The Robot Made Me Do It: Human-Robot Interaction and Risk-Taking Behavior.

Empirical evidence has shown that peer pressure can impact human risk-taking behavior. With robots becoming ever more present in a range of human settings, it is crucial to examine whether robots can have a similar impact. Using the balloon analogue risk task (BART), participants' risk-taking behavior was measured when alone, in the presence of a silent robot, or in the presence of a robot that actively encouraged risk-taking behavior. In the BART, shown to be a proxy for real risk-taking behavior, participants must weigh risk against potential payout. Our results reveal that participants who were encouraged by the robot did take more risks, while the mere presence of the robot in the robot control condition did not entice participants to show more risk-taking behavior. Our results point to both possible benefits and perils that robots might pose to human decision-making. Although increasing risk-taking behavior in some cases has obvious advantages, it could also have detrimental consequences that are only now starting to emerge.

Adaptive training of cortical feature maps for a robot sensorimotor controller.

This work investigates self-organising cortical feature maps (SOFMs) based upon the Kohonen Self-Organising Map (SOM) but implemented with spiking neural networks. In future work, the feature maps are intended as the basis for a sensorimotor controller for an autonomous humanoid robot. Traditional SOM methods require some modifications to be useful for autonomous robotic applications. Ideally the map training process should be self-regulating and not require predefined training files or the usual SOM parameter reduction schedules. It would also be desirable if the organised map had some flexibility to accommodate new information whilst preserving previous learnt patterns. Here methods are described which have been used to develop a cortical motor map training system which goes some way towards addressing these issues. The work is presented under the general term 'Adaptive Plasticity' and the main contribution is the development of a 'plasticity resource' (PR) which is modelled as a global parameter which expresses the rate of map development and is related directly to learning on the afferent (input) connections. The PR is used to control map training in place of a traditional learning rate parameter. In conjunction with the PR, random generation of inputs from a set of exemplar patterns is used rather than predefined datasets and enables maps to be trained without deciding in advance how much data is required. An added benefit of the PR is that, unlike a traditional learning rate, it can increase as well as decrease in response to the demands of the input and so allows the map to accommodate new information when the inputs are changed during training.

Auditory size-deviant detection in adults and newborn infants.

Auditory size perception refers to the ability to make accurate judgements of the size of a sound source based solely upon the sound emitted from the source. Electro-physiological and behavioural data were collected to test whether sound-source size parameters are detected from task-irrelevant sequences in adults and newborn infants. The mismatch negativity (MMN) obtained from adults indexed automatic detection of changes in size for voices, musical instruments and animal calls, regardless of whether the acoustic change indicated larger or smaller sources. Neonates detected changes in the size of a musical instrument. The data are consistent with the notion that auditory size-deviant detection in humans is an innate automatic process. This conclusion is compatible with the theory that the ability to assess the size of sound sources evolved because it provided selective advantage of being able to detect larger (more competent) suitors and larger (more dangerous) predators.