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
.353
27
.353
28
.353
29
.353
30
.353
31
.353
32
.353
33
.353
34
.353
35
.353
36
.351
37
.349
38
.347
39
.346
40
.346
41
.345
42
.343
43
.342
44
.342
45
.341
46
.341
47
.341
48
.341
49
.340
50
.340
51
.339
52
.339
53
.339
54
.339
55
.339
56
.338
57
.337
58
.337
59
.336
60
.336
61
.336
62
.336
63
.335
64
.335
65
.335
66
.334
67
.334
68
.334
69
.334
70
.333
71
.333
72
.332
73
.332
74
.332
75
.332
76
.332
77
.332
78
.331
79
.331
80
.330
81
.330
82
.329
83
.329
84
.329
85
.328
86
.328
87
.328
88
.327
89
.327
90
.327
91
.326
92
.326
93
.325
94
.325
95
.324
96
.324
97
.324
98
.324
99
.323
100
.323
101
.323
102
.322
103
.322
104
.322
105
.322
106
.322
107
.321
108
.321
109
.321
110
.321
111
.321
112
.321
113
.321
114
.320
115
.320
116
.320
117
.320
118
.320
119
.320
120
.320
121
.320
122
.320
123
.320
124
.320
125
.320
126
.319
127
.319
128
.319
129
.318
130
.318
131
.318
132
.318
133
.318
134
.318
135
.317
136
.317
137
.317
138
.317
139
.317
140
.317
141
.316
142
.316
143
.315
144
.315
145
.315
146
.314
147
.313
148
.313
149
.313
150
.313
151
.313
152
.312
153
.312
154
.312
155
.312
156
.312
157
.312
158
.312
159
.311
160
.311
161
.311
162
.311
163
.311
164
.311
165
.311
166
.311
167
.310
168
.310
169
.309
170
.309
171
.309
172
.309
173
.308
174
.308
175
.308
176
.308
177
.308
178
.308
179
.308
180
.307
181
.307
182
.307
183
.307
184
.307
185
.307
186
.307
187
.306
188
.306
189
.306
190
.306
191
.306
192
.306
193
.306
194
.306
195
.306
196
.306
197
.306
198
.305
199
.305
200
.305
201
.305
202
.305
203
.305
204
.304
205
.304
206
.304
207
.304
208
.304
209
.304
210
.304
211
.304
212
.304
213
.303
214
.303
215
.303
216
.302
217
.302
218
.302
219
.302
220
.302
221
.301
222
.301
223
.301
224
.301
225
.301
226
.301
227
.301
228
.300
229
.300
230
.300
231
.299
232
.299
233
.299
234
.299
235
.299
236
.298
237
.298
238
.298
239
.298
240
.297
241
.297
242
.296
243
.296
244
.296
245
.296
246
.295
247
.295
248
.295
249
.295
250
.295
251
.295
252
.295
253
.295
254
.295
255
.295
256
.295
257
.294
258
.294
259
.294
260
.294
261
.294
262
.293
263
.293
264
.293
265
.293
266
.293
267
.293
268
.293
269
.293
270
.293
271
.292
272
.291
273
.291
274
.291
275
.291
276
.291
277
.290
278
.290
279
.289
280
.289
281
.289
282
.288
283
.288
284
.288
285
.288
286
.287
287
.287
288
.287
289
.287
290
.286
291
.286
292
.286
293
.286
294
.285
295
.285
296
.285
297
.285
298
.285
299
.284
300
.284
301
.284
302
.283
303
.283
304
.282
305
.282
306
.281
307
.281
308
.280
309
.279
310
.278
311
.278
312
.278
313
.278
314
.278
315
.276
316
.275
317
.275
318
.274
319
.274
320
.273
321
.273
322
.273
323
.272
324
.272
325
.272
326
.271
327
.270
328
.270
329
.270
330
.269
331
.269
332
.267
333
.267
334
.266
335
.265
336
.265
337
.265
338
.265
339
.265
340
.263
341
.263
342
.263
343
.262
344
.262
345
.261
346
.261
347
.261
348
.260
349
.260
350
.260
351
.260
352
.259
353
.259
354
.258
355
.257
356
.257
357
.256
358
.255
359
.255
360
.255
361
.254
362
.254
363
.252
364
.252
365
.251
366
.251
367
.250
368
.250
369
.249
370
.249
371
.249
372
.248
373
.248
374
.248
375
.247
376
.246
377
.246
378
.246
379
.246
380
.243
381
.243
382
.243
383
.242
384
.238
385
.238
386
.238
387
.238
388
.238
389
.238
390
.238
391
.238
392
.238
393
.238
394
.238
395
.237
396
.237
397
.234
398
.234
399
.233
400
.232
401
.232
402
.232
403
.231
404
.231
405
.231
406
.230
407
.230
408
.230
409
.229
410
.229
411
.228
412
.228
413
.227
414
.226
415
.226
416
.223
417
.222
418
.220
419
.220
420
.218
421
.218
422
.218
423
.218
424
.218
425
.218
426
.218
427
.218
428
.217
429
.217
430
.216
431
.216
432
.215
433
.208
434
.204
435
.202
436
.201
437
.200
438
.198
439
.193
440
.189
441
.189
442
.182
443
.180
444
.176
445
.174
446
.165
447
.165
448
.150
449
.148
450
.132
451
.130
452
.128
453
.126
454
.126
455
.120
456
.116
457
.111
458
.107
459
.056
460
.029
461
.024
462
.008
463
.007
464
.005
465
.003
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480

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