Level-K Classification from EEG Signals Using Transfer Learning

Abstract

Tacit coordination games are games in which communication between the players is not allowed or not possible. In these games, the more salient solutions, that are often perceived as more prominent, are referred to as focal points. The level-k model states that players’ decisions in tacit coordination games are a consequence of applying different decision rules at different depths of reasoning (level-k). A player at Lk=0 will randomly pick a solution, whereas a Lk≥1 player will apply their strategy based on their beliefs regarding the actions of the other players. The goal of this study was to examine, for the first time, the neural correlates of different reasoning levels in tacit coordination games. To that end, we have designed a combined behavioral-electrophysiological study with 3 different conditions, each resembling a different depth reasoning state: (1) resting state, (2) picking, and (3) coordination. By utilizing transfer learning and deep learning, we were able to achieve a precision of almost 100% (99.49%) for the resting-state condition, while for the picking and coordination conditions, the precision was 69.53% and 72.44%, respectively. The application of these findings and related future research options are discussed.

An Electrophysiological Model for Assessing Cognitive Load in Tacit Coordination Games

“…Our findings might suggest that tacit coordination in comparison to picking relies more on system 2 than on system 1. This finding is compatible with the level-k theory, which defines picking as level k = 0 and coordination as level k > 0 [59][60][61][62]. In previous studies, we have shown that considerable variability exits in behavioral coordination ability exists between players [23][24][25]30,[63][64][65][66].…”

Modeling and predicting individual tacit coordination ability

“…3 C, 6 B). Noteworthily, the suggested connection between our findings and level k theory should be further explored especially in view of findings indicating inconsistencies in the level k of players, even in the same game, which imply that the depth of reasoning is stochastic in nature [ 41 , 42 ].…”

“…Consequently, we expect the model we have constructed to be applicable to other contexts where focal points are based on spatial cues, e.g., “Bargaining Table” [ 14 , 35 , 40 , 48 ] and “Moving Discs” [ 18 ]. However, in other cases, where focal points are based on non-spatial features (e.g., the “word Selection” task based on semantic meaning [ 18 , 24 , 42 , 49 ]) our model should be modified accordingly. For example, by updating the number of Gaussians which correspond to different strategic profiles extracted from the game.…”

Predicting Tacit Coordination Success Using Electroencephalogram Trajectories: The Impact of Task Difficulty

“…Until now, the research combining tacit coordination games with electrophysiological measures was mainly based on spectral analysis (e.g., [3][4][5][6]). Power spectrum analysis is an important part of EEG analysis and enables the extraction of key discriminative features associated with different cognitive processes.…”

“…In one of our recent studies [3], we designed a method that predicts the class label of coherence graph patterns associated with different cognitive states extracted out of multi-channel EEG epochs. Furthermore, in our subsequent study [8], our proposed algorithm found an automated solution such that by analyzing spatial data using random walks [9][10][11], we were able to dramatically improve the classification of the task-related classes associated with precision and recall by ~89% and ~90%, respectively.…”