Interactions between rhythmic and feature predictions to create parallel time-content associations
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'The brain is inherently proactive, constantly predicting the when (moment) and what (content) of future input in order to optimize information processing. Previous research on such predictions has mainly studied the ‘when’ or ‘what’ domain separately, missing to investigate the potential integration of both types of predictive information. In the absence of such integration, temporal cues are assumed to enhance any upcoming content at the predicted moment in time (general temporal predictor). However, if the when and what prediction domain were integrated, a much more flexible neural mechanism may be proposed in which temporal-feature interactions would allow for the creation of multiple concurrent time-content predictions (parallel time-content predictor). Here, we used a temporal association paradigm in two experiments in which sound ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["EEG","Prediction","Rhythm","Temporal information"]} <|eot_id|>
 Here is the Description of a dataset. Description:'The brain is inherently proactive, constantly predicting the when (moment) and what (content) of future input in order to optimize information processing. Previous research on such predictions has mainly studied the ‘when’ or ‘what’ domain separately, missing to investigate the potential integration of both types of predictive information. In the absence of such integration, temporal cues are assumed to enhance any upcoming content at the predicted moment in time (general temporal predictor). However, if the when and what prediction domain were integrated, a much more flexible neural mechanism may be proposed in which temporal-feature interactions would allow for the creation of multiple concurrent time-content predictions (parallel time-content predictor). Here, we used a temporal association paradigm in two experiments in which sound ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
The brain is inherently proactive, constantly predicting the when (moment) and what (content) of future input in order to optimize information processing. Previous research on such predictions has mainly studied the ‘when’ or ‘what’ domain separately, missing to investigate the potential integration of both types of predictive information. In the absence of such integration, temporal cues are assumed to enhance any upcoming content at the predicted moment in time (general temporal predictor). However, if the when and what prediction domain were integrated, a much more flexible neural mechanism may be proposed in which temporal-feature interactions would allow for the creation of multiple concurrent time-content predictions (parallel time-content predictor). Here, we used a temporal association paradigm in two experiments in which sound 
EEG; Prediction; Rhythm; Temporal information