Sample stimuli

sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("FreemanZiemba2013.V2-pls")
score = benchmark(my_model)

Model scores

Min Alignment Max Alignment

Rank

Model

Score

1
.332
2
.332
3
.327
4
.324
5
.322
6
.316
7
.313
8
.307
9
.304
10
.301
11
.300
12
.298
13
.298
14
.297
15
.297
16
.297
17
.297
18
.294
19
.294
20
.294
21
.294
22
.291
23
.291
24
.291
25
.291
26
.290
27
.288
28
.286
29
.285
30
.285
31
.283
32
.282
33
.282
34
.282
35
.282
36
.281
37
.281
38
.281
39
.280
40
.280
41
.280
42
.280
43
.280
44
.279
45
.279
46
.278
47
.278
48
.277
49
.277
50
.277
51
.277
52
.276
53
.276
54
.276
55
.276
56
.276
57
.276
58
.275
59
.275
60
.275
61
.275
62
.274
63
.274
64
.274
65
.274
66
.274
67
.274
68
.273
69
.273
70
.273
71
.273
72
.272
73
.272
74
.272
75
.272
76
.271
77
.271
78
.271
79
.270
80
.270
81
.270
82
.270
83
.270
84
.270
85
.269
86
.269
87
.268
88
.268
89
.267
90
.267
91
.267
92
.267
93
.267
94
.266
95
.266
96
.266
97
.266
98
.266
99
.266
100
.266
101
.266
102
.265
103
.265
104
.265
105
.265
106
.265
107
.265
108
.265
109
.264
110
.264
111
.264
112
.264
113
.264
114
.264
115
.264
116
.264
117
.264
118
.264
119
.264
120
.264
121
.264
122
.263
123
.263
124
.263
125
.263
126
.263
127
.262
128
.262
129
.262
130
.262
131
.262
132
.262
133
.262
134
.261
135
.261
136
.261
137
.261
138
.261
139
.261
140
.260
141
.260
142
.260
143
.260
144
.260
145
.259
146
.259
147
.259
148
.259
149
.258
150
.258
151
.258
152
.258
153
.258
154
.258
155
.258
156
.257
157
.257
158
.257
159
.257
160
.257
161
.257
162
.257
163
.257
164
.257
165
.257
166
.256
167
.256
168
.256
169
.256
170
.256
171
.256
172
.255
173
.255
174
.255
175
.255
176
.255
177
.255
178
.255
179
.254
180
.254
181
.254
182
.254
183
.254
184
.254
185
.254
186
.254
187
.254
188
.254
189
.253
190
.253
191
.253
192
.253
193
.253
194
.253
195
.253
196
.253
197
.253
198
.252
199
.252
200
.252
201
.252
202
.252
203
.252
204
.252
205
.252
206
.252
207
.252
208
.252
209
.252
210
.252
211
.251
212
.251
213
.251
214
.251
215
.251
216
.251
217
.251
218
.251
219
.251
220
.251
221
.250
222
.250
223
.250
224
.250
225
.250
226
.249
227
.249
228
.249
229
.249
230
.249
231
.249
232
.248
233
.248
234
.248
235
.248
236
.248
237
.248
238
.248
239
.248
240
.248
241
.247
242
.247
243
.247
244
.247
245
.246
246
.246
247
.246
248
.246
249
.246
250
.246
251
.246
252
.246
253
.246
254
.245
255
.245
256
.244
257
.244
258
.244
259
.244
260
.244
261
.244
262
.244
263
.244
264
.244
265
.243
266
.243
267
.243
268
.243
269
.243
270
.243
271
.243
272
.243
273
.243
274
.243
275
.242
276
.242
277
.242
278
.242
279
.242
280
.241
281
.241
282
.241
283
.241
284
.241
285
.241
286
.241
287
.240
288
.240
289
.240
290
.240
291
.240
292
.240
293
.239
294
.239
295
.239
296
.238
297
.238
298
.238
299
.238
300
.238
301
.237
302
.237
303
.237
304
.237
305
.237
306
.237
307
.236
308
.236
309
.236
310
.236
311
.236
312
.235
313
.235
314
.235
315
.235
316
.235
317
.235
318
.234
319
.234
320
.234
321
.233
322
.233
323
.232
324
.232
325
.232
326
.231
327
.230
328
.230
329
.230
330
.229
331
.229
332
.229
333
.228
334
.227
335
.227
336
.227
337
.226
338
.226
339
.225
340
.225
341
.225
342
.225
343
.224
344
.224
345
.223
346
.223
347
.223
348
.223
349
.222
350
.222
351
.222
352
.221
353
.220
354
.220
355
.219
356
.219
357
.219
358
.219
359
.218
360
.217
361
.217
362
.217
363
.216
364
.216
365
.215
366
.215
367
.215
368
.215
369
.214
370
.214
371
.214
372
.214
373
.214
374
.213
375
.212
376
.211
377
.211
378
.210
379
.210
380
.210
381
.209
382
.209
383
.208
384
.208
385
.207
386
.207
387
.206
388
.206
389
.206
390
.205
391
.205
392
.205
393
.205
394
.205
395
.204
396
.204
397
.204
398
.203
399
.203
400
.203
401
.203
402
.201
403
.200
404
.200
405
.200
406
.197
407
.197
408
.197
409
.197
410
.197
411
.197
412
.197
413
.197
414
.197
415
.196
416
.196
417
.196
418
.196
419
.196
420
.193
421
.193
422
.192
423
.191
424
.191
425
.191
426
.191
427
.190
428
.190
429
.190
430
.190
431
.190
432
.190
433
.189
434
.189
435
.189
436
.188
437
.188
438
.187
439
.187
440
.186
441
.185
442
.184
443
.183
444
.181
445
.181
446
.180
447
.180
448
.180
449
.180
450
.180
451
.180
452
.180
453
.179
454
.179
455
.179
456
.178
457
.178
458
.177
459
.171
460
.168
461
.167
462
.166
463
.165
464
.164
465
.163
466
.159
467
.156
468
.156
469
.152
470
.150
471
.148
472
.145
473
.143
474
.143
475
.136
476
.136
477
.132
478
.130
479
.124
480
.122
481
.109
482
.108
483
.106
484
.104
485
.104
486
.099
487
.096
488
.091
489
.089
490
.046
491
.024
492
.020
493
.007
494
.006
495
.004
496
.003

Benchmark bibtex

@Article{Freeman2013,
                author={Freeman, Jeremy
                and Ziemba, Corey M.
                and Heeger, David J.
                and Simoncelli, Eero P.
                and Movshon, J. Anthony},
                title={A functional and perceptual signature of the second visual area in primates},
                journal={Nature Neuroscience},
                year={2013},
                month={Jul},
                day={01},
                volume={16},
                number={7},
                pages={974-981},
                abstract={The authors examined neuronal responses in V1 and V2 to synthetic texture stimuli that replicate higher-order statistical dependencies found in natural images. V2, but not V1, responded differentially to these textures, in both macaque (single neurons) and human (fMRI). Human detection of naturalistic structure in the same images was predicted by V2 responses, suggesting a role for V2 in representing natural image structure.},
                issn={1546-1726},
                doi={10.1038/nn.3402},
                url={https://doi.org/10.1038/nn.3402}
                }
            

Ceiling

0.82.

Note that scores are relative to this ceiling.

Data: FreemanZiemba2013.V2

315 stimuli recordings from 103 sites in V2

Metric: pls