Content evaluation system and method
Digital content such as games, movies, or interactive media can be evaluated based on cognitive load experienced by users. Evaluation begins with collecting diverse data including metadata, gameplay metrics, user feedback, and the analysis of visual or audio extracts. Information about content length, scope, asset variety, and user interaction patterns is processed using models that consider both general and user-specific factors. These models assess the contributions of individual content properties to overall mental effort, producing values that, for example, relate to complexity. Results may be presented through ranked lists, indicators of user suitability, or suggestions for engagement duration. The approach enables users to select content that matches their preferences and capacities, which can improve satisfaction and streamline navigation and resource use. This evaluation framework also informs device compatibility by relating cognitive demands to hardware features, which can result in enhanced user experience and more efficient allocation of digital resources.
1 . A system for evaluating content, the system comprising:
a display;
one or more processors; and
one or more computer-readable media collectively having stored thereon computer-executable instructions that, when executed with the one or more processors, collectively cause the system to, at least:
obtain data relating to one or more properties of the content;
determine an expected contribution of the one or more properties to a cognitive load for a user, the cognitive load corresponding to a measure of complexity of the content and individual expected contributions corresponding to a complexity score;
determine an expected cognitive load associated with the content in dependence upon a weighted combination of individual complexity scores corresponding to the individual expected contributions;
generate an image for display in dependence upon the determined expected cognitive load, the image comprising an indication of a value of the determined expected cognitive load; and
cause the image to be presented to the user with the display.
2 . The system of claim 1 , wherein the obtained data comprises user-generated feedback relating to the content.
3 . The system of claim 1 , wherein the obtained data comprises gameplay metrics for one or more users.
4 . The system of claim 1 , wherein the obtained data comprises information about a number of assets associated with the content, and at least one complexity score is determined in dependence upon at least a total number of assets or a number of unique assets.
5 . The system of claim 1 , wherein the obtained data comprises one or more screenshots and/or videos of the content, and at least one complexity score is determined in dependence upon a number of objects displayed in a scene of the screenshots and/or videos.
6 . The system of claim 1 , wherein at least one complexity score is determined in dependence upon a user profile of a particular user for whom the image for display is generated, wherein the user profile comprises information about user content preferences and/or experience with other content.
7 . The system of claim 1 , wherein the generated image comprises a ranked list of content ordered according to an expected cognitive load.
8 . The system of claim 1 , wherein the generated image comprises a suggested maximum time for the user to interact with the content in dependence upon the expected cognitive load.
9 . The system of claim 1 , wherein the generated image comprises an indication of the suitability of the content for the system displaying the image in dependence upon the expected cognitive load.
10 . The system of claim 1 , wherein the expected cognitive load is determined for a particular portion of the content.
11 . The system of claim 1 , wherein the expected cognitive load varies throughout at least a portion of the content.
12 . The system of claim 1 , wherein the expected cognitive load is determined in dependence upon a user's time spent using the content and/or a time since the user last used the content.
13 . A method for evaluating content, the method comprising:
obtaining data relating to one or more properties of the content;
determining an expected contribution of the one or more properties to a cognitive load for a user, the cognitive load corresponding to a measure of complexity of the content and individual expected contributions corresponding to a complexity score;
determining an expected cognitive load associated with the content in dependence upon a weighted combination of individual complexity scores corresponding to the individual expected contributions;
generating an image for display in dependence upon the determined expected cognitive load, the image comprising an indication of a value of the determined expected cognitive load; and
causing the image to be presented to the user with a display.
14 . A non-transitory machine-readable storage medium which stores computer software which, when executed by a computer, causes the computer to perform a method for evaluating content, the method comprising:
obtaining data relating to one or more properties of the content;
determining an expected contribution of the one or more of the properties to a cognitive load for a user, the cognitive load corresponding to a measure of complexity of the content and individual expected contributions corresponding to a complexity score;
determining an expected cognitive load associated with the content in dependence upon a weighted combination of individual complexity scores corresponding to the individual expected contributions;
generating an image for display in dependence upon the determined expected cognitive load, the image comprising an indication of a value of the determined expected cognitive load; and
causing the image to be presented to the user with a display.