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.V1-pls")
score = benchmark(my_model)

Model scores

Min Alignment Max Alignment

Rank

Model

Score

1
.378
2
.375
3
.357
4
.355
5
.344
6
.344
7
.343
8
.340
9
.338
10
.336
11
.331
12
.331
13
.331
14
.331
15
.331
16
.331
17
.330
18
.329
19
.328
20
.327
21
.327
22
.327
23
.324
24
.319
25
.313
26
.311
27
.310
28
.309
29
.304
30
.303
31
.301
32
.300
33
.300
34
.298
35
.295
36
.295
37
.292
38
.287
39
.286
40
.285
41
.284
42
.284
43
.284
44
.283
45
.281
46
.281
47
.278
48
.278
49
.277
50
.276
51
.276
52
.276
53
.274
54
.273
55
.271
56
.271
57
.271
58
.270
59
.270
60
.269
61
.267
62
.267
63
.266
64
.265
65
.263
66
.263
67
.263
68
.263
69
.263
70
.263
71
.263
72
.263
73
.263
74
.263
75
.263
76
.262
77
.262
78
.262
79
.262
80
.262
81
.262
82
.262
83
.261
84
.261
85
.261
86
.260
87
.260
88
.260
89
.260
90
.260
91
.260
92
.259
93
.258
94
.258
95
.258
96
.258
97
.257
98
.257
99
.257
100
.257
101
.257
102
.257
103
.257
104
.257
105
.257
106
.257
107
.257
108
.257
109
.256
110
.256
111
.256
112
.256
113
.255
114
.254
115
.254
116
.253
117
.253
118
.253
119
.253
120
.253
121
.253
122
.253
123
.252
124
.252
125
.252
126
.252
127
.252
128
.251
129
.251
130
.251
131
.250
132
.250
133
.249
134
.249
135
.249
136
.249
137
.249
138
.248
139
.248
140
.248
141
.248
142
.248
143
.248
144
.247
145
.247
146
.247
147
.247
148
.247
149
.247
150
.246
151
.246
152
.246
153
.246
154
.246
155
.246
156
.246
157
.245
158
.245
159
.244
160
.244
161
.244
162
.244
163
.244
164
.243
165
.243
166
.243
167
.243
168
.243
169
.243
170
.243
171
.243
172
.243
173
.243
174
.243
175
.243
176
.243
177
.243
178
.243
179
.243
180
.243
181
.243
182
.243
183
.243
184
.242
185
.242
186
.242
187
.242
188
.242
189
.242
190
.242
191
.241
192
.241
193
.241
194
.241
195
.240
196
.240
197
.240
198
.240
199
.240
200
.240
201
.240
202
.240
203
.240
204
.240
205
.239
206
.239
207
.239
208
.239
209
.239
210
.239
211
.239
212
.238
213
.238
214
.238
215
.238
216
.238
217
.238
218
.237
219
.237
220
.237
221
.236
222
.236
223
.236
224
.236
225
.236
226
.236
227
.236
228
.236
229
.236
230
.235
231
.235
232
.235
233
.235
234
.234
235
.234
236
.234
237
.234
238
.233
239
.233
240
.233
241
.232
242
.232
243
.232
244
.232
245
.232
246
.232
247
.231
248
.231
249
.231
250
.231
251
.231
252
.231
253
.231
254
.230
255
.230
256
.230
257
.230
258
.230
259
.230
260
.230
261
.230
262
.229
263
.229
264
.229
265
.229
266
.229
267
.229
268
.229
269
.228
270
.228
271
.228
272
.228
273
.227
274
.227
275
.226
276
.226
277
.226
278
.226
279
.226
280
.226
281
.226
282
.226
283
.225
284
.225
285
.225
286
.225
287
.225
288
.224
289
.224
290
.224
291
.224
292
.224
293
.224
294
.224
295
.224
296
.223
297
.223
298
.223
299
.222
300
.222
301
.222
302
.222
303
.222
304
.222
305
.222
306
.221
307
.221
308
.221
309
.221
310
.221
311
.221
312
.221
313
.221
314
.220
315
.220
316
.219
317
.219
318
.219
319
.218
320
.218
321
.218
322
.218
323
.218
324
.217
325
.217
326
.217
327
.217
328
.217
329
.216
330
.216
331
.216
332
.216
333
.216
334
.215
335
.215
336
.215
337
.215
338
.215
339
.215
340
.214
341
.214
342
.214
343
.214
344
.214
345
.214
346
.213
347
.213
348
.213
349
.213
350
.212
351
.212
352
.212
353
.212
354
.211
355
.211
356
.211
357
.211
358
.211
359
.211
360
.211
361
.211
362
.211
363
.211
364
.211
365
.211
366
.211
367
.210
368
.209
369
.209
370
.209
371
.208
372
.208
373
.208
374
.208
375
.208
376
.208
377
.208
378
.208
379
.207
380
.207
381
.207
382
.207
383
.206
384
.206
385
.205
386
.205
387
.205
388
.205
389
.205
390
.205
391
.204
392
.204
393
.204
394
.204
395
.204
396
.204
397
.203
398
.203
399
.202
400
.202
401
.202
402
.201
403
.201
404
.201
405
.200
406
.200
407
.199
408
.199
409
.199
410
.199
411
.199
412
.197
413
.197
414
.196
415
.195
416
.195
417
.195
418
.195
419
.195
420
.195
421
.194
422
.194
423
.194
424
.194
425
.193
426
.193
427
.193
428
.192
429
.192
430
.192
431
.192
432
.192
433
.191
434
.190
435
.190
436
.190
437
.190
438
.189
439
.188
440
.188
441
.188
442
.188
443
.188
444
.187
445
.186
446
.186
447
.185
448
.184
449
.184
450
.183
451
.182
452
.181
453
.181
454
.180
455
.180
456
.180
457
.180
458
.180
459
.180
460
.180
461
.180
462
.180
463
.180
464
.180
465
.179
466
.175
467
.175
468
.173
469
.173
470
.171
471
.169
472
.169
473
.167
474
.167
475
.167
476
.167
477
.166
478
.164
479
.161
480
.160
481
.160
482
.160
483
.159
484
.159
485
.155
486
.153
487
.151
488
.149
489
.143
490
.140
491
.139
492
.123
493
.117
494
.112
495
.104
496
.101
497
.100
498
.090
499
.078
500
.054
501
.047
502
.042
503
.041
504
.037

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

0.87.

Note that scores are relative to this ceiling.

Data: FreemanZiemba2013.V1

315 stimuli recordings from 102 sites in V1

Metric: pls