Marijuana Effects on Semantic Memory: Verification of Common and Uncommon Category Members

1984 ◽  
Vol 55 (2) ◽  
pp. 503-512 ◽  
Author(s):  
Robert I. Block ◽  
J. R. Wittenborn
Keyword(s):  
Reaction Time ◽  
Semantic Memory ◽  
Real World ◽  
Memory Retrieval ◽  
World Knowledge ◽  
Advance Information ◽  
Normal Difference

Effects of smoked marijuana containing 10 mg delta-9-tetrahydrocannabinol and placebo on retrieval of simple, real-world knowledge in semantic memory were studied. In Exp. 1, subjects (36 men, mean age 23.8 yr.) decided whether an item (e.g. apple) belonged to a specified category (e.g., fruit). In Exp. 2, subjects (40 men, mean age 22.8 yr.) decided whether two items (e.g., apple, peach) belonged to the same category. Marijuana did not alter the normal difference in reaction time between common and uncommon examples of categories, suggesting that effects of marijuana on associations do not derive directly from underlying, general alterations of semantic memory retrieval. Marijuana's effects were not influenced by the demands on memory retrieval or by providing advance information relevant to the required decisions, suggesting memory retrieval was not impaired by this dose of marijuana.

scholarly journals Retrieval-constrained valuation: Toward prediction of open-ended decisions

2021 ◽  
Vol 118 (20) ◽  
pp. e2022685118
Author(s):  
Zhihao Zhang ◽  
Shichun Wang ◽  
Maxwell Good ◽  
Siyana Hristova ◽  
Andrew S. Kayser ◽  
...  
Keyword(s):  
Decision Making ◽  
Semantic Memory ◽  
Real World ◽  
Memory Retrieval ◽  
Cognitive Model ◽  
Evaluation Criteria ◽  
Cognitive Mechanisms ◽  
Problem Structuring ◽  
Out Of Sample ◽  
Classical Models

Real-world decisions are often open ended, with goals, choice options, or evaluation criteria conceived by decision-makers themselves. Critically, the quality of decisions may heavily rely on the generation of options, as failure to generate promising options limits, or even eliminates, the opportunity for choosing them. This core aspect of problem structuring, however, is largely absent from classical models of decision-making, thereby restricting their predictive scope. Here, we take a step toward addressing this issue by developing a neurally inspired cognitive model of a class of ill-structured decisions in which choice options must be self-generated. Specifically, using a model in which semantic memory retrieval is assumed to constrain the set of options available during valuation, we generate highly accurate out-of-sample predictions of choices across multiple categories of goods. Our model significantly and substantially outperforms models that only account for valuation or retrieval in isolation or those that make alternative mechanistic assumptions regarding their interaction. Furthermore, using neuroimaging, we confirm our core assumption regarding the engagement of, and interaction between, semantic memory retrieval and valuation processes. Together, these results provide a neurally grounded and mechanistic account of decisions with self-generated options, representing a step toward unraveling cognitive mechanisms underlying adaptive decision-making in the real world.

A common functional brain network for autobiographical, episodic, and semantic memory retrieval

NeuroImage ◽  
2010 ◽  
Vol 49 (1) ◽  
pp. 865-874 ◽  
Author(s):  
Hana Burianova ◽  
Anthony R. McIntosh ◽  
Cheryl L. Grady
Keyword(s):  
Semantic Memory ◽  
Memory Retrieval ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network

scholarly journals The convergence and divergence of episodic and semantic functions across lateral parietal cortex

2020 ◽  
Author(s):  
Gina F. Humphreys ◽  
JeYoung Jung ◽  
Matthew A. Lambon Ralph
Keyword(s):  
White Matter ◽  
Semantic Memory ◽  
Parietal Cortex ◽  
Memory Retrieval ◽  
Task Difficulty ◽  
Structural Connectivity ◽  
Angular Gyrus ◽  
Episodic Retrieval ◽  
Semantic Retrieval ◽  
White Matter Connectivity

AbstractSeveral decades of neuropsychological and neuroimaging research have highlighted the importance of lateral parietal cortex (LPC) across a myriad of cognitive domains. Yet, despite the prominence of this region the underlying function of LPC remains unclear. Two domains that have placed particular emphasis on LPC involvement are semantic memory and episodic memory retrieval. From each domain, sophisticated models have been proposed as to the underlying function, as well as the more domain-general that LPC is engaged by any form of internally-directed cognition (episodic and semantic retrieval both being examples if this process). Here we directly address these alternatives using a combination of fMRI and DTI white-matter connectivity data. The results show that ventral LPC (angular gyrus) was positively engaged during episodic retrieval but disengaged during semantic memory retrieval. In addition, the level of activity negatively varied with task difficulty in the semantic task whereas episodic activation was independent of difficulty. In contrast, dorsal LPC (intraparietal sulcus) showed domain general activation that was positively correlated with task difficulty. In terms of structural connectivity, a dorsal-ventral and anterior-posterior gradient of connectivity was found to different processing networks (e.g., mid-angular gyrus (AG) connected with episodic retrieval). We propose a unifying model in which LPC as a whole might share a common underlying function (e.g., multimodal buffering) and variations across subregions arise due to differences in the underlying white matter connectivity.

Semantic memory is both impaired and anomalous in schizophrenia

1994 ◽  
Vol 24 (1) ◽  
pp. 193-202 ◽  
Author(s):  
E. Y. H. Chen ◽  
A. J. Wilkins ◽  
P. J. McKenna
Keyword(s):  
Reaction Time ◽  
Semantic Memory ◽  
Reaction Time Task ◽  
Semantic Category ◽  
Semantic Relationship ◽  
Conceptual Category ◽  
Ambiguous Words ◽  
Schizophrenic Patients ◽  
Speed And Accuracy ◽  
Normal Controls

SynopsisThe integrity of semantic memory in schizophrenia was examined in a reaction time task requiring subjects to verify words as members or non-members of a conceptual category, where the words differed in their degree of semantic relationship to the category. Compared to matched normal controls, 28 schizophrenic patients were impaired on the task, showing slower responses in all conditions. In addition, their performance was anomalous in that they took longest to respond to items that were outside the category but semantically related to it, in contrast to the controls who took the longest to respond to ambiguous words at the borderline of the category. The pattern of ‘yes’ and ‘no’ responses of the patients was anomalous in a similar way. In both speed and accuracy of responding, the findings indicate that there is an outward shift of semantic category boundaries in schizophrenia.

DATAtourist

2021 ◽  
Vol 13 (2) ◽  
pp. 62-84
Author(s):  
Boudjemaa Boudaa ◽  
Djamila Figuir ◽  
Slimane Hammoudi ◽  
Sidi mohamed Benslimane
Keyword(s):  
Software Engineering ◽  
Knowledge Acquisition ◽  
Software Architecture ◽  
Recommender Systems ◽  
Real World ◽  
Mobile Application ◽  
Knowledge Bases ◽  
User Requirements ◽  
World Knowledge ◽  
Points Of Interest

Collaborative and content-based recommender systems are widely employed in several activity domains helping users in finding relevant products and services (i.e., items). However, with the increasing features of items, the users are getting more demanding in their requirements, and these recommender systems are becoming not able to be efficient for this purpose. Built on knowledge bases about users and items, constraint-based recommender systems (CBRSs) come to meet the complex user requirements. Nevertheless, this kind of recommender systems witnesses a rarity in research and remains underutilised, essentially due to difficulties in knowledge acquisition and/or in their software engineering. This paper details a generic software architecture for the CBRSs development. Accordingly, a prototype mobile application called DATAtourist has been realized using DATAtourisme ontology as a recent real-world knowledge source in tourism. The DATAtourist evaluation under varied usage scenarios has demonstrated its usability and reliability to recommend personalized touristic points of interest.

Symbols Without Context

2018 ◽  
Author(s):  
Gary Smith
Keyword(s):  
Data Base ◽  
Machine Translation ◽  
Real World ◽  
Language Translation ◽  
Digital Data ◽  
Meaningful Difference ◽  
World Knowledge ◽  
Similarities And Differences ◽  
Words And Images ◽  
The Way

Humans have invaluable real-world knowledge because we have accumulated a lifetime of experiences that help us recognize, understand, and anticipate. Computers do not have real-world experiences to guide them, so they must rely on statistical patterns in their digital data base—which may be helpful, but is certainly fallible. We use emotions as well as logic to construct concepts that help us understand what we see and hear. When we see a dog, we may visualize other dogs, think about the similarities and differences between dogs and cats, or expect the dog to chase after a cat we see nearby. We may remember a childhood pet or recall past encounters with dogs. Remembering that dogs are friendly and loyal, we might smile and want to pet the dog or throw a stick for the dog to fetch. Remembering once being scared by an aggressive dog, we might pull back to a safe distance. A computer does none of this. For a computer, there is no meaningful difference between dog, tiger, and XyB3c, other than the fact that they use different symbols. A computer can count the number of times the word dog is used in a story and retrieve facts about dogs (such as how many legs they have), but computers do not understand words the way humans do, and will not respond to the word dog the way humans do. The lack of real world knowledge is often revealed in software that attempts to interpret words and images. Language translation software programs are designed to convert sentences written or spoken in one language into equivalent sentences in another language. In the 1950s, a Georgetown–IBM team demonstrated the machine translation of 60 sentences from Russian to English using a 250-word vocabulary and six grammatical rules. The lead scientist predicted that, with a larger vocabulary and more rules, translation programs would be perfected in three to five years. Little did he know! He had far too much faith in computers. It has now been more than 60 years and, while translation software is impressive, it is far from perfect. The stumbling blocks are instructive. Humans translate passages by thinking about the content—what the author means—and then expressing that content in another language.

Incremental Knowledge Construction for Real-World Event Understanding

2010 ◽  
Vol 4 (1) ◽  
pp. 65-79 ◽  
Author(s):  
Koji Kamei ◽  
Yutaka Yanagisawa ◽  
Takuya Maekawa ◽  
Yasue Kishino ◽  
Yasushi Sakurai ◽  
...  
Keyword(s):  
Real World ◽  
Knowledge Construction ◽  
A Priori ◽  
Sensor System ◽  
World Knowledge ◽  
Incremental Model ◽  
Proposed Model

The construction of real-world knowledge is required if we are to understand real-world events that occur in a networked sensor environment. Since it is difficult to select suitable ‘events’ for recognition in a sensor environment a priori, we propose an incremental model for constructing real-world knowledge. Labeling is the central plank of the proposed model because the model simultaneously improves both the ontology of real-world events and the implementation of a sensor system based on a manually labeled event corpus. A labeling tool is developed in accordance with the model and is evaluated in a practical labeling experiment.

Real World Knowledge for Databases

1992 ◽  
Vol 3 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Veda C. Storey
Keyword(s):  
Real World ◽  
World Knowledge
Sign in / Sign up

Export Citation Format

Share Document