METHODS OF PREDICTING EMOTIONAL RESPONSE TO SENSORY STIMULI BASED ON INDIVIDUAL TRAITS
Disclosed here are methods and systems for assessing an emotional response of a subject or a group to a sensory stimulus. The methods employ models that infer emotional response based on individual traits of subjects or groups.
1 . A method for inferring an emotional response of a subject to a sensory stimulus comprising:
a) for each of a set of subjects in a cohort of subjects:
(i) exposing the subject to one or more sensory stimuli;
(ii) eliciting and electronically recording subjective response data from the subject to each sensory stimulus and receiving the recorded subjective response data into computer memory;
(iii) electronically measuring objective response data from the subject to each sensory stimulus and receiving the measured objective response data into computer memory, wherein subjective responses and objective responses indicate an emotional response to the sensory stimulus;
(iv) receiving into computer memory responses including trait data about the subject; and
(v) receiving into computer memory data about each sensory stimulus; wherein the received data for a subject constitutes a subject dataset;
b) generating a training dataset by collecting the subject datasets;
c) training a machine learning algorithm on the training dataset to produce a model that infers an emotional response of a subject based on one or more individual trait data;
d) at a user interface associated with a target subject (e.g., who is not in the cohort), inferring an emotional response by the subject to a sensory stimulus based on individual trait data provided by the target subject.
2 . A method comprising:
a) determining a profile comprising trait information about a plurality of individual traits for each of one or more subjects or consumer groups; and
b) for each of one or more sensory stimuli, wherein each stimulus is an odor or a taste, predicting emotional response by each of the subjects or consumer groups to each of the sensory stimuli, based on the trait information.
3 . The method of claim 2 , further comprising:
c) translating the predicted emotional responses into recommendations to each subject or consumer group about attractiveness of products incorporating the sensory stimuli.
4 . A method of generating an emotional response prediction model comprising:
a) providing a dataset that comprises, for each of a plurality of subjects, data including:
(i) a subject profile comprising data on a plurality of individual traits from the subject;
(ii) sensory stimulus data for each of one or a plurality of sensory stimuli to which the subject is exposed; and
(iii) emotional response data for each subject indicating emotional response by the subject to each of the sensory stimuli to which the subject is exposed, wherein the emotional response data comprises one or both of subjective response data and objective response data; and
b) training a learning algorithm to generate a model that infers a subject's emotional response to a sensory stimulus based on the subject's profile.
5 . The method of claim 4 , wherein the sensory stimulus in an odor.
6 . The method of claim 4 , wherein the sensory stimulus is a taste.
7 . The method of claim 4 , wherein the emotional response comprises a subjective response comprising a linguistic expression selected from spoken, written, or signed.
8 . The method of claim 4 , wherein the emotional response data comprises one or a plurality of objective responses selected from the group comprising facial expressions, micro expressions, brain signals, electroencephalography (EEG) signals, functional magnetic resonance imaging (fMRI) signals, body chemical stimuli, body chemical production, pupil dilation, skin conductance, skin potential, skin resistance, skin temperature, respiratory frequency, blood pressure, blood flow, saliva production and flow rate, and any combination thereof.
9 . The method of claim 4 , wherein the emotional response comprises data derived from social media activity of the subject or a group to which the subject belongs.
10 . The method of claim 4 , wherein the emotional response is classified into a discrete or continuous range.
11 . The method of claim 9 , wherein the emotional response is classified as a number, a degree, a level, a range or a bucket.
12 . The method of claim 9 , wherein the emotional response is classified as an image selected by the subject from a group of images.
13 . The method of claim 9 , wherein the emotional response is classified as a subjective feeling verbalized by the subject.
14 . The method of claim 4 , wherein the emotional response is classified into a category within a set of discrete categories, wherein the discrete categories are hierarchically arranged from least positive to most positive emotional response.
15 . The method of claim 14 , wherein the set comprises any of 3, 4, 5, 6, 7, 8, 9 or 10 discrete categories.
16 . The method of claim 14 , wherein the set comprises two discrete categories, including a negative emotional response and a positive emotional response.
17 . The method of claim 14 , wherein the set comprises three discrete categories, including a negative emotional response, a neutral emotional response and a positive emotional response.
18 . The method of claim 4 , wherein the emotional response is classified into a multi variate response, with each variable being measured on a range.
19 . The method of claim 18 , wherein the variables include one or a plurality of responses selected from love, submission, awe, disapproval, remorse, contempt, aggressiveness, and optimism.
20 . The method of claim 4 , wherein the emotional response is selected from one or more of: amused, blissful, calm, cheerful, content, dreamy, ecstatic, energetic, excited, flirty, giddy, good, happy, joyful, loving, mellow, optimistic, peaceful, silly, and sympathetic.
21 - 80 . (canceled)