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
.271
80
.270
81
.270
82
.270
83
.270
84
.270
85
.270
86
.269
87
.269
88
.268
89
.268
90
.267
91
.267
92
.267
93
.267
94
.267
95
.266
96
.266
97
.266
98
.266
99
.266
100
.266
101
.266
102
.266
103
.265
104
.265
105
.265
106
.265
107
.265
108
.265
109
.265
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
.264
123
.263
124
.263
125
.263
126
.263
127
.263
128
.262
129
.262
130
.262
131
.262
132
.262
133
.262
134
.262
135
.261
136
.261
137
.261
138
.261
139
.261
140
.261
141
.260
142
.260
143
.260
144
.260
145
.260
146
.259
147
.259
148
.259
149
.259
150
.258
151
.258
152
.258
153
.258
154
.258
155
.258
156
.258
157
.257
158
.257
159
.257
160
.257
161
.257
162
.257
163
.257
164
.257
165
.257
166
.257
167
.257
168
.257
169
.256
170
.256
171
.256
172
.256
173
.256
174
.256
175
.255
176
.255
177
.255
178
.255
179
.255
180
.255
181
.255
182
.254
183
.254
184
.254
185
.254
186
.254
187
.254
188
.254
189
.254
190
.254
191
.254
192
.253
193
.253
194
.253
195
.253
196
.253
197
.253
198
.253
199
.253
200
.253
201
.252
202
.252
203
.252
204
.252
205
.252
206
.252
207
.252
208
.252
209
.252
210
.252
211
.252
212
.252
213
.252
214
.251
215
.251
216
.251
217
.251
218
.251
219
.251
220
.251
221
.251
222
.251
223
.251
224
.250
225
.250
226
.250
227
.250
228
.250
229
.249
230
.249
231
.249
232
.249
233
.249
234
.249
235
.248
236
.248
237
.248
238
.248
239
.248
240
.248
241
.248
242
.248
243
.248
244
.247
245
.247
246
.247
247
.247
248
.246
249
.246
250
.246
251
.246
252
.246
253
.246
254
.246
255
.246
256
.246
257
.245
258
.245
259
.244
260
.244
261
.244
262
.244
263
.244
264
.244
265
.244
266
.244
267
.244
268
.243
269
.243
270
.243
271
.243
272
.243
273
.243
274
.243
275
.243
276
.243
277
.243
278
.242
279
.242
280
.242
281
.242
282
.242
283
.241
284
.241
285
.241
286
.241
287
.241
288
.241
289
.241
290
.240
291
.240
292
.240
293
.240
294
.240
295
.240
296
.239
297
.239
298
.239
299
.238
300
.238
301
.238
302
.238
303
.238
304
.237
305
.237
306
.237
307
.237
308
.237
309
.237
310
.237
311
.236
312
.236
313
.236
314
.236
315
.236
316
.235
317
.235
318
.235
319
.235
320
.235
321
.235
322
.234
323
.234
324
.234
325
.233
326
.233
327
.232
328
.232
329
.232
330
.231
331
.230
332
.230
333
.230
334
.229
335
.229
336
.229
337
.228
338
.227
339
.227
340
.227
341
.226
342
.226
343
.225
344
.225
345
.225
346
.225
347
.224
348
.224
349
.223
350
.223
351
.223
352
.223
353
.222
354
.222
355
.222
356
.221
357
.220
358
.220
359
.219
360
.219
361
.219
362
.219
363
.218
364
.217
365
.217
366
.217
367
.216
368
.216
369
.215
370
.215
371
.215
372
.215
373
.214
374
.214
375
.214
376
.214
377
.214
378
.213
379
.212
380
.211
381
.211
382
.210
383
.210
384
.210
385
.209
386
.209
387
.208
388
.208
389
.207
390
.207
391
.206
392
.206
393
.206
394
.205
395
.205
396
.205
397
.205
398
.205
399
.204
400
.204
401
.204
402
.203
403
.203
404
.203
405
.203
406
.201
407
.200
408
.200
409
.200
410
.197
411
.197
412
.197
413
.197
414
.197
415
.197
416
.197
417
.197
418
.197
419
.196
420
.196
421
.196
422
.196
423
.196
424
.193
425
.193
426
.192
427
.191
428
.191
429
.191
430
.191
431
.190
432
.190
433
.190
434
.190
435
.190
436
.190
437
.189
438
.189
439
.189
440
.188
441
.188
442
.187
443
.187
444
.186
445
.184
446
.183
447
.181
448
.181
449
.180
450
.180
451
.180
452
.180
453
.180
454
.180
455
.180
456
.179
457
.179
458
.179
459
.178
460
.178
461
.177
462
.171
463
.168
464
.167
465
.166
466
.165
467
.164
468
.163
469
.159
470
.156
471
.156
472
.153
473
.152
474
.151
475
.150
476
.148
477
.145
478
.143
479
.143
480
.142
481
.136
482
.136
483
.132
484
.130
485
.124
486
.122
487
.109
488
.108
489
.106
490
.104
491
.104
492
.099
493
.096
494
.091
495
.089
496
.046
497
.024
498
.020
499
.007
500
.006
501
.004
502
.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