1. A method for predicting affective response of a user to a stream of token instances, comprising: receiving the stream of token instances; partitioning the stream of token instances into consecutive temporal windows of token instances; wherein the temporal windows of token instances are ordered according to their start time; predicting affective response of the user to a first temporal window of token instances by providing a machine learning-based predictor with input data comprising: a vector of values derived from the first temporal window of token instances, and an initial state value derived from a value of a measurement channel of the user taken at time corresponding to start of the first temporal window of token instances; and for each successive temporal window of token instances after the first temporal window of token instances: predicting affective response of the user to the successive temporal window of token instances by providing the machine learning-based predictor with input data comprising: a vector of values derived from the successive temporal window of token instances, and an initial state value derived from prediction of affective response of the user to a temporal window of token instances preceding the successive temporal window of token instances.
1. A method for predicting affective response of a user to a stream of token instances, comprising: receiving the stream of token instances; partitioning the stream of token instances into consecutive temporal windows of token instances; wherein the temporal windows of token instances are ordered according to their start time; predicting affective response of the user to a first temporal window of token instances by providing a machine learning-based predictor with input data comprising: a vector of values derived from the first temporal window of token instances, and an initial state value derived from a value of a measurement channel of the user taken at time corresponding to start of the first temporal window of token instances; and for each successive temporal window of token instances after the first temporal window of token instances: predicting affective response of the user to the successive temporal window of token instances by providing the machine learning-based predictor with input data comprising: a vector of values derived from the successive temporal window of token instances, and an initial state value derived from prediction of affective response of the user to a temporal window of token instances preceding the successive temporal window of token instances. 4. The method of claim 1 , wherein input data provided to the machine learning-based predictor further comprises information related to previous instantiations of token instances in order for the predictor to adjust prediction to account for habituation.