An ERP Research On Mode-free And Model-based Strategies In Football Dynamic Decision-making

Dynamic Decision Making(DDM)refers to a series of interrelated decisions which the current decision is affected by the results of the previous decisions and the options of the subsequent decisions.As an important part of decision-making research,dynamic decision-making has been widely studied in many fields.Although athletes’ decision-making process is continuous and dynamic,previous researches on sports decision-making are mostly limited to examining the athletes’ one-time and static decision-making performance.Therefore,it is of great significance to introduce the dynamic field into the sports area.Based on the dynamic decision theory,this research used the classic Markov two-step decision task paradigm,the football passing decision task and ERP technology to explore the strategic selection of football passing dynamic decision-making and its neural mechanism.Moreover,it revealed the relationship between athletes’ dynamic decision-making selections and their cognitive control types.Two studies was conducted in this research.Study 1 used a 2(level of football ability: Football player,ordinary university student)×2(tasks type of decision-making:Markov two-step decision task,Football dynamic decision task)mixed research design to explore the difference of the decision-making strategies between football players and ordinary college students in both classic dynamic decision-making tasks and the football passing dynamic decision-making tasks.Their corresponding EEG analysis was conducted simultaneously.17 football players and 17 ordinary college students were invited to participate in this study.Tasks and levels of sports have no significant different on model-free,but they have a significant interaction on model-based.The decision-making accuracy of football task has a significant difference between different level of participants,and decision-making strategies selection related positively with decision-making accuracy.Tasks and electrode points have a significant difference on P300.Electrode points have a significant difference on FRN,and there is an interaction between tasks and electrode points on FRN.Study 2 used a 2(level of football ability: Football player,ordinary university student)× 2(tasks type of decision-making: Markov two-step decision task,football dynamic decision task)×2(types of cognitive control: active cognitive control,reactive cognitive control)mixed research design to explore the relationship between the choice of dynamic decision strategies under different cognitive control types.30 football players and 30 ordinary college students were invited to participate in this study.There is no significant relationship between cognitive control types and dynamic decision strategies.Although previous studies have shown that the two have a high degree of overlap in physiological mechanisms and theoretical systems,there is no clear relationship between the two was found in this study.A possible reason for this phenomenon may be due to the difference between the characteristics of football dynamic decision-making and ordinary dynamic decision-making.At present,there is little research in this field,and further research should investigate more in this area.The conclusions of this research are as follows:(1)Football players prefer modelfree decision-making strategy in football dynamic decision-making task,which will lead to better results than using model-based strategy.(2)P300 reflects that football players are more sensitive to unpredictable events in football dynamic decision-making task,which indicates that the model-free decision-making strategy is more dominant than model-based strategy for football players;additionally;FRN reflects that football players are more sensitive to awards than ordinary students in football dynamic decision-making task,which demonstrates that football players are more likely to use model-based strategy than ordinary college students.(3)There is no evidence shows that cognitive control types can predict dynamic decision-making strategic selections.