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Ten Years Applying Psychological Science Inside the U.K. Government is fascinating. It prompted this post and is at 6. below, but first a back story.

TL;DR. Main points:

1. Ron Cumming, my father, pioneered human factors in Australia in the 1960s.
2. Human factors, or user-centred design (UCD), is the design of devices and systems to be safe, easy, and effective for users.
3. Ron and colleagues persuaded Victorian politicians to pass in 1970 the world’s first compulsory seat belt laws–road fatalities dropped immediately.
4. Thaler & Sunstein’s Nudge gave examples of how simple changes in messages and systems can help people make better choices.
5. I taught UCD at La Trobe for many years. Students had great fun identifying easy ways to improve the design of everyday things simply by watching and talking to real users. Some tell me they were inspired to go on and work in the field.
6. In her fascinating column Ten Years Applying Psychological Science Inside the U.K. Government Carla Groom describes how nudge and UCD ideas can be used to make dramatic improvements to people’s lives. And how psychology PhDs can be effective leaders in such non-academic roles.

User Centred Design

A few examples in the first lecture and the students were saying “this is obvious, why isn’t everything done this way?” Don Norman‘s first few pages prompt the same reaction.

It should be obvious whether a door should be pushed or pulled, without labels. A first project: simply observe people as they approach different doors around campus.

Next, ask someone to open an unfamiliar microwave, or turn on the light on the left, or mute this mobile phone. Ask them to ‘think aloud’ as they approach the task, then watch without interrupting.

No RCT, control group, or fancy statistics required. Watch just a few users before–if you can–redesigning the object or system. Then do it again, and again. But you need real users–old, young, left-handed, neurodiverse, not speaking your language, perhaps disabled…

It can be frustrating. You quickly start noting poor design everywhere: why must I type my email in two different places? Why can’t I dislike all the options? Why do I need my glasses to figure out how to turn on the upper shower? …lots of scope for your psychology students to improve the world!

Landing Aircraft Safely

Ron Cumming, my father, was an aeronautical engineer researching crashes at the most dangerous moment in flying–landing. He decided it was largely a perceptual problem–the pilot perhaps needing to land with no visible horizon and only a single line of lights down one side of the runway.

Ron spent 1960 with human factors guru Paul Fitts at the University of Michigan studying psychology. Then he and psychologist Ross Day introduced human factors (ergonomics) to Australia.

Ron and colleagues developed T-VASIS in the 1960s. It was a simple array of lights each side of the runway that indicated to the pilot whether the aircraft was on the ideal glidepath, or needed to fly up or down a little. It was installed in many countries, and some airfields still offer it, despite the widespread use of modern radar systems.

Leading in Road Safety Was Just the Start

After leading the world with compulsory seat belts, in 1976 Victoria was also first with random breath tests of drivers, with .05 as the alcohol limit. Ron and Ross, as the two psychology professors at Monash University, set up the Monash University Accident Research Centre. The good work of MUARC continues.

In 1987 Victoria set up VicHealth as the world’s first health promotion foundation under legislation claimed to set the standard for international best practice by banning tobacco advertising and diverting those advertising dollars to fund anti-smoking campaigns and buy out tobacco sponsorship of sport and the arts.

In 1988 VicHealth funded launch of SunSmart, with the aim of changing behaviour to increase protection against UV in sunlight and thus reduce skin cancer. For example, with its Slip-Slop-Slap campaign.

These and other public behaviour change programs have saved numerous lives, averted much suffering, and reduced health costs. Psychologists continue to play prominent roles in all these programs.

Carla Groom Diagnosed as Autistic at 44

Dr Carla Groom, the then Head of Human Centred Design Science, U. K. Department of Work and Pensions was interviewed about her later-in-life formal diagnosis as autistic, at age 44. Again she is fascinating, here in recounting how the diagnosis explained for her so much about herself and how she worked.

She tells of the strategies she uses to support other neurodiverse people to be their most effective and, more generally, how she builds diverse–usually multi-disciplinary–teams, which tend to be better at problem solving.

Training PhDs to Be Effective in Non-Academic UCD and Nudge Work

Ten Years Applying Psychological Science Inside the U.K. Government offers lessons for how postgraduate education can be broadened to prepare PhDs to work and lead effectively beyond the academy. Including: study qualitative research methods, learn to write for a general audience as well as for academic journals, work in multi-disciplinary groups, and undertake messy real-world projects.

Such as helping pilots to land safely, or nudging everyone to wear a wide-brimmed hat and apply sunscreen.

Geoff

A Chat with ChatGPT by a veteran AI researcher

John Self entered the field of AI in the early 1970s. He’s a distinguished scholar who can claim to have introduced the idea of user model in his 1974 article (while visiting The University of Melbourne). He was writing in the context of AI in Education, a field in which he was a pioneer and long-time leader. Released in 2005, his last AI book is humane and broad, on open access and an excellent read: Whoever Said Computers Would be Intelligent 

John generously hosted what was for me an important sabbatical, in Lancaster in 1988. For a couple of decades back then my research was as a psychologist in AIEd. Many in that field (not John, and not some of the other leading lights) were IT tragics with a view that human learning was little more than turning on the tap to fill the bucket. John’s landmark contribution in 1974 was to recognise that any IT (‘intelligent tutor’, the pretentious term back then) that individualised its response to a student must contain a model of that student. The crudest might be merely a note of where that student was up to in the book. Typically it would comprise a record of previous student work, correct responses and errors, and earlier comments by the IT.

Early Intelligent Tutors (ITs)

Many of those early ITs supported learners to achieve impressive gains on tests, especially in science and computing fields. Interactions resembled those in ‘direct instruction’ classrooms. Direct instruction is highly structured and is coming back into vogue in many countries, often for early reading and numeracy. However, then as now, many students find the interactions stultifying. Grit your teeth and use an IT to quickly get up to speed with LISP! But a fulfilling education, perhaps not 🙁

Learner Models in ITs

My role in AIEd was often to be the maverick critic, despairing of the poverty of typical learner models. I and colleagues studied transcripts of tutoring interactions of expert teachers with individual learners when given occasional chances to interact. Think of a teacher wandering in a class working as individuals. The teacher has a brief interaction with an individual, initiated by a student requesting help, or the teacher walking by and choosing to interrupt.

Human Tutoring and Learner Models

Not surprisingly, the interactions were often brief, just a single Q&A in either direction, or little more. But they were highly diverse. The Q might be about motivation, feelings, seemingly irrelevant things that affect the learner’s work and thinking. Brief explanations can of course be valuable, but possibly the most valuable comments were often much higher-level, about strategy, or motivation. “Inspiring” is possibly the most valuable teacher ability, as hopefully we all remember. Human teachers have learner models for their individual learners. These models are likely fragmentary in many respects, but they are, most importantly, highly diverse. Achieving this richness was the enormous challenge for IT researchers seeking to model good tutoring. It remains a core challenge for any AI intended to be used by a person.

John the Fell Runner and Hill Walker

For decades John was an immensely fit fell runner, spending hours and days in the hills of North-West England. He stopped running in 2017, then from 2018 has been posting online reflections and great photos from his Saunterings in the hills, dales, and moors near Lancaster and surrounds.

ChatGPT’s User Model

John started by asking “What do you think of Saunterings by John Self?” I’m guessing John had front of mind trying to diagnose ChatGPT’s model of its user–John. What did it know about, what did it assume about John? John gives his own commentary, commenting on the AI responses and telling us a little of his own thinking. Lots of fascinating stuff there, especially as John picks apart what seems to be underlying the conversation.

ChatGPT adopts a chatty, deferential, friendly, explanatory style, quick to apologise and explain its own errors. John could of course ask it to adopt a different perspective and style–it immediately did so, and quite convincingly.

John’s Insights

John’s first overall reaction was astonishment that ChatGPT could do so well, and so blindingly fast. Then follows pure gold as John extends his Q&A to investigate various thoughts about how the IT works, what assumptions it makes, how it formulates opinions, and where its boundaries lie. To what extent does it build a model of John? To what extent is it merely integrating the results from a huge number of searches, or is it reasoning about these? John eventually concludes that he cannot trust ChatGPT and cannot follow its reasoning–because there isn’t any. I won’t try to summarise: you need to read John’s final paragraphs to get the rich story.

Extremely well-informed gold!

Geoff