Faithfulness-boost effect: Loyal teammate selection correlates with skill acquisition improvement in online games

The problem of skill acquisition is ubiquitous and fundamental to life. Most tasks in modern society involve the cooperation with other subjects. Notwithstanding its fundamental importance, teammate selection is commonly overlooked when studying learning. We exploit the virtually infinite repository of human behavior available in Internet to study a relevant topic in anthropological science: how grouping strategies may affect learning. We analyze the impact of team play strategies in skill acquisition using a turn-based game where players can participate individually or in teams. We unveil a subtle but strong effect in skill acquisition based on the way teams are formed and maintained during time. “Faithfulness-boost effect” provides a skill boost during the first games that would only be acquired after thousands of games. The tendency to play games in teams is associated with a long-run skill improvement while playing loyally with the same teammate significantly accelerates short-run skill acquisition.Fil: Landfried, Gustavo Andrés. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Slezak, Diego Fernández. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Mocskos, Esteban Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Ctro de Simulación Computacional P/aplicaciones Tecnologicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentin

Evaluation of LSA performance in Spanish using multiple corpus of text

Latent Semantic Analysis is a natural language processing tools that allows estimating semantic distance between terms. The success of LSA is mainly based on the training corpus choice, which have been studied principally in English. This study focuses on studying LSA with regional Spanish corpus and evaluate the performance by identifying synonyms. We found that performance was slightly better than chance, concordantly with previous results. Standard LSA method cannot dynamically increase the training corpus. By using classifiers we combined multiple LSA models and showed that the use of automatic classifiers increase the performance.Sociedad Argentina de Informática e Investigación Operativ

Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database

This summary presents the results obtained in our work, Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database.Sociedad Argentina de Informática e Investigación Operativ

Response Time Distributions in Rapid Chess: A Large-Scale Decision Making Experiment

Rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. In a chess game, players choose consecutively around 40 moves in a finite time budget. The goodness of each choice can be determined quantitatively since current chess algorithms estimate precisely the value of a position. Web-based chess produces vast amounts of data, millions of decisions per day, incommensurable with traditional psychological experiments. We generated a database of response times (RTs) and position value in rapid chess games. We measured robust emergent statistical observables: (1) RT distributions are long-tailed and show qualitatively distinct forms at different stages of the game, (2) RT of successive moves are highly correlated both for intra- and inter-player moves. These findings have theoretical implications since they deny two basic assumptions of sequential decision making algorithms: RTs are not stationary and can not be generated by a state-function. Our results also have practical implications. First, we characterized the capacity of blunders and score fluctuations to predict a player strength, which is yet an open problem in chess softwares. Second, we show that the winning likelihood can be reliably estimated from a weighted combination of remaining times and position evaluation

Human and computer estimations of predictability of words on written language

When we read printed text, we continuously predict the follow words in order to integrate information and direct future eye movements to forthcoming words. Thus the Predictability has become one the most important variables when explaining human behavior and information processing during reading. In this study we present results of word predictability in long Spanish texts, estimated from human responses in a massive web-based task. We used Latent Semantic Analysis (LSA) as a way to estimate human-based predictability values computationally. We validated the human estimation of predictability with local and global properties of the text, and we showed that LSA-distance on adequate timescale captures some semantic aspects of the prediction.Sociedad Argentina de Informática e Investigación Operativ

Human and computer estimations of predictability of words on written language

When we read printed text, we continuously predict the follow words in order to integrate information and direct future eye movements to forthcoming words. Thus the Predictability has become one the most important variables when explaining human behavior and information processing during reading. In this study we present results of word predictability in long Spanish texts, estimated from human responses in a massive web-based task. We used Latent Semantic Analysis (LSA) as a way to estimate human-based predictability values computationally. We validated the human estimation of predictability with local and global properties of the text, and we showed that LSA-distance on adequate timescale captures some semantic aspects of the prediction.Sociedad Argentina de Informática e Investigación Operativ

Human and computer estimations of predictability of words on written language

When we read printed text, we continuously predict the follow words in order to integrate information and direct future eye movements to forthcoming words. Thus the Predictability has become one the most important variables when explaining human behavior and information processing during reading. In this study we present results of word predictability in long Spanish texts, estimated from human responses in a massive web-based task. We used Latent Semantic Analysis (LSA) as a way to estimate human-based predictability values computationally. We validated the human estimation of predictability with local and global properties of the text, and we showed that LSA-distance on adequate timescale captures some semantic aspects of the prediction.Sociedad Argentina de Informática e Investigación Operativ

Mate Marote: a BigData platform for massive scale educational interventions

In this paper we present Mate Marote, a web platform for massive scale educational interventions. We focus on the scaling requirements needed on these kind of deployments. We show the designed architecture, how these decisions solve the imposed requirements and the implementation. To test this development, we performed a small pilot intervention where the whole system was evaluated. We conclude that Mate Marote is ready for production deployment and enabled to middleto- massive scale interventions. For this purpose, we have deployed this platform in CEIBAL program at Uruguay with more than 100K potential users.Sociedad Argentina de Informática e Investigación Operativ

Mate Marote: a BigData platform for massive scale educational interventions

In this paper we present Mate Marote, a web platform for massive scale educational interventions. We focus on the scaling requirements needed on these kind of deployments. We show the designed architecture, how these decisions solve the imposed requirements and the implementation. To test this development, we performed a small pilot intervention where the whole system was evaluated. We conclude that Mate Marote is ready for production deployment and enabled to middleto- massive scale interventions. For this purpose, we have deployed this platform in CEIBAL program at Uruguay with more than 100K potential users.Sociedad Argentina de Informática e Investigación Operativ

Rapid chess: A massive-scale experiment

The proliferation of chess servers on the Internet has turned active chess, blitz and lightning, into a vast cognitive phenomenon involving engaged participants. Here we use this large database of human decision making (rapid chess) as a privileged window to understand human cognition. FICS (Free Internet Chess Server), http://www.freechess.org/ is a free ICS-compatible server for playing chess games through Internet, with more than 300.000 registered users. Using this available chess server in the Internet, we constructed a massive decision-making database. This data includes thousands of million moves of chess games, with the estimated time of each one of them. In order to evaluate the goodness of moves, we used Crafty (an open-source chess engine) to analyse the score of the move. This process is compute expensive, so we parallelized the analysis on a Beowulf cluster. We studied the structure of the time players take to make a move during a game, and using parallelization we were able to analyse a huge amount of moves obtaining a quantification of the quality of the decision made in millions of instances. This approach allowed us to identify a number of statistical fingerprints that uniquely characterize the emergent structure of the game.Sociedad Argentina de Informática e Investigación Operativ