Home Research Research Highlights
Toward optimal learning dynamics

Garrison W. Cottrell and the Temporal Dynamics of Learning Center

Gary CottrellAs outlined in a recent Science article coauthored by members of the TDLC and LIFE centers, transformative advances in the science of learning require collaboration from multiple disciplines, including psychology, neuroscience, machine learning, and education. TDLC has implemented this approach through the formation of research networks, small interdisciplinary teams focused on a common research agenda. By combining approaches from multiple fields, more progress is possible than can be achieved by single-discipline studies. In particular, by combining computational models and experiments, the underlying mechanisms of learning can be elucidated, because the models can be analyzed in ways that brains cannot. This approach has a long history, which we build upon (e.g., Hebb, 1949, Machado, 1997, Shadmehr & Mussa-Ivaldi, 1997, Staddon et al., 2002).

This form of team science is exemplified by the Interacting Memory Systems Network’s discovery of a behavioral function of cell birth (neurogenesis) in the Dentate Gyrus of the hippocampus of mature rats . Brad Aimone, an IMS graduate student in Rusty Gage’s lab (Salk Inst.), wanted to use a model to understand the role of these neurons. He asked IMS member Jeff Elman (UCSD) whose model of hippocampus would be best suited for this investigation, and Jeff sent him to IMS member Janet Wiles (U. Queensland). Together, they added neurogenesis to Janet’s model, which then yielded new predictions of the functional role of these newborn neurons, including that newborn neurons would bind together temporally-adjacent associations with context. New behavioral tasks to verify this prediction were developed by IMS leader Andrea Chiba (UCSD), project scientist Laleh Quinn and graduate student Lara Rangel. The predictions were confirmed. These cells are a new kind of place cell that fires only in a specific place and surrounding context, indicating the coding of space-time in the hippocampus. The Eichenbaum laboratory of CELEST recently discovered “time cells” in the CA1 region of the hippocampus. The existence of temporal coding and contextual encoding at the cellular level in the hippocampus provides a compliment to our earlier finding that internally generated sequences of neural activity in the hippocampus are replayed in the absence of external cues (Pastalkova et al, 2008). Thus, the elements and the ensemble of the hippocampus aggregate to create a sequential record of our personal recollections.

A second, quite different application of this approach is to the study of spacing effects. Spacing of study and testing are well known to influence the duration and effectiveness of learning. We extended the understanding of spacing effects to educationally-relevant time scales, and found that spacing effects are time scale invariant, providing coarse but useful guidance for educators (Cepeda et al., 2009). Based on this data, we developed a new computational theory (the Multiscale Context Model) that successfully predicts the optimal spacing for arbitrary material. We have incorporated MCM into a web-based tool that optimizes study schedules (Mozer et al.,  2009); we are evaluating it 200 Colorado middle school Spanish students.

There are many more applications of this approach; we have applied various machine learning and modeling techniques to automatically detect perceived difficulty of a lecture from facial expressions (Whitehill, et al. 2008), to learn the optimal action to take next in a tutoring context based on examples of human tutoring interactions (Ruvolo, et al. 2008), and to analyze children’s facial expressions while problem solving in order to predict periods of uncertainty (Littlewort, et al. 2011). Likewise, neural recording and behavioral data can inform modeling – ruling out different models of decision-making (Purcell et al., 2010).

Two of the remaining challenges are highlighted here. First, we have used many techniques to build models at various levels of the spatial and temporal hierarchy (from neurons and millisecond scales to the person and year-long scale for spacing effects). Many of these approaches – those that share optimality or Bayesian techniques – are compatible with one another, yet the mappings between levels of the temporal and spatial hierarchy remain to be bridged, although progress has been made (e.g., Lerner, et al., 2011; Poeppel, 2012). Consideration of this problem leads to the insight that interactions between levels depends on the physics of how, for example, molecules (low level) interact at synapses (one level up) and has provided fundamental links between thermodynamics and prediction, showing that in order to be energy-efficient, an organism must be predictive (Still, et al., 2012). However, this is still far from fulfilling the promise of what we call the “levels hypothesis” (Bell, 2007), which is a search for fundamental principles linking the physical levels between, for example, synapses, cells, and organisms. A second challenge is to bridge a collection of findings indicating that an active EEG brain state is necessary for accurate encoding of sensory temporal patterns (Marguet & Harris, 2011; Goard and Dan, 2009; Minces, Harris, & Chiba, In Prep) with data showing that EEG brain state in babies predicts linguistic and cognitive development (Benasich et al, 2008; Gou et al, 2011). This will require reverse engineering human EEG by using animal models, in order to understand the cortical activity and neuromodulatory inputs underlying fast oscillatory activity in human EEG.

And, as the linked paper points out, neurogenesis is enhanced by running – another strong piece of evidence for the crucial role of physical education in K-12 education.