Augmenting Humans Learning From Our Mistakes – Identifying Opportunities for Technology Intervention to Address Everyday Cognitive Failure

—There is growing opportunity for technologies to augment human memory and other cognitive processes – widespread pervasive sensing enables fine-grained traces of human activity, and advances in display technologies enable systems to provide input to user cognition in a wide array of settings. However, systems to date typically either address known cognitive impairment (e.g. autism, Alzheimer’s disease), or look to enhance ones’ general capacity for a specific task. In contrast to these approaches, we argue that a focus on recognition and quantification of human error is key to the design of future systems for augmenting the human mind. By focusing on the errors made in cognition, we can first identify frequent, persistent or severe failures as targets for future pervasive computing systems, and then go on to measure the success of any interventions developed, i.e. by asking have the augmentation systems delivered actually reduced the prevalence, persistence or impact of a specific cognitive error? In this article, we make the general case for the study of human error in order to support the design and evaluation of technology interventions intended to extend cognitive capabilities, before focusing on a case study in the augmentation of human memory.

[1]  Niels Henze,et al.  Impact of Video Summary Viewing on Episodic Memory Recall: Design Guidelines for Video Summarizations , 2016, CHI.

[2]  Elise van den Hoven,et al.  Designing for the Other 'Hereafter': When Older Adults Remember about Forgetting , 2016, CHI.

[3]  Cecilia Mascolo,et al.  Mobile-Based Experience Sampling for Behaviour Research , 2015, Emotions and Personality in Personalized Services.

[4]  Thad Starner,et al.  Towards Passive Haptic Learning of piano songs , 2015, 2015 IEEE World Haptics Conference (WHC).

[5]  P A Hancock,et al.  On the paradoxical decrease of self-reported cognitive failures with age , 2015, Ergonomics.

[6]  Albrecht Schmidt,et al.  Security and Privacy Implications of Pervasive Memory Augmentation , 2015, IEEE pervasive computing.

[7]  Barry A. T. Brown,et al.  Into the wild: challenges and opportunities for field trial methods , 2011, CHI.

[8]  P. Richard,et al.  Augmented Reality for Rehabilitation of Cognitive Disabled Children: A Preliminary Study , 2007, 2007 Virtual Rehabilitation.

[9]  Sven Fuchs,et al.  Augmented Cognition can increase human performance in the control room , 2007, 2007 IEEE 8th Human Factors and Power Plants and HPRCT 13th Annual Meeting.

[10]  Shahram Izadi,et al.  SenseCam: A Retrospective Memory Aid , 2006, UbiComp.

[11]  John Crawford,et al.  The Prospective and Retrospective Memory Questionnaire (PRMQ): Normative data and latent structure in a large non-clinical sample , 2003, Memory.

[12]  Steven M. Smith,et al.  Environmental context-dependent memory: A review and meta-analysis , 2001, Psychonomic bulletin & review.

[13]  Bradley J. Rhodes,et al.  The wearable remembrance agent: A system for augmented memory , 1997, Digest of Papers. First International Symposium on Wearable Computers.

[14]  W. Terry Everyday Forgetting: Data from a Diary Study , 1988 .

[15]  H. F. Crovitz,et al.  Measurements of everyday memory: Toward the prevention of forgetting , 1984 .

[16]  Donald A. Norman,et al.  Design rules based on analyses of human error , 1983, CACM.

[17]  D. Broadbent,et al.  The Cognitive Failures Questionnaire (CFQ) and its correlates. , 1982, The British journal of clinical psychology.

[18]  J. Bennett-Levy,et al.  The Subjective Memory Questionnaire (SMQ). An investigation into the self‐reporting of ‘real‐life’ memory skills , 1980 .

[19]  Albrecht Schmidt,et al.  Augmenting Human Intellect and Amplifying Perception and Cognition , 2017, IEEE Pervasive Computing.