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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