COSTS AND BENEFITS OF INTEGRATING INFORMATION SEQUENCES

Information from the world unfolds over time, and to navigate the everyday world and make future predictions, our brain needs to integrate information over time. For instance, when having a conversation with someone, our brain needs to accumulate information about words and sentences to comprehend the ongoing discussion and respond appropriately. However, ubiquitous accumulation of information can cause interference, especially if we end up combining unrelated information. For instance, the topic of conversation may change from one sentence to the next, in which case combining information from consecutive sentences could cause interference and confusion. These examples demonstrate that integrating information over time is sometimes necessary for successful comprehension and prediction, but it should not be performed indiscriminately. How then should temporal integration mechanisms be implemented, especially in constrained brain-like learning architectures? What kinds of temporal integration and separation mechanisms are employed by contemporary machine learning models? And how do these integration and separation processes compare against what we observe in human behavior? In this thesis, we examined the costs and benefits of integrating and separating information sequences in humans and machines. In the first two projects we focused on learning and tested the performance of biologically-plausible temporal integration mechanisms in neural networks; we characterized the efficacy of these systems in learning categories from a sequence of examples, and investigated how their internal representations are altered by how they integrate information over time. In two further projects we focused on online comprehension and prediction, in the setting of humans reading natural language sequences, and we contrasted our findings with neural network models that predict and generate natural language sequences. We tested how online comprehension and subsequent memory are affected by interruptions in the text that humans are reading. Finally, we tested how neural language models respond to the insertion of incongruent information into a broader coherent text, and we compared these findings against our observations of how humans handle interruptions while reading. Altogether, these studies identify mechanisms by which humans and machines can exploit temporal continuity in the environment, in the service of learning about, understanding and predicting our dynamic world