Learning Neural Representations in Task-Switching Guided by Context Biases

Abstract

AO_SCPLOWBSTRACTC_SCPLOWOur brain can filter and integrate external information with internal representations to accomplish goal-directed behavior. The ability to switch between tasks effectively in response to context and external stimuli is a hallmark of cognitive control. Task switching occurs rapidly and efficiently, allowing us to perform multiple tasks with ease. Similarly, artificial intelligence can be tailored to exhibit multitask capabilities and achieve high performance across domains. In this study, we delve into neural representations learned by task-switching feedforward networks, which use task-specific biases for multitasking mediated by context inputs. Task-specific biases are learned by alternating the tasks the neural network learns during training. By using two-alternative choice tasks, we find that task-switching networks produce representations that resemble other multitasking paradigms, namely parallel networks in the early stages of processing and independent subnetworks in later stages. This transition in information processing is akin to that in the cortex. We then analyze the impact of inserting task contexts in different stages of processing, and the role of its location in the alignment between the task and the stimulus features. To confirm the generality of results, we display neural representations during task switching for different task and data sets. In summary, the use of context inputs improves the interpretability of feedforward neural networks for multitasking, setting the basis for studying architectures and tasks of higher complexity, including biological microcircuits in the brain carrying out context-dependent decision making.