Biofeedback method of modulating digital content to invoke greater pupil radius response
One exemplary implementation displays a visual characteristic associated with an object on a display of a device and utilizes a sensor of the device to obtain physiological data associated with a pupillary response of a user to the visual characteristic. The device adjusts the visual characteristic based on the obtained physiological data to enhance pupillary responses of the user to the object and displays the adjusted visual characteristic to the user. For example, the adjusted visual characteristic may be selected based on previously identified pupillary responses of the user to particular visual characteristics.
1 . A method comprising:
at a device comprising a processor:
obtaining first pupillary data while displaying an element;
detecting, based on the obtained first pupillary data, an intention to interact with the element by identifying pupil diameter changes;
adjusting, based on the obtained first pupillary data and the detected intention to interact with the element, a visual characteristic associated with the element in response to determining that adjusting the visual characteristic will elicit an enhanced pupillary response, wherein determining that adjusting the visual characteristic will elicit the enhanced pupillary response is based on an iterative training process associated with detecting intentions to interact with the element; and
displaying the element with the adjusted visual characteristic.
2 . The method of claim 1 , further comprising:
determining user interaction feedback corresponding to an interaction event associated with the displayed element that includes the adjusted visual characteristic.
3 . The method of claim 2 , wherein the interaction event associated with the displayed element comprises a selection of a user interface object associated with the displayed element.
4 . The method of claim 1 , wherein the iterative training process is based on a training technique that includes a plurality of trials for each of a plurality of interface feedback conditions.
5 . The method of claim 4 , wherein the training technique comprises identifying one or more interface feedback conditions of the plurality of interface feedback conditions that elicits enhanced pupillary responses for a user.
6 . The method of claim 1 , wherein detecting the intention to interact with the element by identifying pupil diameter changes comprises applying a machine learning technique trained to identify patterns in pupil diameter changes corresponding to user intentions.
7 . The method of claim 1 , wherein detecting the intention to interact with the element by identifying pupil diameter changes comprises identifying an intent to execute a movement, make a decision, or select the element at a particular instant in time or in a future period in time.
8 . The method of claim 1 , wherein adjusting the visual characteristic associated with the element comprises animating the visual characteristic during a time period associated with the detected intention to interact with the element.
9 . The method of claim 8 , wherein animating the visual characteristic during the time period comprises at least one of changing a luminance contrast, shifting a hue, shifting a spatial pattern, and moving the visual characteristic with respect to the element.
10 . The method of claim 1 , wherein the visual characteristic is a color attribute of the element, a size of the element, a shape of the element, or an attribute of content proximate to the element.
11 . The method of claim 1 , wherein detecting the intention to interact with the element comprises:
obtaining pupil dilation data corresponding to each of multiple variations of the visual characteristic; and
selecting a variation of the variations to adjust the visual characteristic based on the pupil dilation data.
12 . The method of claim 1 , wherein detecting the intention to interact with the element by identifying pupil diameter changes comprises detecting a pupil dilation pattern.
13 . The method of claim 12 , wherein detecting the pupil dilation pattern comprises accounting for exogenous signals corresponding to pupil diameter changes in the first pupillary data resulting from ambient light changes, chromatic changes, accommodation of an eye, content lighting changes, cyclical pupil dilations, a change in ambient noise, a change in motion of the device, a luminance of a scene, a cognitive load, or a user caffeine intake.
14 . The method of claim 1 further comprising:
obtaining second pupillary data while displaying the element with the adjusted visual characteristic; and
detecting, based on the obtained second pupillary data, an intention to interact with the element by identifying pupil diameter changes associated with the enhanced pupillary response.
15 . The method of claim 1 , wherein the device is a handheld device, a laptop, or a desktop, or a head-mounted-device (HMD).
16 . The method of claim 1 , wherein the first pupillary data is obtained based on identifying an interaction with the displayed element.
17 . A device comprising:
a non-transitory computer-readable storage medium; and
one or more processors coupled to the non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium comprises program instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising:
obtaining first pupillary data while displaying an element;
detecting, based on the obtained first pupillary data, an intention to interact with the element by identifying pupil diameter changes;
adjusting, based on the obtained first pupillary data and the detected intention to interact with the element, a visual characteristic associated with the element in response to determining that adjusting the visual characteristic will elicit an enhanced pupillary response, wherein determining that adjusting the visual characteristic will elicit the enhanced pupillary response is based on an iterative training process associated with detecting intentions to interact with the element; and
displaying the element with the adjusted visual characteristic.
18 . The device of claim 17 , wherein the iterative training process is based on a training technique that includes a plurality of trials for each of a plurality of interface feedback conditions.
19 . The device of claim 18 , wherein the training technique comprises identifying one or more interface feedback conditions of the plurality of interface feedback conditions that elicits enhanced pupillary responses for a user.
20 . A non-transitory computer-readable storage medium, storing program instructions executable on a device to perform operations comprising:
obtaining first pupillary data while displaying an element;
detecting, based on the obtained first pupillary data, an intention to interact with the element by identifying pupil diameter changes;
adjusting, based on the obtained first pupillary data and the detected intention to interact with the element, a visual characteristic associated with the element in response to determining that adjusting the visual characteristic will elicit an enhanced pupillary response, wherein determining that adjusting the visual characteristic will elicit the enhanced pupillary response is based on an iterative training process associated with detecting intentions to interact with the element; and
displaying the element with the adjusted visual characteristic.