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

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