Very superstitious? A preliminary investigation of pigeons’ body position during a matching-to-sample task under differential and common outcome conditions.

The delayed matching-to-sample (DMS) task is widely employed to assess memory in a range of non-human animals. On the standard “common outcomes” (CO) DMS task, correct performance following either sample stimulus results in reinforcement. In contrast, on a “differential outcomes” (DO) DMS task, the outcome following either sample stimulus is different. One of the most consistent findings in the comparative literature is that performance under a DO condition is superior to that under a CO condition. The superior performance is attributed to the fact the DO condition enhances memory for the sample stimulus by tagging each sample with a discrete reward. Here, we investigate an alternative possibility, that pigeons use positional mediation during the delay under DO, but not CO, conditions. To test this, we tracked the head position of pigeons performing a DO (n = 4) or CO (n = 4) task. Consistent with the positional mediation account, all subjects in the DO condition displayed evidence of positional mediation. Surprisingly, positional mediation was not unique to subjects in the DO condition, with subjects in the CO condition also displaying evidence of mediation.&nbsp

Progressive Neural Networks

Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The progressive networks approach represents a step forward in this direction: they are immune to forgetting and can leverage prior knowledge via lateral connections to previously learned features. We evaluate this architecture extensively on a wide variety of reinforcement learning tasks (Atari and 3D maze games), and show that it outperforms common baselines based on pretraining and finetuning. Using a novel sensitivity measure, we demonstrate that transfer occurs at both low-level sensory and high-level control layers of the learned policy

Time-varying Learning and Content Analytics via Sparse Factor Analysis

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc, or the forgetting effect), and (iii) estimates the content organization and intrinsic difficulty of the assessment questions. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and a summary of the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instance. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, as well as analyzing the quality and content organization of learning resources, the question-concept associations, and the question intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses than existing collaborative filtering and knowledge tracing approaches for personalized education