Download ppt - UBham Computer Science, 4pm Thursday 13 November 2008 The 150-year-old science of

UBham Computer Science, 4pm Thursday 13 November 2008

The 150-year-old science of“active virtual machines”

David Boothhttp://www.psychology-people.bham.ac.uk/people/david.booth

School of Psychology, College of Life & Environmental Sciences

Email to request an attachment of this .ppt file: [email protected]

http://www.psychology-people.bham.ac.uk/people/david.booth

Definitionsof terms in the talk’s title

“The 150-year-old science” is Experimental Psychology

in a sense at least as broad as instantiated by Departments of that name at Oxbridge, Bristol, (formerly) Sussex et alii and (better still!) by the School (and College) that I am in.

“Active Virtual Machines” (Aaron Sloman, 2008, e.g. 16 Oct talk at CS)

are programs that are running in (sic) (relatively) intelligent engineered systems,

whether lots of their input/output is material, i.e. robots,or i/o is entirely symbolic, e.g. keyboard & VDU screen.

Psychology is the science of ‘natural AVMs’.

This talk is about a

‘MATH PROOF’THAT I

(AND YOU)DO EXIST- AS AVMs

[Aaron and I share the philosophical position in ontology that can be called Systems Pluralism, understood as a form of Neutral Monism that is opposed to the still dominant Physicalist Monism.]

T.H.E. 16 Oct 08

A basic argument from (sic, not ‘in’) this talk

The (computer) science of AVMs, up to now, lacks a science like that of natural lives/souls/minds

where “nature” includes societies and their cultures as well as the biosphere and the physical cosmos.

The psychologists have been building a Science of Natural Performance for each of the species: human; chimp.; rat; pigeon; etc.

To advance as an engineering science (not just as engineering practice),

the design & implementation of intelligent systems needsa Science of Artificial Performance: - for each effective system of automation

to replace / support human operators;

- for research into possible universes of cognition (AI).

“POEMS” cf. D.A.Booth, 2004 submission to JCS + reviews + reply: http://epapers.bham.ac.uk/54

However, the substance of this talk is solely about what seems to be

the logico-mathematical fundament of Psychology.

Happy to discuss any relevance to Machine Intelligence- but afterwards

-- and from a position of engineering incompetence.

NEXT

However, the substance of this talk is solely about what seems to be

the logico-mathematical fundament of Psychology.

Happy to discuss any relevance to Machine Intelligence- but afterwards

-- and from a position of engineering incompetence.

Aaron’s (16/29 Oct 08) “problem for Psychology” to tackle: “taking seriously some of the conjectures about the contents of the VMs and how they develop and collecting empirical data that could both refine and extend those conjectures, or show how they need to be modified.”

I shall illustrate how academic psychologists started to do this

150 years ago (exactly, as it happens) plus some basic mistakes they made on the way

(repeated to this day by some psychologists and roboticists). Wittgenstein (1953), “Psychology consists of experiments and conceptual confusion.”

Foundational experiments in Psychology

E.H. Weber ~1858-9working with G.T. Fechner (who included results in his 1860 book) e.g. rated sweetness increases with sugar concentration ratio.


E.H. Weber ~1858-9working with G.T. Fechner (who included results in his 1860 book) e.g., rated sweetness increases with sugar concentration ratio.

“Psychophysic[s]” (in Fechner’s sense)

“Link body to mind” – mind as subjective experience, the contents of consciousness [Introspectionism]

Subjectivist fallacy of Direct Scaling: psychophysics asestimation of subjective magnitudes

i.e. strengths of private sensations, such as ‘sweetness’ when you answer the question, “How sweet is it?” with a number on a ‘scale’ formatted by experimenter.




“Link body to mind” – mind as subjective experience, the contents of consciousness

Subjectivist fallacy of Direct Scaling: psychophysics asestimation of subjective magnitudes

Empathy as a magic power: “I just feel that person’s emotion.” – cp. Aleksander with his AVM; a Turing Machine seeming human.

Philosophers’ criterion of consciousness: just say you’re aware.

Non-psychologists’ approach to a crow hooking a worm:intuit what inputs are controlling the outputs or even what the crow is (?I’d be) trying to do.




“Link body to mind” – mind as subjective experience, the contents of consciousness

Subjectivist fallacy of Direct Scaling: psychophysics as estimation of subjective magnitudes

Empathy as a magic power: “I just feel that person’s emotion.”– cp. Aleksander with his AVM

Philosophers’ criterion of consciousness: say you are aware

Non-psychologists’ approach to a crow hooking a worm:intuit what the crow is trying to do.

• All one big mistake: AVMs as awarenesses

“Psychophysics” in 20th century perceptual scienceA link across the mind – mentation as achievement, not awareness

(with consciousness as a type of achievement by the system)

Objectivist view(s) of the psychophysical function:- influence of input on output (causation)- sensitivity of output to input (performance)

[Behaviorism sees only the output and the input, as influences to and from the environment for the “O”= “Organism” as a 0(zero)! – empty cipher / mindless.]

“Psychophysics” in 20th century perceptual scienceA link across the mind – mentation as achievement (with consciousness as a type of achievement by the system)

Objectivist view of the psychophysical function:- influence of input on output (causation)- sensitivity of output to input (performance)

[Behaviorism sees only the output and the input, as organism’s influences to/from environment.]

E.g.

• Empathy as one system’s performance on another system’s performance

- evidence from tests of what insight Aleksander achieves

- evidence on the truth of the projection (Edmondson/Beale)

“Psychophysics” in 20th-century perceptual scienceA link across the mind – mentation as objective achievement (with consciousness as a type of performance of the system)

Objectivist view of the psychophysical function:- influence of input on output (causation)- sensitivity of output to input (performance)

(Behaviorism sees only the output and the input, as the organism’s influences to and from the environment.)

• Empathy as performance on a system’s performance - evidence from tests of what insight Aleksander achieves - evidence on the truth of the projection (Edmondson/Beale)

• Psychologists’ criterion of consciousness: able to be explicit what you are (also) unspokenly achieving.

- requires measurement of TWO psy/phy functions / mental processes

Example in next slide: taste of caffeine in coffee

• Psychologists’ criterion of consciousness requires the determination of two i/o functions:

- one that does show sensitivity to the stimulus, non-symbolically - one describing stimulus, that may or may not show sensitivity

Measures of sensitivity to stimulus at levels near to noise: detection, with/without recognition

e.g. P.M.Merikle et al., 1995 onwards: subliminal perception. Also, multiple detection analysis.

at higher, normal levels: discrimination achieved by each outputN = 1 multiple discrimination diagnosis: Booth & Freeman, 1993; Co-Pro tool (in Java) 2004, 2006

An example of the discriminations criterion (our first): the taste of caffeine in my usual drink of coffee

Preliminary report: Booth, Conner & Gibson (1989) Ann NY Acad Sci 561, 226-242.Full report: Conner, Sharpe & Booth, in revision, “Discrimination without description of differences:

subconscious or implicit?”

Main result: in about half of the individuals taking part, discrimination of caffeine was better (smaller ratio of concentrations) for degree of preference, rated on “I’d choose this coffee always / never”,

than it was for degree of bitterness - “This is more / less bitter than my usual”.

“Psychophysics” in 20th-century perceptual scienceA link across the mind – mentation as achievement Objectivist view of the psychophysical function:

- influence of input on output (causation)- sensitivity of output to input (performance)

• Empathy as performance on a system’s performance - evidence from tests of what Aleksander’s AVM achieves - evidence on the truth of the projection (Edmondson/Beale)

• Psychologists’ criterion of consciousness: coffcaff – TWO processes

able to be explicit what you are (also) unspokenly achieving.

• Psychologists’ approach to a crow hooking a worm:- construct a testable input/output-transformation hypothesis from existing theory of info.-processing dynamics/structures;- vary the input(s) to test that hypothesis on the outputs;- if hypothesis refuted, change the theory to cover the new finding;- if not refuted, find a more critical or powerful test of the theory.

• Psychological approach to a ‘line’ of robots [POEMS] : as per crow.

Running AVMs perform info.-transfer

Second set of big mistakes by psychophysicists (to this day) [The first was (and is) Introspectionism / Fallacy of Direct Scaling]

Lab. purity, not life realismMistake: the ‘analytical’ stancePure chemicals can be isolated; therefore we must isolate pure concepts. ‘Labelled line’ theory of sensory perception Booth on R.P. Erickson BBS 2008

Correction: the ‘integrative’ stanceScience needs to ‘carve nature at its joints,’ not to abandon it at lab door. Ecological validity of laboratory experiments. Mental revivication of real-life situations

(but with disconfounded variations in influential features).

Extremes assumed to work like usual levelsMistake: the search for linearity (i.e. one function for all levels of input)Laws of physics for all conditions; hence a law of the mind also must be. ignore end-effects & other biases on quantitative judgments ECPoulton

The Power Law – explicandum perverted; explicative degeneracyCorrection: zero all judgments on the adaptive normFunctions differ (i) near noise, (ii) for receptor saturation, (iii) in discomfort

Physics as the ideal, not engineering scienceMistake: organisms/machines are piles of atoms.Correction: natural and artificial systems are ‘levels’ of causal networks.

There are no abstract psychophysical functions.

So, what was Weber’s discovery?!Without overextending the evidence (as old-style psychophysicists still do),

differences in “Psychic” output, i.e. a symbolic response, are produced by ratios of “Physical” input, i.e. a material stimulus.

This rule is limited to levels of input well above noise and below saturation or discomfort

Weber discovered a working principle (NOT the Weber-Fechner “Law”)

OK, so let’s use the rule while keeping to middling levels.

With just one input and one input, no evidence can exist on which of any potential mediators is operative, e.g.- the observed transform itself (behaviorism)- an experienced sensation (introspectionism)- a ‘low-level’ sensory process (even neural processes)- a ‘high level’ perceptuo-cognitive process- a linguistic process (cultural role of the descriptor)Hey, but what about several outputs and/or inputs?Such data can distinguish hypothesised mediations, at least within a well-tested theory of mental furniture.

Plan of the rest of the talk (well, of the slides at least)

A single discrimination functionGeneral principles (5 slides)Illustrative results

3 examples (5 slides)

Performance of multiple discriminations General principles (8 slides)Illustrative results

Craving for chocolate (3 slides)Emoticons – annoying or friendly (2 slides)Empathy for an invalid (2 slides)fMRI & cognition (2 slides)

Categorical perception – in or out of scope? (2 slides)

“Psychophysics” in 20th-century perceptual scienceThe basic logic

E.g., five data on causation of output by input

Output Input (starting (starting first)subsequently)

Absent Present

Present √ √ √

Absent √ √

(An experimenter causes an input -- but, with observational data, the sequence of starts is diagnostic of the direction of causation.)

This slide and the next few are based on my Inaugural Lecture, 1988.Nevertheless they are very old ideas in psychology:

the above is a scientist’s take on Hume.

Data for a psychophysical function (the basic math)(with input levels scaled theoretically to give linearity)

Just two pointsspecify a straight line“...” = system’s view

^ X | | X

Levels X X

(amounts) X X “NORM” response

of X

output X X

pattern X “ZERO” response

?endeffect

Levels (amounts) of input pattern -------- >

~Zero Norm point point N.B. Other low

anchors

The Norm and the Discrimination Unit• Use middling levels only in any experiment: stay close to anchor response on most stable of common levels of stimulus,

e.g., “as usual in the world”, “exactly right for me” = Norm.

• Use a measure of sensitivity for any i/o functione.g., slope of straight line, r-value of linear regression.

Weber’s ‘constant’ of sensitivity of that response to that stimulus, in a model by L.L. Thurstone (1927), turns out to be (Torgerson, 1956):

f [ RMSE of responses / Slope ]– see graph on next slide. N.B. in stimulus Dimensions (a ratio of levels).Subjectivist name: “Just Noticeable Difference” in intensities of a sensation: JND

An objective name: “Half-Discriminated Disparity” in input levels by output = HDD

• Use HDDs from Norm as a universal scale for stimuli e.g., on previous slide, perceptual Zero might be 4.1 HDDs from the Norm.

The HDD as a unit of structural info. The Bit is a unit of the quantity of processable information

– a static ‘Informon’.The HDD is a unit of the content of information processing

– a dynamic ‘Logon’.

Half

S1 S2

Regression line through the data

Mean R1

Mean R2

75%/25%

Levels of Response

Levels of Stimulus (in ratios of physical measurement)

Half-DiscriminatedDisparity(HDD)

The level ofstimulus S2above level S1at which half the Responses areindistinguishablebetween S1 & S2- halfway between100% discrimination& 0% discrimination.

Twice z-score for 25%times RMSE over SlopeConner et al. (1988) J Appl Psyc

If material quantities act in ratios,then symbolic quantities act as differences.

“SWEET”: “SWEET”: “SWEET” X X X

X X X

AS JUST JUSTUSUAL RIGHT RIGHT

X JUST JUST

TOO X TOO XLITTLE LITTLE

X NONE X XATALL

Sugar (log concentration) Sugar (log concentration) “SWEET” score Usual / None

Ratios Ratios Differences

observed observed re-plotted data

Discrete i/o functions picked up from a complex dynamic

CRUMBS!hypothesised channels of biscuit texture perception

via breakage of the food item by the teeth

using just 7 variants of a shortbread biscuit recipe (with up to 50% confounding of inputs)

Assessor B

5

[6]

4

3

[2][1]

[7]

r2 = 0.78p < 0.01

-4

-2

0

2

4

6

8

0.50 0.60 0.70 0.80

log10 Percentage of fractures > 1 N

Cru

nchi

ness

ratin

gs

Assessor D

5

[6]

4

3

[2,7]

1

r2 = 0.67p < 0.05

-4

-2

0

2

4

6

8

0.50 0.60 0.70 0.80

log10 Percentage of fractures > 1 N

Cru

nch

ine

ss r

atin

gs

0 = “as crunchy as usual”

[ ] = unfolded(below peak rating)

Loudness of crackles during the first bite (from penetrometry at CCFRA)

Discrete i/o functions picked up from a complex dynamic

Assessor D

1

3

4 5

6,2 7

r2 = 0.85p < 0.005

-4

-2

0

2

4

6

8

1.00 1.05 1.10 1.15 1.20 1.25

log10 Complete fracture force

Ha

rdn

ess

ra

ting

Assessor A

6

7,14

3

2,5

r2 = 0.69p < 0.05

-6

-4

-2

0

2

4

6

1.63 1.65 1.67 1.69 1.71 1.73

log10 Percentage of fractures < 0.1 N

Cris

pnes

s ra

tings

0 = “as hard as usual”

0 = “as crisp as usual”

Force needed to snap the biscuit Low-force cracks [‘sogginess’ (?or ‘quietness’)] [?offload of peri-odontal pressure; ?noise]

(again)

Booth, D.A., Earl, T., & Mobini, S. (2003). Perceptual channels for the texture of a food. Appetite 40, 69-76. [Funded by BBSRC AF]

An affordance from the internal environmentOutput Response: size of meal. Input Stimulus: volume in stomach

Inhibitory i/o transform: a ‘satiety signal’Note: a physiological signal to behaviour meets the criteria for a mental process (Booth, 2008).N.B. Just one output and one input can’t tell if the data process is subjectively experienced or not..

HDD =1.40

No prior snack

rd Test Day

An affordance from the internal environment

Poisson function(?s) for HDDs of Gastric Amount by Test Meal Size

Kissileff, H.R., Booth, D.A., Thornton, J.C., Pi-Sunyer, F.X., Pierson, R.N., & Lee, J. (in prep’n). Food intake as a measure of sensitivity to gastric distension signals in healthy human adults.

Limit of HDD = ~ 1.3i.e., Weber Fraction of ~30%

Vagalsensitivity?

The “Disparity” is a Ratio because input is material

?Non-compliance with morning fast

Inconstancy of internal Norm Point for size of lunch on an increasing number of Test Days

The Recognition [Preference] Triangle of distances from Norm [Ideal] as a learnt preference ‘generalisation decrement’ (dissimilarity function)

The YES line between the two NO-NOs

Top quality Ideal Just right YES, accept, YES

Yes. Yes.

Yes? Yes?

NO, NO,too little too much- reject | | | - reject def RP IP RP xs

Amount of sensory or conceptual ‘driver’ of quality A no-no A no-no

With Mark Conner, 1986-9, e.g. (with HDDs) Conner et al.. (1988) JAP, JFS etc.

The truncated TriangleIf you use lousy samples (i.e., a poor quality ‘background’), you can’t get the response “I’d always choose this one” when the tested feature (‘foreground’) is at its ideal level. The tip of the isosceles triangle is chopped off -

an ‘hedonic ceiling.’

e.g. Booth & Shepherd, 1988

maximum preferencefor that type of sample best

observable responses

Contextual defects in data on two features of a familiar complex

mouse-drawn doodles, on data published in book by D.A. Booth (1994) from R.P.J. Freeman’s PhD Thesis.

Influences of acid and sugar in vended orange drink:showing an integrated defect in the non-taste factors

Raw ratings of choice from one assessor (data “unfolded”)

Lin. reg.linear

Contextual defect in measures of one feature

Talk to 1st Pangborn Symposium, 1991; Booth & Freeman (1993) Acta Psychologica 85, xxx-xxx

Quality Response = (varied Stimulus A2 + constant Complex B2) -0.5

Fitn

ess

for

use

/ ac

cept

abili

ty

IDEALvaried Factor A

constant Factor B

Quality factors in context of use: off the Cone’s peak

QUALITY DEFECT

DEFICIT IN B

CONIC SECTION:OBSERVED QUALITY AT DIFFERENT LEVELS OF FACTOR A WHEN FACTOR B IS OFF-IDEAL

Theoretical equation: Quality = (varied A2 + constant B2)-0.5

theoretical isosceles triangle

STRAIGHT LINE (FOLDED): OBSERVED QUALITY AT DIFFERENT LEVELS OF FACTOR A WHEN FACTOR B IS AT IDEAL POINT / TOP QUALITY

inaccurately drawn curve

The familiar context is a unitary but complex mental process (configurated CS); thus, the isosceles triangle of each normed process forms

a (hyper)cone in HDD units of two (or more) distinct processes in interaction.

The cone of object/situation recognition

Fit

nes

s fo

r u

se /

ac

cep

tab

ilit

y

IDEALFactor A

Factor B

- 1 HDD

-1 HDD +

1 HDD

IDEAL RANGES OF A & B

+ 1 HDD

unacceptable:too little of A and B

unacceptable:too much of A and B

HDD: Half-Discriminable Disparity (“JND”)

JUST “OFF”

HDD: the unit of multiple discriminative performance Analysis of data: fit a perpendicular conic section (right hyperbola)

to the raw data for each i/o - psychophysical function, with the Norm Response scored as zero – e.g., the graphs below;

then rescale the levels of the Stimulus in HDDs from Norm Point (‘discrimination distances’).

graphs from CoPro2.29, coded by Oli Sharpe on BBSRC funds

“salty” “sweet” “sour” “bitter”

Mono-Sodium Glutamate (MSG) in logarithms of concentration in tomato juice

Sodium chloride Sucrose Citric acid Caffeineall in log concentrations in t.j.

HDD: the unit of multiple discriminative performance

Information-transmitting channels as mental dimensions

What’s the same among inputs runs over the same channel. What differs is processed over separate channels.

Representing a channel as a Euclidean dimension,- aspects that are the same summate on a dimension (+)- disparate aspects are processed orthogonally (rtSoS:

Γ = big gamma; keyboard character = ¬)

MD models from two inputs on one (integrative) outputSimple 1D: A (no interaction: an ‘element’)Simple 1D: B (no interaction)Complex 1D: A + B With 4 elements, could be A+B+C+D

2D: A ¬ B3D: A ¬ B ¬ (A + B)

DA Booth & RPJ Freeman, 1993, Acta Psychologica 84, 1-16.Discriminative measurement of feature integration in object recognition.

A BA+B


Deeper mental processingHypothetical causal explanations (models) of an integrative output (response to be modelled: RM), e.g. - familiarity of the object or situation: recognition;- disposition to act in a particular way towards the object

in the situation: preference.Design

Manipulated or selected stimulus features = S1, S2, S3 etc. (either graded/quantitative or present-absent/categorical)

Monitored response features (quantitative) = R1, R2, R3 etc. - most easily interpreted if R1 is specific to S1, etc.

i.e., successful “descriptive analysis”.Models

S: stimulatory processing - could be sub- or unconscious.R: conceptual processing - ideational decision-makingSR: descriptive processing - relates concept to stimulusSSR: perceptual processing - stimulus under a descriptionSRR: affective processing - intention or emotionRR: reasoned processing – e.g. deducing decisive concept

Co-Pro calculates best explanation of the data: often, r2 = 0.85 - 1.0.


Craving for chocolateThe investigator describes 8 different chocolatey foods

i.e., solely symbolic input (written words).

Four aspects of the foods are varied (near-)orthogonally- each food has a higher or lower level of each feature.

S1. Some chocolate itself vs chocolate flavouring S2. Contents of sugar (implied) S3. Amount of the food, i.e. volume and calories S4. Enrichment with “vitamins” mentioned or not.

After reading each description, the participant assesses the food for (a) craving, (b) sweet, (c) filling, (d) comforting.

Scale each hyperbola-fitted i/o function in its HDD.Calculate differences & samenesses (dimensionality). Compute best fitting multidimensional processing model.

Illustrative results from two people (one a woman, one a man) [Guess which – as a test of your stereotyping of the genders!]

Craving for chocolate

• Person A

Diagnosed processing (“best model”): r2 = 0.93Cravings explained by two different emotions (E ¬ F),

i.e., S/R//R mentation:

(i) a feeling in common (X + Y) between filling-sugar chocolatiness & filling-amount comfort

(ii) mixed-up feelings about vitamins (+s) - (not)comforting, chocolatey, sweet, filling

Later recall of what was in mind during the evaluations:”[my] tiredness … hunger”; ”sweet”, “size”, “like” of each

item. “I’m averse to addition of vitamins to make a food

‘healthier.’”

N.B. a Third-Person account of a First-Person viewpoint.

Craving for chocolate

• Person B

Diagnosed processing (‘best model’): r2 = 0.98Cravings explained by two different (¬) sensations,

i.e., S//S/R mentation:

(i) a sensation in common between sweet and filling in chocolate, seen in the word “sugar”;

(ii) sensation in common between chocolatey chocolate and comforting sugar, seen in the amount described.

Immediate recall of what was in mind during evaluations:“sweetness of the chocolate”; “fun chocolate”;

“comforting when tired”.

In each individual, the best mental model matched retrospective free recall.


Analysing the impact of Emoticons

Rate how “happy” (R1) is the expression on each “face”Then rate how “cross” (R2) each face looks.

Finally, if you like, rate expressions for something (R3) you choose.

Eight bunches of keyboard characters (arranged in a column):

|;-)) |;-|) }:-(| |:-|| };-}) };-|| }:-() |;-(|

Feature codes: S1 fhd (crease=1); S2 eye (crease=1); S3 lipup (1); S4 lipdn (1); S5 jaw (open = 1).

Greatest confounding (34%) for eye & lipup and lipdn & jaw, both r = 0.58.

Best Models of “happy”LG: lipdn+fhd ¬ lipup Contributions 54+2 ¬ 42% of r2 = 0.91. (lack of) upturned something lip in common between ( & }

RM: jaw+lipup ¬ lipdn+fhd 47+11 ¬ 23+16% of r2 = 0.77.

AS: lipup ¬ fhd 72 ¬ 27% of r2 = 0.60, modelled from a single rating (of first face) as very slightly “happy”. (This person stated emoticons to be very annoying.)

Emoticons

|;-)) |;-|) }:-(| |:-|| };-}) };-|| }:-() |;-(|S1 fhd (crease=1); S2 eye (crease=1); S3 lipup (1); S4 lipdn (1); S5 jaw (open = 1).

Best Models of “cross”

LG: lipdn ¬ lipup 56 ¬ 42% of r2 = 0.92.

RM: lipdn+eye ¬ lipup ¬ jaw 76+5 ¬ 9 ¬ 7% of r2 = 0.89.

AS: jaw ¬ eye 50 ¬ 49% of r2 = 0.71,rating only two of the emoticons as at all “cross”

Model of “friendly” (term chosen by RM) r2 = 0.88:S3//S1/R2+S5//S3/R2 ¬ S3//S4/R1 ¬ S4//S2/R1 ¬ S1//S4/R1Sensation in common smiling lip glum lip foreheadbetween upturned lip seen as seen as describedand open jaw (laugh), “happy” “happy” like happydescribed as forehead lip-down. eyelid. downturned or upturned lip as lip. (not) being “cross.”


Empathy, sympathy and compassion Eight vignettes of an appeal for donations, e.g. “You see an appeal for funds for someone who needs a wheelchair costing about £200 in order to move anywhere from a seat or bed but is not currently eligible for a motability grant .” “You see an appeal for contributions towards the £2000 cost of a wheelchair. The chair is required by someone who can walk about 20 metres but needs the chair for longer distances, and is eligible for motability grant but of only half the cost.” Rate each imagined situation for your likelihood of contributing (compassion), the person’s need (empathy) and how bad the person feels (sympathy).

Codes in best discrimination-scaled models from CoPro:-Cost = cost of wheelchair, Moblty = level of mobility, Grant = eligibility for grant; need = perceived level of need of invalid, badfl = how bad the invalid feels; donate = likelihood of contributing to appeal.

R SR S

DB1 Open: (need + Moblty/badfl) ¬ Cost 58% 4% 36% of r2 = 0.91 Closed: Cost/need ¬ Grant/badfl

78% 21% of r2 = 0.87DB2 Open: (Cost/badfl + need/badfl) ¬ Cost/need

47% 23% 28% of r2 = 0.96 Closed: Cost ¬ (Grant + Moblty)

88% 5% 5% of r2 =0.88


Empathy, sympathy and compassionCost = cost of wheelchair, Moblty = level of mobility, Grant = eligibility for grant; need = perceived level of need of invalid, badfl = how bad the invalid feels;

MODELS OF donate = likelihood of contributing to appeal

LS1 Open: Moblty + Cost/badfl 92% 7% of r2 = 0.91

Closed: Moblty + Cost 90% 9% of r2 = 0.90

LS2 Open: need/badfl 100% of r2 = 0.91

Closed: need 100% of r2 = 0.91

How the system functions depends on the task taken onor

The results are only as good as the experiment(“revivification” / ‘ecological validity’)

Nevertheless,we are ‘reading’ the momentary dynamic in the mind.


Appetite for pictured foods:cognition while rating interest [Arousal] and attractiveness [Valence]

[another mistake of over-abstractness?]

Brain imaging while viewing pictures of foods and non-foodsmatched in shape and colour

Food-specific activation of orbito-frontal cortex e.g. expected consequences of eating corpus striatum, e.g. sensory motivation by foods (cingulate, e.g. emotional reactions to foods)

- most strongly by FAT contents, somewhat by SUGAR, hardly at all by AMOUNT pictured

View and rate same pictures out of the fMRI scanner

Rate strength of “desire to eat” (appetite for) each food, but also the supposed basic elements of affect (arousal & valence) – “how interesting” & “how pleasing” each pictured food and non-food is.Best Model (r2 = 0.97-0.07*) of rated Appetite in most participants is R or RR i.e., using the concepts of Arousal and Valence, not features of foods, and only a minority of these R models are driven predominantly by the Fats input. So, look at the S-driven models that fit each person’s data least badly.

NEXT SLIDE

*Low variances-a/c-for show that Best Models with high r-sq’ds are not artefacts from random data!

Arie Nouwen, Suzanne Higgs, David Booth (B’ham), Neils Birbaumer (Tuebingen), SSIB abstract 2008


Rating arousal and valence on appetite for pictured foods

R1 = food arousal; R2 = food valence; [R3 = desire to eat: Response Modelled] R4 = non-food arousal (interesting); R5 = non-food valence (pleasing).S1 = Amount of food; S2 = Sugars contents; S3 = Fats contents.

+ = shared signal; ¬ = different signals - from pictured foods to appetite (R3).

Best stimulus-driven models (S, SSR, SR, SRR)The 3 out of each group of 12 with the best r-sq’ds in that group (mostly SRR)

Health interesting shape/colour r2

S3/R1//R1 ¬ S1/R4//R4 ¬ S1/R1//R1 S1: 42% ¬ S3: 56% of 0.38S3/R5//R1 ¬ S2/R4//R1 S2: 45% ¬ S3: 54% 0.29S3//S2/R2 ¬ S2//S1/R5 (the only SSR) S2: 13% ¬ S3: 86% 0.30Nicely sweet fat Prettily large sugary food

Diabetes Prettiness of lots of fatty food ¬ attractiveness of sweet food

S3/R5+S1/R5 ¬ S2/R2 S3: 77%+S1: 11% ¬ S2: 10% 0.61

S1/R2//R1 ¬ S3/R4//R5+S2/R5//R1 ¬ S3/R4//R5+S2/R4//R1 ¬ S3/R1//R2 S1: 51% ¬ S2: 2% ¬ S3: 43% 0.38

S1/R2//R5 ¬ S3/R1//R4 S1: 74% N.B. ¬ S3: 25% 0.25

Another big mistake in EP (and in PD)?

‘Categorical perception’= rated “(dis)similarities” between items

Distances between categorically different objects?(!)

(1) Nothing in common: don’t answers have to be random? hence central limit theorem (Shepard; Nosovsky)fit data with free parameters, incl. Minkowski (Lamberts)

(2) One or more features in common: ?HDDs on those features – but how combine distances??any allowance for incommensurable features

The “omega function” NEXT SLIDE

Fitn

ess

for

use

/ ac

cept

abili

ty

IDEALFactor A

Factor B

Quality factors OUT of context of use: the Omega Artefact

IDEAL RANGES OF A & B

JUST “OFF”

“Anti-ideals” in individually modelled MD-PREF can come from testing a diversity of products in one session: the assessor’s ‘mind-set’ on one product & use turns other product-uses to No-Nos: delta becomes omega; the fitting of a quadratic gets trapped in an outer limb of an observed quartic.

THE REAL DELTA FUNCTION OF PRODUCT ACCEPTANCE

THE “DOWN” NO-NO CUBIC

THE “UP” NO-NO CUBIC

A CUBIC AT EACH END MAKES A QUARTIC = DELTA IS TWISTED INTO OMEGA.

Another big mistake in EP (and in PD)?

‘Categorical perception’= rated “(dis)similarities” between items

Distances between categorically different objects?(!)

(1) Nothing in common: don’t answers have to be random? hence central limit theorem (Shepard; Nosovsky)fit data with free parameters, incl. Minkowski (Lamberts)

(2) One or more features in common: ?HDDs on those features – but how combine distances??any allowance for incommensurable features

The “omega function” NEXT SLIDE

Why bother? Keep to real tasks of choosing between alternatives

- not exploring heuristics in using the word “similar” - not comparing “interesting” bunches of products

Thank you for your attention.

Spare slides

ABSTRACT of talk

The 150-year-old science of ‘active virtual machines’ – David Booth

In 1888-9, E.H. Weber discovered one of the first basic principles of Experimental Psychology: equal ratios of the quantity of stimulation to the senses were rated as equally different in intensity, when the levels of input were moderate. This semilog linear range of an input/output function for physical or chemical stimuli is plain linear when the stimuli are symbolic such as quantitative descriptions. This discriminative sensitivity of an output can therefore be used as a scaling unit for quantities of any input. Furthermore, inputs that are treated as the same by an output will summate in discrimination units from the level to which the person or animal has learnt: that is, an information-transmitting channel through an adapted intelligent system constitutes a mental dimension. If two transforms operate over different channels, then their interaction is orthogonal. Hence the simplest account of a mind is as a Euclidean hyperspace of distinct causal processes. When two outputs are observed from one input, two distinct ways of processing the input may be distinguished. With sufficiently independent multiple inputs tested on specific outputs as well as on an overall output of interest, the set of possible processes and their interactions can be tested against each other on the individual’s multiple discrimination performance in acting on variants of a specific situation (Booth & Freeman, 1993; data-analytic program ‘Co-Pro’, 2006). Several examples of such cognitive diagnosis will be given. An argument offered for discussion -- made in a response to MoC 2003 in an MS now on epapers.bham.ac.uk -- is that the development of intelligent robots needs to include a science of artificial performance, analogous to this psychological science of natural performance – i.e., ‘POEMS’, psychology of emerging machine souls / sentients / symbolisers / subjectivities!