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
.291
27
.291
28
.291
29
.291
30
.291
31
.291
32
.291
33
.291
34
.291
35
.291
36
.291
37
.290
38
.288
39
.286
40
.285
41
.285
42
.285
43
.283
44
.282
45
.282
46
.282
47
.282
48
.281
49
.281
50
.281
51
.280
52
.280
53
.280
54
.280
55
.280
56
.279
57
.279
58
.278
59
.278
60
.277
61
.277
62
.277
63
.277
64
.276
65
.276
66
.276
67
.276
68
.276
69
.276
70
.275
71
.275
72
.275
73
.274
74
.274
75
.274
76
.274
77
.274
78
.274
79
.273
80
.273
81
.272
82
.272
83
.272
84
.272
85
.271
86
.271
87
.271
88
.270
89
.270
90
.270
91
.270
92
.269
93
.269
94
.268
95
.268
96
.267
97
.267
98
.267
99
.267
100
.267
101
.266
102
.266
103
.266
104
.266
105
.266
106
.266
107
.265
108
.265
109
.265
110
.265
111
.265
112
.265
113
.265
114
.264
115
.264
116
.264
117
.264
118
.264
119
.264
120
.264
121
.264
122
.264
123
.264
124
.264
125
.264
126
.264
127
.263
128
.263
129
.263
130
.263
131
.263
132
.262
133
.262
134
.262
135
.262
136
.262
137
.262
138
.261
139
.261
140
.261
141
.261
142
.261
143
.260
144
.260
145
.260
146
.259
147
.259
148
.259
149
.258
150
.258
151
.258
152
.258
153
.258
154
.258
155
.257
156
.257
157
.257
158
.257
159
.257
160
.257
161
.257
162
.257
163
.257
164
.257
165
.256
166
.256
167
.256
168
.256
169
.256
170
.256
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
.253
184
.253
185
.253
186
.253
187
.253
188
.253
189
.253
190
.253
191
.253
192
.252
193
.252
194
.252
195
.252
196
.252
197
.252
198
.252
199
.252
200
.252
201
.252
202
.252
203
.252
204
.252
205
.251
206
.251
207
.251
208
.251
209
.251
210
.251
211
.251
212
.251
213
.251
214
.251
215
.250
216
.250
217
.250
218
.249
219
.249
220
.249
221
.249
222
.249
223
.248
224
.248
225
.248
226
.248
227
.248
228
.248
229
.248
230
.248
231
.248
232
.247
233
.247
234
.247
235
.246
236
.246
237
.246
238
.246
239
.246
240
.246
241
.246
242
.246
243
.245
244
.245
245
.244
246
.244
247
.244
248
.244
249
.244
250
.244
251
.244
252
.244
253
.243
254
.243
255
.243
256
.243
257
.243
258
.243
259
.243
260
.243
261
.243
262
.243
263
.242
264
.242
265
.242
266
.242
267
.242
268
.241
269
.241
270
.241
271
.241
272
.241
273
.241
274
.241
275
.240
276
.240
277
.240
278
.240
279
.240
280
.239
281
.239
282
.239
283
.238
284
.238
285
.238
286
.238
287
.238
288
.237
289
.237
290
.237
291
.237
292
.237
293
.236
294
.236
295
.236
296
.236
297
.235
298
.235
299
.235
300
.235
301
.235
302
.235
303
.234
304
.234
305
.234
306
.233
307
.233
308
.232
309
.232
310
.232
311
.231
312
.230
313
.230
314
.230
315
.229
316
.229
317
.229
318
.228
319
.227
320
.227
321
.227
322
.226
323
.226
324
.225
325
.225
326
.225
327
.225
328
.224
329
.224
330
.223
331
.223
332
.223
333
.223
334
.222
335
.222
336
.221
337
.220
338
.220
339
.219
340
.219
341
.219
342
.219
343
.218
344
.217
345
.217
346
.217
347
.216
348
.216
349
.215
350
.215
351
.215
352
.215
353
.214
354
.214
355
.214
356
.214
357
.214
358
.213
359
.212
360
.212
361
.211
362
.210
363
.210
364
.210
365
.209
366
.209
367
.208
368
.208
369
.207
370
.207
371
.206
372
.206
373
.205
374
.205
375
.205
376
.205
377
.205
378
.204
379
.204
380
.203
381
.203
382
.203
383
.203
384
.201
385
.200
386
.200
387
.200
388
.197
389
.197
390
.197
391
.197
392
.197
393
.197
394
.197
395
.197
396
.197
397
.196
398
.196
399
.196
400
.196
401
.193
402
.193
403
.192
404
.191
405
.191
406
.191
407
.191
408
.190
409
.190
410
.190
411
.190
412
.190
413
.189
414
.189
415
.188
416
.188
417
.187
418
.187
419
.186
420
.184
421
.183
422
.181
423
.181
424
.180
425
.180
426
.180
427
.180
428
.180
429
.180
430
.180
431
.179
432
.179
433
.179
434
.178
435
.178
436
.177
437
.171
438
.168
439
.167
440
.166
441
.165
442
.163
443
.159
444
.156
445
.156
446
.150
447
.149
448
.145
449
.143
450
.136
451
.136
452
.124
453
.122
454
.109
455
.108
456
.106
457
.104
458
.104
459
.099
460
.096
461
.091
462
.089
463
.046
464
.024
465
.020
466
.007
467
.006
468
.004
469
.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