scholarly journals Complexity Reduction in the Negotiation of New Lexical Conventions

2018 ◽  
Author(s):  
William Schueller ◽  
Vittorio Loreto ◽  
Pierre-Yves Oudeyer
Keyword(s):  
Ad Hoc ◽  
Short Term Memory ◽  
Fine Tuning ◽  
Global State ◽  
High Complexity ◽  
Short Term ◽  
New Words ◽  
Efficient Control ◽  
New Strategy ◽  
Multi Agent

In the process of collectively inventing new words for new con-cepts in a population, conflicts can quickly become numerous,in the form of synonymy and homonymy. Remembering all ofthem could cost too much memory, and remembering too fewmay slow down the overall process. Is there an efficient be-havior that could help balance the two? The Naming Game isa multi-agent computational model for the emergence of lan-guage, focusing on the negotiation of new lexical conventions,where a common lexicon self-organizes but going through aphase of high complexity. Previous work has been done onthe control of complexity growth in this particular model, byallowing agents to actively choose what they talk about. How-ever, those strategies were relying on ad hoc heuristics highlydependent on fine-tuning of parameters. We define here a newprincipled measure and a new strategy, based on the beliefsof each agent on the global state of the population. The mea-sure does not rely on heavy computation, and is cognitivelyplausible. The new strategy yields an efficient control of com-plexity growth, along with a faster agreement process. Also,we show that short-term memory is enough to build relevantbeliefs about the global lexicon.

scholarly journals Energy-Efficient Control of Mobile Processors Based on Long Short-Term Memory

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 80552-80560 ◽  
Author(s):  
Jaehwan Lee ◽  
Sanghyuck Nam ◽  
Sangoh Park
Keyword(s):  
Energy Efficient ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Efficient Control ◽  
Long Short Term Memory

scholarly journals Universal Response-Adaptation Relation in Bacterial Chemotaxis

2014 ◽  
Vol 197 (2) ◽  
pp. 307-313 ◽  
Author(s):  
Anna K. Krembel ◽  
Silke Neumann ◽  
Victor Sourjik
Keyword(s):  
Short Term Memory ◽  
Bacterial Chemotaxis ◽  
Fine Tuning ◽  
Universal Relation ◽  
Short Term ◽  
Content Type ◽  
Cooperative Interactions ◽  
Memory Time ◽  
Chemotaxis Receptors ◽  
Activity Dependent

The bacterial strategy of chemotaxis relies on temporal comparisons of chemical concentrations, where the probability of maintaining the current direction of swimming is modulated by changes in stimulation experienced during the recent past. A short-term memory required for such comparisons is provided by the adaptation system, which operates through the activity-dependent methylation of chemotaxis receptors. Previous theoretical studies have suggested that efficient navigation in gradients requires a well-defined adaptation rate, because the memory time scale needs to match the duration of straight runs made by bacteria. Here we demonstrate that the chemotaxis pathway ofEscherichia colidoes indeed exhibit a universal relation between the response magnitude and adaptation time which does not depend on the type of chemical ligand. Our results suggest that this alignment of adaptation rates for different ligands is achieved through cooperative interactions among chemoreceptors rather than through fine-tuning of methylation rates for individual receptors. This observation illustrates a yet-unrecognized function of receptor clustering in bacterial chemotaxis.

Components of Short-Term Memory and Their Relation to Language Processing

2005 ◽  
Vol 14 (4) ◽  
pp. 204-208 ◽  
Author(s):  
Randi C. Martin
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
Semantic Information ◽  
Short Term Memory ◽  
Verbal Working Memory ◽  
Parietal Region ◽  
Short Term ◽  
Term Memory ◽  
New Words

Verbal working memory consists of separable capacities for the retention of phonological and semantic information. Within the phonological domain, there are independent capacities for retaining input-phonological codes and output-phonological codes. The input-phonological capacity does not appear to be critical for language comprehension but is involved in verbatim repetition and long-term learning of new words. The semantic capacity is critical for both comprehension and production and for the learning of new semantic information. Different neural structures appear to underlie these capacities, with a left-parietal region involved in input-phonological retention and a left-frontal region involved in semantic retention.

scholarly journals An explicit, generative, and automatic syntactic representation of multi-digit numbers

2021 ◽  
Author(s):  
Dror Dotan ◽  
Nadin Brutman
Keyword(s):  
Ad Hoc ◽  
Short Term Memory ◽  
Syntactic Structure ◽  
Short Term ◽  
Generative Process ◽  
Size Limit ◽  
Number Words ◽  
Chunk Size ◽  
Syntactic Representation ◽  
External Cues

Representing the base-10 structure of numbers is a challenging cognitively ability, unique to humans, but it is yet unknown how precisely this is done. Here, we examined whether and how literate adults represent a number’s full syntactic structure. In 5 experiments, the participants repeated sequences of 6-7 number words, and we systematically varied the order of words within the sequence. Repetition was more accurate when the sequence was grammatical (e.g., ninety-seven) than when it was not (seven-ninety). The performance monotonously improved for sequences with increasingly longer grammatical segments, up to a limit of ~4 words per segment, irrespectively of the number of digits, and worsened thereafter. We conclude that at least for numbers up to 6 digits long, participants represented the number’s full syntactic structure and used it to merge number words into chunks in short-term memory. Short chunks improved memorization, but oversized chunks disrupted memorization. The existence of a chunk size limit suggests that the chunks are not memorized templates, whose size limit is not expected to be so low. Rather, they are created ad-hoc by a generative process, such as the hierarchical syntactic representation hypothesized in Michael McCloskey’s number-processing model. Chunking occurred even when it disrupted performance, and even when external cues for chunking were controlled for or were removed; we conclude that the above generative process operates automatically rather than voluntarily.

An Agent-Based Model for the Role of Short-Term Memory Enhancement in the Emergence of Grammatical Agreement

2018 ◽  
Vol 24 (2) ◽  
pp. 119-127
Author(s):  
Javier Vera
Keyword(s):  
Short Term Memory ◽  
Language Evolution ◽  
Language Game ◽  
Memory Capacity ◽  
Language Games ◽  
Short Term ◽  
Memory Enhancement ◽  
Term Memory ◽  
Grammatical Agreement ◽  
Multi Agent

What is the influence of short-term memory enhancement on the emergence of grammatical agreement systems in multi-agent language games? Agreement systems suppose that at least two words share some features with each other, such as gender, number, or case. Previous work, within the multi-agent language-game framework, has recently proposed models stressing the hypothesis that the emergence of a grammatical agreement system arises from the minimization of semantic ambiguity. On the other hand, neurobiological evidence argues for the hypothesis that language evolution has mainly related to an increasing of short-term memory capacity, which has allowed the online manipulation of words and meanings participating particularly in grammatical agreement systems. Here, the main aim is to propose a multi-agent language game for the emergence of a grammatical agreement system, under measurable long-range relations depending on the short-term memory capacity. Computer simulations, based on a parameter that measures the amount of short-term memory capacity, suggest that agreement marker systems arise in a population of agents equipped at least with a critical short-term memory capacity.

scholarly journals Beat-to-Beat Continuous Blood Pressure Estimation Using Bidirectional Long Short-Term Memory Network

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 96
Author(s):  
Dongseok Lee ◽  
Hyunbin Kwon ◽  
Dongyeon Son ◽  
Heesang Eom ◽  
Cheolsoo Park ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Short Term Memory ◽  
Fine Tuning ◽  
Estimation Model ◽  
Short Term ◽  
Term Memory ◽  
Training Time ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Continuous Blood Pressure

Continuous blood pressure (BP) monitoring is important for patients with hypertension. However, BP measurement with a cuff may be cumbersome for the patient. To overcome this limitation, various studies have suggested cuffless BP estimation models using deep learning algorithms. A generalized model should be considered to decrease the training time, and the model reproducibility should be taken into account in multi-day scenarios. In this study, a BP estimation model with a bidirectional long short-term memory network is proposed. The features are extracted from the electrocardiogram, photoplethysmogram, and ballistocardiogram. The leave-one-subject-out (LOSO) method is incorporated to generalize the model and fine-tuning is applied. The model was evaluated using one-day and multi-day tests. The proposed model achieved a mean absolute error (MAE) of 2.56 and 2.05 mmHg for the systolic and diastolic BP (SBP and DBP), respectively, in the one-day test. Moreover, the results demonstrated that the LOSO method with fine-tuning was more compatible in the multi-day test. The MAE values of the model were 5.82 and 5.24 mmHg for the SBP and DBP, respectively.

scholarly journals Adaptive Semiparametric Language Models

2021 ◽  
Vol 9 ◽  
pp. 362-373
Author(s):  
Dani Yogatama ◽  
Cyprien de Masson d’Autume ◽  
Lingpeng Kong
Keyword(s):  
Ad Hoc ◽  
Nearest Neighbor ◽  
Short Term Memory ◽  
Language Model ◽  
Language Models ◽  
Local Context ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory

Abstract We present a language model that combines a large parametric neural network (i.e., a transformer) with a non-parametric episodic memory component in an integrated architecture. Our model uses extended short-term context by caching local hidden states—similar to transformer-XL—and global long-term memory by retrieving a set of nearest neighbor tokens at each timestep. We design a gating function to adaptively combine multiple information sources to make a prediction. This mechanism allows the model to use either local context, short-term memory, or long-term memory (or any combination of them) on an ad hoc basis depending on the context. Experiments on word-based and character-based language modeling datasets demonstrate the efficacy of our proposed method compared to strong baselines.

Contribution of the Phonological Loop to Phonological Learning of New Vocabulary

1996 ◽  
Vol 82 (3) ◽  
pp. 769-770 ◽  
Author(s):  
Costanza Papagno
Keyword(s):  
Short Term Memory ◽  
Human Memory ◽  
Phonological Loop ◽  
Short Term ◽  
Specific Component ◽  
Term Memory ◽  
New Words ◽  
Substantial Role ◽  
Phonological Learning

A specific component of human memory, the phonological short-term memory, plays a substantial role in the acquisition of new words. Both the short-term store and the rehearsal components of the system appear to be involved.

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