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
.240
195
.240
196
.240
197
.240
198
.240
199
.240
200
.240
201
.240
202
.240
203
.240
204
.239
205
.239
206
.239
207
.239
208
.239
209
.239
210
.239
211
.238
212
.238
213
.238
214
.238
215
.238
216
.238
217
.237
218
.237
219
.237
220
.236
221
.236
222
.236
223
.236
224
.236
225
.236
226
.236
227
.236
228
.236
229
.235
230
.235
231
.235
232
.235
233
.234
234
.234
235
.234
236
.234
237
.233
238
.233
239
.233
240
.232
241
.232
242
.232
243
.232
244
.232
245
.232
246
.231
247
.231
248
.231
249
.231
250
.231
251
.231
252
.231
253
.230
254
.230
255
.230
256
.230
257
.230
258
.230
259
.230
260
.230
261
.229
262
.229
263
.229
264
.229
265
.229
266
.229
267
.229
268
.228
269
.228
270
.228
271
.228
272
.227
273
.227
274
.226
275
.226
276
.226
277
.226
278
.226
279
.226
280
.226
281
.226
282
.225
283
.225
284
.225
285
.225
286
.225
287
.224
288
.224
289
.224
290
.224
291
.224
292
.224
293
.224
294
.223
295
.223
296
.223
297
.222
298
.222
299
.222
300
.222
301
.222
302
.222
303
.222
304
.221
305
.221
306
.221
307
.221
308
.221
309
.221
310
.221
311
.221
312
.220
313
.220
314
.219
315
.219
316
.219
317
.218
318
.218
319
.218
320
.218
321
.217
322
.217
323
.217
324
.217
325
.217
326
.216
327
.216
328
.216
329
.216
330
.216
331
.215
332
.215
333
.215
334
.215
335
.215
336
.215
337
.214
338
.214
339
.214
340
.214
341
.214
342
.214
343
.213
344
.213
345
.213
346
.213
347
.212
348
.212
349
.212
350
.212
351
.211
352
.211
353
.211
354
.211
355
.211
356
.211
357
.211
358
.211
359
.211
360
.211
361
.211
362
.211
363
.210
364
.209
365
.209
366
.209
367
.208
368
.208
369
.208
370
.208
371
.208
372
.208
373
.208
374
.208
375
.207
376
.207
377
.207
378
.207
379
.206
380
.206
381
.205
382
.205
383
.205
384
.205
385
.205
386
.205
387
.204
388
.204
389
.204
390
.204
391
.204
392
.204
393
.203
394
.203
395
.202
396
.202
397
.202
398
.201
399
.201
400
.201
401
.200
402
.200
403
.199
404
.199
405
.199
406
.199
407
.199
408
.197
409
.197
410
.196
411
.195
412
.195
413
.195
414
.195
415
.195
416
.195
417
.194
418
.194
419
.194
420
.194
421
.193
422
.193
423
.193
424
.192
425
.192
426
.192
427
.192
428
.192
429
.191
430
.190
431
.190
432
.190
433
.190
434
.189
435
.188
436
.188
437
.188
438
.188
439
.188
440
.187
441
.186
442
.186
443
.185
444
.184
445
.184
446
.183
447
.182
448
.181
449
.181
450
.180
451
.180
452
.180
453
.180
454
.180
455
.180
456
.180
457
.180
458
.180
459
.180
460
.180
461
.179
462
.175
463
.175
464
.173
465
.173
466
.171
467
.169
468
.169
469
.167
470
.167
471
.167
472
.167
473
.166
474
.164
475
.161
476
.160
477
.160
478
.160
479
.159
480
.159
481
.155
482
.153
483
.151
484
.149
485
.143
486
.140
487
.139
488
.123
489
.117
490
.112
491
.100
492
.078
493
.054
494
.047
495
.042
496
.041
497
.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