Task complexity interacts with state-space uncertainty in the arbitration between model-based and model-free learning

It has previously been shown that the relative reliability of model-based and model-free reinforcement-learning (RL) systems plays a role in the allocation of behavioral control between them. However, the role of task complexity in the arbitration between these two strategies remains largely unknown. Here, using a combination of novel task design, computational modelling, and model-based fMRI analysis, we examined the role of task complexity alongside state-space uncertainty in the arbitration process. Participants tended to increase model-based RL control in response to increasing task complexity. However, they resorted to model-free RL when both uncertainty and task complexity were high, suggesting that these two variables interact during the arbitration process. Computational fMRI revealed that task complexity interacts with neural representations of the reliability of the two systems in the inferior prefrontal cortex

“Show me, how does it look now”: Remote Help-giving in Collaborative Design

This paper examines the role of visual information in a remote help-giving situation involving the collaborative physical task of designing a prototype remote control. We analyze a set of video recordings captured within an experimental setting. Our analysis shows that using gestures and relevant artefacts and by projecting activities on the camera, participants were able to discuss several design-related issues. The results indicate that with a limited camera view (mainly faces and shoulders), participants’ conversations were centered at the physical prototype that they were designing. The socially organized use of our experimental setting provides some key implications for designing future remote collaborative systems

A task based 'design for all' support tool

The ‘Design for All’ philosophy promotes the development of products that meet the requirements of a broader section of the population, including those who are older or disabled, to minimise the need for bespoke designs and individual customisations. Such an approach begins to meet the needs of a population containing an ever increasing proportion of these excluded groups, whilst providing opportunities to manufacturers to maximise the available market for any given product. Most design activity embodies some form of task analysis that involves identifying users and the tasks they perform. Computer based human modelling systems are becoming increasingly important in this task analysis role combined with the established ergonomics technique of fitting trials, in which a product or environment is evaluated through trials using a carefully selected user group. This research addresses the lack of existing data necessary for the accurate representation of human form and capability in the older and disabled populations for use in these modelling systems. A small-scale survey is being undertaken to collect this important information. In addition, existing modelling systems in this area rely on expert ergonomics knowledge in performing task based analysis, which in addition can be a time consuming and repetitive task. Methods are being developed to streamline this process and to place the emphasis on good design and ergonomics principles as opposed to ‘driving’ the system. These methods involve the development of a simplified process for computer based task analysis and a means of determining the percentage accommodated by any given design. Further research will eventually focus on extending the data collection, refining the task model and look at a means of suggesting design solutions in response to the analysis results

A task based 'design for all' support tool

The ‘Design for All’ philosophy promotes the development of products that meet the requirements of a broader section of the population, including those who are older or disabled, to minimise the need for bespoke designs and individual customisations. Such an approach begins to meet the needs of a population containing an ever increasing proportion of these excluded groups, whilst providing opportunities to manufacturers to maximise the available market for any given product. Most design activity embodies some form of task analysis that involves identifying users and the tasks they perform. Computer based human modelling systems are becoming increasingly important in this task analysis role combined with the established ergonomics technique of fitting trials, in which a product or environment is evaluated through trials using a carefully selected user group. This research addresses the lack of existing data necessary for the accurate representation of human form and capability in the older and disabled populations for use in these modelling systems. A small-scale survey is being undertaken to collect this important information. In addition, existing modelling systems in this area rely on expert ergonomics knowledge in performing task based analysis, which in addition can be a time consuming and repetitive task. Methods are being developed to streamline this process and to place the emphasis on good design and ergonomics principles as opposed to ‘driving’ the system. These methods involve the development of a simplified process for computer based task analysis and a means of determining the percentage accommodated by any given design. Further research will eventually focus on extending the data collection, refining the task model and look at a means of suggesting design solutions in response to the analysis results

Learning from the past: uncovering design process models using an enriched process mining

Design documents and design project footprints accumulated by corporate IT systems have increasingly become valuable sources of evidence for design information and knowledge management. Identification and extraction of such embedded information and knowledge into a clear and usable format will greatly accelerate continuous learning from past design efforts for competitive product innovation and efficient design process management in future design projects. Different from existing systems, this paper proposes a methodology of learning and extracting useful knowledge using past design project documents from design process perspective based on process mining techniques. A new process mining approach that is able to directly handle textual data is proposed at the first stage of the proposed methodology. The outcome is a hierarchical process model that reveals the actual design process hidden behind a large amount of design documents and enables the connection of various design information from different perspectives. At the second stage, the discovered process model is further refined to learn multi-faceted knowledge patterns by applying a number of statistical analysis methods. The outcomes range from task dependency study from workflow analysis, identification of irregular task execution from performance analysis, cooperation pattern discovery from social net analysis, to evaluation of personal contribution based on role analysis. Relying on the knowledge patterns extracted, lessons and best practices can be uncovered which offer great support to decision makers in managing any future design initiatives. The proposed methodology was tested using an email dataset from a university-hosted multi-year multidisciplinary design project

Benefits of Matching Domain Structure for Planning Software: The Right Stuff

We investigated the role of domain structure in software design. We compared 2 planning applications, for a Mission Control group (International Space Station), and measured users speed and accuracy. Based on our needs analysis, we identified domain structure and used this to develop new prototype software that matched domain structure better than the legacy system. We took a high-fidelity analog of the natural task into the laboratory and found (large) periformance differences, favoring the system that matched domain structure. Our task design enabled us to attribute better periormance to better match of domain structure. We ran through the whole development cycle, in miniature, from needs analysis through design, development, and evaluation. Doing so enabled inferences not just about the particular systems compared, but also provided evidence for the viability of the design process (particularly needs analysis) that we are exploring