Carpenter plots of Subject 1, raw

>> load S1_bruto
>> cd ..
>> [corr_S1(1),proc_S1(1)]=salida2procesados(S1_f50);
>> [corr_S1(2),proc_S1(2)]=salida2procesados(S1_f80);
>> corr_S1=correctos2correctos_filtrado(corr_S1,[1 1.5]);
>> 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.-‘)

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Learning in the reaction times experiments

>> clear
>> cd resultados
>> load S1_bruto
>> cd ..
>> [corr_S1(1),proc_S1(1)]=salida2procesados(S1_f50);
>> [corr_S1(2),proc_S1(2)]=salida2procesados(S1_f80);
>> corr_S1=correctos2correctos_filtrado(corr_S1,[1 1.5]);
>> 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,’.-‘)