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