About this Research Topic
Originally, modalities were processed to form specific indices for the emotional and cognitive assessment, following a knowledge-driven process. It is worth noting that the relationship between each index and the related inner state is mainly correlational; additionally, most of the indices lack both a stable measurement scale and a set of reliable benchmark values. These aspects negatively impact their predictive performances. Moreover, high-level behaviours cannot be directly predicted, but only inferred from the measured low-level states. To overcome these limitations, a data-driven approach has been recently proposed. Accordingly, the modalities are transformed into a set of features that are fed into a Machine-Learning (ML) or Deep Learning (DL) model, whose parameters are tuned to maximise the accuracy of the desired outcome (that can be even a high-level behaviour). More importantly, the prediction performance is not bounded by the actual knowledge but can be increased with the amount of available data, making the models flexible and adaptive.
This research topic aims to collect theoretical and experimental papers on the use of ML/DL techniques with bioelectrical (e.g., EEG, ECG, SC, EMG) and biometric/behavioural (e.g., facial expressions, eye tracking, speech features) features, with applications to the Consumer Neuroscience and Neuromarketing fields. Potential topics include, but are not limited to:
• Original ML/DL models for emotion recognition, with applications in the Consumer Neuroscience or Neuromarketing fields
• Original ML/DL models for mental state estimation, with applications in the Consumer Neuroscience or Neuromarketing fields
• Evaluations of pre-existing ML/DL models for emotion recognition and mental state estimation, with applications in the Consumer Neuroscience or Neuromarketing fields
• Reviews and metanalysis on the use of ML/DL methods in the Consumer Neuroscience or Neuromarketing fields
• Original datasets to train/benchmark ML/DL models for emotion recognition and mental estimation
• Experimental studies in Consumer Neuroscience or Neuromarketing fields using ML/DL techniques
Keywords: Consumer neuroscience, Neuromarketing; Consumer Behaviour, Artificial Intelligence, Machine Learning, Deep Learning, Affect Detection, Mental state estimation, EEG, Skin-Conductance, Eye-Tracker
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.