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

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