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Brain Training with Cognitive Simulations

Today we will continue our review of the benefits of brain training for specific occupations: in this case, pilots and basketball players. The lessons can be relevant not only for corporate training but also for education and brain health & wellness.

To do so, we will select quotes from our interview last year with one of the major scientists in the field of cognitive simulations, Professor Daniel Gopher. You can read the full interview here.

Prof. Gopher published an award-winning article in 1994, Gopher, D., Weil, M. and Baraket, T. (1994), Transfer of skill from a computer game trainer to flight, Human Factors 36, 1–19., that constitutes a key milestone in the cognitive engineering field.

On Cognitive Training and Cognitive Simulations

AF: Tell us a bit about your overall research interests

DG: My main interest has been how to expand the limits of human attention, information processing and response capabilities which are critical in complex, real-time decision-making, high-demand tasks such as flying a military jet or playing professional basketball. Using a tennis analogy, my goal has been, and is, how to help develop many “Wimbledon”-like champions. Each with their own styles, but performing to their maximum capacity to succeed in their environments.

What research over the last 15-20 years has shown is that cognition, or what we call thinking and performance, is really a set of skills that we can train systematically. And that computer-based cognitive trainers or “cognitive simulations” are the most effective and efficient way to do so.

This is an important point, so let me emphasize it. What we have discovered is that a key factor for an effective transfer from training environment to reality is that the training program ensures “Cognitive Fidelity“, this is, it should faithfully represent the mental demands that happen in the real world. Traditional approaches focus instead on physical fidelity, which may seem more intuitive, but less effective and harder to achieve. They are also less efficient, given costs involved in creating expensive physical simulators that faithfully replicate, let’s say, a whole military helicopter or just a significant part of it.

AF: Very interesting. In the Serious Games Summit this week we are seeing a number of simulations for military training that try to be as realistic as possible. Are you saying that they may not be the best approach for training?

DG: The need for physical fidelity is not based on research, at least for the type of high-performance training we are talking about. In fact, a simple environment may be better in that it does not create the illusion of reality. Simulations can be very expensive and complex, sometimes even costing as much as the real thing, which limits the access to training. Not only that, but the whole effort may be futile, given that some important features can not be replicated (such as gravitation free tilted or inverted flight), and even result in negative transfer, because learners pick up on specific training features or sensations that do not exist in the real situation.

Main studies and applications

AF: What are the main studies have you conducted?

DG: in this field of work, I would mention two. In one, which constituted the basis for the 1994 paper, we showed that 10 hours of training for flight cadets, in an attention trainer instantiated as a computer game-Space Fortress- resulted in 30% improvement in their flight performance. The results led the trainer to be integrated into the regular training program of the flight school. It was used in the training of hundreds of flight cadets for several years. In the other one, sponsored by NASA, we compared the results of the cognitive trainer vs. a sophisticated, pictorial and high-level-graphic and physical-fidelity-based computer simulation of a Blackhawk helicopter. The result: the Space Fortress cognitive trainer was very successful in improving performance, while the alternative was not. The study was published in the proceedings of the Human Factors and Ergonomic Society: Hart S. G and Battiste V. (1992), Flight test of a video game trainer. Proceedings of the Human Factors Society 26th Meeting (pp. 1291-1295).

AF: What have been to date the main applications of your computer-based cognitive simulations?

DG: in summary, I’d say

– Flying high-performance airplanes: in 10 hours, we showed an increase in 30% flight performance

– Flying with HMD (helmet mounted displays)

Touch-typing skills

– Teaching old adults to cope with high workload attention demands.

Developing Basketball “game-intelligence” for professional players, to improve the performance of individuals and teams

Trainer for basketball “game-intelligence”

AF: talk to us about the basketball example. I am sure many readers will find that fascinating.

DG: I served as a scientific advisor to ACE, who developed the program called IntelliGym. Although the context is different, the approach and basic principles are the same of those of developing a trainer for the task of flying a high performance jet airplane. First, one needs to analyze what cognitive skills are involved in playing at top level, and then develop a computer-based cognitive simulation that trains those skills. What most people don’t realize is that top players are not born top players. We are not just talking about instincts. We are talking about skills that can be trained.

AF: what are the results of the program so far?

DG: Well, first let me say that the company has had to overcome huge cultural barriers to get adoption by a good number of university teams and some NBA players. Coaches see the value of this tool very quickly, but administrators are harder to convince in the beginning. We have seen that the teams and individuals using Intelligym have improved their performance significantly. From the cognitive training, or skill development point of view, we have seen that players improve their positional awareness-of themselves, their mates and opponents, and ability to predict what is going on in the game and to make fast and good decisions. Players quickly develop attention allocation strategies that enable them better participate in the game, and also improve their spatial orientation.

Summary of key findings

AF: Fascinating real-world experience. Can you summarize your research findings across all these examples and fields, and how you see the field evolving?

DG: In short, I’d summarize by saying that

Cognitive performance can be substantially improved with proper training.

– It is not rigidly constrained by innate, fixed abilities.

Cognitive task analysis enables us to extract major cognitive skills involved in any task.

– Attention control and attention allocation strategies are a critical determinants in performing at top level in complex, real-time decision-making environments

– Those skills, and other associated, can be improved through training

– Research shows that stand-alone, inexpensive, PC-based training is effective to transfer and generalize performance.

– The key for success is to ensure Cognitive fidelity, this is, that the cognitive demands in training resemble those of the real life task.

I can think of many other applications. Probably currency and options traders would benefit from a system like this. Now, we will need to increase awareness, and will need to find champions willing to take risks. The cognitive simulation approach is less intuitive that traditional ones.

Professor Wayne Shebilske, at Wright State University Psychology department, is conducting additional research on applications, such as outlined on the paper Shebilske, Wayne L., et al, “Revised Space Fortress: A Validation Study” (accepted for Behavior Research Methods, Instruments and computers).

(Professor Shebilske was kind enough to write a great comment below, giving us 2 detailed references:

Shebilske, W. L., Volz, R. A., Gildea, K. M., Workman, J. W., Nanjanath, M., Cao, S., & Whetzel, J. (2005). Revised Space Fortress: A validation study. Behavior Research Methods, 37, 591-601.

Volz, R.A., Johnson, J.C., Cao, S., Nanjanath, M., Whetzel, J., Ioerger, T.R., Raman, B., Shebilske, W.L., and Xu, Dianxiang (2005). Fine-Grained data acquisition and agent oriented tools for distributed training protocol research: Revised Space Fortress. Down Load Technical Supplement, Psychonomic Society Web-based Archive (see 37,591-601).

AF: are you doing something to spread the word?

DG: apart from conferences and journals, I have written the chapter Emphasis change as a training protocol for high demands tasks, in the book Applied Attention: From Theory to Practice, A. Kramer, D. Wiegman, A. Kirlik (Eds): Oxford Psychology Press, about to be released.

A more in-depth view of his cognitive simulation approach

AF: Great. For readers who may be interested in more specific details about your specific approach to cognitive training, could you give us some lessons learned?

DG: Good question. There are different types of cognitive training. The one we have specialized in focuses on the development of attention-control, attention-allocation strategies, which are bottleneck in some high-performing, high-mental-workload- environments. Our approach is called Emphasis Change Protocol, and is based on the introduction of systematic variability in training, while maintaining the overall task intact. We just change the emphasis on sub-components of a complex task during performance. In our research, this has proven to be the most effective way to train attention management skills, task switching and control processes, such as the ability to initiate, coordinate, synchronize and regulate goal-directed behavior.

This “whole task” approach increases transfer and adaptation capabilities, vs. traditional part task training, which decomposes the complex task and trains elements in isolation. However, whole task training is harder at the beginning-there is slower progress at early stages of training.

Other principles we use, based on our and others literature, is the need for intermittent schedules of feedback (vs. full one), to help retention and transfer (at the cost of making learning slower), and the encouragement to explore alternatives to reach a general optimum. This exploration is important: we want to help the user find a flexible, and personal best, match between his abilities and task demands, out of localized peaks. Coming back to the tennis example, we know that McEnroe and Boris Becker have different styles, but both are Wimbledon winners. We want to make sure the user increases sensitivity to real-time changes in the environment and expands his or her ability to cope with them.

AF: Professor Gopher, it has been a pleasure to talk to you. Thank you for your time.

————————————–

After we published this interview, Professor Shebilske wrote the great following comment:

“Your excellent interview with Dr. Gopher reminded me why so many of us have followed his lead in training complex skills. I hope that your interview inspires others. They will find that he is generous with his ideas, time, energy, and infectious positive spirit. Working with him to replicate experiments and extend ideas is both productive and enjoyable.
Your interview includes a reference to an article by my colleagues and me. I want to update the reference and provide a related reference to a Web-based Archive.

Shebilske, W. L., Volz, R. A., Gildea, K. M., Workman, J. W., Nanjanath, M., Cao, S., & Whetzel, J. (2005). Revised Space Fortress: A validation study. Behavior Research Methods, 37, 591-601.

Volz, R.A., Johnson, J.C., Cao, S., Nanjanath, M., Whetzel, J., Ioerger, T.R., Raman, B., Shebilske, W.L., and Xu, Dianxiang (2005). Fine-Grained data acquisition and agent oriented tools for distributed training protocol research: Revised Space Fortress. Down Load Technical Supplement, Psychonomic Society Web-based Archive (see 37,591-601). .

The journal articles’ abstract describes both:

Abstract
We describe briefly the redevelopment of Space Fortress (SF), a research tool widely used to study training of complex tasks involving both cognitive and motor skills, to execute on current generation systems with significantly extended capabilities, and then compare the performance of human participants on an original PC version of SF with the Revised Space Fortress (RSF). Participants trained on SF or RSF for 10 sets of 8 3-min practice trials and 2 3-min test trials. They then took tests on retention, resistance to secondary task interference, and transfer to a different control system. They then switched from SF to RSF or from RSF to SF for two sets of final tests and completed rating scales comparing RSF and SF. Slight differences were predicted based on a scoring error in the original version of SF used and on slightly more precise joystick control in RSF. The predictions were supported. The SF group started better, but did worse when they transferred to RSF. Despite the disadvantage of having to be cautious in generalizing from RSF to SF, RSF has many advantages, which include accommodating new PC hardware and new training techniques. A monograph that presents the methodology used in creating RSF, details on its performance and validation, and directions on how to download free copies of the system may be downloaded from www.psychonomic.org/archive/.

The extended capabilities for RSF include a) being executable on current generation platforms, b) being written in a mostly platform independent manner, c) being executable in a distributed environment, d) having hooks built in for the incorporation of intelligent agents to play various roles, such as partners and coaches e) providing a general experiment definition mechanism, f) supporting teamwork through being able to flexibly assign different input controls to different members of a team, g) maintaining all data in a central database rather than having to manually merge data sets after the fact, and h) having playback capability, which enables researchers to review all actions that occurred during an experiment and to take new measures. Experimenters can design measures before an experiment to test specific hypotheses with a rigorous laboratory task. They can also use playback to discover and explore unanticipated events. Although simpler and more complex synthetic task environments can be advantageous for some goals, Danny Gopher, our colleagues, and I believe that Space Fortress remains an important tool for scientists and trainers. Please feel free to contact me (wayne.shebilske@wright.edu) for additional help downloading and using RSF.”

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