“Maelzel’s Chess Player”: Science, Trickery and Spectatorship

Lee Eun Kim

doi:10.22505/jas.2020.52.3.01

Rousseau as a Chess Player

Scientific American ◽

10.1038/scientificamerican07061878-2090dsupp ◽

1878 ◽

Vol 6 (131supp) ◽

pp. 2090-2090

Keyword(s):

Chess Player

Download Full-text

,,Er hätte am liebsten ununterbrochen gespielt“. : Schach in Elias Canettis Roman ,,Die Blendung“

Zeitschrift für Germanistik ◽

10.3726/92167_649 ◽

2020 ◽

Vol 30 (3) ◽

pp. 649-661

Author(s):

Carl Philipp Roth

Keyword(s):

Social Context ◽

20Th Century ◽

Chess Player ◽

Early 20Th Century ◽

The Novel ◽

Strategic Skills

Abstract Der Beitrag untersucht die Bedeutung des Schachspiels in Elias Canettis Roman Die Blendung zum einen auf der Ebene der historischen und sozialen Kontexte, in denen der Schachspieler Siegfried Fischer im Wien des frühen 20. Jahrhunderts steht. Er fokussiert zum anderen die Bedeutung des Schachspiels auf Handlungsebene. Denn Siegfried Fischer – genannt Fischerle – überträgt seine strategischen Fähigkeiten im Schach auf die ihn umgebende Welt und bringt so Peter Kien ,Zug um Zug‘ um dessen Reichtum.The article examines the significance of chess in Elias Canetti’s novel Die Blendung in the historical and social context of early 20th century Vienna. It further focuses on the function of chess within the novel: The actor and chess player Siegfried Fischer – called Fischerle – transfers his strategic skills from chess to his surroundings, thus depriving Peter Kien of his wealth ‘move by move’.

Download Full-text

Deep learning investigation for chess player attention prediction using eye-tracking and game data

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications - ETRA '19 ◽

10.1145/3314111.3319827 ◽

2019 ◽

Author(s):

Justin Le Louedec ◽

Thomas Guntz ◽

James L. Crowley ◽

Dominique Vaufreydaz

Keyword(s):

Deep Learning ◽

Eye Tracking ◽

Chess Player

Download Full-text

Champ versus Chump: Viewing an Opponent’s Face Engages Attention but Not Reward Systems

Games ◽

10.3390/g12030062 ◽

2021 ◽

Vol 12 (3) ◽

pp. 62

Author(s):

Ralph S. Redden ◽

Greg A. Gagliardi ◽

Chad C. Williams ◽

Cameron D. Hassall ◽

Olave E. Krigolson

Keyword(s):

Reinforcement Learning ◽

Chess Player ◽

Reward Systems ◽

The Third ◽

Reward Positivity ◽

Eeg Data ◽

Attentional Engagement ◽

Level Of Difficulty ◽

Competitive Games ◽

Reinforcement Model

When we play competitive games, the opponents that we face act as predictors of the outcome of the game. For instance, if you are an average chess player and you face a Grandmaster, you anticipate a loss. Framed in a reinforcement learning perspective, our opponents can be thought of as predictors of rewards and punishments. The present study investigates whether facing an opponent would be processed as a reward or punishment depending on the level of difficulty the opponent poses. Participants played Rock, Paper, Scissors against three computer opponents while electroencephalographic (EEG) data was recorded. In a key manipulation, one opponent (HARD) was programmed to win most often, another (EASY) was made to lose most often, and the third (AVERAGE) had equiprobable outcomes of wins, losses, and ties. Through practice, participants learned to anticipate the relative challenge of a game based on the opponent they were facing that round. An analysis of our EEG data revealed that winning outcomes elicited a reward positivity relative to losing outcomes. Interestingly, our analysis of the predictive cues (i.e., the opponents’ faces) demonstrated that attentional engagement (P3a) was contextually sensitive to anticipated game difficulty. As such, our results for the predictive cue are contrary to what one might expect for a reinforcement model associated with predicted reward, but rather demonstrate that the neural response to the predictive cue was encoding the level of engagement with the opponent as opposed to value relative to the anticipated outcome.

Download Full-text