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