By segmenting a sequence of elements into blocks, or chunks, information becomes easier to retain and recall in the correct order. In the paper published in PLoS Computational Biology this week, the researchers examined the temporal dynamics behind our method of memorizing sequences. This strategy of breaking down cognitive or behavioral sequences into chunks is employed in a wide variety of tasks, such as memorizing telephone numbers, but the principles behind it remain unknown.
Jordi and his collaborators used computational models to demonstrate both learning and recall of a chunking representation of sequences. “We hypothesized that sequences can be learned and stored in a manner that could be compared to a ball rolling down a pinball machine,” explains Jordi. “Learning can be identified with the gradual placement of the pins. After learning, the pins are placed in a way that, at each run, the ball follows the same trajectory (recall of the same sequence) that encodes the perceptual sequence. Simulations show that the dynamics share several features observed in behavioral experiments, such as increased delays before new chunks are loaded.”
Because chunking is a hallmark of the brain’s organization, efforts to understand its dynamics can provide valuable insights into the brain and its disorders. Failures in learning chunking sequences provide new insights into the dynamical causes of neurological disorders such as Parkinson’s disease and schizophrenia.
Figure: Two-layer network for learning chunking dynamics. In this example, the input sequence (a, b, c, d, e) is presented repeatedly. Initially, all the synaptic connections within a matrix are similar with small random variations. After several sequence presentations, the input patterns and their order are learned according to a hierarchical order: at a lower layer composed of elementary modes and at a higher level composed of chunking modes. In the elementary layer, the weights Vii′ in the directions a to b, b to c, and d to e are weakened (arrow thickness denotes coupling strength), while the weights in the opposite direction are strengthened. Similarly, the weights Wjj′ learn the trajectories along three chunks: ab, c and de.
Jordi Fonollosa, Emre Neftci & Mikhail Rabinovich (2015). “Learning of Chunking Sequences in Cognition and Behavior.” Plos Computational Biology, 19;11(11):e1004592