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("Ferguson2024gray_hard-value_delta")
score = benchmark(my_model)

Model scores

Min Alignment Max Alignment

Rank

Model

Score

1
1.0
2
1.0
3
1.0
4
1.0
5
1.0
6
1.0
7
1.0
8
1.0
9
1.0
10
1.0
11
1.0
12
1.0
13
1.0
14
1.0
15
1.0
16
1.0
17
1.0
18
1.0
19
.971
20
.971
21
.971
22
.971
23
.971
24
.942
25
.942
26
.942
27
.942
28
.942
29
.942
30
.942
31
.942
32
.942
33
.942
34
.942
35
.942
36
.942
37
.942
38
.942
39
.942
40
.883
41
.883
42
.883
43
.883
44
.883
45
.883
46
.855
47
.855
48
.855
49
.855
50
.855
51
.802
52
.802
53
.802
54
.777
55
.777
56
.777
57
.777
58
.777
59
.777
60
.777
61
.728
62
.728
63
.728
64
.728
65
.728
66
.728
67
.728
68
.728
69
.706
70
.706
71
.706
72
.706
73
.706
74
.706
75
.706
76
.706
77
.706
78
.706
79
.662
80
.662
81
.662
82
.662
83
.641
84
.641
85
.641
86
.641
87
.641
88
.641
89
.641
90
.641
91
.641
92
.641
93
.641
94
.641
95
.641
96
.601
97
.601
98
.601
99
.583
100
.583
101
.583
102
.583
103
.583
104
.583
105
.583
106
.583
107
.583
108
.546
109
.546
110
.546
111
.546
112
.529
113
.529
114
.529
115
.529
116
.529
117
.496
118
.496
119
.481
120
.481
121
.481
122
.481
123
.481
124
.481
125
.481
126
.451
127
.451
128
.437
129
.437
130
.437
131
.437
132
.437
133
.437
134
.437
135
.437
136
.437
137
.437
138
.437
139
.437
140
.437
141
.437
142
.410
143
.410
144
.410
145
.397
146
.397
147
.397
148
.397
149
.397
150
.397
151
.397
152
.397
153
.397
154
.397
155
.397
156
.397
157
.397
158
.397
159
.372
160
.372
161
.361
162
.361
163
.361
164
.361
165
.338
166
.338
167
.328
168
.328
169
.328
170
.307
171
.298
172
.298
173
.298
174
.298
175
.298
176
.298
177
.279
178
.279
179
.270
180
.270
181
.270
182
.270
183
.270
184
.270
185
.270
186
.270
187
.270
188
.270
189
.270
190
.270
191
.253
192
.253
193
.246
194
.246
195
.246
196
.246
197
.246
198
.246
199
.230
200
.223
201
.223
202
.223
203
.223
204
.223
205
.223
206
.203
207
.203
208
.203
209
.190
210
.184
211
.184
212
.184
213
.184
214
.184
215
.173
216
.173
217
.167
218
.167
219
.167
220
.167
221
.167
222
.157
223
.152
224
.152
225
.152
226
.152
227
.152
228
.152
229
.138
230
.138
231
.126
232
.126
233
.126
234
.126
235
.114
236
.114
237
.104
238
.104
239
.086
240
.078
241
.078
242
.073
243
.071
244
.071
245
.064
246
.064
247
.064
248
.053
249
.048
250
.048
251
.044
252
.033
253
.033
254
.020
255
256
257
258
259
260
261
262
263
264

Benchmark bibtex

        @misc{ferguson_ngo_lee_dicarlo_schrimpf_2024,
         title={How Well is Visual Search Asymmetry predicted by a Binary-Choice, Rapid, Accuracy-based Visual-search, Oddball-detection (BRAVO) task?},
         url={osf.io/5ba3n},
         DOI={10.17605/OSF.IO/5BA3N},
         publisher={OSF},
         author={Ferguson, Michael E, Jr and Ngo, Jerry and Lee, Michael and DiCarlo, James and Schrimpf, Martin},
         year={2024},
         month={Jun}
}

Ceiling

0.86.

Note that scores are relative to this ceiling.

Data: Ferguson2024gray_hard

Metric: value_delta