Affective response predictor for a stream of stimuli

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.

2. The method of claim 1 , further comprising forwarding a predicted affective response of the user to the stream of token instances; wherein the predicted affective response comprises a value derived from a prediction of affective response of the user to a temporal window of token instances with a start time that occurs after the start time of the first temporal window of token instances.

3. The method of claim 1 , wherein predictions of affective response of the user are expressed as at least one of: values of a measurement channel of the user, and emotional responses.

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.

5. The method of claim 1 , wherein input data provided to the machine learning-based predictor further comprises a baseline value for the user of a measurement channel of the user.

6. The method of claim 1 , wherein input data provided to the machine learning-based predictor further comprises values corresponding to a situation of the user while the user is exposed to the token instances.

7. The method of claim 1 , wherein the machine learning-based predictor utilizes a neural network.

8. The method of claim 1 , wherein the machine learning-based predictor utilizes a regression model.

9. The method of claim 1 , wherein the machine learning-based predictor utilizes a maximum entropy model classifier.

10. The method of claim 1 , wherein the machine learning-based predictor utilizes a Hidden Markov Model.

11. A system configured to predict an affective response of a user to a stream of token instances, comprising:

a sample generator configured to receive the stream of token instances, and to partition 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; and

a sequential machine learning-based predictor configured to receive input data comprising: a vector of values derived from a 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 predict, based on the input data, an affective response of the user to the first temporal window of token instances; and

for each successive temporal window of token instances after the first temporal window of token instances, the sequential machine learning-based predictor is further configured to: receive 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; and to predict, based on the input data, an affective response of the user to the successive temporal window of token instances.

12. The system of claim 11 , wherein the sequential machine learning-based predictor is further configured to forward a predicted affective response of the user to the stream of token instances; wherein the predicted affective response comprises a value derived from a prediction of affective response of the user to a temporal window of token instances with a start time that occurs after the start time of the first temporal window of token instances.

13. The system of claim 11 , wherein predictions of affective response of the user are expressed as at least one of: values of a measurement channel of the user, and emotional responses.

14. The system of claim 11 , wherein the input data provided to the sequential 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.

15. The system of claim 11 , wherein the input data provided to the sequential machine learning-based predictor further comprises a baseline value for the user of a measurement channel of the user.

16. The system of claim 11 , wherein the input data provided to the predictor further comprises values corresponding to a situation of the user while the user is exposed to the token instances.

17. The system of claim 11 , wherein the sequential machine learning-based predictor utilizes a neural network.

18. The system of claim 11 , wherein the sequential machine learning-based predictor utilizes a regression model.

19. The system of claim 11 , wherein the sequential machine learning-based predictor utilizes a maximum entropy model classifier.

20. The system of claim 11 , wherein the sequential machine learning-based predictor utilizes a Hidden Markov Model.