Abstract: Described herein are methods for training a predictor of a user's emotional response to stimuli (e.g., digital content). In order to more accurately learn the nature of the emotional response of the user to the stimuli, in some embodiments, the training of the predictor involves collection of attention level data that indicates to which objects the user paid attention. The attention level data may be utilized to weight token instances representing visual objects from the stimuli. Such a weighting can help to train the emotional response predictor to better determine which objects influence the user's affective response and/or the extent of their influence on the user's affective response. In different embodiments, attention level information may come from different sources, such as eye tracking data of the user, and a model for predicting an interest level of the user in various visual objects.

Abstract: Generating a situation-dependent library comprising a user's expected response to tokens representing stimuli that influence the user's affective state, including: receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods and are spread over a long period of time that spans different situations; wherein at least one token is expected to elicit from the user a noticeably different affective response in the different situations; receiving target values corresponding to the temporal windows of token instances; the target values represent the user's responses to the token instances from the temporal windows of token instances; training a machine learning-based user response model using the samples and the corresponding target values; and analyzing the machine learning-based user response model to generate the situation-dependent library comprising the user's expected response to tokens, which accounts for the

Abstract: A data structure stored in memory including: token instances representing stimuli that influence a user's affective state; the token instances are spread over a long period of time that spans different situations, and a plurality of the token instances have overlapping instantiation periods; data representing levels of user attention in some of the token instances used by an application program to improve the accuracy of a machine learning based affective response model for the user; annotations representing emotional states of the user; the annotations are spread over a long period of time that spans different situations; and linkage information between the token instances, the data representing levels of user attention, and the annotations.

Abstract: Described herein are methods for identifying situations. The methods receive samples, each comprising a temporal window of token instances to which a user was exposed and an affective response annotation. One embodiment clusters the samples into a plurality of clusters utilizing a distance function that computes a distance between a pair comprising first and second samples. Another embodiment utilizes an Expectation-Maximization approach to assign situation identifiers. And still another embodiment involves training, utilizing the samples, a machine learning-based classifier to assign situation identifiers.

Abstract: Described herein are systems for identifying situations. The system receive samples, each comprising a temporal window of token instances to which a user was exposed and an affective response annotation. One embodiment uses a clustering algorithm to cluster the samples into a plurality of clusters utilizing a distance function that computes a distance between a pair comprising first and second samples. Another embodiment utilizes an Expectation-Maximization approach to assign situation identifiers. And another embodiment involves training, utilizing the samples, a machine learning-based classifier to assign situation identifiers.

Abstract: Computing token-dependent affective response baseline levels for a user involves: receiving a certain temporal window of token instances, receiving from a database affective response annotations corresponding to temporal windows of token instances for which a difference from the certain temporal window of token instances is below a threshold, and computing a baseline level based on the affective response annotations. Wherein the baseline level includes a value that represents at least one of: an emotional response, and a value of a user measurement channel.

Abstract: Generating a habituation-compensated library comprising a user's expected response to tokens representing stimuli that influence the user's affective state, the method comprising: receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods; the samples further comprise data on previous instantiations of at least one of the token instances from the temporal windows; receiving target values corresponding to the temporal windows of token instances; the target values represent the user's response to the token instances from the temporal windows of token instances; training a machine learning-based user response model using the samples, the data on previous instantiations, and the corresponding target values; and analyzing the machine learning-based user response model to generate the habituation-compensated library, which accounts for the influence of the user's previous exposure to tokens.

Abstract: Computing a situation-dependent affective response baseline levels for a user involves: receiving an identifier of a situation, receiving affective response indicator values from a database corresponding to time periods in which the user was in the situation, and computing the situation-dependent baseline level corresponding to the situation based on data comprising the affective response indicator values.

Abstract: Predicting a user's response to a stream of token instances, including: receiving a stream of token instances; partitioning the stream of token instances into consecutive temporal windows of token instances; predicting response of the user to temporal windows of token instances; predicting response of the user to a certain temporal window of token instances; and forwarding the prediction of the user to the stream of token instances.

Abstract: Described herein are methods for training a machine learning-based predictor of affective response to stimuli. The methods involve receiving samples comprising temporal windows of token instances to which a user was exposed, and target values representing affective response annotations of the user in response to the temporal windows of token instances. This data is used for the training of the predictor along with values indicative of the number of the token instances in the temporal windows of token instances, which are used to compensate for non-linear effects resulting from saturation of the user.

Abstract: Generating a situation-dependent library comprising a user's expected response to tokens representing stimuli that influence the user's affective state, including: receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods and are spread over a long period of time that spans different situations; wherein at least one token is expected to elicit from the user a noticeably different affective response in the different situations; receiving target values corresponding to the temporal windows of token instances; the target values represent the user's responses to the token instances from the temporal windows of token instances; training a machine learning-based user response model using the samples and the corresponding target values; and analyzing the machine learning-based user response model to generate the situation-dependent library comprising the user's expected response to tokens, which accounts for the

Abstract: Methods for detecting and validating an estimated situation using a situation-dependent predictor of a user response to token instances, including: receiving a temporal window of token instances and a putative situation for a user; predicting an expected response of the user to being exposed to the temporal window of token instances; receiving a value of a measurement channel of the user taken during exposure of the user to the temporal window of token instances; identifying that difference between the received value of the user measurement channel and the predicted expected response of the user is above a predefined threshold; and indicating that the putative situation is wrong.

Abstract: Predicting a user's response to a stream of token instances, including: receiving a stream of token instances; partitioning the stream of token instances into consecutive temporal windows of token instances; predicting response of the user to temporal windows of token instances; predicting response of the user to a certain temporal window of token instances; and forwarding the prediction of the user to the stream of token instances.

Abstract: A data structure stored in memory including: token instances representing stimuli that influence a user's affective state; the token instances are spread over a long period of time that spans different situations, and a plurality of the token instances have overlapping instantiation periods; data representing levels of user attention in some of the token instances used by an application program to improve the accuracy of a machine learning based affective response model for the user; annotations representing emotional states of the user; the annotations are spread over a long period of time that spans different situations; and linkage information between the token instances, the data representing levels of user attention, and the annotations.

Abstract: Calculating a situation-dependent baseline value for a user response to token instances representing stimuli that influence the user's affective state, utilizing large time windows and rapid adjustments to changing situations, including: accessing a database storing annotations representing the user's response to token instances originating from multiple distinct token sources; calculating a first situation-dependent baseline value by weighting annotations retrieved from the database and associated with a first situation identifier, which are spread over a long period of time ‘T’; calculating a second situation-dependent baseline value by weighting annotations retrieved from the database and associated with a second situation identifier; wherein the difference between the first and second situation-dependent baseline values is significant, and the method rapidly adjusts to the situation change by exhibiting an extremely shorter transient time between the first and the second situation-dependent baselines than

Abstract: Creating a machine learning-based habituation-compensated predictor of a user's response to token instances representing stimuli that influence the user's affective state, comprising: receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods; the samples further comprise data on previous instantiations of at least one of the token instances from the temporal windows; receiving target values corresponding to the temporal windows of token instances; the target values represent the user's responses to the token instances from the temporal windows of token instances; training the machine learning-based habituation-compensated predictor to predict the user's response to token instances, while accounting for the influence of the user's previous exposure to tokens; wherein the training uses the samples, the data on previous instantiations, and the corresponding target values

Abstract: Creating a machine learning-based affective response predictor of a user when there are significantly more samples than target values available for training, comprising: receiving samples comprising temporal windows of token instances to which the user was exposed; the token instances are spread over a long period of time; receiving intermittent target values corresponding to a subset of the temporal windows of token instances; the target values represent affective response annotations of the user; creating the machine learning-based affective response predictor of the user, by running a semi-supervised machine learning training procedure on the samples and the intermittent corresponding target values; wherein the machine learning-based affective response predictor is more accurate than a predictor created when training only on the samples that have corresponding target values, since it is capable of learning additional information from the samples comprising temporal windows of token instances without corres

Abstract: Creating a machine learning-based affective response predictor to predict a user's emotional state after being exposed to tokens representing stimuli that influence the user's affective state, comprising: receiving samples comprising temporal windows of token instances to which the user was exposed; the token instances are spread over a long period of time, and a subset of the token instances originate from same source and have overlapping instantiation periods; receiving target values, which represent affective response annotations of the user and correspond to the temporal windows of token instances; and creating the machine learning-based affective response predictor for the user, which compensates for non-linear effects resulting from the user being exposed to the subset of token instances originating from the same source and having overlapping instantiation periods, by running a machine learning training procedure on input data comprising the samples and the corresponding target values.

Abstract: Generating a habituation-compensated library comprising a user's expected response to tokens representing stimuli that influence the user's affective state, the method comprising: receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods; the samples further comprise data on previous instantiations of at least one of the token instances from the temporal windows; receiving target values corresponding to the temporal windows of token instances; the target values represent the user's response to the token instances from the temporal windows of token instances; training a machine learning-based user response model using the samples, the data on previous instantiations, and the corresponding target values; and analyzing the machine learning-based user response model to generate the habituation-compensated library, which accounts for the influence of the user's previous exposure to tokens

Abstract: Methods and devices for stimulating the development of phonetic categories by an infant. The device includes a memory element operative to store auditory pieces comprising isolated phonemes in a voice similar to the voice of the infant's parent. The method includes the steps of recording the infant's parent pronouncing isolated phonemes, and playing to the infant auditory pieces comprising isolated phonemes based on the recordings of the infant's parent.