Methods for training emotional response predictors utilizing attention in visual objects

A method for training an emotional response predictor that utilizes eye tracking, comprising:

• receiving temporal windows of token instances to which a user was exposed;
• wherein each temporal window of token instances comprises at least two token instances that have overlapping instantiation periods, with each of the at least two token instances representing a different visual object;
• receiving, for each temporal window of token instances, eye tracking data of the user, which is indicative of objects the user gazed at while viewing content represented by the temporal window of token instances, and determining attention levels in at the least two token instances from the temporal window of token instances that have overlapping instantiation periods and represent different visual objects.
• generating samples corresponding to the temporal windows of token instances;
• wherein for each temporal window of token instances, values in its corresponding sample are indicative of weights of the token instances belonging to the temporal window of token instances;
• and wherein values in the sample that correspond to the at least two token instances of the temporal window of token instances, are further weighted according to their corresponding attention levels;
• receiving target values corresponding to the temporal windows of token instances;
• wherein each target value corresponding to the temporal window of token instances represents an affective response of the user to being exposed to the token instances in the temporal window of token instances;
• and
• training the emotional response predictor utilizing a machine learning-based training algorithm that uses the samples and the target values as training data.

A method for training an emotional response predictor for a user by utilizing eye tracking data of other users, comprising:

• receiving temporal windows of token instances to which a user was exposed;
• wherein each temporal window of token instances comprises at least two token instances that have overlapping instantiation periods, with each of the at least two token instances representing a different visual object;
• receiving, for each temporal window of token instances, eye tracking data of the other users, which is indicative of objects the other users gazed at while viewing content represented by the temporal window of token instances, and determining attention levels in at the least two token instances from the temporal window of token instances that have overlapping instantiation periods and represent different visual objects.
• generating samples corresponding to the temporal windows of token instances;
• wherein for each temporal window of token instances, values in its corresponding sample are indicative of weights of the token instances belonging to the temporal window of token instances;
• and wherein values in the sample that correspond to the at least two token instances of the temporal window of token instances, are further weighted according to their corresponding attention levels;
• receiving target values corresponding to the temporal windows of token instances;
• wherein each target value corresponding to the temporal window of token instances represents an affective response of the user to being exposed to the token instances in the temporal window of token instances;
• and
• training the emotional response predictor utilizing a machine learning-based training algorithm that uses the samples and the target values as training data.