  This paper introduces the M&M model, a novel multimodal-multitask learning
framework, applied to the AVCAffe dataset for cognitive load assessment (CLA).
M&M uniquely integrates audiovisual cues through a dual-pathway architecture,
featuring specialized streams for audio and video inputs. A key innovation lies
in its cross-modality multihead attention mechanism, fusing the different
modalities for synchronized multitasking. Another notable feature is the
model's three specialized branches, each tailored to a specific cognitive load
label, enabling nuanced, task-specific analysis. While it shows modest
performance compared to the AVCAffe's single-task baseline, M\&M demonstrates a
promising framework for integrated multimodal processing. This work paves the
way for future enhancements in multimodal-multitask learning systems,
emphasizing the fusion of diverse data types for complex task handling.
