EyeANn

eyeANn is a term used in speculative AI literature to describe an eye-centered artificial neural network architecture designed to model and predict human gaze behavior. The name combines "eye" with "ANN" (artificial neural network) to emphasize the integration of oculomotor signals into learning and inference. It is not a standardized term, but it signals a family of models oriented to gaze-aware processing.

Concept and architecture: A typical eyeANn framework fuses eye-tracking data (fixations, saccades, pupil dilation) with visual input to create a temporally aware model. Components may include a sensor encoding module, a sequence model (such as an LSTM or transformer), and a gaze-decoder that outputs predicted gaze coordinates or attention maps. The architecture is designed to handle asynchronous streams and to align gaze signals with dynamic scenes.

Training and data requirements: EyeANn models are trained on synchronized datasets that pair video frames with eye-tracker streams. Loss functions often combine gaze regression with attention-related objectives, and calibration procedures can influence accuracy across individuals and contexts. Training emphasizes temporal alignment and robustness to variability in gaze behavior.

Applications and implications: Potential uses include human–computer interaction, usability testing, adaptive displays, accessibility tools, and cognitive psychology research. Because gaze signals carry rich information about attention, eyeANn-based systems aim to offer real-time predictions or personalized content delivery.

Limitations and reception: As a largely speculative construct, eyeANn faces questions about privacy, data quality, generalization across users, and interpretability. In practice, similar concepts exist under names such as gaze estimation networks or eye-tracking neural models.

See also: eye tracking, artificial neural network, gaze estimation, human–computer interaction.