task Set ◽  
Criterion Task ◽  
Number Of False Alarms ◽  
High Level

This paper summarizes gender differences in performing various elements of the Criterion Task Set. Performance data and Subjective Workload Assessment Technique ratings were analyzed for 28 men and 28 women who participated in a large-scale CTS validation study. In general, women tended to perform slightly better than men on the majority of tasks. In particular, performance by women was better on Grammatical Reasoning, Linguistic Processing, Mathematical Processing, and Memory Search. Response times on Probability Monitoring were faster for women but at the expense of a greater number of False Alarms. Men performed better only on the high level of Continuous Recall and the medium level of Unstable Tracking. Women tended to give lower subjective ratings than men to those tasks with a high memory component and gave higher ratings than men to those tasks involving input/output and spatial elements.

scholarly journals SPARQA: Skeleton-Based Semantic Parsing for Complex Questions over Knowledge Bases

2020 ◽  
Vol 34 (05) ◽  
pp. 8952-8959
Author(s):  
Yawei Sun ◽  
Lingling Zhang ◽  
Gong Cheng ◽  
Yuzhong Qu
Keyword(s):  
Knowledge Base ◽  
Level Structure ◽  
Knowledge Bases ◽  
Coarse Grained ◽  
Semantic Parsing ◽  
Fine Grained ◽  
Sentence Level ◽  
Natural Language Question ◽  
Parsing Algorithm ◽  
High Level

Semantic parsing transforms a natural language question into a formal query over a knowledge base. Many existing methods rely on syntactic parsing like dependencies. However, the accuracy of producing such expressive formalisms is not satisfying on long complex questions. In this paper, we propose a novel skeleton grammar to represent the high-level structure of a complex question. This dedicated coarse-grained formalism with a BERT-based parsing algorithm helps to improve the accuracy of the downstream fine-grained semantic parsing. Besides, to align the structure of a question with the structure of a knowledge base, our multi-strategy method combines sentence-level and word-level semantics. Our approach shows promising performance on several datasets.

Face and Word Recognition in Patients with Left and Right Hemispheric Lesions: Evidence from Reaction Times and ERPs

2002 ◽  
Vol 13 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Stefan R. Schweinberger ◽  
Thomas Klos ◽  
Werner Sommer
Keyword(s):  
Slow Wave ◽  
Reaction Times ◽  
Memory Search ◽  
Event Related Potentials ◽  
Performance Deficits ◽  
Set Size ◽  
Left Brain ◽  
Related Potentials ◽  
Abstract Words ◽  
Left Brain Damage

Abstract: We recorded reaction times (RTs) and event-related potentials (ERPs) in patients with unilateral lesions during a memory search task. Participants memorized faces or abstract words, which were then recognized among new ones. The RT deficit found in patients with left brain damage (LBD) for words increased with memory set size, suggesting that their problem relates to memory search. In contrast, the RT deficit found in patients with RBD for faces was apparently related to perceptual encoding, a conclusion also supported by their reduced P100 ERP component. A late slow wave (720-1720 ms) was enhanced in patients, particularly to words in patients with LBD, and to faces in patients with RBD. Thus, the slow wave was largest in the conditions with most pronounced performance deficits, suggesting that it reflects deficit-related resource recruitment.

Time after time: support for memory accounts of temporal preparation.

2019 ◽  
Author(s):  
Joe Butler ◽  
Samuel Ngabo ◽  
Marcus Missal
Keyword(s):  
Response Times ◽  
Reaction Times ◽  
Evidence Base ◽  
Higher Order ◽  
Order Effects ◽  
Previous Trial ◽  
Adaptive Responses ◽  
Temporal Preparation ◽  
Subsequent Trial ◽  
Higher Order Effects

Complex biological systems build up temporal expectations to facilitate adaptive responses to environmental events, in order to minimise costs associated with incorrect responses, and maximise the benefits of correct responses. In the lab, this is clearly demonstrated in tasks which show faster response times when the period between warning (S1) and target stimulus (S2) on the previous trial was short and slower when the previous trial foreperiod was long. The mechanisms driving such higher order effects in temporal preparation paradigms are still under debate, with key theories proposing that either i) the foreperiod leads to automatic modulation of the arousal system which influences responses on the subsequent trial, or ii) that exposure to a foreperiod results in the creation of a memory trace which is used to guide responses on the subsequent trial. Here we provide data which extends the evidence base for the memory accounts, by showing that previous foreperiod exposures are cumulative with reaction times shortening after repeated exposures; whilst also demonstrate that the higher order effects associated with a foreperiod remain active for several trials.

A challenge for historical research: Making data FAIR using a collaborative ontology management environment (OntoME)

Semantic Web ◽  
2020 ◽  
pp. 1-16
Author(s):  
Francesco Beretta
Keyword(s):  
Semantic Analysis ◽  
Historical Research ◽  
Data Modelling ◽  
Fine Grained ◽  
Fair Principles ◽  
Active Research ◽  
New Research ◽  
High Level ◽  
Conceptual Data ◽  
Ontology Management

This paper addresses the issue of interoperability of data generated by historical research and heritage institutions in order to make them re-usable for new research agendas according to the FAIR principles. After introducing the symogih.org project’s ontology, it proposes a description of the essential aspects of the process of historical knowledge production. It then develops an epistemological and semantic analysis of conceptual data modelling applied to factual historical information, based on the foundational ontologies Constructive Descriptions and Situations and DOLCE, and discusses the reasons for adopting the CIDOC CRM as a core ontology for the field of historical research, but extending it with some relevant, missing high-level classes. Finally, it shows how collaborative data modelling carried out in the ontology management environment OntoME makes it possible to elaborate a communal fine-grained and adaptive ontology of the domain, provided an active research community engages in this process. With this in mind, the Data for history consortium was founded in 2017 and promotes the adoption of a shared conceptualization in the field of historical research.

One-IPC high-level simulation of microthreaded many-core architectures

2016 ◽  
Vol 31 (2) ◽  
pp. 152-162 ◽  
Author(s):  
Irfan Uddin
Keyword(s):  
Design Space Exploration ◽  
Instruction Set ◽  
Efficient Design ◽  
Simulation Framework ◽  
Fine Grained ◽  
Detailed Simulation ◽  
High Level ◽  
Many Core ◽  
The Cost ◽  
Multiple Clusters

The microthreaded many-core architecture is comprised of multiple clusters of fine-grained multi-threaded cores. The management of concurrency is supported in the instruction set architecture of the cores and the computational work in application is asynchronously delegated to different clusters of cores, where the cluster is allocated dynamically. Computer architects are always interested in analyzing the complex interaction amongst the dynamically allocated resources. Generally a detailed simulation with a cycle-accurate simulation of the execution time is used. However, the cycle-accurate simulator for the microthreaded architecture executes at the rate of 100,000 instructions per second, divided over the number of simulated cores. This means that the evaluation of a complex application executing on a contemporary multi-core machine can be very slow. To perform efficient design space exploration we present a co-simulation environment, where the detailed execution of instructions in the pipeline of microthreaded cores and the interactions amongst the hardware components are abstracted. We present the evaluation of the high-level simulation framework against the cycle-accurate simulation framework. The results show that the high-level simulator is faster and less complicated than the cycle-accurate simulator but with the cost of losing accuracy.

Stories as Technology: Past, Present, and Future

2021 ◽  
Author(s):  
Roger’s Bacon ◽  
Sergey Samsonau ◽  
Dario Krpan ◽  
◽  
Keyword(s):  
Social Cognitive ◽  
Fine Grained ◽  
Good Story ◽  
High Level ◽  
Future Technologies

What is it about a good story that causes it to have life-changing effects on one person and not another? I wonder if future technologies will enable us to develop the type of truly deep and fine-grained understanding of stories as social, cognitive, and emotional technologies that might allow us to answer this question with a high-level of precision.

scholarly journals Entity Type Recognition for Heterogeneous Semantic Graphs

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 75-86 ◽  
Author(s):  
Jennifer Sleeman ◽  
Tim Finin ◽  
Anupam Joshi
Keyword(s):  
Machine Learning ◽  
Background Knowledge ◽  
Knowledge Bases ◽  
Heterogeneous Data ◽  
Unstructured Data ◽  
Supervised Machine Learning ◽  
Coreference Resolution ◽  
Multiple Sources ◽  
Fine Grained ◽  
High Level

We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. Big data problems that involve integrating data from multiple sources can benefit from our approach when the datas ontologies are unknown, inaccessible or semantically trivial. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.

Traffic Signal Color Recognition Is a Problem for Both Protan and Deutan Color-Vision Deficients

2003 ◽  
Vol 45 (3) ◽  
pp. 495-503 ◽  
Author(s):  
David A. Atchison ◽  
Carol A. Pedersen ◽  
Stephen J. Dain ◽  
Joanne M. Wood
Keyword(s):  
Color Vision ◽  
Response Times ◽  
Reaction Times ◽  
Error Rates ◽  
Traffic Signal ◽  
White Background ◽  
Signal Design ◽  
Traffic Light ◽  
Light Signals ◽  
Signal Color

We investigated the effect of color-vision deficiency on reaction times and accuracy of identification of traffic light signals. Participants were 20 color-normal and 49 color-deficient males, the latter divided into subgroups of different severity and type. Participants performed a tracking task. At random intervals, stimuli simulating standard traffic light signals were presented against a white background at 5° to right or left. Participants identified stimulus color (red/yellow/green) by pressing an appropriate response button. Mean response times for color normals were 525, 410, and 450 ms for red, yellow, and green lights, respectively. For color deficients, response times to red lights increased with increase in severity of color deficiency, with deutans performing worse than protans of similar severity: response times of deuteranopes and protanopes were 53% and 35% longer than those of color normals. A similar pattern occurred for yellow lights, with deuteranopes and protanopes having increased response times of 85% and 53%, respectively. For green lights, response times of all groups were similar. Error rates showed patterns similar to those of response times. Contrary to previous studies, deutans performed much worse than protans of similar severity. Actual or potential applications of this research include traffic signal design and driver licensing.

Hands-Free versus Hand-Held Cell Phone Conversation on a Braking Response by Young Drivers

2007 ◽  
Vol 105 (2) ◽  
pp. 514-522 ◽  
Author(s):  
Joy L. Hendrick ◽  
Jamie R. Switzer
Keyword(s):  
Cell Phone ◽  
Response Times ◽  
Cell Phones ◽  
Reaction Times ◽  
Free Cell ◽  
Visual Signal ◽  
Young Drivers ◽  
Speed Of Information Processing ◽  
Additional Support ◽  
The Right

As some states allow motorists to use hands-free cell phones only while driving, this study was done to examine some braking responses to see if conversing on these two types of cell phones affects quick responding. College-age drivers ( n = 25) completed reaction time trials in go/no-go situations under three conditions: control (no cell phone or conversation), and conversing on hands-free and hand-held cell phones. Their task involved moving the right foot from one pedal to another as quickly as possible in response to a visual signal in a lab setting. Significantly slower reaction times, movement times, and total response times were found for both cell phone conditions than for the control but no differences between hands-free and hand-held phone conditions. These findings provide additional support that talking on cell phones, regardless if it is hands-free or hand-held, reduces speed of information processing.

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