U.S. patent application number 17/061073 was filed with the patent office on 2021-01-28 for method for predicting efficacy of a stimulus by measuring physiological response to stimuli.
The applicant listed for this patent is NEUROMATTERS, LLC. Invention is credited to Charles Phillip Cloud, Paul Deguzman, Jacek Dmochowski, Lucas Parra, Daniel Rosenthal, Paul Sajda.
Application Number | 20210022637 17/061073 |
Document ID | / |
Family ID | 1000005139008 |
Filed Date | 2021-01-28 |
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United States Patent
Application |
20210022637 |
Kind Code |
A1 |
Parra; Lucas ; et
al. |
January 28, 2021 |
METHOD FOR PREDICTING EFFICACY OF A STIMULUS BY MEASURING
PHYSIOLOGICAL RESPONSE TO STIMULI
Abstract
Methods are disclosed for determining an efficacy of a stimulus
based on one or more measurable physiological responses to one or
more stimuli including one or more stimulus features. Data is
acquired on physiological responses of a group of one or more
subjects to presentation of one or more stimuli including one or
more stimulus features. The data on the one or more physiological
responses of the one or more subjects is correlated with the
presentation of the one or more stimulus features included in the
one or more stimuli. The correlated data on the one or more
physiological responses are associated with a separately-determined
efficacy of the one or more stimuli to form a stimulus efficacy
model. From this information, a projected efficacy of a stimulus is
determinable by comparing one or more subsequently-measured
physiological responses to the stimulus with the stimulus efficacy
model.
Inventors: |
Parra; Lucas; (New York,
NY) ; Sajda; Paul; (New York, NY) ; Deguzman;
Paul; (Valley Cottage, NY) ; Rosenthal; Daniel;
(New York, NY) ; Cloud; Charles Phillip; (New
York, NY) ; Dmochowski; Jacek; (Montclair,
NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NEUROMATTERS, LLC |
New York |
NY |
US |
|
|
Family ID: |
1000005139008 |
Appl. No.: |
17/061073 |
Filed: |
October 1, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
14836316 |
Aug 26, 2015 |
10835147 |
|
|
17061073 |
|
|
|
|
62041706 |
Aug 26, 2014 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/378 20210101;
A61B 3/113 20130101; A61B 5/38 20210101; A61B 3/11 20130101; A61B
5/7225 20130101; A61B 5/0205 20130101; A61B 5/318 20210101; A61B
2503/12 20130101; A61B 5/7275 20130101 |
International
Class: |
A61B 5/0484 20060101
A61B005/0484; A61B 3/113 20060101 A61B003/113; A61B 5/00 20060101
A61B005/00; A61B 5/0205 20060101 A61B005/0205; A61B 3/11 20060101
A61B003/11; A61B 5/0402 20060101 A61B005/0402 |
Claims
1.-20. (canceled)
21. A method to predict an efficacy of a stimulus based on one or
more previously-measured physiological responses to one or more
stimuli including one or more stimulus features, the method
comprising: acquiring data on physiological responses of one or
more subjects to presentation of one or more stimuli including one
or more stimulus features; forming a first data matrix tracking the
one or more stimulus features included in the one or more stimuli
tracked against a time over which the one or more stimulus features
included in the one or more stimuli were presented; forming a
second data matrix including one or more physiological responses of
the one or more subjects to which the one or more stimulus features
included in the one or more stimuli were presented tracked against
the time over which the one or more stimulus features included in
the one or more stimuli were presented; associating a
separately-determined efficacy of the one or more stimuli as
determined from at least one of the one or more subjects and an
additional group of one or more subjects with the one or more
resulting physiological responses; and applying statistical
processing to correlate the separately-determined efficacy of the
one or more stimuli with the data on the one or more physiological
responses to determine a projected efficacy of a stimulus based on
subsequently-measured physiological responses to the stimulus.
22. The method of claim 21, wherein the data on the one or more
physiological responses are acquired by one or more of:
electroencephalography (EEG); electrocardiography (EKG); galvanic
skin response; heart rate monitoring; respiration rate monitoring;
eye tracking; and pupillometry.
23. The method of claim 22, wherein when the data on the one or
more physiological responses are measured by
electroencephalography, further comprising forming the model by
temporally filtering the presentation of each of the one or more
stimulus features included in the one or more stimuli by spatially
filtering the one or more physiological responses from the
electroencephalography.
24. The method of claim 21, wherein a statistical process is used
to regress the first data matrix of onto the second data
matrix.
25. The method of claim 24, wherein the statistical process
includes canonical correlation analysis.
26. The method of claim 26, wherein correlation between the one or
more stimulus features included in the one or more stimuli and the
one or more physiological responses is represented by one or more
eigenvalues.
27. The method of claim 21, wherein correlating the data on the one
or more physiological responses of the one or more subjects
includes inter-subject correlation between the one or more
subjects.
28. The method of claim 27, wherein the inter-subject correlation
includes a group-level inter-subject correlation.
29. (canceled)
Description
RELATED APPLICATION
[0001] This application claims the benefit of U.S. Provisional
Patent Application Ser. No. 62/041,706, titled "METHOD FOR
MEASURING PHYSIOLOGICAL IMPACT OF STIMULUS FEATURES TO PREDICT
RESPONSE OF SUBJECTS TO STIMULI INCLUDING SUCH STIMULUS FEATURES,"
filed on Aug. 26, 2014, the entire contents of which are hereby
incorporated in its entirety herein.
BACKGROUND
[0002] Every day, countless sums of money are expended in trying to
influence opinions or behavior of individuals or groups, or to
determine what types of images, sounds, or other stimuli may
successfully influence the opinions or behavior of individuals or
groups. Just to list a few examples, movie producers, advertisers,
politicians, video game developers, and many others invest huge
amounts of time and money in trying to engage their audience and
generate a positive response to their offerings. To this end, for
instance, a great deal of time, money, and energy is invested in
conducting focus groups, administering consumer surveys, evaluating
user behavior, and in conducting other processes in the attempt to
create media events that will attract consumers or other
supporters.
[0003] Despite the desire to be able to predict a response to media
stimuli or other stimuli, it has proven difficult to make
predictions of what stimuli will engage an audience and elicit a
desirable response. Existing methods often prove unsatisfactory.
For example, in focus groups, participants may be asked to rate or
rank advertisements based on their individual preferences and the
quality of the ads. However, asking participants to rate or rank
advertisements may not yield accurate responses for several
reasons. First, when participants are brought into a focus group or
otherwise asked to watch the advertisements, they are actively
requested to watch the advertisements and, thus, may react
differently than ordinary viewers, who may passively watch the
advertisement when it is inserted in a break within a program. The
different mindset can result in the focus group having a very
different response to the advertisement than an ordinary viewer.
Similarly, being asked to deliberately form an opinion or a
preference may bias a subject to report what the subject believes
that the person administering wants to hear.
[0004] It thus would be a significant advance in the art to be able
to derive a more accurate way of gauging a response of one or more
subjects to a set of stimuli as may be included in an audio and/or
video an advertisement, a television program, video game, a movie,
music, or other media, or in any other form of stimuli.
SUMMARY
[0005] Methods are disclosed for determining an efficacy of a
stimulus based on one or more measurable physiological responses to
one or more stimuli including one or more stimulus features. Data
is acquired on physiological responses of a group of one or more
subjects to presentation of one or more stimuli including one or
more stimulus features. The data on the one or more physiological
responses of the one or more subjects is correlated with the
presentation of the one or more stimulus features included in the
one or more stimuli. The correlated data on the one or more
physiological responses are associated with a separately-determined
efficacy of the one or more stimuli to form a stimulus efficacy
model. From this information, a projected efficacy of a stimulus is
determinable by comparing one or more subsequently-measured
physiological responses to the stimulus with the stimulus efficacy
model.
[0006] In another aspect, a method to project an efficacy of a
stimulus based on one or more measurable physiological responses is
disclosed. Data is acquired on physiological responses of a group
of one or more subjects to presentation of one or more stimuli
included one or more stimulus features. A first data matrix is
formed, where the first data matrix includes one or more stimuli
tracked against a time over which the one or more stimulus features
included in the one or more stimuli were presented. A second data
matrix including one or more physiological responses of the one or
more subjects to which the one or more stimulus features included
in the one or more stimuli were presented tracked against the time
over which the one or more stimulus features included in the one or
more stimuli were presented. A separately-determined efficacy of
the one or more stimuli as determined from at least one of the one
or more subjects and an additional group of one or more subjects is
associated with the one or more resulting physiological responses.
Statistical processing is applied to generate a model associating
the separately-determined efficacy of the one or more stimuli with
the one or more physiological responses to determine a projected
efficacy of a stimulus based on subsequently-measured physiological
responses to the stimulus.
[0007] In still another aspect, a method of predicting a response
of one or more subjects to a stimulus includes subjecting a group
of one or more subjects to one or more stimuli including one or
more stimulus features. Data is acquired on one or more
physiological responses of the group of one of more subjects to
presentation of the one or more stimulus features included in the
one or more stimuli. An efficacy of the stimuli is determined from
at least one of the one or more subjects and an additional group of
one or more subjects. A model is generated correlating the one or
more physiological responses to the presentation of the one or more
stimulus features included in the one or more stimuli. The model is
associated with the efficacy of the one or more stimuli to form a
stimulus efficacy model. The stimulus efficacy model may be used to
predict efficacy of a stimulus by measuring one or more
physiological responses of prospective subjects to the stimulus and
comparing the one or more physiological responses of the
prospective subjects with the stimulus efficacy model.
[0008] Other methods are further described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIGS. 1 and 2 are schematic diagrams depicting analysis of
responses of a group of one or more subjects to one or more stimuli
including one or more stimulus features according to aspects of the
disclosure;
[0010] FIGS. 3 and 4 are block diagrams of systems used to analyze
responses of one or more subjects to one or more stimulus features
included in one or more stimuli according to aspects of the
disclosure;
[0011] FIG. 5 is a schematic diagram of a process of assessing
separately-determined efficacy of one or more stimuli according to
aspects of the disclosure;
[0012] FIGS. 6-8 are flow diagrams of exemplary methods of
acquiring and/or using data on physiological responses of one or
more subjects to one or more stimuli including one or more stimulus
features according to aspects of the disclosure; and
[0013] FIG. 9 is a block diagram of an exemplary computing system
that may be used to acquire and process data on physiological
responses to one or more stimuli including one or more stimulus
features and data on separately-determined efficacy of the one or
more stimuli according to aspects of the disclosure.
DETAILED DESCRIPTION
[0014] Methods of the present disclosure assist in predicting the
response of one or more subjects to one or more visual or audible
stimuli including one or more stimulus features to be presented to
the one or more subjects.
[0015] FIG. 1 is a schematic diagram of a process 100 depicting an
analysis of physiological responses of a group of one or more
subjects 110 to presentation 120 of one or stimuli 121. In the
process to acquire data on physiological responses to one or more
stimuli 124, the group of one or more subjects 110 is subjected to
one or more stimuli 121, such as visual or auditory stimuli. The
one or more stimuli 121 possess one or more stimulus features 122.
For example, an auditory stimulus, such as a particular sound, may
include stimulus features such as pitch trajectory, sound envelope,
speech envelope, spectral power, rhythm, volume, and pitch. It is
known that a human mind and body have measureable reactions to
stimuli, including changes in heart rate, respiration rate, eye
activity, brain activity, and other biophysical reactions. A
process to measure and record data on the physiological responses
123 may include electroencephalography (EEG), electrocardiography
(EKG), galvanic skin response (GSR), and other techniques to
measure heart rate, respiration rate, eye activity, or other
physiological responses to stimuli.
[0016] A process 130 correlates the data acquired on the
physiological responses with the presentation of the one or more
stimulus features included in the one or more stimuli, such as by
correlating a time at which the one or more stimulus features 122
of the one or more stimuli 121 were presented with the acquired
physiological responses. Using statistical processes described
further below with reference to FIGS. 3 and 4, a process 140 is
used to associate the correlation between the resulting one or more
physiological responses and the one or more stimuli 121 including
the one or more stimulus features 122 to a separately-determined
efficacy of the one or more stimuli 145. The separately-determined
efficacy of the one or more stimuli 145 is ascertained from the
group of one or more subjects 110 and/or a separate group of one or
more subjects. The separately-determined efficacy data is
determined by polling subjects, interviewing subjects, subjecting
subjects to testing to determine their responses while being
subjected to one or more stimuli, as further described with
reference to FIG. 5.
[0017] The determined association of the degree of correlation
between the physiological responses and the one or more stimuli 121
including the one or more stimulus features 122 with the
separately-determined efficacy of the one or more stimuli 121 may
be used to create a stimulus efficacy model 150. The stimulus
efficacy model 150 thus may be used in a process to determine a
projected efficacy of a stimulus 160. In such a process 160,
subsequently-acquired physiological data acquired in response to a
stimulus may be used to determine the efficacy of that stimulus to
determine whether the stimulus is desirable for inclusion in an
audio, visual, or audiovisual presentation, such as an
advertisement, a program, a video game, etc. Thus, using the
stimulus efficacy model 150 generated via the process 100 may
enable one to create a media presentation that includes a stimulus
or stimuli that have a desired efficacy.
[0018] FIG. 2 is a schematic diagram of a process 200 depicting an
analysis of responses of a group one or more subjects 210 to
presentation 220 of one or more stimuli 221 including one or more
stimulus features 222 according to another aspect of the
disclosure. In the process 200, while the group of one or more
subjects 210 is presented 220 with the one or more stimuli 221
including the one or more stimulus features 222, physiological
responses 230 of the group of one or more subjects 210 are acquired
as described with reference to FIG. 1. The magnitude of the
physiological responses may be included in the acquired
physiological data 230. In addition, a separately-determined
efficacy 245 of each of the one or more stimuli 221 is determined.
As previously stated, a process by which a separately-determined
efficacy of the one or more stimuli 221 is described with reference
to FIG. 5.
[0019] In order to develop a model that may identify physiological
responses to the one or more stimulus features 222 included in the
one or more stimuli 221, a first data matrix 240 tracking the
presentation of the one or more stimuli 221 including the one or
more stimulus features 222 is created, tracking the one or more
stimuli 221 and the one or more stimulus features 222 over a period
of time during which they are presented. Also, a second data matrix
250 tracking the physiological responses of the group of one or
more subjects 210 is formed, tracking the measured responses over
the time period during which the one or more stimulus features 222
included in the one or more stimuli 221 are presented. The
collected data in the first matrix 240 may include a quantified
magnitude 235 of each of the one or more stimulus features 222
included in the one or more stimuli 220, and the second matrix 250
may incorporate the quantified degree of the physiological
responses to the one or more stimulus features 222 included in the
one or more stimuli 221 in the acquired physiological data 230.
[0020] The first data matrix 240 and the second data matrix 250 are
subject to correlation processing 260, which may include
statistical regression of the physiological responses to the one or
more stimulus features 222 included in the one or more stimuli 221.
The correlation processing results in a model 270 that includes the
physiological responses correlated with the stimuli.
[0021] As described with reference to FIG. 1, the model can be used
to evaluate whether any of the stimuli 121 is likely to achieve a
desired effect by measuring the physiological responses of subjects
to the one or more stimuli 121. The resulting model may be
consulted to determine what type or stimuli should be included in
an audio, visual, or audiovisual work being developed so that the
work will have a desired effect on its audience.
[0022] FIG. 3 is a block diagram of a system 300 used to analyze
responses of one or more subjects to sets of stimuli including one
or more stimulus features according to an aspect of the disclosure.
One or more subjects, such as subject 310, are subjected to a
stimulus 320, which may incorporate one or more identifiable
stimulus features. The stimulus 320 may, for example, include an
audio, visual, or audiovisual presentation that includes one or
more stimulus features. A feature extraction subsystem 325
identifies a plurality of features, such as features f.sub.1 326,
f.sub.2 327, and f.sub.n 328, and captures each as a function of
time. Data representing the extracted features are combined by an
aggregator 345. As further described below, a filter 435 applies
weights, such as weights w.sub.1 336, w.sub.2 337, and w.sub.n 338,
are assigned to the extracted feature data to optimize the yield of
the model. The resulting output is weighted, extracted feature data
tracked over time, y.sub.1(t) 357.
[0023] In a particular aspect, the features are used as independent
variables to which resulting physiological responses are correlated
as dependent variables. In a visual presentation or a visual
component of an audiovisual presentation, features extracted may
include, for example, orientation energy, optical flow, luminance
and chrominance, local spatial contrast, and temporal contrast. For
an audio presentation or an audio component of an audio visual
presentation, the features extracted may include pitch trajectory,
sound envelope, speech envelope and spectral power or powers. The
physiological responses may include data yielded by
electroencephalography (EEG), electrocardiography (EKG), galvanic
skin response, heart rate monitoring, respiration rate monitoring,
eye tracking, and pupillometry.
[0024] Correspondingly, the subject 310 is presented with the
stimulus 320 while being monitored by physiological measuring
devices. In FIG. 3, the subject 310 is being monitored by an
electroencephalography system that monitors physiological responses
of the subject over time, including spatially-arrayed input leads
features x.sub.1(t) 311, x.sub.2(t) 312, and x.sub.n(t) 313. A
filter 315 applies weights, such as weights v.sub.1 316, v.sub.2
317, and v.sub.n 318 to the measured responses. Again, as further
described below, the weights are assigned to the extracted feature
data to optimize the yield of the model. Data representing the
physiological responses are combined by an aggregator 325. The
resulting output is weighted, physiological response data tracked
over time, y.sub.2(t) 359.
[0025] The weighted, extracted feature data tracked over time,
y.sub.1(t) 357, and the weighted, physiological response data
tracked over time, y.sub.2(t) 359, is submitted to a correlator 355
and then an optimizer 365. Output of the optimizer 365 is fed back
to the filters 315 and 335 to adjust the applied weights to improve
the signal-to-noise ratio inherent in the correlated data, as well
as to account for delays resulting from a subject's physiological
response to various stimuli. In a particular aspect, a
block-Toeplitz structure is used so that a temporal filter
represented by the weights applied by the filter 335 filters across
a temporal aperture, allowing for short-time integration of the
multiple stimuli. In a particular aspect, a spatiotemporal matrix
of EEG responses is constructed such that the filter 315 linearly
filters one or more underlying neuronal sources. Temporally
filtering the stimuli while spatially filtering the neuronal
responses leads to pairs of optimal filters which uncover the
covariation of specific temporal frequencies of the stimulus to
specific neuronal sources or patterns of sources in the brain. For
example, activity in an occipital component of the EEG may be found
to covary with a visual stimulus feature, while activity in a
temporal component may be found to covary with an audio stimulus
feature. Each of these pairs is associated with an optimal
correlation value, such as a generalized eigenvalue that conveys a
level of physiological drive elicited by a selected identified
stimulus.
[0026] The system may employ a variety of physiological responses
recorded before, during and after a given stimulus. This includes,
but is not limited to, recordings from EEG, EKG, Eye Tracking,
Pupillometry, Heart Rate/Heart Rate Variability, GSR, and
Respiratory Rate. A variety of models can be used to relate the
physiological responses to the stimuli, including, but not limited
to, temporal filters, spatial filters, spectral models, neural
networks, and deep (learning) networks. These models all serve to
learn an optimal representation between a set of computed stimuli
and a corresponding set of physiological signals.
[0027] FIG. 4 is a block diagram of a system 400 that may be used
to derive inter-subject correlation among a plurality of subjects.
The system 400 shares many common elements with the system 300 of
FIG. 3. However, the system 400 focuses on correlating
physiological responses between subjects 405 and 410 in response to
a stimulus including one or more stimulus features 401, rather than
correlating physiological responses of a subject to one or more
stimuli, as is the case in FIG. 3. It should be appreciated that
the systems 300 and 400 may be combined to inter-relate responses
of multiple subjects to a plurality of stimuli.
[0028] Each of the one or more subjects 405 and 410 are subjected
to the stimulus 401. The subjects 405 and 410 are monitored by
electroencephalography systems that monitor physiological responses
of the subject over time. The subject 405 is monitored by
spatially-arrayed input leads features x.sub.11(t) 406, x.sub.12(t)
407, and x.sub.1n(t) 408. A filter 435 applies weights, such as
weights v.sub.11 436, v.sub.12 437, and v.sub.1n 438 to the
measured responses. Data representing the physiological responses
are combined by an aggregator 445. The resulting output is
weighted, physiological response data tracked over time, y.sub.1(t)
457. The subject 410 is monitored by spatially-arrayed input leads
features x.sub.21(t) 411, x.sub.22(t) 412, and x.sub.2n(t) 413. A
filter 415 applies weights, such as weights v.sub.21416, v.sub.22
417, and v.sub.2n 418 to the measured responses. Data representing
the physiological responses are combined by an aggregator 425. The
resulting output is weighted, physiological response data tracked
over time, y.sub.2(t) 459.
[0029] The weighted physiological response feature data tracked
over time, y.sub.1(t) 457 and the weighted, physiological response
data tracked over time, y.sub.2(t) 459 are submitted to a
correlator 455 and then an optimizer 465. Output of the optimizer
465 is fed back to the filters 415 and 435 to adjust the applied
weights to improve the signal-to-noise ratio inherent in the
correlated data, as well as to account for delays resulting from a
subject's physiological response to various stimuli. The
inter-subject data is correlated with the stimulus feature data
(not shown in FIG. 4) to yield physiological response data
collected from multiple subjects to the stimulus feature data.
[0030] FIG. 5 is a schematic diagram of a process 500 of assessing
separately-determined efficacy of one or more stimuli. To provide a
body of information for association with the correlation between
the physiological data and the stimuli, a group of one or more
subjects 510, which may be the same group from which physiological
data is acquired as described with reference to FIGS. 1 and 2,
and/or a second group of one or more subjects 511, are subjected to
a process 520 in which one or more stimuli 522 are presented. The
stimuli, as previously described, may include one or more visual
stimuli, audible stimuli, or a combination of visual and audible
stimuli. Upon being subjected to the one or more stimuli subjects
in the first group and/or the second group (if more than one group
is used) are subjected to a process 530 to evaluate their response
to the one or more stimuli. For example, the subjects may be polled
regarding their subjective responses to the one or more stimuli.
Also, the one or more subjects may be interviewed individually or
as part of a group (e.g., a focus group) regarding their responses
to the one or more stimuli. Further, the subjects may be observed
to acquire their reactions to the one or more stimuli as the
stimuli are presented. Alternatively, the one or more subjects may
be submitted to testing to gauge their performance subsequent to or
during the presentation of the stimuli to determine whether the
stimuli have an effect on the subjects' cognitive performance on
such testing. Additional methods of evaluating the response of
subjects to the one or more stimuli also may be used. It should be
appreciated that one or more of these methods of evaluating the
subjects' responses may be used.
[0031] FIG. 6 is a flow diagram of an exemplary method 600 of
analyzing responses of one or more subjects to sets of stimuli
according to aspects of the disclosure. At 602, data is acquired on
one or more physiological responses of one or more subjects to
presentation of one or more stimuli including one or more stimulus
features. As previously described with reference to FIGS. 1-4, the
data may be acquired by EKG, GSR, heart rate, respiration rate,
eye-gaze, fixation duration, pupilometry, or other measurements. At
604, the data on the one or more physiological responses of the one
or more subjects are correlated with the presentation of the one or
more stimulus features included in the one or more stimuli. At 606,
the correlated data on the one or more physiological responses are
associated with a separately-determined efficacy of the one or more
stimuli to form a stimulus efficacy model. As a result, a projected
efficacy of a stimulus is determinable from one or more
subsequently-measured physiological responses to the stimulus.
[0032] FIG. 7 is a flow diagram of an exemplary method 700 of
analyzing responses of one or more subjects to sets of stimuli
according to aspects of the disclosure. At 702, data is acquired on
physiological responses of one or more subjects to presentation of
one or more stimuli including one or more stimulus features. As
previously described with reference to FIGS. 1-4, the data may be
acquired by EKG, GSR, heart rate, respiration rate, eye-gaze,
fixation duration, pupilometry, or other measurements. At 704, a
first data matrix is formed, where the first matrix includes one or
more stimulus features included in the one or more stimuli tracked
against a time over which the one or more stimuli were presented.
At 706, a second data matrix is formed that includes one or more
physiological responses of a first group of one or more subjects
that were exposed to the one or more stimulus features included in
the one or more stimuli tracked against the time over which the one
or more stimuli were presented. At 708, a separately-determined
efficacy of the set of stimuli as determined from at least one of
the first group of one or more subjects and a second group of one
or more subjects are associated with the one or more resulting
physiological responses. The separately-determined efficacy is
measured by polling, interviewing, observing, testing, or other
methods as described with reference to FIG. 6. At 710, statistical
processing is applied to correlate the separately-determined
efficacy of the set of stimuli with the one or more physiological
responses to determine a projected efficacy of a stimulus based on
subsequently-measured physiological responses.
[0033] FIG. 8 is a flow diagram of another exemplary method of
analyzing responses of one or more subjects to sets of stimuli
according to aspects of the disclosure. At 802, one or more
subjects are subjected to one or more stimuli including one or more
stimulus features. At 804, data on one or more physiological
responses of the one of more subjects to the one or more stimulus
features included in the one or more stimuli are acquired. As
previously described with reference to FIGS. 1-4, the data may be
acquired by EKG, GSR, heart rate, respiration rate, eye-gaze,
fixation duration, pupilometry, or other measurements. At 806,
efficacy of one or more stimuli from at least one of the one or
more subjects and an additional group of one or more subjects is
determined. The separately-determined efficacy is measured by
polling, interviewing, observing, testing, or other methods as
described with reference to FIG. 5. At 808, a model correlating the
one or more measurable physiological responses to the presentation
of the one or more stimulus features included in the one or more
stimuli is generated. At 810, the model is correlated with the
efficacy of the one or more stimuli. The model may be used to
determine prospective efficacy of a stimulus by subjecting one or
more additional subjects to the stimulus and measuring the one or
more measurable physiological responses of the one or more
additional subjects.
[0034] FIG. 9 is a block diagram of an exemplary computing system
900 that may be used for performing computer-implemented methods or
executing computer-executable instructions for generating or using
a model correlating physiological responses with one or more
stimuli including one or more stimulus features according to the
present disclosure.
[0035] Referring to FIG. 9, the computing system 900 may include
any of a number of forms of stationary or mobile computing devices.
The computing device 900 typically includes at least one processing
unit 904 and a system memory 906. Depending on the exact
configuration and type of computing device, the system memory 906
may be volatile (such as RAM), non-volatile (such as ROM, flash
memory, and the like) or some combination of the two. The system
memory 906 typically maintains an operating system 908, one or more
applications 910, and program data 912.
[0036] The computing device 900 may also have additional features
or functionality. For example, the computing device 900 may also
include additional data storage devices (removable and/or
non-removable) such as, for example, magnetic disks, optical disks,
or tape. Such additional storage is illustrated in FIG. 9 by
removable storage 914 and non-removable storage 916. Computer
storage media may include volatile and non-volatile, removable and
non-removable media implemented in any method or technology for
storage of information, such as computer readable instructions,
data structures, program modules or other data. The system memory
906, the removable storage 914, and the non-removable storage 916
are all examples of computer storage media. Available types of
computer storage media include, but are not limited to, RAM, ROM,
EEPROM, flash memory (in both removable and non-removable forms) or
other memory technology, CD-ROM, digital versatile disks (DVD) or
other optical storage, magnetic cassettes, magnetic tape, magnetic
disk storage or other magnetic storage devices, or any other medium
which can be used to store the desired information and which can be
accessed by the computing device 900. Any such computer storage
media may be part of the computing device 1000.
[0037] The computing device 1000 may also have input device(s) 918
such as a keyboard, mouse, pen, voice input device, touch input
device, etc. Further, in accordance with aspects of the present
disclosure, physiological measurement devices to track EEG, EKG,
heart rate, respiration rate, eye-gaze, fixation duration,
pupilometry, or other measurements may be operatively coupled as
input devices to the computing device 900 to acquire physiological
data. Output device(s) 920 such as a display, speakers, printer,
etc., may also be included. In accordance with aspects of the
disclosure, such devices may be used to present the one or more
stimuli. The computing device 900 thus may be used to present
stimuli, capture physiological data, and correlate the data with
the stimuli and included stimulus features. The computing device
900 may also be used to receive separately-determined data on the
efficacy of the stimulus. Similarly, suitable computer-readable
instructions may be used to generate models and perform statistical
processing on the data in accordance with aspects of the
disclosure.
[0038] The computing device 900 also may include one or more
communication connections 922 that allow the computing device 900
to communicate with other computing devices 924, such as over a
network or a wireless network. The one or more communication
connections 922 are an example of communication media. Available
forms of communication media typically carry computer readable
instructions, data structures, program modules or other data in a
modulated data signal such as a carrier wave or other transport
mechanism and includes any information delivery media. The term
"modulated data signal" may include a signal that has one or more
of its characteristics set or changed in such a manner as to encode
information in the signal. By way of example, and not limitation,
communication media may include wired media such as a wired network
or direct-wired connection, and wireless media such as acoustic,
RF, infrared and other wireless media. The term computer readable
media as used herein includes both storage media and communication
media.
[0039] While the disclosure has been has been set forth herein in
reference to specific aspects, features and illustrative aspects,
it will be appreciated that the utility of the disclosure is not
thus limited, but rather extends to and encompasses numerous other
variations, modifications and alternative aspects, as will suggest
themselves to those of ordinary skill in the field of the present
disclosure, based on the description herein. Any of various
elements or features recited herein is contemplated for use with
other features or elements disclosed herein, unless specified to
the contrary. Correspondingly, the invention that may be
hereinafter claimed is intended to be broadly construed and
interpreted, as including all such variations, modifications and
alternative aspects, within its spirit and scope.
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