Analysis of the "naming game" with learning errors in communications.
Ontology highlight
ABSTRACT:
SUBMITTER: Lou Y
PROVIDER: S-EPMC4503979 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Naming game simulates the evolution of vocabulary in a population of agents. Through pairwise interactions in the games, agents acquire a set of vocabulary in their memory for object naming. The existing model confines to a one-to-one mapping between a name and an object. Focus is usually put onto name consensus in the population rather than knowledge learning in agents, and hence simple learning model is usually adopted. However, the cognition system of human being is much more complex and knowledge is usually presented in a complicated form. Therefore, in this work, we extend the agent learning model and design a new game to incorporate domain learning, which is essential for more complicated form of knowledge. In particular, we demonstrate the evolution of color categorization and naming in a population of agents. We incorporate the human perceptive model into the agents and introduce two new concepts, namely subjective perception and subliminal stimulation, in domain learning. Simulation results show that, even without any supervision or pre-requisition, a consensus of a color naming system can be reached in a population solely via the interactions. Our work confirms the importance of society interactions in color categorization, which is a long debate topic in human cognition. Moreover, our work also demonstrates the possibility of cognitive system development in autonomous intelligent agents.
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Project description:Complex networks are often organized in groups or communities of agents that share the same features and/or functions, and this structural organization is built naturally with the formation of the system. In social networks, we argue that the dynamic of linguistic interactions of agreement among people can be a crucial factor in generating this community structure, given that sharing opinions with another person bounds them together, and disagreeing constantly would probably weaken the relationship. We present here a computational model of opinion exchange that uncovers the community structure of a network. Our aim is not to present a new community detection method proper, but to show how a model of social communication dynamics can reveal the (simple and overlapping) community structure in an emergent way. Our model is based on a standard Naming Game, but takes into consideration three social features: trust, uncertainty and opinion preference, that are built over time as agents communicate among themselves. We show that the separate addition of each social feature in the Naming Game results in gradual improvements with respect to community detection. In addition, the resulting uncertainty and trust values classify nodes and edges according to role and position in the network. Also, our model has shown a degree of accuracy both for non-overlapping and overlapping communities that are comparable with most algorithms specifically designed for topological community detection.
| S-EPMC5552283 | biostudies-other
Project description:To investigate how consensus is reached on a large self-organized peer-to-peer network, we extended the naming game model commonly used in language and communication to Naming Game in Groups (NGG). Differing from other existing naming game models, in NGG everyone in the population (network) can be both speaker and hearer simultaneously, which resembles in a closer manner to real-life scenarios. Moreover, NGG allows the transmission (communication) of multiple words (opinions) for multiple intra-group consensuses. The communications among indirectly-connected nodes are also enabled in NGG. We simulated and analyzed the consensus process in some typical network topologies, including random-graph networks, small-world networks and scale-free networks, to better understand how global convergence (consensus) could be reached on one common word. The results are interpreted on group negotiation of a peer-to-peer network, which shows that global consensus in the population can be reached more rapidly when more opinions are permitted within each group or when the negotiating groups in the population are larger in size. The novel features and properties introduced by our model have demonstrated its applicability in better investigating general consensus problems on peer-to-peer networks.
| S-EPMC4139946 | biostudies-literature
Project description:The Naming Game has proven to be an important model of opinion dynamics in complex networks. It is significantly enriched by the introduction of nodes committed to a single opinion. The resulting model is still simple but captures core concepts of opinion dynamics in networks. This model limitation is rigid commitment which never changes. Here we study the effect that making commitment variable has on the dynamics of the system. Committed nodes are assigned a commitment strength, w, defining their willingness to lose (in waning), gain (for increasing) or both (in variable) commitment to an opinion. Such model has committed nodes that can stick to a single opinion for some time without losing their flexibility to change it in the long run. The traditional Naming Game corresponds to setting w at infinity. A change in commitment strength impacts the critical fraction of population necessary for a minority consensus. Increasing w lowers critical fraction for waning commitment but increases this fraction for increasing commitment. Further, we show that if different nodes have different values of w, higher standard deviation of w increases the critical fraction for waning commitment and decrease this fraction for increasing commitment.
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Project description:INTRODUCTION AND OBJECTIVES:Aim of this study is to determine the setting, causes, and the harm of medication errors (ME) which are notified by Primary Health Care. MATERIAL AND METHODS:Setting: Primary Care Regional Health Service of Madrid. 2016. DESIGN:Descriptive and cross-sectional study. PARTICIPANTS:All ME (1,839) which were notified by Primary Care Centres by notification system of safety incidents between January 1st 2016 and November 17th 2016. MAIN MEASUREMENTS:Setting, real harm, potential harm, and cause of error. These items were classified by one researcher. Concordance was checked with another researcher. RESULTS:Just under half (47%) (95% CI: 44.8%-49.3%) of ME occurred in Primary Care Centre, 26.5% (95% CI: 24.5%-28.6%) of ME were patient medication errors, and 27.5% (95% CI: 24.1%-30.8%) of ME were potential severe harm errors. Prescribing errors were the cause of most ME in Primary Care Centre [27.4% (95% CI: 24.4%-30.4%)]. Communication between patients and doctors were the cause of most patient medication errors [66% (95% CI: 61.8%-70.2%)]. Patient mistakes and forgetfulness were also causes of patient medication errors. CONCLUSIONS:Half of all mediation errors hppened at Primary Care Center while one quarter of them were patient medication errors. One quarter of all ME were potential severe harm errors. The main causes were prescribing errors, failure of communication between patients and doctors, and patient mistakes and forgetfulness. Prescribing aid systems, communication improvements and patients aids should be implemented.
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Project description:In order to gain a better understanding of aphasia one must consider the complex combinations of language impairments along with the pattern of paraphasias. Despite the fact that both deficits and paraphasias feature in diagnostic criteria, most research has focused only on the lesion correlates of language deficits, with minimal attention on the pattern of patients' paraphasias. In this study, we used a data-driven approach (principal component analysis - PCA) to fuse patient impairments and their pattern of errors into one unified model of chronic post-stroke aphasia. This model was subsequently mapped onto the patients' lesion profiles to generate the triangulation of language-cognitive impairments, naming errors and their neural correlates. Specifically, we established the pattern of co-occurrence between fifteen error types, which avoids focussing on a subset of errors or the use of experimenter-derived methods to combine across error types. We obtained five principal components underlying the patients' errors: omission errors; semantically-related responses; phonologically-related responses; dysfluent responses; and a combination of circumlocutions with mixed errors. In the second step, we aligned these paraphasia-related principal components with the patients' performance on a detailed language and cognitive assessment battery, utilising an additional PCA. This omnibus PCA revealed seven unique fused impairment-paraphasia factors: output phonology; semantics; phonological working memory; speech quanta; executive-cognitive skill; phonological (input) discrimination; and the production of circumlocution errors. In doing so we were able to resolve the complex relationships between error types and impairments. Some are relatively straightforward: circumlocution errors formed their own independent factor; there was a one-to-one mapping for phonological errors with expressive phonological abilities and for dysfluent errors with speech fluency. In contrast, omission-type errors loaded across both semantic and phonological working memory factors, whilst semantically-related errors had the most complex relationship by loading across four factors (phonological ability, speech quanta, executive-cognitive skills and circumlocution-type errors). Three components had unique lesion correlates: phonological working memory with the primary auditory region; semantics with the anterior temporal region; and fluency with the pre-central gyrus, converging with existing literature. In conclusion, the data-driven approach allowed derivation of the triangulation of deficits, error types and lesion correlates in post-stroke aphasia.
| S-EPMC5683039 | biostudies-literature
Project description:Diverse features of motor learning have been reported by numerous studies, but no single theoretical framework concurrently accounts for these features. Here, we propose a model for motor learning to explain these features in a unified way by extending a motor primitive framework. The model assumes that the recruitment pattern of motor primitives is determined by the predicted movement error of an upcoming movement (prospective error). To validate this idea, we perform a behavioural experiment to examine the model's novel prediction: after experiencing an environment in which the movement error is more easily predictable, subsequent motor learning should become faster. The experimental results support our prediction, suggesting that the prospective error might be encoded in the motor primitives. Furthermore, we demonstrate that this model has a strong explanatory power to reproduce a wide variety of motor-learning-related phenomena that have been separately explained by different computational models.
| S-EPMC4316743 | biostudies-other
Project description:Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the "wisdom of the crowds." Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., "funky jazz with saxophone," "spooky electronica," etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data.
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Project description:Language recovery following acute left hemisphere (LH) stroke is notoriously difficult to predict. Global language measures (e.g., overall aphasia severity) and gross lesion metrics (e.g., size) provide incomplete recovery predictions. In this study, we test the hypothesis that the types of naming errors patients produce, combined with dysfunctional brain tissue metrics, can provide additional insight into recovery following acute LH stroke. One hundred forty-eight individuals who were hospitalized with a new LH stroke completed clinical neuroimaging and assessments of naming and global language skills. A subset of participants again completed language testing at subacute, early (5-7 months post-stroke), and late (?11 months post-stroke) chronic phases. At each time point, we coded naming errors into four types (semantic, phonological, mixed and unrelated) and determined error type totals and proportions. Dysfunctional tissue measures included the percentage of damage to language network regions and hypoperfusion in vascular territories. A higher proportion of semantic errors was associated with better acute naming, but higher proportions of other error types was related to poorer accuracy. Naming and global language skills significantly improved over time , but naming error profiles did not change. Fewer acute unrelated errors and less damage to left angular gyrus resulted in optimal naming and language recovery by the final testing time point, yet patients with more acute errors and damage to left middle temporal gyrus demonstrated the greatest increases in language over time. These results illustrate that naming error profiles, particularly unrelated errors, add power to predictions of language recovery after stroke.
| S-EPMC7546715 | biostudies-literature
Project description:To determine whether the naming impairment in aphasia is influenced by error learning and whether error learning is related to type of retrieval strategy.Nine participants with aphasia and 10 neurologically intact controls named familiar proper noun concepts. When experiencing tip-of-the-tongue naming failure (TOT) in an initial TOT-elicitation phase, participants were instructed to adopt phonological or semantic self-cued retrieval strategies. In the error learning manipulation, items evoking TOT states during TOT elicitation were randomly assigned to a short or long time condition in which participants were encouraged to continue to try to retrieve the name for either 20 s (short interval) or 60 s (long). The incidence of TOT on the same items was measured on a post-test after 48 hr. Error learning was defined as a higher rate of recurrent TOTs (TOT at both TOT elicitation and post-test) for items assigned to the long (versus short) time condition.In the phonological condition, participants with aphasia showed error learning, whereas controls showed a pattern opposite to error learning. There was no evidence for error learning in the semantic condition for either group.Error learning is operative in aphasia but is dependent on the type of strategy used during naming failure.
| S-EPMC3781174 | biostudies-literature