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