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

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