Spike-based decision learning of nash equilibria in two-player games

Humans and animals face decision tasks in an uncertain multi-agent environment where an agent's strategy may change in time due to the co-adaptation of others strategies. The neuronal substrate and the computational algorithms underlying such adaptive decision making, however, is largely unknown. We propose a population coding model of spiking neurons with a policy gradient procedure that successfully acquires optimal strategies for classical game-theoretical tasks. The suggested population reinforcement learning reproduces data from human behavioral experiments for the blackjack and the inspector game. It performs optimally according to a pure (deterministic) and mixed (stochastic) Nash equilibrium, respectively. In contrast, temporal-difference(TD)-learning, covariance-learning, and basic reinforcement learning fail to perform optimally for the stochastic strategy. Spike-based population reinforcement learning, shown to follow the stochastic reward gradient, is therefore a viable candidate to explain automated decision learning of a Nash equilibrium in two-player games.

Interference during the implicit learning of two different motor sequences

It has been demonstrated that learning a second motor task after having learned a first task may interfere with the long-term consolidation of the first task. However, little is known about immediate changes in the representation of the motor memory in the early acquisition phase within the first minutes of the learning process. Therefore, we investigated such early interference effects with an implicit serial reaction time task in 55 healthy subjects. Each subject performed either a sequence learning task involving two different sequences, or a random control task. The results showed that learning the first sequence led to only a slight, short-lived interference effect in the early acquisition phase of the second sequence. Overall, learning of neither sequence was impaired. Furthermore, the two processes, sequence-unrelated task learning (i.e. general motor training) and the sequence learning itself did not appear to interfere with each other. In conclusion, although the long-term consolidation of a motor memory has been shown to be sensitive to other interfering memories, the present study suggests that the brain is initially able to acquire more than one new motor sequence within a short space of time without significant interference.

Perceptual learning of motion discrimination by mental imagery.

Perceptual learning can occur when stimuli are only imagined, i.e., without proper stimulus presentation. For example, perceptual learning improved bisection discrimination when only the two outer lines of the bisection stimulus were presented and the central line had to be imagined. Performance improved also with other static stimuli. In non-learning imagery experiments, imagining static stimuli is different from imagining motion stimuli. We hypothesized that those differences also affect imagery perceptual learning. Here, we show that imagery training also improves motion direction discrimination. Learning occurs when no stimulus at all is presented during training, whereas no learning occurs when only noise is presented. The interference between noise and mental imagery possibly hinders learning. For static bisection stimuli, the pattern is just the opposite. Learning occurs when presented with the two outer lines of the bisection stimulus, i.e., with only a part of the visual stimulus, while no learning occurs when no stimulus at all is presented.

Governing individual learning in the transition phase of software maintenance offshoring: a dynamic perspective

Prior studies suggest that clients need to actively govern knowledge transfer to vendor staff in offshore outsourcing. In this paper, we analyze longitudinal data from four software maintenance offshore out-sourcing projects to explore why governance may be needed for knowledge transfer and how governance and the individual learning of vendor engineers inter-act over time. Our results suggest that self-control is central to learning, but may be hampered by low levels of trust and expertise at the outset of projects. For these foundations to develop, clients initially need to exert high amounts of formal and clan controls to enforce learning activities against barriers to knowledge sharing. Once learning activities occur, trust and expertise increase and control portfolios may show greater emphases on self-control.