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
.316
53
.316
54
.316
55
.316
56
.316
57
.316
58
.316
59
.316
60
.316
61
.316
62
.314
63
.313
64
.311
65
.310
66
.310
67
.310
68
.309
69
.308
70
.305
71
.305
72
.304
73
.303
74
.301
75
.301
76
.301
77
.301
78
.301
79
.301
80
.301
81
.301
82
.301
83
.301
84
.301
85
.300
86
.300
87
.300
88
.300
89
.300
90
.299
91
.299
92
.299
93
.299
94
.299
95
.298
96
.298
97
.298
98
.298
99
.298
100
.297
101
.297
102
.296
103
.295
104
.295
105
.295
106
.295
107
.295
108
.294
109
.294
110
.294
111
.294
112
.294
113
.294
114
.294
115
.294
116
.294
117
.294
118
.293
119
.293
120
.293
121
.292
122
.291
123
.290
124
.290
125
.290
126
.290
127
.290
128
.290
129
.289
130
.289
131
.289
132
.289
133
.289
134
.289
135
.288
136
.288
137
.287
138
.287
139
.287
140
.286
141
.286
142
.285
143
.285
144
.285
145
.285
146
.285
147
.284
148
.284
149
.284
150
.284
151
.283
152
.283
153
.283
154
.283
155
.282
156
.282
157
.282
158
.282
159
.282
160
.282
161
.282
162
.281
163
.281
164
.281
165
.280
166
.279
167
.279
168
.279
169
.279
170
.279
171
.278
172
.278
173
.278
174
.278
175
.278
176
.278
177
.278
178
.278
179
.278
180
.278
181
.278
182
.278
183
.278
184
.278
185
.278
186
.278
187
.278
188
.278
189
.278
190
.278
191
.277
192
.277
193
.277
194
.277
195
.277
196
.277
197
.276
198
.276
199
.276
200
.275
201
.275
202
.275
203
.275
204
.275
205
.274
206
.274
207
.274
208
.274
209
.274
210
.274
211
.274
212
.274
213
.273
214
.273
215
.273
216
.273
217
.273
218
.272
219
.272
220
.272
221
.271
222
.271
223
.271
224
.271
225
.271
226
.271
227
.270
228
.270
229
.270
230
.270
231
.270
232
.269
233
.269
234
.269
235
.269
236
.268
237
.268
238
.267
239
.267
240
.267
241
.267
242
.267
243
.266
244
.266
245
.266
246
.265
247
.265
248
.265
249
.264
250
.264
251
.264
252
.264
253
.264
254
.263
255
.263
256
.263
257
.263
258
.263
259
.263
260
.262
261
.262
262
.262
263
.262
264
.261
265
.261
266
.261
267
.260
268
.260
269
.259
270
.259
271
.259
272
.259
273
.258
274
.258
275
.258
276
.258
277
.258
278
.258
279
.257
280
.257
281
.256
282
.256
283
.256
284
.256
285
.255
286
.255
287
.254
288
.254
289
.254
290
.254
291
.254
292
.253
293
.253
294
.253
295
.253
296
.252
297
.252
298
.251
299
.251
300
.251
301
.250
302
.249
303
.249
304
.249
305
.248
306
.248
307
.248
308
.248
309
.248
310
.247
311
.247
312
.247
313
.247
314
.247
315
.246
316
.246
317
.246
318
.246
319
.246
320
.246
321
.245
322
.245
323
.245
324
.245
325
.245
326
.245
327
.244
328
.244
329
.244
330
.244
331
.243
332
.243
333
.243
334
.242
335
.242
336
.242
337
.242
338
.242
339
.242
340
.242
341
.241
342
.241
343
.241
344
.241
345
.241
346
.241
347
.240
348
.239
349
.239
350
.239
351
.239
352
.238
353
.238
354
.238
355
.238
356
.238
357
.238
358
.237
359
.237
360
.236
361
.236
362
.236
363
.235
364
.235
365
.235
366
.235
367
.234
368
.234
369
.234
370
.234
371
.233
372
.233
373
.233
374
.233
375
.233
376
.232
377
.232
378
.231
379
.231
380
.230
381
.229
382
.229
383
.228
384
.228
385
.227
386
.227
387
.227
388
.226
389
.225
390
.224
391
.223
392
.223
393
.223
394
.223
395
.223
396
.223
397
.222
398
.222
399
.222
400
.221
401
.221
402
.221
403
.220
404
.220
405
.220
406
.220
407
.219
408
.219
409
.218
410
.217
411
.217
412
.217
413
.217
414
.216
415
.215
416
.215
417
.215
418
.215
419
.215
420
.213
421
.213
422
.212
423
.210
424
.210
425
.209
426
.208
427
.208
428
.207
429
.206
430
.206
431
.206
432
.206
433
.206
434
.206
435
.206
436
.206
437
.206
438
.206
439
.206
440
.205
441
.200
442
.198
443
.198
444
.196
445
.193
446
.191
447
.190
448
.184
449
.183
450
.183
451
.183
452
.182
453
.175
454
.173
455
.160
456
.133
457
.129
458
.114
459
.089
460
.061
461
.053
462
.048
463
.047
464
.043
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481

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