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("Ferguson2024round_f-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
.961
12
.961
13
.961
14
.961
15
.961
16
.961
17
.961
18
.906
19
.906
20
.906
21
.906
22
.867
23
.867
24
.867
25
.867
26
.867
27
.867
28
.818
29
.818
30
.818
31
.783
32
.783
33
.783
34
.783
35
.783
36
.783
37
.783
38
.783
39
.739
40
.739
41
.707
42
.707
43
.707
44
.707
45
.707
46
.707
47
.667
48
.667
49
.667
50
.667
51
.667
52
.667
53
.638
54
.638
55
.638
56
.638
57
.638
58
.638
59
.638
60
.638
61
.638
62
.602
63
.576
64
.576
65
.576
66
.576
67
.576
68
.576
69
.544
70
.544
71
.544
72
.544
73
.544
74
.544
75
.544
76
.544
77
.520
78
.520
79
.470
80
.470
81
.470
82
.470
83
.470
84
.443
85
.443
86
.424
87
.424
88
.424
89
.424
90
.424
91
.424
92
.400
93
.400
94
.400
95
.383
96
.383
97
.383
98
.383
99
.346
100
.346
101
.346
102
.346
103
.346
104
.346
105
.346
106
.326
107
.326
108
.312
109
.312
110
.312
111
.312
112
.312
113
.282
114
.282
115
.282
116
.282
117
.282
118
.282
119
.255
120
.255
121
.255
122
.255
123
.255
124
.255
125
.255
126
.255
127
.230
128
.230
129
.230
130
.230
131
.230
132
.230
133
.230
134
.208
135
.208
136
.208
137
.208
138
.208
139
.208
140
.208
141
.187
142
.187
143
.187
144
.187
145
.187
146
.187
147
.187
148
.187
149
.187
150
.187
151
.169
152
.169
153
.160
154
.160
155
.153
156
.153
157
.153
158
.153
159
.153
160
.153
161
.153
162
.138
163
.138
164
.138
165
.138
166
.138
167
.138
168
.138
169
.125
170
.113
171
.113
172
.113
173
.113
174
.113
175
.102
176
.102
177
.102
178
.102
179
.102
180
.102
181
.092
182
.092
183
.092
184
.092
185
.092
186
.083
187
.075
188
.075
189
.075
190
.075
191
.075
192
.075
193
.068
194
.061
195
.061
196
.055
197
.055
198
.050
199
.050
200
.033
201
.033
202
.030
203
.027
204
.018
205
.012

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

0.87.

Note that scores are relative to this ceiling.

Data: Ferguson2024round_f

Metric: value_delta