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
.290
27
.288
28
.286
29
.285
30
.285
31
.283
32
.282
33
.282
34
.282
35
.282
36
.281
37
.281
38
.281
39
.280
40
.280
41
.280
42
.280
43
.280
44
.279
45
.279
46
.278
47
.278
48
.277
49
.277
50
.277
51
.277
52
.276
53
.276
54
.276
55
.276
56
.276
57
.276
58
.275
59
.275
60
.275
61
.274
62
.274
63
.274
64
.274
65
.274
66
.274
67
.273
68
.273
69
.272
70
.272
71
.272
72
.272
73
.271
74
.271
75
.271
76
.270
77
.270
78
.270
79
.270
80
.269
81
.269
82
.268
83
.268
84
.267
85
.267
86
.267
87
.267
88
.267
89
.266
90
.266
91
.266
92
.266
93
.266
94
.266
95
.265
96
.265
97
.265
98
.265
99
.265
100
.265
101
.265
102
.264
103
.264
104
.264
105
.264
106
.264
107
.264
108
.264
109
.264
110
.264
111
.264
112
.264
113
.264
114
.264
115
.263
116
.263
117
.263
118
.263
119
.263
120
.262
121
.262
122
.262
123
.262
124
.262
125
.262
126
.261
127
.261
128
.261
129
.261
130
.261
131
.260
132
.260
133
.260
134
.259
135
.259
136
.259
137
.258
138
.258
139
.258
140
.258
141
.258
142
.258
143
.258
144
.257
145
.257
146
.257
147
.257
148
.257
149
.257
150
.257
151
.257
152
.257
153
.257
154
.256
155
.256
156
.256
157
.256
158
.256
159
.256
160
.255
161
.255
162
.255
163
.255
164
.254
165
.254
166
.254
167
.254
168
.254
169
.254
170
.254
171
.254
172
.253
173
.253
174
.253
175
.253
176
.253
177
.253
178
.253
179
.253
180
.253
181
.252
182
.252
183
.252
184
.252
185
.252
186
.252
187
.252
188
.252
189
.252
190
.252
191
.252
192
.252
193
.252
194
.251
195
.251
196
.251
197
.251
198
.251
199
.251
200
.251
201
.251
202
.251
203
.251
204
.250
205
.250
206
.250
207
.249
208
.249
209
.249
210
.249
211
.249
212
.248
213
.248
214
.248
215
.248
216
.248
217
.248
218
.248
219
.248
220
.248
221
.247
222
.247
223
.247
224
.246
225
.246
226
.246
227
.246
228
.246
229
.246
230
.246
231
.246
232
.245
233
.245
234
.244
235
.244
236
.244
237
.244
238
.244
239
.244
240
.244
241
.244
242
.243
243
.243
244
.243
245
.243
246
.243
247
.243
248
.243
249
.243
250
.243
251
.243
252
.242
253
.242
254
.242
255
.242
256
.242
257
.241
258
.241
259
.241
260
.241
261
.241
262
.241
263
.241
264
.240
265
.240
266
.240
267
.240
268
.240
269
.240
270
.239
271
.239
272
.239
273
.238
274
.238
275
.238
276
.238
277
.238
278
.237
279
.237
280
.237
281
.237
282
.237
283
.236
284
.236
285
.236
286
.236
287
.235
288
.235
289
.235
290
.235
291
.235
292
.235
293
.234
294
.234
295
.234
296
.233
297
.233
298
.232
299
.232
300
.232
301
.231
302
.230
303
.230
304
.230
305
.229
306
.229
307
.229
308
.228
309
.227
310
.227
311
.227
312
.226
313
.226
314
.225
315
.225
316
.225
317
.225
318
.224
319
.224
320
.223
321
.223
322
.223
323
.223
324
.222
325
.222
326
.221
327
.220
328
.220
329
.219
330
.219
331
.219
332
.219
333
.218
334
.217
335
.217
336
.217
337
.216
338
.216
339
.215
340
.215
341
.215
342
.215
343
.214
344
.214
345
.214
346
.214
347
.214
348
.213
349
.212
350
.212
351
.211
352
.210
353
.210
354
.210
355
.209
356
.209
357
.208
358
.208
359
.207
360
.207
361
.206
362
.206
363
.205
364
.205
365
.205
366
.205
367
.205
368
.204
369
.204
370
.203
371
.203
372
.203
373
.203
374
.201
375
.200
376
.200
377
.200
378
.197
379
.197
380
.197
381
.197
382
.197
383
.197
384
.197
385
.197
386
.197
387
.196
388
.196
389
.196
390
.196
391
.193
392
.193
393
.192
394
.191
395
.191
396
.191
397
.191
398
.190
399
.190
400
.190
401
.190
402
.190
403
.189
404
.189
405
.188
406
.188
407
.187
408
.187
409
.186
410
.185
411
.184
412
.183
413
.181
414
.181
415
.180
416
.180
417
.180
418
.180
419
.180
420
.180
421
.180
422
.179
423
.179
424
.179
425
.178
426
.178
427
.177
428
.171
429
.168
430
.167
431
.166
432
.165
433
.163
434
.159
435
.156
436
.156
437
.150
438
.149
439
.145
440
.143
441
.136
442
.136
443
.124
444
.122
445
.109
446
.108
447
.106
448
.104
449
.104
450
.099
451
.096
452
.091
453
.089
454
.046
455
.024
456
.020
457
.007
458
.006
459
.004
460
.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