Immersive ecosystem

A computer-implemented method for immersive training comprising:

• measuring, with one or more sensors, at least one response of a user to a three-dimensional (3D) asset of a 3D rendering that is being rendered on a virtual reality (VR) display, the 3D rendering comprising a plurality of 3D assets;
• and
• executing, on a processor, a set of instructions stored on a computer readable medium that is operable to:
• generate a personal performance profile specific to the user and based on prior stored measured user responses to one or more of the 3D assets of the plurality of assets of the 3D rendering;
• receive, in real-time, the at least one measured response of the user to the 3D asset,
• update the user-specific personal performance profile based on the at least one measured response of the user to the 3D asset;
• compare, in real-time, the received at least one measured response of the user at a given time with a corresponding predetermined user response value at a corresponding predetermined time value to the 3D asset;
• generate a dynamic decision matrix customized for the user based on the user-specific personal performance profile and a neural efficiency score of the user, and
• modify, in real-time, at least one of the plurality of 3D assets of the 3D rendering based on the dynamic decision matrix customized for the user and the user response comparison.

A non-transitory computer-readable medium comprising a set of instructions stored thereon, the set of instructions being operable when executed on a processor to:

• receive, in real-time, from one or more sensors, at least one measured response ofa user to a three-dimensional (3D) asset ofa 3D rendering that is being rendered on a virtual reality (VR) display, the 3D rendering comprising a plurality of 3D assets;
• generate a personal performance profile specific to the user and based on prior stored measured user responses to one or more of the 3D assets of the plurality of assets of the 3D rendering;
• update the user-specific personal performance profile based on the at least one measured response of the user to the 3D asset;
• compare, in real-time, the received at least one measured response of the user at a given time with a corresponding predetermined user response value to the 3D asset at a corresponding predetermined time value to the 3D asset;
• generate a dynamic decision matrix customized for the user based on the user-specific personal performance profile and a neural efficiency score of the user;
• and
• modify, in real-time, at least one of the plurality of 3D assets of the 3D rendering based on the dynamic decision matrix customized for the user and the user response comparison.