While the human brain efficiently handles various computations with a limited number of neurons, traditional deep learning networks require a significant increase in parameters to improve performance. Yet, these parameters are used inefficiently as the networks employ the same amount of computation for inputs of the same size, regardless of the input's complexity. We address this inefficiency by introducing self-introspection capabilities to the network, enabling it to adjust the number of used parameters based on the internal representation of the task and adapt the computation time based on the task complexity. This enables the network to adaptively reuse parameters across tasks, dynamically adjusting the computational effort to match the complexity of the input. We demonstrate the effectiveness of this method on language modeling and computer vision tasks. Notably, our model achieves 96.62\% accuracy on ImageNet with just a three-layer network, surpassing much larger ResNet-50 and EfficientNet. When applied to a transformer architecture, the approach achieves 95.8\%/88.7\% F1 scores on the SQuAD v1.1/v2.0 datasets at negligible parameter cost. These results showcase the potential for dynamic and reflective computation, contributing to the creation of intelligent systems that efficiently manage resources based on input data complexity.