Dynamic Analysis of Learning in Behavioral Experiments

By Anne C. Smith, Loren M. Frank, Sylvia Wirth, Marianna Yanike, D. Hu, Yasuo Kubota, Ann M. Graybiel, Wendy A. Suzuki, Emery N. Brown. Journal of Neuroscience, 2004

311 citations recorded by the source corpus.

Understanding how an animal's ability to learn relates to neural activity or is altered by lesions, different attentional states, pharmacological interventions, or genetic manipulations are central questions in neuroscience. Although learning is a dynamic process, current analyses do not use dynamic estimation methods, require many trials across many animals to establish the occurrence of learning, and provide no consensus as how best to identify when learning has occurred. We develop a state-space model paradigm to characterize learning as the probability of a correct response as a function of trial number (learning curve). We compute the learning curve and its confidence intervals using a state-space smoothing algorithm and define the learning trial as the first trial on which there is reasonable certainty (>0.95) that a subject performs better than chance for the balance of the experiment. For a range of simulated learning experiments, the smoothing algorithm estimated learning curves with smaller mean integrated squared error and identified the learning trials with greater reliability than commonly used methods. The smoothing algorithm tracked easily the rapid learning of a monkey during a single session of an association learning experiment and identified learning 2 to 4 d earlier than accepted criteria for a rat in a 47 d procedural learning experiment. Our state-space paradigm estimates learning curves for single animals, gives a precise definition of learning, and suggests a coherent statistical framework for the design and analysis of learning experiments that could reduce the number of animals and trials per animal that these studies require.

Subfields: Cell Biology, Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/455ccf2afbdbc4d0dfa4df8069f05e631904bd57

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cell Biology

In Cognitive Neuroscience

In Molecular Biology

Citations in the corpus

Electroencephalogram signatures of loss and recovery of consciousness from propofol (2013, methodology): “We estimated the probability of response to the click and verbal stimuli and the difference in probability of response by using Bayesian Monte Carlo methods to fit a state-space model to these data (31, 74).”
Coherent theta oscillations and reorganization of spike timing in the hippocampal- prefrontal network upon learning. (2010, unknown): “…HpcPfc-q-coherence at the choice point was higher at a learning stage when the rat reliably complied with the rewarded rule (compared with trials prior to criterion performance; Figure 2F, t test, p < 0.05), as assessed by high values of the ‘‘certainty’’ measure developed by Smith et al. (2004).”
Dopamine signaling and the distal reward problem (2007, methodology): “For each turn, we obtained a measure of the trial-to-trial probability of making a correct choice [13].”
Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis. (2012, background): “gives probabilistic estimates about the probability of a correct response at each trial (Smith et al., 2004).”
Rewarded Outcomes Enhance Reactivation of Experience in the Hippocampus (2009, unknown): “Reward Enhances Reactivation in the Hippocampus we also used a dynamic state-space smoothing algorithm (Smith et al., 2004, 2007) to estimate the animals' probability of an accurate response for each sequence on each trial and to compute confidence intervals for the estimated probability.”
Network dynamics underlying the formation of sparse, informative representations in the hippocampus (2008, unknown): “For this analysis data were combined across animals as a pool of ones (correct decisions) and zeroes (incorrect decisions) for each day, and using a previously described dynamic state space algorithm (Smith et al., 2004), we estimated the probability of an incorrect decision with confidence limits a”
Cortical control of arm movements: a dynamical systems perspective. (2013, background): “Does the firing of cortical cells drive muscle contraction with little intervening translation, so that cortical activity resembles muscle activity; or does it encode the intended movement end point or path, to be transformed by the spinal cord into commands that contract the muscles?”