Topic: psychophysical tasks

One of the most essential language skills that humans possess is the ability to correctly execute actions based on linguistic instructions. Here we use the latest advances in Natural Language Processing (NLP) to create a neural model of this cognitive ability. Models are trained on a set of 50 psychophysical tasks which require cognitive competencies well studied in the literature. Information about tasks demands is embedded using a transformer architecture pre-trained on an NLP task. Our best performing model, clipNet, uses 33M parameters pre-trained on embedding images and their correspondin

One of humans’ most astonishing cognitive feats is the dual ability to interpret linguistic instructions to perform novel tasks in very few practice trials and conversely produce a linguistic description for a task once it has been learned. In contrast, it typically takes thousands of trials for animals to learn even the simplest behavioral tasks. To explore the neural mechanisms that underpin these remarkable abilities, we trained recurrent neural networks to perform a set of common psychophysical tasks simultaneously with task type information provided by the output of a pre-trained transf