The current testing methods in the MindSet: Vision toolbox have some limitations that could be addressed for a more comprehensive evaluation of DNN-human alignment. One limitation is the reliance on pre-trained DNNs, which may not capture the full range of human cognitive processes. To improve this, researchers could consider training DNNs from scratch on the provided datasets to assess their ability to learn human-like representations.
Another limitation is the focus on specific tasks and stimuli, which may not cover the full spectrum of human cognitive abilities. To enhance the toolbox, researchers could incorporate a wider variety of tasks, including more complex cognitive processes like reasoning, problem-solving, and decision-making. Additionally, incorporating feedback mechanisms and recurrent connections in the neural network models could better simulate the iterative nature of human cognition.
Furthermore, the toolbox could benefit from including datasets that involve multimodal stimuli, combining visual, auditory, and linguistic inputs to mimic real-world cognitive challenges. By expanding the range of tasks, stimuli, and model architectures, the MindSet: Vision toolbox can offer a more holistic evaluation of DNN-human alignment across various domains of human psychology.