## Re-analysis of Carpenter plots

Sara:

>> cd ..
>> t_reaccion2histog_carpenter(correctos.t_reaccion(~correctos.dcha,1),20,’r.-‘);
>> hold on
>> t_reaccion2histog_carpenter(correctos.t_reaccion(correctos.dcha,1),20,’.-‘);

Agh!

I take only the last 250 trials (see post “Learning for Sara”):
>> buenos=false(1,size(correctos.t_reaccion,1));
>> buenos(250:end)=true;
>> t_reaccion2histog_carpenter(correctos.t_reaccion(~correctos.dcha & buenos’,1),20,’r.-‘);
>> hold on
>> t_reaccion2histog_carpenter(correctos.t_reaccion(correctos.dcha & buenos’,1),20,’.-‘);

## Amount of data needed to get a clean Carpenter plot

It seems that about 100 stimuli are enough to get a clean plot.

>> a=randn(1,50)+5;
>> t_reaccion2histog_carpenter(1./a,10,’k.-‘);

>> a=randn(1,100)+5;
>> t_reaccion2histog_carpenter(1./a,10,’k.-‘);

>> a=randn(1,200)+5;
>> t_reaccion2histog_carpenter(1./a,10,’k.-‘);

>> a=randn(1,500)+5;
>> t_reaccion2histog_carpenter(1./a,10,’k.-‘);

>> a=randn(1,1000)+5;
>> t_reaccion2histog_carpenter(1./a,10,’k.-‘);

## Learning for Sara

>> cd ..
>> aprendizaje=correctos2aprendizaje(correctos,50,1);

Seems stable in the last 250 trials.

## Analysis of Eloisa’s 1000 trials with feedback

>> aprendizaje=correctos2aprendizaje(corr_filt,50,1);

A clear bias due to previous knowledge: Faster to the right from the beginning.

At least, seems quite stable.

>> [histog,bins]=hist_norm(corr_filt.t_reaccion(corr_filt.dcha,1),30);
>> close all
>> plot(bins,histog)
>> hold on
>> [histog,bins]=hist_norm(corr_filt.t_reaccion(~corr_filt.dcha,1),20);
>> plot(bins,histog,’r’)
>> legend(‘Right’,’Left’)

>> t_reaccion2histog_carpenter(corr_filt.t_reaccion(corr_filt.dcha,1),20);
>> hold on
>> t_reaccion2histog_carpenter(corr_filt.t_reaccion(~corr_filt.dcha,1),20,’r.-‘);

## ¡ERROR!

The program t_reaccion2histog_carpenter was wrong. All Carpenter-like plots in previous posts are wrong.

## Effect of rotation in the fits to symmetric ellipsoids

PENTACAM DATA

I generate datos_fig_pap_pentacam_rot by running fig_pap_correlpentacam_03, and changing in the code c_cuadros=5:5.

>> R_rot=R; Q_rot=Q;

First subject:
>> close all
subplot(2,2,1)
hist(R{1,1})
title(‘Cara anterior, sin rotación’)
subplot(2,2,2)
hist(R_rot{1,1})
title(‘Cara anterior, con rotación’)
subplot(2,2,3)
hist(R{1,2})
title(‘Cara posterior, sin rotación’)
subplot(2,2,4)
hist(R_rot{1,2})
title(‘Cara posterior, con rotación’)

Second subject:

>> close all
subplot(2,2,1)
hist(R{2,1})
title(‘Cara anterior, sin rotación’)
subplot(2,2,2)
hist(R_rot{2,1})
title(‘Cara anterior, con rotación’)
subplot(2,2,3)
hist(R{2,2})
title(‘Cara posterior, sin rotación’)
subplot(2,2,4)
hist(R_rot{2,2})
title(‘Cara posterior, con rotación’)

Third subject:

close all
subplot(2,2,1)
hist(R{3,1})
title(‘Cara anterior, sin rotación’)
subplot(2,2,2)
hist(R_rot{3,1})
title(‘Cara anterior, con rotación’)
subplot(2,2,3)
hist(R{3,2})
title(‘Cara posterior, sin rotación’)
subplot(2,2,4)
hist(R_rot{3,2})
title(‘Cara posterior, con rotación’)

Asphericities, for the three subjects:

>> close all
subplot(2,2,1)
hist(Q{1,1})
title(‘Cara anterior, sin rotación’)
subplot(2,2,2)
hist(Q_rot{1,1})
title(‘Cara anterior, con rotación’)
subplot(2,2,3)
hist(Q{1,2})
title(‘Cara posterior, sin rotación’)
subplot(2,2,4)
hist(Q_rot{1,2})
title(‘Cara posterior, con rotación’)
figure
subplot(2,2,1)
hist(Q{2,1})
title(‘Cara anterior, sin rotación’)
subplot(2,2,2)
hist(Q_rot{2,1})
title(‘Cara anterior, con rotación’)
subplot(2,2,3)
hist(Q{2,2})
title(‘Cara posterior, sin rotación’)
subplot(2,2,4)
hist(Q_rot{2,2})
title(‘Cara posterior, con rotación’)
figure
subplot(2,2,1)
hist(Q{3,1})
title(‘Cara anterior, sin rotación’)
subplot(2,2,2)
hist(Q_rot{3,1})
title(‘Cara anterior, con rotación’)
subplot(2,2,3)
hist(Q{3,2})
title(‘Cara posterior, sin rotación’)
subplot(2,2,4)
hist(Q_rot{3,2})
title(‘Cara posterior, con rotación’)

ATLAS DATA

Only horizontal meridian.

First subject:

subplot(2,2,1)
subplot(2,2,2)
subplot(2,2,3)
subplot(2,2,4)

Second subject:

>> close all
>> subplot(2,2,1)
subplot(2,2,2)
subplot(2,2,3)
subplot(2,2,4)

## Influence of eylids in the Placido topographies

>> fi=0:.01:2*pi;
for c=1:4
subplot(2,2,c)
plot(cornearaw{c}(:,1),cornearaw{c}(:,2),’.’)
hold on
plot(3*cos(fi),3*sin(fi),’r’,’LineWidth’,2)
end

Efectivamente, la zona de ajuste queda bien cubierta. Pero también es cierto que algunos agujeros quedan muy cerca.

## Carpenter plots of Subject 1, raw

>> cd ..
>> t_reaccion2histog_carpenter(corr_S1(1).t_reaccion(:,1),20)
>> hold on
>> t_reaccion2histog_carpenter(corr_S1(2).t_reaccion(corr_S1(2).dcha,1),20,’r.-‘)
>> t_reaccion2histog_carpenter(corr_S1(2).t_reaccion(~corr_S1(2).dcha,1),20,’k.-‘)

## Learning in the reaction times experiments

>> clear
>> cd ..
>> apren1=correctos2aprendizaje(corr_S1(1),100,3);

First box is the ratio between reaction time to the right and reaction time to the left (errorbars are a bit cutres, and might be wrong. But should represent the 95% confidence interval). Second box is correlation coefficient between reaction time and time between the two stimuli. Third box is the p-value of this correlation. In this case both sides were 50%.

Case with 80% right, 20% left:

>> apren2=correctos2aprendizaje(corr_S1(2),100,3);

Comparison of reaction times ratios:

>> close all
>> errorbar(apren1.ratiotiempos(1,:),apren1.ratiotiempos(2,:))
>> hold on
>> errorbar(apren2.ratiotiempos(1,:),apren2.ratiotiempos(2,:),’r’)
>> legend(‘50% – 50%’,’80% – 20%’)

## Test of fits to conic curves

Different asphericities with constant radius (R=20000)

>> x=-3000:100:3000;
>> p=-50:10:10;
>> for c_p=1:length(p)
Z=ZConica_otraformula(x,20000,p(c_p));
conica=ajustaconica([x(:) Z(:)],[],1);
p_fit(c_p)=conica(2);
end
>> plot(p,p_fit,’.-‘)
>> xlabel(‘Real p’)
>> ylabel(‘Fitted p’)

Same, with noise:

>> for c_p=1:length(p)
Z=ZConica_otraformula(x,20000,p(c_p))+randn(1,length(x))*2;
conica=ajustaconica([x(:) Z(:)],[],1);
p_fit(c_p)=conica(2);
end
plot(p,p_fit,’.-‘)