Biological learning and artificial intelligence

It was once taken for granted that learning in animals and man could be explained with a simple set of general learning rules, but over the last hundred years, a substantial amount of evidence has been accumulated that points in a quite different direction. In animal learning theory, the laws of learning are no longer considered general. Instead, it has been necessary to explain behaviour in terms of a large set of interacting learning mechanisms and innate behaviours. Artificial intelligence is now on the edge of making the transition from general theories to a view of intelligence that is based on anamalgamate of interacting systems. In the light of the evidence from animal learning theory, such a transition is to be highly desired

Speech Development by Imitation

The Double Cone Model (DCM) is a model of how the brain transforms sensory input to motor commands through successive stages of data compression and expansion. We have tested a subset of the DCM on speech recognition, production and imitation. The experiments show that the DCM is a good candidate for an artificial speech processing system that can develop autonomously. We show that the DCM can learn a repertoire of speech sounds by listening to speech input. It is also able to link the individual elements of speech to sequences that can be recognized or reproduced, thus allowing the system to imitate spoken language

First Steps Toward a Computational Theory of Autism

A computational model with three interacting components for context sensitive reinforcement learning, context processing and automation can autonomously learn a focus attention and a shift attention task. The performance of the model is similar to that of normal children, and when a single parameter is changed, the performance on the two tasks approaches that of autistic children

Event Prediction and Object Motion Estimation in the Development of Visual Attention

A model of gaze control is describes that includes mechanisms for predictive control using a forward model and event driven expectations of target behavior. The model roughly undergoes stages similar to those of human infants if the influence of the predictive systems is gradually increased

Cognitive modeling with context sensitive reinforcement learning

We describe how a standard reinforcement learning algorithm can be changed to include a second contextual input that is used to modulate the learning in the original algorithm. The new algorithm takes the context into account during relearning when the previously learned actions are no longer valid. The algorithm was tested on a number of cognitive experiment and shown to reproduce the learning in both a task switching test and in the Wisconsin Card Sorting Test. In addition, the algorithm was able to learn a context sensitive categorization of objects in the Labov experiment

A reinforcement-learning model of top-down attention based on a potential-action map.

No abstract availabl

Nobody's Perfect : On Trust in Social Robot Failures

With robots increasingly succeeding in exhibiting more human-like behaviours, humans may be more likely to ‘forgive’ their errors and continue to trust them as a result of ascribing higher, more human-like intelligence to them. If an integral aspect of successful HRI is to accurately communicate the competence of a robot, it can be argued that the technical success of the robot in exhibiting human-like behaviour can, in some cases, lead to a failure of the interaction by resulting in misperceived human-like competence. We highlight this through the example of speech in robots, and discuss the implications of failures and their role in HRI design

Testing the Error Recovery Capabilities of Robotic Speech

Trust in Human-Robot Interaction is a widely studied subject, and yet, few studies have examined the ability to speak and how it impacts trust towards a robot. Errors can have a negative impact on perceived trustworthiness of a robot. However, there seem to be mitigating effects, such as using a humanoid robot, which has been shown to be perceived as more trustworthy when having a high error-rate than a more mechanical robot with the same error- rate. We want to use a humanoid robot to test whether speech can increase anthropomorphism and mitigate the effects of errors on trust. For this purpose, we are planning an experiment where participants solve a sequence completion task, with the robot giv- ing suggestions (either verbal or non-verbal) for the solution. In addition, we want to measure whether the degree of error (slight error vs. severe error) has an impact on the participants’ behaviour and the robot’s perceived trustworthiness, since making a severe error would affect trust more than a slight error. Participants will be assigned to three groups, where we will vary the degree of accu- racy of the robot’s answers (correct vs. almost right vs. obviously wrong). They will complete ten series of a sequence completion task and rate trustworthiness and general perception (Godspeed Questionnaire) of the robot. We also present our thoughts on the implications of potential results

The Absolute Threshold of Colour Vision in the Horse

Arrhythmic mammals are active both during day and night if they are allowed. The arrhythmic horses are in possession of one of the largest terrestrial animal eyes and the purpose of this study is to reveal whether their eye is sensitive enough to see colours at night. During the day horses are known to have dichromatic colour vision. To disclose whether they can discriminate colours in dim light a behavioural dual choice experiment was performed. We started the training and testing at daylight intensities and the horses continued to choose correctly at a high frequency down to light intensities corresponding to moonlight. One Shetland pony mare, was able to discriminate colours at 0.08 cd/m2, while a half blood gelding, still discriminated colours at 0.02 cd/m2. For comparison, the colour vision limit for several human subjects tested in the very same experiment was also 0.02 cd/m2. Hence, the threshold of colour vision for the horse that performed best was similar to that of the humans. The behavioural results are in line with calculations of the sensitivity of cone vision where the horse eye and human eye again are similar. The advantage of the large eye of the horse lies not in colour vision at night, but probably instead in achromatic tasks where presumably signal summation enhances sensitivity