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
.432
2
.430
3
.409
4
.407
5
.394
6
.394
7
.392
8
.389
9
.387
10
.385
11
.379
12
.379
13
.379
14
.379
15
.379
16
.379
17
.378
18
.376
19
.375
20
.375
21
.374
22
.374
23
.370
24
.365
25
.358
26
.355
27
.353
28
.347
29
.347
30
.344
31
.343
32
.343
33
.341
34
.338
35
.338
36
.334
37
.329
38
.327
39
.326
40
.325
41
.325
42
.325
43
.324
44
.322
45
.322
46
.318
47
.318
48
.317
49
.316
50
.316
51
.316
52
.314
53
.313
54
.311
55
.310
56
.310
57
.310
58
.309
59
.308
60
.305
61
.305
62
.304
63
.303
64
.301
65
.301
66
.301
67
.301
68
.301
69
.301
70
.301
71
.301
72
.301
73
.301
74
.301
75
.300
76
.300
77
.300
78
.300
79
.300
80
.299
81
.299
82
.299
83
.299
84
.299
85
.298
86
.298
87
.298
88
.298
89
.298
90
.297
91
.297
92
.296
93
.295
94
.295
95
.295
96
.295
97
.295
98
.294
99
.294
100
.294
101
.294
102
.294
103
.294
104
.294
105
.294
106
.294
107
.294
108
.293
109
.293
110
.293
111
.292
112
.291
113
.290
114
.290
115
.290
116
.290
117
.290
118
.290
119
.289
120
.289
121
.289
122
.289
123
.289
124
.289
125
.288
126
.288
127
.287
128
.287
129
.287
130
.286
131
.286
132
.285
133
.285
134
.285
135
.285
136
.284
137
.284
138
.284
139
.284
140
.284
141
.283
142
.283
143
.283
144
.283
145
.282
146
.282
147
.282
148
.282
149
.282
150
.282
151
.282
152
.281
153
.281
154
.281
155
.280
156
.279
157
.279
158
.279
159
.279
160
.279
161
.278
162
.278
163
.278
164
.278
165
.278
166
.278
167
.278
168
.278
169
.278
170
.278
171
.278
172
.278
173
.278
174
.278
175
.278
176
.278
177
.278
178
.278
179
.278
180
.278
181
.277
182
.277
183
.277
184
.277
185
.277
186
.277
187
.276
188
.276
189
.276
190
.275
191
.275
192
.275
193
.275
194
.275
195
.274
196
.274
197
.274
198
.274
199
.274
200
.274
201
.274
202
.274
203
.273
204
.273
205
.273
206
.273
207
.272
208
.272
209
.272
210
.271
211
.271
212
.271
213
.271
214
.271
215
.270
216
.270
217
.270
218
.270
219
.270
220
.269
221
.269
222
.269
223
.269
224
.268
225
.267
226
.267
227
.267
228
.267
229
.267
230
.266
231
.266
232
.266
233
.265
234
.265
235
.265
236
.264
237
.264
238
.264
239
.264
240
.264
241
.263
242
.263
243
.263
244
.263
245
.263
246
.263
247
.263
248
.262
249
.262
250
.262
251
.262
252
.261
253
.261
254
.261
255
.260
256
.260
257
.259
258
.259
259
.259
260
.259
261
.259
262
.258
263
.258
264
.258
265
.258
266
.258
267
.258
268
.257
269
.257
270
.256
271
.256
272
.256
273
.256
274
.255
275
.255
276
.254
277
.254
278
.254
279
.254
280
.254
281
.253
282
.253
283
.253
284
.253
285
.252
286
.252
287
.251
288
.251
289
.251
290
.250
291
.249
292
.249
293
.249
294
.248
295
.248
296
.248
297
.248
298
.248
299
.247
300
.247
301
.247
302
.247
303
.247
304
.246
305
.246
306
.246
307
.246
308
.246
309
.246
310
.245
311
.245
312
.245
313
.245
314
.245
315
.245
316
.244
317
.244
318
.244
319
.244
320
.243
321
.243
322
.243
323
.242
324
.242
325
.242
326
.242
327
.242
328
.242
329
.242
330
.241
331
.241
332
.241
333
.241
334
.241
335
.241
336
.240
337
.239
338
.239
339
.239
340
.239
341
.238
342
.238
343
.238
344
.238
345
.238
346
.238
347
.237
348
.237
349
.236
350
.236
351
.236
352
.235
353
.235
354
.235
355
.235
356
.234
357
.234
358
.234
359
.234
360
.233
361
.233
362
.233
363
.233
364
.233
365
.232
366
.232
367
.231
368
.231
369
.230
370
.229
371
.229
372
.228
373
.228
374
.227
375
.227
376
.227
377
.226
378
.225
379
.224
380
.223
381
.223
382
.223
383
.223
384
.223
385
.223
386
.222
387
.222
388
.222
389
.221
390
.221
391
.221
392
.220
393
.220
394
.220
395
.220
396
.219
397
.219
398
.218
399
.217
400
.217
401
.217
402
.217
403
.216
404
.215
405
.215
406
.215
407
.215
408
.215
409
.213
410
.213
411
.212
412
.210
413
.210
414
.209
415
.208
416
.208
417
.207
418
.206
419
.206
420
.206
421
.206
422
.206
423
.206
424
.206
425
.206
426
.206
427
.206
428
.206
429
.205
430
.200
431
.198
432
.198
433
.196
434
.193
435
.191
436
.190
437
.184
438
.183
439
.183
440
.183
441
.182
442
.175
443
.173
444
.160
445
.133
446
.129
447
.114
448
.089
449
.061
450
.053
451
.048
452
.047
453
.043
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470

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