8. A method for generating a habituation-compensated library, comprising: receiving samples comprising temporal windows of token instances to which a user was exposed, wherein the temporal windows of token instances comprise a window comprising instantiations of first and second tokens that have overlapping instantiation periods; receiving data on previous instantiations of the first and second tokens, to which the user was exposed; receiving target values corresponding to the temporal windows of token instances; the target values, which are derived from values of a measurement channel of the user, represent affective responses of the user to the token instances from the temporal windows of token instances; wherein the affective responses are values comprising representations of values of the measurement channel of the user; training a machine learning-based user response model using data comprising: the samples, the data on previous instantiations of the first and second tokens, and the corresponding target values; and generating, based on the machine learning-based user response model, the habituation-compensated library that comprises for each token of the first and second tokens: a first expected affective response of the user to an instance of the token after a first number of previous exposures to instantiations of the token, and a second expected affective response of the user to an instance of the token after a second number, that is greater than the first number, of previous exposures to instantiations of the token; wherein for the first token, the first expected affective response is stronger than the second expected affective response, while for the second token, the first expected affective response is weaker than the second expected affective response.
8. A method for generating a habituation-compensated library, comprising: receiving samples comprising temporal windows of token instances to which a user was exposed, wherein the temporal windows of token instances comprise a window comprising instantiations of first and second tokens that have overlapping instantiation periods; receiving data on previous instantiations of the first and second tokens, to which the user was exposed; receiving target values corresponding to the temporal windows of token instances; the target values, which are derived from values of a measurement channel of the user, represent affective responses of the user to the token instances from the temporal windows of token instances; wherein the affective responses are values comprising representations of values of the measurement channel of the user; training a machine learning-based user response model using data comprising: the samples, the data on previous instantiations of the first and second tokens, and the corresponding target values; and generating, based on the machine learning-based user response model, the habituation-compensated library that comprises for each token of the first and second tokens: a first expected affective response of the user to an instance of the token after a first number of previous exposures to instantiations of the token, and a second expected affective response of the user to an instance of the token after a second number, that is greater than the first number, of previous exposures to instantiations of the token; wherein for the first token, the first expected affective response is stronger than the second expected affective response, while for the second token, the first expected affective response is weaker than the second expected affective response. 11. The method of claim 8 , wherein the step of training the machine learning-based user response model comprises training a neural network; and the habituation-compensated library comprises partial derivatives of output values of the neural network, with respect to input neurons that correspond to token instances.