Appendix B.

The tables below shows 64 stimulus vectors created from the corresponding 64 six-dimensional binary vectors by substituting a random number between 0.0 and 0.1 for each “0” and another random number between 0.9 and 1.0 for each “1”, and subsequently used in design optimization for the two categorization models

Stimulus	Feature element
Number	1	2	3	4	5	6
1	0.0223	0.0254	0.0226	0.0342	0.0723	0.0036
2	0.0240	0.0498	0.0615	0.0898	0.0452	0.9643
3	0.0626	0.0312	0.0874	0.0600	0.9810	0.0999
4	0.0343	0.0067	0.0527	0.0306	0.9385	0.9658
5	0.0053	0.0397	0.0742	0.9615	0.0632	0.0987
6	0.0361	0.0036	0.0385	0.9633	0.0993	0.9974
7	0.0107	0.0339	0.0811	0.9053	0.9250	0.0634
8	0.0355	0.0932	0.0917	0.9591	0.9284	0.9240
9	0.0232	0.0561	0.9736	0.0947	0.0655	0.0908
10	0.0840	0.0267	0.9317	0.0765	0.0041	0.9049
11	0.0577	0.0133	0.9567	0.0066	0.9340	0.0682
12	0.0340	0.0608	0.9955	0.0918	0.9943	0.9804
13	0.0578	0.0359	0.9726	0.9094	0.0420	0.0241
14	0.0254	0.0189	0.9417	0.9140	0.0868	0.9543
15	0.0232	0.0168	0.9339	0.9455	0.9808	0.0021
16	0.0713	0.0227	0.9438	0.9084	0.9045	0.9858
17	0.0485	0.9996	0.0116	0.0872	0.0674	0.0763
18	0.0235	0.9190	0.0127	0.0478	0.0329	0.9682
19	0.0099	0.9835	0.0641	0.0714	0.9057	0.0165
20	0.0190	0.9262	0.0784	0.0068	0.9516	0.9709
21	0.0299	0.9836	0.0858	0.9550	0.0289	0.0209
22	0.0936	0.9256	0.0450	0.9809	0.0529	0.9564
23	0.0218	0.9585	0.0673	0.9635	0.9641	0.0804
24	0.0261	0.9040	0.0969	0.9812	0.9077	0.9347
25	0.0918	0.9603	0.9682	0.0225	0.0293	0.0470
26	0.0579	0.9074	0.9738	0.0803	0.0097	0.9801
27	0.0134	0.9016	0.9875	0.0340	0.9155	0.0924
28	0.0557	0.9978	0.9978	0.0453	0.9645	0.9825
29	0.0405	0.9124	0.9151	0.9380	0.0742	0.0592
30	0.0682	0.9275	0.9400	0.9505	0.0043	0.9860
31	0.0394	0.9081	0.9820	0.9334	0.9800	0.0386
32	0.0921	0.9613	0.9728	0.9866	0.9277	0.9655
33	0.9615	0.0256	0.0957	0.0653	0.0729	0.0091
34	0.9610	0.0227	0.0763	0.0055	0.0910	0.9057
35	0.9800	0.0604	0.0407	0.0249	0.9113	0.0061
36	0.9230	0.0075	0.0832	0.0354	0.9392	0.9177
37	0.9442	0.0694	0.0115	0.9934	0.0127	0.0140
38	0.9495	0.0427	0.0857	0.9302	0.0989	0.9070
39	0.9158	0.0449	0.0838	0.9978	0.9196	0.0947
40	0.9990	0.0398	0.0211	0.9889	0.9486	0.9459
41	0.9592	0.0594	0.9590	0.0225	0.0039	0.0668
42	0.9022	0.0311	0.9522	0.0502	0.0503	0.9069
43	0.9551	0.0203	0.9287	0.0756	0.9041	0.0237
44	0.9703	0.0903	0.9139	0.0335	0.9539	0.9843
45	0.9163	0.0408	0.9444	0.9542	0.0135	0.0819
46	0.9458	0.0784	0.9596	0.9869	0.0724	0.9205
47	0.9680	0.0405	0.9994	0.9427	0.9523	0.0864
48	0.9255	0.0740	0.9801	0.9763	0.9749	0.9381
49	0.9037	0.9061	0.0795	0.0148	0.0642	0.0054
50	0.9101	0.9570	0.0471	0.0737	0.0963	0.9766
51	0.9073	0.9713	0.0827	0.0049	0.9770	0.0881
52	0.9582	0.9942	0.0909	0.0227	0.9721	0.9899
53	0.9440	0.9099	0.0704	0.9422	0.0656	0.0750
54	0.9484	0.9234	0.0422	0.9046	0.0643	0.9654
55	0.9388	0.9241	0.0455	0.9321	0.9503	0.0658
56	0.9792	0.9265	0.0798	0.9841	0.9365	0.9178
57	0.9154	0.9208	0.9505	0.0944	0.0148	0.0118
58	0.9024	0.9288	0.9828	0.0704	0.0270	0.9164
59	0.9517	0.9641	0.9645	0.0200	0.9251	0.0809
60	0.9513	0.9108	0.9376	0.0763	0.9966	0.9169
61	0.9018	0.9055	0.9452	0.9271	0.0697	0.0259
62	0.9654	0.9328	0.9404	0.9581	0.0068	0.9714
63	0.9058	0.9031	0.9671	0.9913	0.9373	0.0277
64	0.9422	0.9951	0.9118	0.9047	0.9427	0.9384