nm1208: er [1.9] so first thing i'll say is that er from [0.3] er beginning of 
next week Monday [0.4] er these [0.2] er lectures will be moving into brand 
spanking new [0.5] sort of flashy lecture theatre [0.6] er in the new 
agriculture building [0.3] and er [1.5] this is er where it is [0.5] so er 
approach the front entry in meteorology sort of right [0.3] down there [0.3] er 
[2.5] go outsi-, go round outside left side of meteorology to meet in reach the 
entrance to the new ag-, agriculture building [0.5] lectures [0.4] theatres one 
and two are on a lower ground floor [1.1] use stairs or the lift [2.3] and 
there will be no access to the new building from the old [0.2] agriculture [2.
2] right [0.4] does that [0.3] make sense to everybody [1.5] cool [1.2] right 
[0.2] er what i'm going to talk about today is expertise [1.7] and er the first 
thing [0.2] we need to do [0.5] before [0.4] we embark on an investigation of 
any sort of area [0.7] is to [0.2] define [0.9] what we're talking about so 
that we know that we're actually talking [0.4] about the same sort of thing [1.
7] so [0.3] what constitutes [0.2] an expert [6.3] right [0.2] here's er one 
example from [0.2] New Forest Post ninth of March [0.3] nineteen-ninety-five [1.
0] er 
front page article driving death off the road [0.4] young drivers at a Forest 
School have been undergoing psychological tests that can help slash [0.4] the 
death toll on Britain's roads an expert [0.2] from namex University [0.3] has 
been putting forty-five Brockenhurst College students through hazard perception 
tests this week [0.7] a hazard perception test could well be part of the 
driving test in two years time said Dr namex in nineteen-ninety-five [laughter] 
[2.3] never mind er [laughter] so in this case this journalist from New Forest 
Post [0.4] has labelled [0.3] me an expert because somehow i managed to 
convince him [0.5] that i knew marginally more about the subject area [0.2] 
than he did [1.0] however what does it say [0.5] in the dictionary [8.0] [2.5] 
okay it's a [1.2] bog standard er Oxford Dictionary [2.6] er [1.0] an expert 
means [0.2] being practised skilful and [0.2] well informed [1.1] and obviously 
the sort of problem with this [0.4] definition is how [0.3] exactly practised 
skilful and well informed do you actually have to be [0.3] before we start 
calling you [0.4] an expert [1.5] and er 
one area where the definition [0.2] of expertise is [1.6] vital is within the 
justice system [0.6] so er expertise can actually be officially [0.4] er 
conferred [0.2] by a judge [0.4] for the purpose of court room testimony [0.6] 
and in fact f-, quite a few of my colleagues have been called up to court to 
give evidence as [0.3] psychologists on various [0.5] sort of different aspects 
[0.2] particularly driving dri-, driver fatigue [0.5] and various things to do 
with driving [1.3] er [2.5] and in this case [0.8] the er [0.2] definition of 
expertise [0.5] is usually [0.2] one on training this is er [1.7] an official 
one this is actually in your handout [6.1] okay so er for the purposes of 
justice the courts describe an expert as one who's qualified [0.6] by training 
or experience to form a definitive opinion [0.6] in areas of science arts or 
department of trade [0.4] in which laypersons [0.3] are not [0.3] prepared to 
reach accurate conclusions [2.0] but the problem with this sort of definition 
[0.2] is that there is evidence that training [0.7] and experience [0.3] are [0.
2] poor criterion for judging the quality of an expert [0.4] something we'll 
[0.4] explore [0.6] er later [2.9] so er [0.5] moving on to [0.2] more 
psychological [0.4] like definitions [0.9] expertise has been seen as the 
amount and complexity of knowledge [0.6] gained through experience in a domain 
[0.6] okay by the way this er [0.5] these er [0.2] definitions are in your 
handout so i think they are yeah they should be in your handout so [0.3] don't 
need to scribble them down [1.3] that's all right [2.0] okay this is good old 
Eysenck and Keane [1.0] our course textbook [1.7] and er they stated that 
expertise means being good at specific [0.2] problems [0.7] within a specific 
domain [1.4] in a lot of domains [0.2] expertise [0.2] is defined by peer 
review [1.4] for example with driving [0.2] top er police officers [0.5] er top 
police drivers are often put forward [0.4] as [0.3] the model of driving [0.4] 
so they can perform [0.3] a range of skills [0.6] which have [0.2] been defined 
[0.2] by other [0.5] police drivers [0.5] as being important [0.2] to good 
driving [0.3] like er skid control hazard perception [0.5] vehicle control et 
cetera [1.6] and most of the time this may be entirely sensible [0.8] so [0.3] 
you can assume that they must know something about what they're 
doing [1.4] however police drivers have got a job to do [0.6] and that job 
involves driving [0.2] as fast as possible [0.2] from A [0.4] to [0.2] B [2.5] 
and with some [0.2] advanced driving courses [0.5] er which are usually run [0.
3] by [0.2] police drivers [0.8] er [0.3] one problem with this is that people 
are often taught to drive [0.2] like policemen [1.0] and while this includes 
skills [0.3] which have been shown to be beneficial to reducing accident 
involvement [0.4] like hazard perception [1.3] it can also legitimize [0.2] 
practices [0.3] which are inappropriate for people [0.2] who aren't [0.7] 
driving police cars [0.5] so [0.2] things like driving fast or making progress 
[3.5] i'm very bitter about that 'cause i recently failed my motorbike test for 
failing to make progress by driving too slow round roundabouts [0.7] [laughter] 
and er [3.0] here's another definition put forward by some psychologists [3.0] 
er [0.4] Ericsson and Lehmann nineteen-ninety-six [1.8] argued that er [0.4] 
expertise expert performance involves [0.2] an individual [0.8] being able to 
produce a [0.3] consistent performance [0.3] on demand [0.5] over a range of 
tasks that are representative of
of a domain [0.9] that is [0.3] expert perf-, er expert performers [0.5] are 
those individuals who can reliably [0.2] reproduce [0.8] their skill [0.2] 
their superior skill whenever [0.3] you ask them to [2.9] Ericsson and Lehmann 
[0.2] argued that though this is quite a narrow definition [0.3] the advantage 
[0.4] of this sort of definition [0.6] is that at last we have an opportunity 
[0.5] for measuring [0.3] expertise [0.6] okay what we're doing here is 
operationalizing expertise [0.5] so [0.3] by by definition that means [0.4] we 
can start measuring it in laboratory [0.3] experiments the problem with all the 
previous [0.3] definitions [0.2] is because they're so nicely woolly and vague 
[0.4] it's very hard to actually sit down and measure what we mean [0.8] er [0.
2] by narrowing down the definition like this [0.2] we can actually start 
measuring it [0.4] which means we can start doing some science on it [3.7] but 
as you can imagine [0.5] evaluating [0.2] performance is a real [0.2] problem 
[0.2] for this [0.2] field [4.1] for example what's a [0.3] laboratory test for 
whether someone is an expert art [0.2] critic [1.3] so we could [0.3] do 
something like [0.5] develop a test of 
their knowledge [0.5] of history of art but would that [0.2] be a fair test of 
whether they were a good critic or not [2.2] so there's great difficulty in 
operi-, operisati-, er [0.2] operationalizing [0.3] expertise [0.5] in many 
domains [0.2] so [0.3] and actually measuring expertise [0.2] quantitatively [1.
3] and [0.4] what we find with our police drivers' peer review [0.5] is often 
the only [0.5] measure of success [1.1] and er [0.6] Goodyear in nineteen-
ninety-seven [0.5] brought up this point with respect to professional 
psychologists [0.8] so with professional counselling [0.2] psychologists for 
example [0.5] how can we actually measure the quality of their work you know is 
it the number of people they successfully treat in the year [0.3] in a year or 
something like that [1.0] and [0.3] with academic psychologists like me is it 
the number of papers published the number of research contracts i've won [0.5] 
not the quality of the papers published [0.4] okay [0.2] and these are all 
things that [0.2] you know are actually used to evaluate us [0.4] in ra-, real 
life [0.7] but you know [0.2] are they [0.2] fair measures [2.5] okay [0.3] i 
mean and 
you can see there's potential problems for that so for example [0.4] er [0.5] 
er we've we get although we get judged for teaching [0.4] on a sort of 
department wide basis [0.3] the teaching quality isn't actually [0.2] formally 
assessed on a person by person basis [0.2] okay apart from [0.4] we get 
informal bits of feedback from the sort of sheets you fill in [0.4] but er [0.
3] the actual formal assessment takes place on a department wide proc-, er 
process [0.4] so at the moment [0.4] er i actually have no personal s-, 
incentive for my career to [0.2] sort of [0.5] bother to sort of do any good 
teaching [0.3] my all my incentive is based on research because that's [0.2] 
what i personally get judged on [0.2] not the teaching [1.4] okay so there are 
problems [0.3] in judging expertise [0.3] in this sort of way [1.6] so [0.7] 
into the science how have people [0.2] tried to get around [0.5] these sort of 
problems [1.7] well first thing they did [0.3] was to choose [0.2] a [0.2] an 
appropriate domain [0.7] and they chose a domain [0.4] where expertise is well 
defined [0.6] easy to operationalize [0.4] and [0.4] measurable [1.1] and that 
domain [0.9] was [0.2] chess [10.0] okay [0.3] so now [0.4] chess was the er [0.
2] 
original domain for studying [0.2] expert [0.2] performance [2.0] and the nice 
thing about chess is that there are [0.2] clear criterion [0.4] distinguishing 
novices [0.2] from experts basically experts will beat [0.5] novices [1.2] and 
it's also [0.2] a very well defined game set within a very rigid [0.2] 
framework of rules [0.8] and this makes it [0.2] really nice for [0.2] 
experimental work [1.6] on the face of it it should be straightforward [0.4] 
okay there's no [0.3] er chance involved there's no do-, dice rolling or 
anything that that [0.6] and there are a finite [0.3] in theory finite set of 
moves that can be made at any one time [1.5] but it's the sheer number [0.2] of 
[0.3] these possible moves which is overwhelming [0.6] so just [0.3] three 
moves into a chess game [0.5] gives you more than nine-million [1.7] distinct 
[0.3] board positions [5.4] and er on the face of it [0.3] this should be an 
ideal task for a computer [3.0] however attempts to design computers that can 
match [0.3] the best chess players [0.3] hasn't proved to be easy [1.0] so [0.
7] what er computer chess brai-, er programs tend to do [0.3] is search [0.4] 
all available options [1.1] for example a 
a recent one [0.4] developed by I-B-M [0.5] and known as Deep Blue [2.6] 
considered about ninety-billion [0.6] moves [0.4] per turn [2.1] at a rate of 
about nine [0.2] billion [0.7] per second [5.0] okay and this is a computer 
that i think it eventually managed to beat Gary Kasparov but it was a close run 
thing [1.5] but the point is [0.7] that people [0.2] can't do that [0.8] our 
brains literally can't think [0.3] at anywhere near that sort of speed [1.4] so 
how come the best [0.2] chess players human chess players still manage [0.2] to 
beat computers most [0.4] of the time [1.3] well what are the best chess 
players doing [0.5] that the computers [0.2] and the novice chess players 
aren't doing [3.0] originally [1.7] it was assumed that chess experts were 
better [0.4] as a result of superior intellectual capacity [3.0] however [0.2] 
it's er since been shown that expert chess players [0.3] aren't actually any [0.
3] cleverer [0.5] than anyone [0.2] else or [0.2] cleverer than a matched 
control group i should say [0.9] so [0.2] study found that their I-Qs are no 
higher [0.2] than matched [0.3] controls [5.5] so [0.7] if their mental 
capacity [0.8] doesn't appear to be any higher [0.2] 
than normal [1.1] then presumably it must be their strategies [0.8] which are 
superior [4.2] so [0.5] how has a chess performance been measured [1.2] er lot 
of the [0.6] original work which was carried out [0.6] by [0.3] de Groot [1.7] 
and er [0.4] what he did was get er [0.2] two groups of chess players [1.9] the 
first group were what he called experts [0.3] and they were [0.3] top local 
club players [1.3] that's his definition of expert [0.7] and he compared with 
an [1.0] with an even better group [1.1] the grand masters [0.9] so [0.2] grand 
masters are world class chess players [0.2] so his comparison is between [0.7] 
one really high level of expertise and a [0.3] still a pretty high level of 
expertise and what [0.2] he wanted to find out what is the difference [0.2] 
between [0.2] these two [0.4] groups [2.3] and what he did was er measure it 
measure [0.5] er their performance and what they're thinking about [0.5] with 
er [0.2] verbal protocols [0.6] and what verbal protocols are is simply getting 
people to talk aloud [0.3] when they're doing the task [0.2] and [0.2] then 
transcribing what they're saying and trying to analyse it [1.1] okay so he 
analysed their performance by 
getting them to talk aloud [0.4] and he also timed [0.6] their responses [4.1] 
and er he presented them with a [0.3] game [0.3] position [1.5] and asked them 
to [0.5] think aloud [0.5] while deciding [0.6] what move to make [5.5] and [1.
0] he found that people were only considering [0.3] about thirty [0.3] 
alternative [0.2] moves [0.6] searching at most [0.5] to a depth [0.2] of six 
moves [0.7] and [0.3] frequently less [3.3] okay [0.2] and there's a [0.2] nice 
summary of all this sort of stuff in Eysenck and Keane [5.2] so the grand 
masters didn't consider more moves [0.4] and they didn't search [0.3] more 
deeply than the experts [0.6] but they were [0.3] neverlethe-, nevertheless 
slightly faster [0.5] at getting to the right [0.3] move [2.0] and they also 
came to their chosen move [0.2] earlier [0.3] in their search through possible 
alternatives [5.1] and er [0.4] that was supported by the finding that the 
quality of the chess moves that these people were making [0.7] remained high 
even when you [0.2] forcibly reduced their search time [0.4] okay so the [0.3] 
the best players [0.4] er still searching similar number of options [0.3] but 
they're coming to the right option earlier [0.4] in their search [4.8] and in a-
, in 
addition [0.2] they found that the grand masters [0.7] er actually came up with 
better moves [0.4] which was assessed by independent [0.7] raters [5.2] and 
what er de Groot argued here [0.7] is that the chess master doesn't generate 
moves by [0.4] a simple search [0.5] through possible alternatives [0.7] which 
is exactly what our chess computers are doing [0.2] to generate all possible 
altern-, alternatives and then go through them [0.4] one by one [0.9] instead 
our grand masters [0.3] are using [0.7] cued [0.2] recall [0.4] from memory [11.
4] okay so as i said the depth of search how many moves people are thinking 
ahead [0.5] er increases up the level of our experts [0.5] but it doesn't 
actually increase when people move on to be [0.2] grand masters [3.0] and de 
Groot suggests that the difference between these experts and these grand 
masters is that the grand masters have a [0.2] superior knowledge [0.7] of 
different [0.2] board positions [0.6] and what the best move from each position 
would be [8.6] okay so the idea is that the expert [0.2] can recognize [0.6] 
the position [0.7] and [0.3] knows a good move from there [0.5] and that means 
you don't 
have to think about [0.2] all of the irrelevant moves [0.4] which you know in 
advance are a load of rubbish [0.4] because [0.4] you've already you can 
recognize this pattern from your memory [0.5] so you don't have to do what the 
computer is doing and go though all of the [0.4] irrelevant [0.3] s-, er 
possible moves as well [0.7] and ma-, that makes it much more efficient [0.4] 
and it means that you don't have to do [0.5] do this er ninety-billion [0.4] 
option search every time you make a move [4.3] and er [0.7] de Groot's 
colleagues found further evidence to support this in an experiment that 
involved chess master and expert players' [0.4] recall of board positions [2.1] 
they found that if the position was from an actual game [0.7] than the grand 
masters 
[0.4] were [0.3] significantly [0.4] better [0.8] at remembering [0.3] the 
position [4.1] but [0.7] if [0.2] the position was random [2.5] so you just [0.
2] pick your chess pieces [0.2] whack them anywhere on the board [0.2] nothing 
to do with the game [0.7] then you found no difference [0.3] between [0.6] er 
the two [0.2] exp-, [0.5] expert groups no difference between the experts and 
the grand masters [1.4] okay so y-, [0.2] grand masters only get their 
advantage [0.3] if they're trying to memorize an actual board position [0.2] 
and they argued [0.3] that tha-, this is because [0.2] the grand masters simply 
[0.3] knew more [0.7] game [0.4] positions [6.7] so [2.0] according to [0.3] 
move on to Chase and Simon who followed up this work what [0.2] differentiates 
[0.7] expert chess players from novice chess players [0.8] is that experts have 
stored [0.4] and 
organized in memory [0.5] many tens of thousands [0.5] of [0.2] different [0.2] 
game [0.2] positions [2.4] and the idea is when they saw sensible game 
positions they could use the knowledge they had [0.3] in memory [0.9] to help 
them remember [0.2] the various [0.3] positions [0.2] as integrated [0.3] 
organized [0.4] chunks [0.7] of [0.9] information [3.7] and [0.3] this is the 
sort of thing that Chase and Simon [0.2] nineteen-seventy-three [0.6] were 
looking at [1.6] okay if you remember back to er [0.3] about this time last 
year [1.0] er [0.7] and my first year practical on [0.2] memory chunking [1.1] 
you'll remember that you could remember [0.4] the same [0.3] stimuli [0.4] 
better [0.6] when they were [0.2] sort of chunked into groups [0.3] that linked 
in [0.7] with your own personal knowledge basis so remember i presented you 
with a list of [0.5] sort of letters [0.4] when i arranged them in one group in 
in certain groups they made no sense and so you're trying to remember each 
individual letter [0.4] when i arranged them in other groups [0.2] they 
actually formed [0.2] er acronyms [0.3] sort of common acronyms er [0.4] so 
that instead of having to remember every single letter [0.2] you could just 
remember [0.4] 
the fewer number [0.2] of acronyms [1.4] okay so this is all Miller's stuff on 
there being room for [0.5] seven plus-or-minus two [0.3] items in short term 
memory [1.4] but the size [0.4] of [0.4] what [0.2] one of those items is [0.3] 
depends on your [0.4] personal knowle-, knowledge [1.3] and Chase an-, ar-, 
Simon argued that [0.6] chess players are chunking [0.5] the board [0.3] and 
the more expert you get at chess [0.3] the bigger more complex [0.3] your 
chunks [0.6] become [6.0] and er [0.4] chess experts were found [1.0] to be 
able to memorize board positions [1.6] by breaking them down into seven or so 
[0.2] familiar patterns or units [1.6] and the difference between the [0.2] 
novice and expert [0.4] lay in the amount of information they could hold [0.2] 
in one chunk [15.2] so in er one experiment they did [0.9] people had two chess 
boards in front of them [0.8] on the first board was an arrangement of pieces 
[0.9] and the second board [0.3] was blank [2.1] and what participants had to 
do was recreate the position they saw on the first board [1.5] with the pos-, 
er [0.5] sorry [0.2] say that again [0.4] what participants had to do was 
recreate the position they could see on 
the first board [0.3] on the second board [1.2] and what they measured [0.4] 
was the number of glances [0.3] that these people [0.5] had between the two 
boards [0.8] and how much [0.6] they remembered [0.2] in [0.2] each of these 
glances [1.9] okay [0.5] so [0.2] they argued that each glance between these 
two boards could be viewed as [0.2] a chunk [3.9] and better players could rem-,
r-, recognize a chunk faster [1.5] and [0.2] their chunks were also [0.2] 
bigger [10.8] and er work [0.2] published [1.0] in nineteen-ninety-six by Gobel 
[0.2] and Simon [5.2] confirms this [9.7] and er [0.7] they demonstrated that 
[0.2] retrieval processes involving [0.8] recognition of board [0.2] 
arrangements [1.8] are actually instrumental [1.9] in grand master level chess 
players' success [0.4] compared with [0.7] er [0.9] less experienced [0.4] 
other players [7.8] okay so even when [0.2] the grand masters were time 
constrained [0.6] so they couldn't engage their looking ahead processes their 
performance isn't much affected [0.9] okay [0.6] and they argued [0.7] this 
time that this suggests that an organized [0.2] knowledge system [1.0] is 
relatively more important to [0.2] experts' performance [0.5] than even the 
processes 
involved in predicting future moves [0.4] okay so [0.3] the key thing is 
organization [0.4] of information [0.7] er [0.3] organization of knowledge [6.
8] having said that [0.6] er Holding and Reynolds [2.2] did experiment in 
nineteen- [0.2] eighty-two [0.7] er they reran de Groot's experiment using a 
random board position [0.5] okay so again chess board [0.4] and you just whack 
on the pieces at random [0.4] nothing to do with the game [1.2] and [0.4] by 
comparing chess players of different levels [0.7] on the move they would make 
from [0.5] the random [0.2] position [1.7] they found that the experts produced 
better moves [0.4] on [0.3] even from these totally random board positions [0.
8] okay [0.3] so the idea is here is that er [0.4] the experts couldn't 
possibly have the board position previously analysed [0.3] because it was a 
totally random board position [1.1] er but they inst-, still generated better 
moves on from those random board positions so [0.4] they argued that er [1.3] 
we shouldn't be so one-sided about this [0.3] and [2.4] there is a important 
element of chess expertise that does involve [0.8] evaluating [0.2] board 
position as well 
as [0.5] remembering it [4.1] okay [1.2] right so [0.4] half an hour in so er 
[0.6] kind of thought [0.4] er s-, skill [0.6] and learning things this is when 
your attention should be sort of plummeting downhill [0.3] so i shall attempt 
to raise it again by doing a quick demonstration [0.2] of [0.4] one of these 
experiments [0.5] if i get you to stick your hand up if you've ever been in a 
chess club [2.1] [laughter] can i get y-, stick your hand up if you've ever 
played chess before [0.8] [laughter] bollocks [laughter] right [0.2] okay could 
you put your hand up if you think you're intermediate chess player [0.2] y-, [1.
3] sort of okayish [1.8] it's getting tricky i'm going to have to pick at 
random i think [0.2] okay put your hand up if you've never ever played chess 
before [1.3] right out of those people who couldn't even name the pieces on a 
chess board [1.3] brilliant [0.4] er cou-, could i can i get you to come out [2.
1] right [0.5] okay and out of the rest of you i need to find someone who er [0.
4] okay who who's been playing since they were er a child who has played as 
chess as a child and has played recently as 
well [0.5] can you stick your hand up [2.0] [laughter] who's played chess 
within the last four years [laughter] ah brilliant namex
sf1210: oh no [0.2] 
nm1208: it's all right [laughter] this is undermining [0.3] okay what i can i 
get you to stand over there and look in that direction [0.6] okay [0.9] right 
sf1210: look in which direction 
nm1208: s-, o-, over there [0.2] just just don't don't look over here [0.2] 
okay [laughter] [0.5] and what i'm going to show you [0.5] i've er what i've 
designed here is er my acme patent overhead [0.4] chess set [2.7] [laughter] 
carefully constructed there [1.1] okay [0.6] and basically what i'm going to do 
is er i've got a chess board position [0.3] from a famous game [0.4] for namex 
versus namex played this Tuesday [laughter] [0.7] and er if you come and stand 
here [1.7] er i'm going to show that for thirty seconds [0.3] 
nm1208: oh God [0.3] 
nm1208: and then i'm going to cover it up [0.2] and then i want you to recreate 
that position on that board [0.4] as f-, [0.2] as er fast as you can [0.3] and 
you've got about two minutes or just tell me y-, [0.2] when you give up [0.4] 
okay [laughter] right [1.4] and we'll s-, see [0.2] okay so you remember in the 
original experiment this was actually a comparison between er expert club 
players [0.3] and a grand master [0.2] so we're actually looking at very 
different levels of expertise but we can see whether we can [0.5] sort of 
replicate the same sort of effect so are you ready [0.7] and here goes [2.3] 
for those of you can't see this board position this is probably a very boring 
part of the lecture [laughter] [laughter] [1.5] okay [0.7] t-, [0.2] ten 
seconds [9.0] twenty seconds [8.8] thirty seconds okay off you go [3.5] okay [1.
7] and of course we have the additional problem here is that er these pieces 
are really fiddly to move around [6.5] [9.2] okay and what we're going to try 
and do here is er score this by just counting the number of [0.6] pieces [0.2] 
that she gets in the right place [15.8] okay if you wanted to do this 
scientifically of course then er i'd actually 
sf1209: guessed the rest [0.9] 
nm1208: you're guessing the rest okay that's [0.7] right [0.4] er [0.6] i don't 
think you did too badly there [1.1] okay er right looking my original piece 
you've got all the [1.0] one two three four five six seven [1.2] eight [0.6] 
nine [3.0] you get nine points [1.3] brilliant thanks very much indeed 
[laughter] that's it [laughter] right [1.1] okay i'll just er quickly scramble 
this er [11.4] all right [1.6] 
okay [2.3] right er and could i have my er chess grand master [laughter] [1.4] 
sf1210: [0.4] 
nm1208: okay [0.8] right do you want to stand here [0.5] and [0.2] first i'm 
going to show you this er real chess board position [0.2] for thirty seconds 
then i want you to [0.6] er going to cover it up and then you've got about two 
minutes to reproduce as much as you can remember [0.3] okay 
sf1210: what on this [0.4] 
nm1208: er yeah well on er yeah that's right yeah [0.7] okay you're ready [1.0] 
sf1210: sorry [0.4] 
nm1208: [laugh] [0.6] and [0.6] go [1.2] [laughter] er er [11.9] okay that's 
ten seconds [13.7] okay twenty-seven seconds three [0.7] two [0.5] one [0.3] go 
[0.8] okay right get you to reproduce it [2.7] okay so according to [0.2] de 
Groot someone who has a basic knowledge of chess [0.6] er [0.3] should be able 
to er remember [0.4] slightly more [0.2] positions [0.3] if you noticed with 
the [0.3] on the on the actual sort of stimuli that the de Groot was using [0.
3] er [0.4] a-, actually no sorry this is ch-, er Chase and Simon this is [0.4] 
er [0.3] obviously because they're using much more higher level of chess 
players er the actual chess boards were like [0.2] much further into the game 
which is wh-, [0.4] i deliberately chose one where we-, we're only we're only 
about sort of think ten moves into the game there [0.6] or something like that 
so a lot of the pieces are in their original positions [1.0] but er [0.9] 
[laughter] [7.2] 
sf1210: i've mixed the colours up does it matter [0.2] 
nm1208: no [1.2] [laughter] okay in the scientist's conditions then we would 
care about the colours [laughter] and er [0.5] okay and er [0.6] also if this 
is is this is proper science of course i'll be testing like fifty people in 
each group [0.4] and er and also er control for manual dexterity in moving bits 
of tiny acetate around [laughter] 
sf1212: probably better than you did it [0.4] 
nm1208: 
okay that's cool thanks very much indeed [0.6] right so [3.1] right er we have 
[0.3] one [4.0] two [laugh] no [laughter] three four five [2.2] six [0.6] seven 
eight nine ten eleven twelve [1.9] twelve er [0.5] twelve [0.6] ha [laughter] 
[0.9] hurray [1.5] [laughter] i don't know whether that means it's stati-, 
statistically significant thanks thank you very much you two [0.8] brilliant [1.
8] er [2.1] okay [0.3] i'm not but i'm not going to pretend that's science in 
action there [laughter] [1.3] okay [1.5] right so there we have chess nicely 
defined [0.3] domain [0.4] but [0.4] the problem is [1.8] how do we know [0.3] 
that other more complicated [0.2] and sort of every day [0.4] domains [0.4] er 
actually [0.2] involving the same sort of processes [0.8] in the real world 
problems are seldom as 
well defined as they are in chess [0.2] so what research is there looking at 
expertise [0.4] in domains with less rigid rules [1.4] and in the literature 
areas commonly study studied [0.8] involve [1.6] things like [2.1] chess [0.3] 
medicine [0.3] and [0.6] computing [3.9] so many of the features that 
characterize the expert chess players [0.6] also seem to characterize experts 
in [0.4] other domains [1.2] for example the idea of information chunking [0.4] 
was found to be important in the domain of computer programming [1.3] computer 
programming it's been suggested that expert programmers [0.9] have [0.3] large 
chunks of code [0.7] in memory that they can rearrange [0.7] in order to solve 
a problem [1.6] so the idea is that expert programmers can remember more code 
[0.3] than novices [0.5] and as Chase and Simon then showed with chess [0.5] 
they can [0.2] fit more information [0.2] into one [0.3] sort of memory chunk 
[0.5] of code [0.2] if you like [3.4] okay [0.6] as i said er [1.9] well that 
was research by Adelson in nineteen-eighty-one [2.3] one way of er analysing 
difference between experts and novices [0.2] as i said previously [0.2] is to 
get people to talk about 
what they're thinking of [0.5] when their solv-, when they solve a problem [0.
7] and these are verbal protocols [1.5] and er [1.1] people have done this 
looking at these sort of domains [0.6] and they've compared they've transcribed 
the commentaries and compared the statements people are making [0.6] er between 
the expert and novice groups and they've also compared the length of time [0.3] 
people have spent on various aspects [0.6] of the problems [0.9] and also the 
relationship between these sort of strategies [0.5] used [0.3] and the 
solutions reached [2.9] and [1.3] it is using that sort of methods [0.6] that 
er [0.5] Glaser and Chi [1.0] nineteen-eighty-eight [9.2] found that er [0.6] 
one difference [0.2] between experts and novices [0.2] is their different 
schemas [1.1] for [0.5] er solving problems within their own domain of 
expertise [0.5] okay [0.5] this bit of er overhead is actually on the handout 
so you don't need to scribble it down [0.7] okay [0.3] and so our schema [0.5] 
what i mean by a schema [0.2] is a sort of a plan [0.2] an outline a structure 
[0.2] framework a program [0.7] okay so [0.4] in this sort of context [0.2] 
think of a schema [0.5] as a sort 
of cognitive mental plan [0.4] sort of guide for action [1.0] okay especially 
in this case some sort of organized [0.2] framework [0.8] for solving problems 
[1.1] and the [0.2] schemas [0.3] of experts [1.0] have been argued to involve 
[0.2] large [0.3] highly intercon-, interrelated [0.6] units of knowledge [3.7] 
which are organized according to [0.2] underlying structural similarities [0.8] 
amongst these knowledge units [2.0] okay [1.8] so our experts large [0.2] 
highly interconnected units of knowledge [0.8] and connected together [0.4] er 
[0.5] by underlying structural similarities [0.4] a contrast to that is the 
novices [0.3] er the idea is that the sort of schemas [0.2] that novices use [0.
4] in a domain [0.5] are relatively small [0.3] disconnected bits of 
information [0.7] which are organized according to superficial [0.5] 
similarities [0.2] not structural [0.4] similarities [2.7] and er [0.9] Glaser 
and Chi [0.2] this is er [0.5] fun-, er th-, this bit here is actually from er 
Steinberg [0.3] nineteen-ninety-ni wo-, nine which i'll put in the er reprint 
collection [0.7] er [0.2] this is the difference between novices and experts [0.
2] and schemas [0.4] it can be noted in how they 
classify different various problems [1.0] er [0.9] the idea's that experts and 
novices also describe the essential nature of various problems differently [1.
1] and [0.3] determine how to solve [0.5] various problems [0.6] differently [0.
9] okay so [0.3] three key expert [0.2] novice differences [5.8] and another 
thing that er researchers have found [0.5] is that experts tend to spend much 
more time [0.2] determining how [0.5] to [0.3] represent a problem [0.2] than 
novices [0.8] that's research by [0.5] Lesgold et al [0.4] nineteen-eighty-
eight [3.0] okay so experts spend more time representing the problem [0.3] but 
[0.2] they spent less time [0.3] implementing [0.4] that solu-, [0.2] the 
solution [0.4] or the strategy for th-, the solution [7.8] and in contrast [0.
2] novices tend to dive into a problem [0.3] without so much of this [0.5] er 
[0.3] initial analysis and hence spent much more time [0.3] trying to figure 
out [0.4] solution [0.2] to the problem [3.4] so differences between experts 
and novices in these sort of domains [0.3] in their expenditure of time could 
be viewed in terms of the focus [0.5] and the direction of their problem 
solving [0.3] experts 
spend more time figuring out what they [0.3] already know about the problem [0.
4] and how the information given in a problem [0.2] maps on to what [0.7] they 
already know [0.9] and the idea is once the expert finds a previously existing 
strategy for solving [0.4] the problem they can just put it down out of memory 
[0.4] and implement it [0.6] without too much bother [1.9] okay [0.9] and [0.2] 
another way to describe that [0.2] is that experts are [0.3] working forward [1.
4] from the given information [0.4] to find [0.7] their unknown information [0.
6] okay [5.4] okay so they're going from what do i know to what do i need to 
find out [1.4] implementing the correct sequence of steps based on strategies 
they've retrieved [0.2] from their [0.2] schemas in long term memory [0.9] okay 
in contrast [1.1] to this [0.3] the novices [0.5] tend to work [0.2] backwards 
[0.7] so what they tend to do is generate a set of alternative [0.4] solutions 
[0.9] to start with and then trying to work out [0.4] which one would be the 
best one to proceed with [0.7] okay so little time is spent trying to represent 
the problem initially [1.0] and er [0.4] to illustrate this difference between 
forward 
processing and [0.2] backwards processing here is an example [0.2] that's in 
Steinberg [2.3] imagine that we have an expert doctor [0.5] and [0.3] a totally 
green [0.5] er novice [0.3] n-, medical student [0.6] and they're both 
presented with a patient [0.5] who has a series of symptoms [0.9] first of all 
what does the novice do [0.4] well he's not sure [0.4] exactly what to make of 
the symptoms and so what he does is goes and orders a [0.3] whole long series 
of expensive [0.4] er [0.2] medical tests to be done [0.5] in the hope [0.2] 
that once he's got [0.2] the full [0.3] information in front of him full 
symptomatic information he may be able to [0.6] then [0.2] go ahead and make 
his diagnosis [0.5] and he realizes that this illness could be [0.2] any number 
of a wide range of things [0.3] and he works backwards [0.4] to try and [0.3] 
sort eliminate them and work out exactly which one [0.2] it is [1.6] our more 
experienced doctor on the other hand [0.3] is far more likely to recognize [0.
3] the initial [0.6] set of symptoms he's being presented with [0.8] as being 
as fitting [0.2] a diagnostic pattern [0.3] or one of a small number of 
patterns that she holds in her [0.5] 
long term memory [2.1] she's therefore worked forward from these initial 
symptoms [0.2] to a much smaller [0.6] set of possible [0.2] illnesses [0.3] 
and therefore she only needs [0.4] a very small number [0.3] of highly targeted 
tests [0.5] to choose [0.3] the correct [0.2] diagnosis [0.2] from among the [0.
2] limited number of possibilities that she's generated [3.8] okay [0.6] er [0.
5] okay a word of warning with that though [0.3] is that obviously that's a 
that's a simple way [0.3] of describing it and as you can imagine in real life 
[0.3] get's much more [0.4] fiddly than that [0.9] okay those are sort of very 
sort of gross [1.4] it's a very gross generalization on how [0.9] people 
operate 'cause obviously as i described at the beginning it depends on how you 
define your expert [0.4] and how you [0.4] define your novice [3.6] okay [0.4] 
and er [1.9] another thing to bear in mind [0.2] is [0.6] what a w-, first of 
all well wh-, d-, w-, how do we define our expertise and also [0.2] how [0.5] 
people in the study have chosen to measure [0.7] that expertise [2.1] and [0.7] 
this actually makes the whole area of expertise quite tricky [0.5] and when 
you're actually 
looking at research in this area [0.3] you should bear [0.2] that sort of thing 
in mind [0.3] okay [0.3] because despite the example i've just given you [0.7] 
er what you actually find with doctors [0.4] is that [2.6] for most [0.2] cases 
[3.3] you don't actually get that much difference between novice and expert 
doctors [1.2] so it's been found that [0.3] diagnostic performance doesn't 
actually seem to improve [0.5] much beyond the first year [0.4] of residency [0.
4] for [0.2] typical [0.2] diagnostic cases [3.4] okay and er [0.2] there are 
some small differences between doctors with different lengths of experience for 
everyday agn-, diagnosis [0.8] er [0.7] though it has been shown [0.3] there 
are tend to be much bigger differences [0.3] as soon as you start moving on to 
more difficult cases [0.9] okay [11.3] and [0.7] Boshuizen [0.3] and Schmidt 
nineteen-ninety-two [2.9] found that the expert doctors seem to have [0.3] 
easier access to higher level [0.8] structured diagnostic information [0.6] 
whereas the medical students [0.6] tended to have to go through this sort of 
cumbersome biomedical reasoning [0.5] to get to their solutions [0.6] okay [1.
3] but [0.2] say we've got to be 
very careful that we don't [0.2] overgeneralize here [0.2] because it matters 
what we're actually calling a novice doctor here and what we're calling an 
expert doctor [0.4] and [0.2] how we're measuring [0.6] their expertise [2.1] 
another example [0.6] of a [0.9] skilled domain where the problem may not be [0.
2] as well defined as we would like [0.6] is [0.2] physics [3.2] and er [0.9] 
it's covered by a lot of research by Chi et al [1.7] and they tested er expert 
and novice physicists on a range of physics problems [1.0] 
and they found that the novices tend to use the surface features [0.3] of the 
problem [0.5] while the experts [0.4] er encode far more deeply [2.5] er in 
this domain experts solved problems four times faster [0.2] than novices [0.6] 
though they spent longer analysing the problems [0.3] and Chi et al argued that 
these differences [0.4] between novices and experts were [0.3] as a result [1.
1] of differences in strategy [0.5] and [0.2] er knowledge [2.1] so [0.8] like 
with the chess [0.4] the experts who use their superior knowledge of previous 
problems [0.8] er [0.2] but er is found that the experts not only had a greater 
quantity of knowledge [0.2] 
they also organized [0.3] that knowledge [0.3] better [1.7] and er [0.3] 
experts also tend to exhibit superior memory [0.2] on unexpected recall tasks 
[0.3] possibly as a result of their deeper [0.4] encoding [0.5] of the problem 
[3.5] okay [0.3] and [2.1] more research by Larkin et al [0.2] where they [0.2] 
took people [0.5] with equivalent knowledge in solving [0.2] physics problems 
[0.2] but who differed on their level differed on their level of expertise and 
they found that [0.3] when we actually controlled for the amount of knowledge 
people knew [0.5] the er the experts still showed a superior performance [0.6] 
okay [0.3] so [0.3] it's not th-, the am-, not just the amount of knowledge [0.
2] it's how you structure that knowledge [0.2] which is important [0.3] and 
then [0.2] they described it as [0.2] pattern based [0.6] retrieval [0.4] from 
memory [0.9] and like medicine [0.5] er they found that [0.3] experts tended to 
use a forward working strategy [0.6] whereas [0.2] the novices tended to use a 
backwards [0.3] working strategy [2.6] and also this er expertise was found to 
be very domain suspic-, specific [0.3] so [0.2] being an expert physicist [0.2] 
doesn't make you [0.2] good at anything 
else [0.2] whatsoever [1.1] okay and this may reflect the finding [0.4] that i 
talked about earlier where basic [0.2] differences like things like I-Q [0.5] 
er have surprisingly little [0.2] bearing [0.3] on expert [0.3] performance [4.
5] okay [1.5] er running out of time so [0.2] i'm going to [2.6] pop on to [0.
4] some people who've tried to summarize [0.2] this sort of research [4.2] okay 
[0.3] er summarizing this sort of research [1.8] [0.7] is a problem [0.2] 
'cause as i said [0.2] before we have to be very careful that [0.5] first of 
all we know what definition of expert and novice we're using [1.1] secondly [0.
3] we've got to [0.4] be very careful about exactly how we're measuring [0.2] 
expertise [0.5] and the third thing we've got to watch out [0.6] is [0.2] that 
we don't overgeneralize from our results [0.4] okay so it could be [0.3] that 
er we say ah this is a novi-, this is an expert and the expert can do this this 
and this [0.3] but then we discover [0.2] that's only true of experts [0.4] er 
solving maths problems [0.2] and it has no bearing on experts [0.7] er doing 
chess and things like that [0.6] so you've got to bear all of these sort of 
these those three things in mind when you're analysing this research [0.8] but 
er [0.2] Glaser and Chi [1.8] again have come [0.2] come up with er a list [0.
8] of things they 
argued were common [0.2] between different domains [0.2] of expertise [1.4] and 
[0.3] these are all things that we've met previously in the lecture [1.0] okay 
[0.2] so [0.9] they argued that er experts excel mainly in their own domains [1.
8] experts perceive large meaningful patterns in domains [0.7] experts tend to 
be faster at solving problems [1.1] experts have superior short and long term 
memory [1.2] experts see and represent problems in their domain at a deeper 
level than novices [0.8] and experts spend more time [0.4] analysing [0.4] 
problems [0.4] qualitatively [1.4] and something we haven't talked about is 
experts have very strong [0.2] self-monitoring [0.5] skills [0.4] okay [0.5] 
and you'll see that [0.5] similar pattern reflected [0.2] in another example 
i've put in your handout [0.5] er by Green and Gilhooly who tried to do the 
same thing and came up with [0.3] five maxims [1.3] okay [1.2] right so we've 
looked at the differences between novices and experts [0.3] one point we 
haven't addressed [0.2] is how people [0.2] become [0.2] experts in the first 
place so how do you become skilful at something [0.5] how can a-, someone 
anyone become an expert at something or does [0.5] er can anyone become an 
expert at something or does genetics play an important role [0.6] and those [0.
2] are the issues [0.2] we'll be looking at [0.3] next week [0.2] i'll see you 
then