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
.402
2
.402
3
.397
4
.393
5
.391
6
.384
7
.380
8
.373
9
.369
10
.365
11
.364
12
.362
13
.361
14
.360
15
.360
16
.360
17
.360
18
.357
19
.356
20
.356
21
.356
22
.353
23
.353
24
.353
25
.353
26
.351
27
.349
28
.347
29
.346
30
.346
31
.343
32
.342
33
.342
34
.341
35
.341
36
.341
37
.341
38
.340
39
.340
40
.339
41
.339
42
.339
43
.339
44
.339
45
.338
46
.337
47
.337
48
.336
49
.336
50
.336
51
.336
52
.335
53
.335
54
.335
55
.334
56
.334
57
.334
58
.334
59
.333
60
.333
61
.332
62
.332
63
.332
64
.332
65
.332
66
.332
67
.331
68
.331
69
.330
70
.330
71
.329
72
.329
73
.329
74
.328
75
.328
76
.328
77
.327
78
.327
79
.327
80
.326
81
.326
82
.325
83
.325
84
.324
85
.324
86
.324
87
.324
88
.323
89
.323
90
.323
91
.322
92
.322
93
.322
94
.322
95
.322
96
.321
97
.321
98
.321
99
.321
100
.321
101
.321
102
.321
103
.320
104
.320
105
.320
106
.320
107
.320
108
.320
109
.320
110
.320
111
.320
112
.320
113
.320
114
.320
115
.319
116
.319
117
.319
118
.318
119
.318
120
.318
121
.318
122
.318
123
.318
124
.318
125
.317
126
.317
127
.317
128
.317
129
.317
130
.317
131
.316
132
.316
133
.315
134
.315
135
.315
136
.314
137
.313
138
.313
139
.313
140
.313
141
.313
142
.312
143
.312
144
.312
145
.312
146
.312
147
.312
148
.312
149
.312
150
.311
151
.311
152
.311
153
.311
154
.311
155
.311
156
.311
157
.311
158
.310
159
.310
160
.309
161
.309
162
.309
163
.309
164
.308
165
.308
166
.308
167
.308
168
.308
169
.308
170
.308
171
.307
172
.307
173
.307
174
.307
175
.307
176
.307
177
.307
178
.306
179
.306
180
.306
181
.306
182
.306
183
.306
184
.306
185
.306
186
.306
187
.306
188
.306
189
.305
190
.305
191
.305
192
.305
193
.305
194
.305
195
.304
196
.304
197
.304
198
.304
199
.304
200
.304
201
.304
202
.304
203
.304
204
.303
205
.303
206
.303
207
.302
208
.302
209
.302
210
.302
211
.302
212
.301
213
.301
214
.301
215
.301
216
.301
217
.301
218
.301
219
.300
220
.300
221
.299
222
.299
223
.299
224
.299
225
.299
226
.298
227
.298
228
.298
229
.298
230
.297
231
.297
232
.296
233
.296
234
.296
235
.296
236
.296
237
.295
238
.295
239
.295
240
.295
241
.295
242
.295
243
.295
244
.295
245
.295
246
.295
247
.295
248
.294
249
.294
250
.294
251
.294
252
.294
253
.293
254
.293
255
.293
256
.293
257
.293
258
.293
259
.293
260
.293
261
.293
262
.292
263
.291
264
.291
265
.291
266
.291
267
.291
268
.290
269
.290
270
.289
271
.289
272
.289
273
.288
274
.288
275
.288
276
.288
277
.287
278
.287
279
.287
280
.287
281
.286
282
.286
283
.286
284
.286
285
.285
286
.285
287
.285
288
.285
289
.285
290
.284
291
.284
292
.284
293
.283
294
.283
295
.282
296
.282
297
.281
298
.281
299
.280
300
.279
301
.278
302
.278
303
.278
304
.278
305
.278
306
.276
307
.275
308
.275
309
.274
310
.274
311
.273
312
.273
313
.273
314
.272
315
.272
316
.272
317
.271
318
.270
319
.270
320
.270
321
.269
322
.269
323
.267
324
.267
325
.266
326
.265
327
.265
328
.265
329
.265
330
.265
331
.263
332
.263
333
.263
334
.262
335
.262
336
.261
337
.261
338
.261
339
.260
340
.260
341
.260
342
.260
343
.259
344
.259
345
.258
346
.257
347
.256
348
.255
349
.255
350
.255
351
.254
352
.254
353
.252
354
.252
355
.251
356
.251
357
.250
358
.250
359
.249
360
.249
361
.249
362
.248
363
.248
364
.248
365
.247
366
.246
367
.246
368
.246
369
.246
370
.243
371
.243
372
.243
373
.242
374
.238
375
.238
376
.238
377
.238
378
.238
379
.238
380
.238
381
.238
382
.238
383
.238
384
.238
385
.237
386
.237
387
.234
388
.234
389
.233
390
.232
391
.232
392
.232
393
.231
394
.231
395
.231
396
.230
397
.230
398
.230
399
.229
400
.229
401
.228
402
.228
403
.227
404
.226
405
.226
406
.223
407
.222
408
.220
409
.220
410
.218
411
.218
412
.218
413
.218
414
.218
415
.218
416
.218
417
.218
418
.217
419
.217
420
.216
421
.216
422
.215
423
.208
424
.204
425
.202
426
.201
427
.200
428
.198
429
.193
430
.189
431
.189
432
.182
433
.176
434
.174
435
.165
436
.165
437
.150
438
.148
439
.132
440
.130
441
.128
442
.126
443
.126
444
.120
445
.116
446
.111
447
.107
448
.056
449
.029
450
.024
451
.008
452
.007
453
.005
454
.003
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469

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