-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.html
2139 lines (2131 loc) · 110 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<html lang="en">
<head>
<meta name="charset" content="UTF-8">
<meta name="author" content="ViCCo Group i.A. Anna Wolff">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="description" content="DNN vs. brain and behavior">
<title>DNN vs. brain_behavior </title>
<link rel="stylesheet" type="text/css" href="sortable.css" />
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<link rel="stylesheet" type="text/css" href="buttons.css" />
</head>
<body>
<div class="container">
<h1 style="text-align: center;">DNN vs. brain and behavior</h1>
</div>
<div class="Intro">
<p style="text-align:center;">Would like to add new papers or update existing ones? Click<a href="https://docs.google.com/spreadsheets/d/1Dgbqh19xYWZ8MSJwEOu9XSaZzCWD-DbmL7oq395-prk/edit?usp=sharing"_blank"> here.</a> We will update the website regulary. </p>
<p style="text-align:center;">This list is the updated (but surely not complete!) literature collection by <a href="https://twitter.com/AnnaWol45981764"target= "_blank">Anna Wolff </a> and <a href="https://twitter.com/martin_hebart"target= "_blank">Martin Hebart</a> (<a href="https://hebartlab.com/"target= "_blank">ViCCo Group</a>). </p>
</div>
<div id='filter_section' >
<button id='reset_filter'>Reset</button></br>
</div>
<p><div id="table">
<table class="sortable">
<thead>
<tr>
<th>Title</th>
<th>Authors</th>
<th>Year</th>
<th>Journal</th>
<th>Link</th>
<th>Keywords</th>
</tr>
</thead>
<tbody>
<tr>
<td>Sharpening of Hierarchical Visual Feature Representations of Blurred Images</td>
<td>Abdelhack, Mohamed; Kamitani, Yukiyasu</td>
<td>2018</td>
<td>eNeuro</td>
<td><a href="https://dx.doi.org/10.1523/ENEURO.0443-17.2018" target= "_blank">10.1523/ENEURO.0443-17.2018</a</td>
<td>brain_imaging; fMRI; visual</td>
</tr>
<tr>
<td>Conflicting Bottom-up and Top-down Signals during Misrecognition of Visual Objects</td>
<td>Abdelhack, Mohamed; Kamitani, Yukiyasu</td>
<td>2019</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/521252" target= "_blank">10.1101/521252</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes</td>
<td>Antolík, Ján; Hofer, Sonja B.; Bednar, James A.; Mrsic-Flogel, Thomas D.</td>
<td>2016</td>
<td>PLoS computational biology</td>
<td><a href="https://dx.doi.org/10.1371/journal.pcbi.1004927" target= "_blank">10.1371/journal.pcbi.1004927</a</td>
<td>brain_imaging; electrophysiology; rodent; visual</td>
</tr>
<tr>
<td>What deep learning can tell us about higher cognitive functions like mindreading?</td>
<td>Aru, Jaan; Vicente, Raul</td>
<td>2018</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1803.10470v2" target= "_blank">1803.10470v2</a</td>
<td>review</td>
</tr>
<tr>
<td>Training for object recognition with increasing spatial frequency: A comparison of deep learning with human vision</td>
<td>Avberšek, Lev Kiar; Zeman, Astrid A.; Op de Beeck, Hans</td>
<td>2021</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/2021.01.24.427905" target= "_blank">10.1101/2021.01.24.427905</a</td>
<td>brain_imaging; human; visual</td>
</tr>
<tr>
<td>Deep convolutional networks do not classify based on global object shape</td>
<td>Baker, Nicholas; Lu, Hongjing; Erlikhman, Gennady; Kellman, Philip J.</td>
<td>2018</td>
<td>PLoS computational biology</td>
<td><a href="https://dx.doi.org/10.1371/journal.pcbi.1006613" target= "_blank">10.1371/journal.pcbi.1006613</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning</td>
<td>Bakhtiari, Shahab; Mineault, Patrick; Lillicrap, Tim; Pack, Christopher C.; Richards, Blake A.</td>
<td>2021</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/2021.06.18.448989v2" target= "_blank">10.1101/2021.06.18.448989v2</a</td>
<td>brain_imaging; rodent; visual; electrophysiology</td>
</tr>
<tr>
<td>Vector-based navigation using grid-like representations in artificial agents</td>
<td>Banino, Andrea; Barry, Caswell; Uria, Benigno; Blundell, Charles; Lillicrap, Timothy P.; Mirowski, Piotr; Pritzel, Alexander; Chadwick, Martin J.; Degris, Thomas; Modayil, Joseph; Wayne, Greg; Soyer, Hubert; Viola, Fabio; Zhang, Brian; Goroshin, Ross; Rabinowitz, Neil; Pascanu, Razvan; Beattie, Charlie; Petersen, Stig; Sadik, Amir; Gaffney, Stephen; King, Helen; Kavukcuoglu, Koray; Hassabis, Demis; Hadsell, Raia; Kumaran, Dharshan</td>
<td>2018</td>
<td>Nature</td>
<td><a href="https://dx.doi.org/10.1038/s41586-018-0102-6" target= "_blank">10.1038/s41586-018-0102-6</a</td>
<td>brain_imaging; electrophysiology; rodent; visual</td>
</tr>
<tr>
<td>The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks</td>
<td>Bankson, B. B.; Hebart, Martin N.; Groen, Iris I. A.; Baker, Chris I.</td>
<td>2018</td>
<td>NeuroImage</td>
<td><a href="https://dx.doi.org/10.1016/j.neuroimage.2018.05.037" target= "_blank">10.1016/j.neuroimage.2018.05.037</a</td>
<td>brain_imaging; MEG; semantic; visual</td>
</tr>
<tr>
<td>Analyzing biological and artificial neural networks: challenges with opportunities for synergy?</td>
<td>Barrett, David G. T.; Morcos, Ari S.; Macke, Jakob H.</td>
<td>2018</td>
<td>Current Opinion in Neurobiology</td>
<td><a href="http://arxiv.org/abs/1810.13373v1" target= "_blank">1810.13373v1</a</td>
<td>review</td>
</tr>
<tr>
<td>Neural Population Control via Deep ANN Image Synthesis</td>
<td>Bashivan, Pouya; Kar, Kohitij; DiCarlo, James J.</td>
<td>2018</td>
<td>Cognitive Computational Neuroscience</td>
<td></td>
<td>brain_imaging; electrophysiology; monkey; visual</td>
</tr>
<tr>
<td>Neural population control via deep image synthesis</td>
<td>Bashivan, Pouya; Kar, Kohitij; DiCarlo, James J.</td>
<td>2019</td>
<td>Science</td>
<td><a href="https://dx.doi.org/10.1126/science.aav9436" target= "_blank">10.1126/science.aav9436</a</td>
<td>brain_imaging; electrophysiology; monkey; visual</td>
</tr>
<tr>
<td>Modeling Human Categorization of Natural Images Using Deep Feature Representations</td>
<td>Battleday, Ruairidh M.; Peterson, Joshua C.; Griffiths, Thomas L.</td>
<td>2017</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1711.04855v1" target= "_blank">1711.04855v1</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Capturing human categorization of natural images by combining deep networks and cognitive models</td>
<td>Battleday, Ruairidh M.; Peterson, Joshua C.; Griffiths, Thomas L.</td>
<td>2020</td>
<td>Nature Communications</td>
<td><a href="https://dx.doi.org/10.1038/s41467-020-18946-z" target= "_blank">10.1038/s41467-020-18946-z</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>From convolutional neural networks to models of higher-level cognition (and back again)</td>
<td>Battleday, Ruairidh M.; Peterson, Joshua C.; Griffiths, Thomas L.</td>
<td>2021</td>
<td>Annals of the New York Academy of Sciences</td>
<td><a href="https://dx.doi.org/10.1111/nyas.14593" target= "_blank">10.1111/nyas.14593</a</td>
<td>human; review; visual</td>
</tr>
<tr>
<td>Minimal videos: Trade-off between spatial and temporal information in human and machine vision</td>
<td>Ben-Yosef, Guy; Kreiman, Gabriel; Ullman, Shimon</td>
<td>2020</td>
<td>Cognition</td>
<td><a href="https://dx.doi.org/10.1016/j.cognition.2020.104263" target= "_blank">10.1016/j.cognition.2020.104263</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Computational mechanisms underlying cortical responses to the affordance properties of visual scenes</td>
<td>Bonner, Michael F.; Epstein, Russell A.</td>
<td>2018</td>
<td>PLoS computational biology</td>
<td><a href="https://dx.doi.org/10.1371/journal.pcbi.1006111" target= "_blank">10.1371/journal.pcbi.1006111</a</td>
<td>brain_imaging; fMRI; visual</td>
</tr>
<tr>
<td>Reinforcement Learning, Fast and Slow</td>
<td>Botvinick, Matthew M.; Ritter, Sam; Wang, Jane X.; Kurth-Nelson, Zeb; Blundell, Charles; Hassabis, Demis</td>
<td>2019</td>
<td>Trends in Cognitive Sciences</td>
<td><a href="https://dx.doi.org/10.1016/j.tics.2019.02.006" target= "_blank">10.1016/j.tics.2019.02.006</a</td>
<td>learning; review</td>
</tr>
<tr>
<td>Object-scene conceptual regularities reveal fundamental differences between 3 biological and artificial object vision</td>
<td>Bracci, Stefania; Mraz, Jakob; Zeman, Astrid A.; Leys, Gaëlle; Op de Beeck, Hans</td>
<td>2021</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/2021.08.13.456197" target= "_blank">10.1101/2021.08.13.456197</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>The Ventral Visual Pathway Represents Animal Appearance over Animacy, Unlike Human Behavior and Deep Neural Networks</td>
<td>Bracci, Stefania; Ritchie, J. Brendan; Kalfas, Ioannis; Op de Beeck, Hans</td>
<td>2019</td>
<td>Journal of Neuroscience</td>
<td><a href="https://dx.doi.org/10.1523/JNEUROSCI.1714-18.2019" target= "_blank">10.1523/JNEUROSCI.1714-18.2019</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Learning divisive normalization in primary visual cortex</td>
<td>Burg, Max F.; Cadena, Santiago A.; Denfield, George H.; Walker, Edgar Y.; Tolias, Andreas S.; Bethge, Matthias; Ecker, Alexander S.</td>
<td>2021</td>
<td>PLoS computational biology</td>
<td><a href="https://dx.doi.org/10.1371/journal.pcbi.1009028" target= "_blank">10.1371/journal.pcbi.1009028</a</td>
<td>brain_imaging; electrophysiology; monkey; visual</td>
</tr>
<tr>
<td>Deep convolutional models improve predictions of macaque V1 responses to natural images</td>
<td>Cadena, Santiago A.; Denfield, George H.; Walker, Edgar Y.; Gatys, Leon A.; Tolias, Andreas S.; Bethge, Matthias; Ecker, Alexander S.</td>
<td>2019</td>
<td>PLoS computational biology</td>
<td><a href="https://dx.doi.org/10.1371/journal.pcbi.1006897" target= "_blank">10.1371/journal.pcbi.1006897</a</td>
<td>brain_imaging; electrophysiology; monkey; visual</td>
</tr>
<tr>
<td>How well do deep neural networks trained on object recognition characterize the mouse visual system?</td>
<td>Cadena, Santiago A.; Sinz, Fabian H.; Muhammad, Taliah; Froudarakis, Emmanouil; Cobos, Erick; Walker, Edgar Y.; Reimer, Jake; Bethge, Matthias; Tolias, Andreas S.; Ecker, Alexander S.</td>
<td>2019</td>
<td>NeurIPS workshop Neuro-AI</td>
<td></td>
<td>brain_imaging; electrophysiology; rodent; visual</td>
</tr>
<tr>
<td>Deep neural networks rival the representation of primate IT cortex for core visual object recognition</td>
<td>Cadieu, Charles F.; Hong, Ha; Yamins, Daniel L. K.; Pinto, Nicolas; Ardila, Diego; Solomon, Ethan A.; Majaj, Najib J.; DiCarlo, James J.</td>
<td>2014</td>
<td>PLoS computational biology</td>
<td><a href="https://dx.doi.org/10.1371/journal.pcbi.1003963" target= "_blank">10.1371/journal.pcbi.1003963</a</td>
<td>brain_imaging; electrophysiology; monkey; visual</td>
</tr>
<tr>
<td>BOLD5000, a public fMRI dataset while viewing 5000 visual images</td>
<td>Chang, Nadine; Pyles, John A.; Marcus, Austin; Gupta, Abhinav; Tarr, Michael J.; Aminoff, Elissa M.</td>
<td>2019</td>
<td>Scientific Data</td>
<td><a href="https://dx.doi.org/10.1038/s41597-019-0052-3" target= "_blank">10.1038/s41597-019-0052-3</a</td>
<td>brain_imaging; fMRI; human; semantic; visual</td>
</tr>
<tr>
<td>The Roles of Statistics in Human Neuroscience</td>
<td>Chén, Oliver Y.</td>
<td>2019</td>
<td>Brain Sciences</td>
<td><a href="https://dx.doi.org/10.3390/brainsci9080194" target= "_blank">10.3390/brainsci9080194</a</td>
<td>review</td>
</tr>
<tr>
<td>DNNBrain: A Unifying Toolbox for Mapping Deep Neural Networks and Brains</td>
<td>Chen, Xiayu; Zhou, Ming; Gong, Zhengxin; Xu, Wei; Liu, Xingyu; Huang, Taicheng; Zhen, Zonglei; Liu, Jia</td>
<td>2020</td>
<td>Frontiers in Computational Neuroscience</td>
<td><a href="https://dx.doi.org/10.3389/fncom.2020.580632" target= "_blank">10.3389/fncom.2020.580632</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Models of primate ventral stream that categorize and visualize images</td>
<td>Christensen, Elijah D.; Zylberberg, Joel</td>
<td>2020</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/2020.02.21.958488" target= "_blank">10.1101/2020.02.21.958488</a</td>
<td>brain_imaging; electrophysiology; monkey; visual</td>
</tr>
<tr>
<td>Deep Neural Networks as Scientific Models</td>
<td>Cichy, Radoslaw M.; Kaiser, Daniel</td>
<td>2019</td>
<td>Trends in Cognitive Sciences</td>
<td><a href="https://dx.doi.org/10.1016/j.tics.2019.01.009" target= "_blank">10.1016/j.tics.2019.01.009</a</td>
<td>review</td>
</tr>
<tr>
<td>Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence</td>
<td>Cichy, Radoslaw M.; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude</td>
<td>2016</td>
<td>Scientific Reports</td>
<td><a href="https://dx.doi.org/10.1038/srep27755" target= "_blank">10.1038/srep27755</a</td>
<td>brain_imaging; fMRI; human; MEG; visual</td>
</tr>
<tr>
<td>Neural dynamics of real-world object vision that guide behaviour</td>
<td>Cichy, Radoslaw M.; Kriegeskorte, Nikolaus; Jozwik, Kamila M.; van den Bosch, Jasper J. F.; Charest, Ian</td>
<td>2017</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/147298" target= "_blank">10.1101/147298</a</td>
<td>brain_imaging; fMRI; human; MEG; semantic; visual</td>
</tr>
<tr>
<td>Human perception in computer vision</td>
<td>Dekel, Ron</td>
<td>2017</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1701.04674v1" target= "_blank">1701.04674v1</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway</td>
<td>Devereux, Barry J.; Clarke, Alex; Tyler, Lorraine K.</td>
<td>2018</td>
<td>Scientific Reports</td>
<td><a href="https://dx.doi.org/10.1038/s41598-018-28865-1" target= "_blank">10.1038/s41598-018-28865-1</a</td>
<td>brain_imaging; fMRI; semantic; visual</td>
</tr>
<tr>
<td>Disentangled behavioral representations</td>
<td>Dezfouli, A., Ashtiani, H., Ghattas, O., Nock, R., Dayan, P., & Ong, C. S.</td>
<td>2019</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/658252" target= "_blank">10.1101/658252</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Models that learn how humans learn: The case of decision-making and its disorders</td>
<td>Dezfouli, Amir; Griffiths, Kristi; Ramos, Fabio; Dayan, Peter; Balleine, Bernard W.</td>
<td>2019</td>
<td>PLoS computational biology</td>
<td><a href="https://dx.doi.org/10.1371/journal.pcbi.1006903" target= "_blank">10.1371/journal.pcbi.1006903</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models</td>
<td>Dezfouli, Amir; Morris, Richard; Ramos, Fabio; Dayan, Peter; Balleine, Bernard W.</td>
<td>2018</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/328849" target= "_blank">10.1101/328849</a</td>
<td>behavior; brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Human and DNN Classification Performance on Images With Quality Distortions: A Comparative Study</td>
<td>Dodge, Samuel; Karam, Lina</td>
<td>2019</td>
<td>Association for Computing Machinery</td>
<td><a href="https://dx.doi.org/10.1145/3306241" target= "_blank">10.1145/3306241</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Crowding reveals fundamental differences in local vs. global processing in humans and machines</td>
<td>Doerig, Adrien; Bornet, A.; Choung, O. H.; Herzog, Michael H.</td>
<td>2020</td>
<td>Vision Research</td>
<td><a href="https://dx.doi.org/10.1016/j.visres.2019.12.006" target= "_blank">10.1016/j.visres.2019.12.006</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Capsule Networks as Recurrent Models ofGrouping and Segmentation</td>
<td>Doerig, Adrien; Schmittwilken, Lynn; Sayim, Bilge; Manassi, Mauro; Herzog, Michael H.</td>
<td>2020</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/747394" target= "_blank">10.1101/747394</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>What do adversarial images tell us about human vision?</td>
<td>Dujmović, Marin; Malhotra, Gaurav; Bowers, Jeffrey S.</td>
<td>2020</td>
<td>eLife</td>
<td><a href="https://dx.doi.org/10.7554/eLife.55978" target= "_blank">10.7554/eLife.55978</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Unveiling functions of the visual cortex using task-specific deep neural networks</td>
<td>Dwivedi, Kshitij; Bonner, Michael F.; Cichy, Radoslaw M.; Roig, Gemma</td>
<td>2021</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/2020.11.27.401380" target= "_blank">10.1101/2020.11.27.401380</a</td>
<td>brain_imaging; fMRI; human; semantic; visual</td>
</tr>
<tr>
<td>Unraveling Representations in Scene-selective Brain Regions Using Scene-Parsing Deep Neural Networks</td>
<td>Dwivedi, Kshitij; Cichy, Radoslaw M.; Roig, Gemma</td>
<td>2020</td>
<td>Journal of Cognitive Neuroscience</td>
<td><a href="https://dx.doi.org/10.1162/jocn\_a\_01624" target= "_blank">10.1162/jocn\_a\_01624</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Task-specific vision models explain task-specific areas of visual cortex</td>
<td>Dwivedi, Kshitij; Roig, Gemma</td>
<td>2018</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/402735" target= "_blank">10.1101/402735</a</td>
<td>brain_imaging; visual</td>
</tr>
<tr>
<td>How Deep is the Feature Analysis underlying Rapid Visual Categorization?</td>
<td>Eberhardt, Sven; Cader, Jonah; Serre, Thomas</td>
<td>2016</td>
<td>Advances in Neural Information Processing Systems</td>
<td><a href="http://arxiv.org/abs/1606.01167v1" target= "_blank">1606.01167v1</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>A rotation-equivariant convolutional neural network model of primary visual cortex</td>
<td>Ecker, Alexander S.; Sinz, Fabian H.; Froudarakis, Emmanouil; Fahey, Paul G.; Cadena, Santiago A.; Walker, Edgar Y.; Cobos, Erick; Reimer, Jacob; Tolias, Andreas S.; Bethge, Matthias</td>
<td>2018</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1809.10504v1" target= "_blank">1809.10504v1</a</td>
<td>brain_imaging; electrophysiology; rodent; visual</td>
</tr>
<tr>
<td>Seeing it all: Convolutional network layers map the function of the human visual system</td>
<td>Eickenberg, Michael; Gramfort, Alexandre; Varoquaux, Gaël; Thirion, Bertrand</td>
<td>2017</td>
<td>NeuroImage</td>
<td><a href="https://dx.doi.org/10.1016/j.neuroimage.2016.10.001" target= "_blank">10.1016/j.neuroimage.2016.10.001</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Adversarial Examples that Fool both Computer Vision and Time-Limited Humans</td>
<td>Elsayed, Gamaleldin F.; Shankar, Shreya; Cheung, Brian; Papernot, Nicolas; Kurakin, Alex; Goodfellow, Ian; Sohl-Dickstein, Jascha</td>
<td>2018</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1802.08195v3" target= "_blank">1802.08195v3</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Relating Visual Production and Recognition of Objects in Human Visual Cortex</td>
<td>Fan, Judith E.; Wammes, Jeffrey D.; Gunn, Jordan B.; Yamins, Daniel L. K.; Norman, Kenneth A.; Turk-Browne, Nicholas B.</td>
<td>2020</td>
<td>Journal of Neuroscience</td>
<td><a href="https://dx.doi.org/10.1523/JNEUROSCI.1843-19.2019" target= "_blank">10.1523/JNEUROSCI.1843-19.2019</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Common Object Representations for Visual Production and Recognition</td>
<td>Fan, Judith E.; Yamins, Daniel L. K.; Turk-Browne, Nicholas B.</td>
<td>2018</td>
<td>Cognitive Science</td>
<td><a href="https://dx.doi.org/10.1111/cogs.12676" target= "_blank">10.1111/cogs.12676</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans</td>
<td>Farzmahdi, Amirhossein; Rajaei, Karim; Ghodrati, Masoud; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi</td>
<td>2016</td>
<td>Scientific Reports</td>
<td><a href="https://dx.doi.org/10.1038/srep25025" target= "_blank">10.1038/srep25025</a</td>
<td>behavior; human; monkey; visual</td>
</tr>
<tr>
<td>Training neural networks to mimic the brain improves object recognition performance</td>
<td>Federer, Callie; Xu, Haoyan; Fyshe, Alona; Zylberberg, Joel</td>
<td>2020</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1905.10679v2" target= "_blank">1905.10679v2</a</td>
<td>brain_imaging; electrophysiology; human; monkey; visual</td>
</tr>
<tr>
<td>Comparing continual task learning in minds and machines</td>
<td>Flesch, Timo; Balaguer, Jan; Dekker, Ronald; Nili, Hamed; Summerfield, Christopher</td>
<td>2018</td>
<td>Proceedings of the National Academy of Sciences</td>
<td><a href="https://dx.doi.org/10.1073/pnas.1800755115" target= "_blank">10.1073/pnas.1800755115</a</td>
<td>human; learning; visual</td>
</tr>
<tr>
<td>Using human brain activity to guide machine learning</td>
<td>Fong, Ruth C.; Scheirer, Walter J.; Cox, David D.</td>
<td>2018</td>
<td>Scientific Reports</td>
<td><a href="https://dx.doi.org/10.1038/s41598-018-23618-6" target= "_blank">10.1038/s41598-018-23618-6</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Constrained sampling from deep generative image models reveals mechanisms of human target detection</td>
<td>Fruend, Ingo</td>
<td>2020</td>
<td>Journal of Vision</td>
<td><a href="https://dx.doi.org/10.1167/jov.20.7.32" target= "_blank">10.1167/jov.20.7.32</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Human sensitivity to perturbations constrained by a model of the natural image manifold</td>
<td>Fruend, Ingo; Stalker, Elee</td>
<td>2018</td>
<td>Journal of Vision</td>
<td><a href="https://dx.doi.org/10.1167/18.11.20" target= "_blank">10.1167/18.11.20</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Five points to check when comparing visual perception in humans and machines</td>
<td>Funke, Christina M.; Borowski, Judy; Stosio, Karolina; Brendel, Wieland; Wallis, Thomas S. A.; Bethge, Matthias</td>
<td>2021</td>
<td>Journal of Vision</td>
<td><a href="https://dx.doi.org/10.1167/jov.21.3.16" target= "_blank">10.1167/jov.21.3.16</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Do Primates and Deep Artificial Neural Networks Perform Object Categorization in a Similar Manner?</td>
<td>Gangopadhyay, Prabaha; Das, Jhilik</td>
<td>2019</td>
<td>Journal of Neuroscience</td>
<td><a href="https://dx.doi.org/10.1523/JNEUROSCI.2458-18.2018" target= "_blank">10.1523/JNEUROSCI.2458-18.2018</a</td>
<td>behavior; electrophysiology; fMRI; human; monkey; visual</td>
</tr>
<tr>
<td>Visual Object Recognition: Do We (Finally) Know More Now Than We Did?</td>
<td>Gauthier, Isabel; Tarr, Michael J.</td>
<td>2016</td>
<td>Annual Review of Vision Science</td>
<td><a href="https://dx.doi.org/10.1146/annurev-vision-111815-114621" target= "_blank">10.1146/annurev-vision-111815-114621</a</td>
<td>review; visual</td>
</tr>
<tr>
<td>Comparing deep neural networks against humans: object recognition when the signal gets weaker</td>
<td>Geirhos, Robert; Janssen, David H. J.; Schütt, Heiko H.; Rauber, Jonas; Bethge, Matthias; Wichmann, Felix A.</td>
<td>2017</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1706.06969v2" target= "_blank">1706.06969v2</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Generalisation in humans and deep neural networks</td>
<td>Geirhos, Robert; Medina Temme, Carlos R.; Rauber, Jonas; Schütt, Heiko H.; Bethge, Matthias; Wichmann, Felix A.</td>
<td>2018</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1808.08750v3" target= "_blank">1808.08750v3</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency</td>
<td>Geirhos, Robert; Meding, Kristof; Wichmann, Felix A.</td>
<td>2020</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/2006.16736v3" target= "_blank">2006.16736v3</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Partial success in closing the gap between human and machine vision</td>
<td>Geirhos, Robert; Narayanappa, Kantharaju; Mitzkus, Benjamin; Thieringer, Tizian; Bethge, Matthias; Wichmann, Felix A.; Brendel, Wieland</td>
<td>2021</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/2106.07411v1" target= "_blank">2106.07411v1</a</td>
<td>brain_imaging; human; visual</td>
</tr>
<tr>
<td>ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness</td>
<td>Geirhos, Robert; Rubisch, Patricia; Michaelis, Claudio; Bethge, Matthias; Wichmann, Felix A.; Brendel, Wieland</td>
<td>2018</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1811.12231v2" target= "_blank">1811.12231v2</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Feedforward object-vision models only tolerate small image variations compared to human</td>
<td>Ghodrati, Masoud; Farzmahdi, Amirhossein; Rajaei, Karim; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi</td>
<td>2014</td>
<td>Frontiers in Computational Neuroscience</td>
<td><a href="https://dx.doi.org/10.3389/fncom.2014.00074" target= "_blank">10.3389/fncom.2014.00074</a</td>
<td>brain_imaging; electrophysiology; monkey; visual</td>
</tr>
<tr>
<td>The Roles of Supervised Machine Learning in Systems Neuroscience</td>
<td>Glaser, Joshua I.; Benjamin, Ari S.; Farhoodi, Roozbeh; Kording, Konrad P.</td>
<td>2018</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1805.08239v2" target= "_blank">1805.08239v2</a</td>
<td>review</td>
</tr>
<tr>
<td>Controversial stimuli: Pitting neural networks against each other as models of human cognition</td>
<td>Golan, Tal; Raju, Prashant C.; Kriegeskorte, Nikolaus</td>
<td>2020</td>
<td>Proceedings of the National Academy of Sciences of the United States of America</td>
<td><a href="https://dx.doi.org/10.1073/pnas.1912334117" target= "_blank">10.1073/pnas.1912334117</a</td>
<td>behavior; human; review; visual</td>
</tr>
<tr>
<td>Visual scenes are categorized by function</td>
<td>Greene, Michelle R.; Baldassano, Christopher; Esteva, Andre; Beck, Diane M.; Fei-Fei, Li</td>
<td>2016</td>
<td>Journal of Experimental Psychology</td>
<td><a href="https://dx.doi.org/10.1037/xge0000129" target= "_blank">10.1037/xge0000129</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Shared spatiotemporal category representations in biological and artificial deep neural networks</td>
<td>Greene, Michelle R.; Hansen, Bruce C.</td>
<td>2018</td>
<td>PLoS computational biology</td>
<td><a href="https://dx.doi.org/10.1371/journal.pcbi.1006327" target= "_blank">10.1371/journal.pcbi.1006327</a</td>
<td>brain_imaging; EEG; human; visual</td>
</tr>
<tr>
<td>Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior</td>
<td>Groen, Iris I. A.; Greene, Michelle R.; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M.; Baker, Chris I.</td>
<td>2018</td>
<td>eLife</td>
<td><a href="https://dx.doi.org/10.7554/eLife.32962" target= "_blank">10.7554/eLife.32962</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Convergent evolution of face spaces across human face-selective neuronal groups and deep convolutional networks</td>
<td>Grossman, Shany; Gaziv, Guy; Yeagle, Erin M.; Harel, Michal; Mégevand, Pierre; Groppe, David M.; Khuvis, Simon; Herrero, Jose L.; Irani, Michal; Mehta, Ashesh D.; Malach, Rafael</td>
<td>2019</td>
<td>Nature Communications</td>
<td><a href="https://dx.doi.org/10.1038/s41467-019-12623-6" target= "_blank">10.1038/s41467-019-12623-6</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Perceptual Dominance in Brief Presentations of Mixed Images: Human Perception vs. Deep Neural Networks</td>
<td>Gruber, Liron Z.; Haruvi, Aia; Basri, Ronen; Irani, Michal</td>
<td>2018</td>
<td>Frontiers in Computational Neuroscience</td>
<td><a href="https://dx.doi.org/10.3389/fncom.2018.00057" target= "_blank">10.3389/fncom.2018.00057</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Brains on Beats</td>
<td>Güçlü, Umut; Thielen, Jordy; Hanke, Michael; van Gerven, Marcel A. J.</td>
<td>2016</td>
<td>Advances in Neural Information Processing Systems</td>
<td></td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream</td>
<td>Güçlü, Umut; van Gerven, Marcel A. J.</td>
<td>2015</td>
<td>Journal of Neuroscience</td>
<td><a href="https://dx.doi.org/10.1523/JNEUROSCI.5023-14.2015" target= "_blank">10.1523/JNEUROSCI.5023-14.2015</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Modeling the Dynamics of Human Brain Activity with Recurrent Neural Networks</td>
<td>Güçlü, Umut; van Gerven, Marcel A. J.</td>
<td>2017</td>
<td>Frontiers in Computational Neuroscience</td>
<td><a href="https://dx.doi.org/10.3389/fncom.2017.00007" target= "_blank">10.3389/fncom.2017.00007</a</td>
<td>brain_imaging; fMRI; human</td>
</tr>
<tr>
<td>Increasingly complex representations of natural movies across the dorsal stream are shared between subjects</td>
<td>Güçlü, Umut; van Gerven, Marcel A. J.</td>
<td>2017</td>
<td>NeuroImage</td>
<td><a href="https://dx.doi.org/10.1016/j.neuroimage.2015.12.036" target= "_blank">10.1016/j.neuroimage.2015.12.036</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Reconstructing perceived faces from brain activations with deep adversarial neural decoding</td>
<td>Güçlütürk, Yagmur; Güçlü, Umut; Seeliger, Katja; Bosch, Sander; van Lier, Rob; van Gerven, Marcel A. J.</td>
<td>2017</td>
<td>Advances in Neural Information Processing Systems 30 (NIPS 2017) Pre-Proceedings</td>
<td></td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Levels of Representation in a Deep Learning Model of Categorization</td>
<td>Guest, Olivia; Love, Bradley C.</td>
<td>2019</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/626374" target= "_blank">10.1101/626374</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Performance-optimized hierarchical models only partially predict neural responses during perceptual decision making</td>
<td>Gwilliams, Laura; King, Jean-Rémi</td>
<td>2017</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/221630" target= "_blank">10.1101/221630</a</td>
<td>brain_imaging; human; MEG; visual</td>
</tr>
<tr>
<td>Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex</td>
<td>Han, Kuan; Wen, Haiguang; Shi, Junxing; Lu, Kun-Han; Zhang, Yizhen; Fu, Di; Liu, Zhongming</td>
<td>2019</td>
<td>NeuroImage</td>
<td><a href="https://dx.doi.org/10.1016/j.neuroimage.2019.05.039" target= "_blank">10.1016/j.neuroimage.2019.05.039</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Neuroscience-Inspired Artificial Intelligence</td>
<td>Hassabis, Demis; Kumaran, Dharshan; Summerfield, Christopher; Botvinick, Matthew M.</td>
<td>2017</td>
<td>Neuron</td>
<td><a href="https://dx.doi.org/10.1016/j.neuron.2017.06.011" target= "_blank">10.1016/j.neuron.2017.06.011</a</td>
<td>review</td>
</tr>
<tr>
<td>Explicit information for category-orthogonal object properties increases along the ventral stream</td>
<td>Hong, Ha; Yamins, Daniel L. K.; Majaj, Najib J.; DiCarlo, James J.</td>
<td>2016</td>
<td>Nature Neuroscience</td>
<td><a href="https://dx.doi.org/10.1038/nn.4247" target= "_blank">10.1038/nn.4247</a</td>
<td>brain_imaging; electrophysiology; monkey; visual</td>
</tr>
<tr>
<td>Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features</td>
<td>Horikawa, Tomoyasu; Kamitani, Yukiyasu</td>
<td>2017</td>
<td>Frontiers in Computational Neuroscience</td>
<td><a href="https://dx.doi.org/10.3389/fncom.2017.00004" target= "_blank">10.3389/fncom.2017.00004</a</td>
<td>brain_imaging; fMRI; human</td>
</tr>
<tr>
<td>Generic decoding of seen and imagined objects using hierarchical visual features</td>
<td>Horikawa, Tomoyasu; Kamitani, Yukiyasu</td>
<td>2017</td>
<td>Nature Communications</td>
<td><a href="https://dx.doi.org/10.1038/ncomms15037" target= "_blank">10.1038/ncomms15037</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>The visual and semantic features that predict object memory: Concept property norms for 1,000 object images</td>
<td>Hovhannisyan, Mariam; Clarke, Alex; Geib, Benjamin R.; Cicchinelli, Rosalie; Monge, Zachary; Worth, Tory; Szymanski, Amanda; Cabeza, Roberto; Davis, Simon W.</td>
<td>2021</td>
<td>Memory & Cognition</td>
<td><a href="https://dx.doi.org/10.3758/s13421-020-01130-5" target= "_blank">10.3758/s13421-020-01130-5</a</td>
<td>behavior; human; semantic; visual</td>
</tr>
<tr>
<td>Comparing the Visual Representations and Performance of Humans and Deep Neural Networks</td>
<td>Jacobs, Robert A.; Bates, Christopher J.</td>
<td>2019</td>
<td>Current Directions in Psychological Science</td>
<td><a href="https://dx.doi.org/10.1177/0963721418801342" target= "_blank">10.1177/0963721418801342</a</td>
<td>human; learning; review; visual</td>
</tr>
<tr>
<td>Noise-robust recognition of objects by humans and deep neural networks</td>
<td>Jang, Hojin; McCormack, Devin; Tong, Frank</td>
<td>2021</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/2020.08.03.234625" target= "_blank">10.1101/2020.08.03.234625</a</td>
<td>behavior; fMRI; human; visual</td>
</tr>
<tr>
<td>Convolutional neural networks trained with a developmental sequence of blurry to clear images reveal core differences between face and object processing</td>
<td>Jang, Hojin; Tong, Frank</td>
<td>2021</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/2021.05.25.444835" target= "_blank">10.1101/2021.05.25.444835</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>General object-based features account for letter perception better than specialized letter features</td>
<td>Janini, Daniel; Hamblin, Chris; Deza, Arturo; Konkle, Talia</td>
<td>2021</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/2021.04.21.440772" target= "_blank">10.1101/2021.04.21.440772</a</td>
<td>behavior; human; semantic; visual</td>
</tr>
<tr>
<td>Relating Simple Sentence Representations in Deep Neural Networks and the Brain</td>
<td>Jat, Sharmistha; Tang, Hao; Talukdar, Partha; Mitchell, Tom</td>
<td>2019</td>
<td>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</td>
<td><a href="https://dx.doi.org/10.18653/v1/P19-1507" target= "_blank">10.18653/v1/P19-1507</a</td>
<td>brain_imaging; human; MEG; semantic</td>
</tr>
<tr>
<td>Extracting low-dimensional psychological representations from convolutional neural networks</td>
<td>Jha, Aditi; Peterson, Joshua C.; Griffiths, Thomas L.</td>
<td>2020</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/2005.14363v1" target= "_blank">2005.14363v1</a</td>
<td>behavior; human; review; visual</td>
</tr>
<tr>
<td>Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments</td>
<td>Jozwik, Kamila M.; Kriegeskorte, Nikolaus; Storrs, Katherine R.; Mur, Marieke</td>
<td>2017</td>
<td>Frontiers in Psychology</td>
<td><a href="https://dx.doi.org/10.3389/fpsyg.2017.01726" target= "_blank">10.3389/fpsyg.2017.01726</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Face dissimilarity judgements are predicted by representational distance in deep neural networks and principal-component face space</td>
<td>Jozwik, Kamila M.; O’Keeffe, Jonathan; Storrs, Katherine R.; Kriegeskorte, Nikolaus</td>
<td>2021</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/2021.04.09.438859" target= "_blank">10.1101/2021.04.09.438859</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>To find better neural network models of human vision, find better neural network models of primate vision</td>
<td>Jozwik, Kamila M.; Schrimpf, Martin; Kanwisher, Nancy; DiCarlo, James J.</td>
<td>2019</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/688390" target= "_blank">10.1101/688390</a</td>
<td>brain_imaging; electrophysiology; human; monkey; visual</td>
</tr>
<tr>
<td>Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons</td>
<td>Kalfas, Ioannis; Kumar, Satwant; Vogels, Rufin</td>
<td>2017</td>
<td>eNeuro</td>
<td><a href="https://dx.doi.org/10.1523/ENEURO.0113-17.2017" target= "_blank">10.1523/ENEURO.0113-17.2017</a</td>
<td>brain_imaging; electrophysiology; monkey; visual</td>
</tr>
<tr>
<td>Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior</td>
<td>Kar, Kohitij; Kubilius, Jonas; Schmidt, Kailyn; Issa, Elias B.; DiCarlo, James J.</td>
<td>2019</td>
<td>Nature Neuroscience</td>
<td><a href="https://dx.doi.org/10.1038/s41593-019-0392-5" target= "_blank">10.1038/s41593-019-0392-5</a</td>
<td>brain_imaging; electrophysiology; monkey; visual</td>
</tr>
<tr>
<td>Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition</td>
<td>Karimi-Rouzbahani, Hamid; Bagheri, Nasour; Ebrahimpour, Reza</td>
<td>2017</td>
<td>Neuroscience</td>
<td><a href="https://dx.doi.org/10.1016/j.neuroscience.2017.02.050" target= "_blank">10.1016/j.neuroscience.2017.02.050</a</td>
<td>brain_imaging; EEG; human; visual</td>
</tr>
<tr>
<td>Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models</td>
<td>Karimi-Rouzbahani, Hamid; Bagheri, Nasour; Ebrahimpour, Reza</td>
<td>2017</td>
<td>Scientific Reports</td>
<td><a href="https://dx.doi.org/10.1038/s41598-017-13756-8" target= "_blank">10.1038/s41598-017-13756-8</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>How do targets, nontargets, and scene context influence real-world object detection?</td>
<td>Katti, Harish; Peelen, Marius V.; Arun, Sripati P.</td>
<td>2017</td>
<td>Attention, Perception & Psychophysics</td>
<td><a href="https://dx.doi.org/10.3758/s13414-017-1359-9" target= "_blank">10.3758/s13414-017-1359-9</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Machine vision benefits from human contextual expectations</td>
<td>Katti, Harish; Peelen, Marius V.; Arun, Sripati P.</td>
<td>2019</td>
<td>Scientific Reports</td>
<td><a href="https://dx.doi.org/10.1038/s41598-018-38427-0" target= "_blank">10.1038/s41598-018-38427-0</a</td>
<td>behavior; brain_imaging; human; visual</td>
</tr>
<tr>
<td>Principles for models of neural information processing</td>
<td>Kay, Kendrick N.</td>
<td>2018</td>
<td>NeuroImage</td>
<td><a href="https://dx.doi.org/10.1016/j.neuroimage.2017.08.016" target= "_blank">10.1016/j.neuroimage.2017.08.016</a</td>
<td>review</td>
</tr>
<tr>
<td>Deep neural network models of sensory systems: windows onto the role of task constraints</td>
<td>Kell, Alexander J. E.; McDermott, Josh H.</td>
<td>2019</td>
<td>Current Opinion in Neurobiology</td>
<td><a href="https://dx.doi.org/10.1016/j.conb.2019.02.003" target= "_blank">10.1016/j.conb.2019.02.003</a</td>
<td>brain_imaging; fMRI; human; review</td>
</tr>
<tr>
<td>A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy</td>
<td>Kell, Alexander J. E.; Yamins, Daniel L. K.; Shook, Erica N.; Norman-Haignere, Sam V.; McDermott, Josh H.</td>
<td>2018</td>
<td>Neuron</td>
<td><a href="https://dx.doi.org/10.1016/j.neuron.2018.03.044" target= "_blank">10.1016/j.neuron.2018.03.044</a</td>
<td>brain_imaging; fMRI; human</td>
</tr>
<tr>
<td>Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models</td>
<td>Khaligh-Razavi, Seyed-Mahdi; Henriksson, Linda; Kay, Kendrick N.; Kriegeskorte, Nikolaus</td>
<td>2017</td>
<td>Journal of Mathematical Psychology</td>
<td><a href="https://dx.doi.org/10.1016/j.jmp.2016.10.007" target= "_blank">10.1016/j.jmp.2016.10.007</a</td>
<td>backpropagation; brain_imaging; fMRI; human</td>
</tr>
<tr>
<td>Deep supervised, but not unsupervised, models may explain IT cortical representation</td>
<td>Khaligh-Razavi, Seyed-Mahdi; Kriegeskorte, Nikolaus</td>
<td>2014</td>
<td>PLoS computational biology</td>
<td><a href="https://dx.doi.org/10.1371/journal.pcbi.1003915" target= "_blank">10.1371/journal.pcbi.1003915</a</td>
<td>brain_imaging; electrophysiology; monkey; visual</td>
</tr>
<tr>
<td>Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder</td>
<td>Kheradpisheh, Saeed R.; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée</td>
<td>2016</td>
<td>Frontiers in Computational Neuroscience</td>
<td><a href="https://dx.doi.org/10.3389/fncom.2016.00092" target= "_blank">10.3389/fncom.2016.00092</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition</td>
<td>Kheradpisheh, Saeed R.; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée</td>
<td>2016</td>
<td>Scientific Reports</td>
<td><a href="https://dx.doi.org/10.1038/srep32672" target= "_blank">10.1038/srep32672</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Deep neural networks in computational neuroscience</td>
<td>Kietzmann, Tim C.; McClure, Patrick; Kriegeskorte, Nikolaus</td>
<td>2019</td>
<td>Oxford Research Encyclopaedia of Neuroscience</td>
<td><a href="https://dx.doi.org/10.1093/acrefore/9780190264086.013.46" target= "_blank">10.1093/acrefore/9780190264086.013.46</a</td>
<td>review</td>
</tr>
<tr>
<td>Recurrence is required to capture the representational dynamics of the human visual system</td>
<td>Kietzmann, Tim C.; Spoerer, Courtney J.; Sörensen, Lynn K. A.; Cichy, Radoslaw M.; Hauk, Olaf; Kriegeskorte, Nikolaus</td>
<td>2019</td>
<td>Proceedings of the National Academy of Sciences of the United States of America</td>
<td><a href="https://dx.doi.org/10.1073/pnas.1905544116" target= "_blank">10.1073/pnas.1905544116</a</td>
<td>brain_imaging; human; MEG; visual</td>
</tr>
<tr>
<td>Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure</td>
<td>Kim, Been; Reif, Emily; Wattenberg, Martin; Bengio, Samy; Mozer, Michael C.</td>
<td>2019</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1903.01069v4" target= "_blank">1903.01069v4</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Using deep learning to reveal the neural code for images in primary visual cortex</td>
<td>Kindel, William F.; Christensen, Elijah D.; Zylberberg, Joel</td>
<td>2017</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1706.06208v1" target= "_blank">1706.06208v1</a</td>
<td>brain_imaging; electrophysiology; monkey; visual</td>
</tr>
<tr>
<td>Neural system identification for large populations separating “what” and “where”</td>
<td>Klindt, David; Ecker, Alexander S.; Euler, Thomas; Bethge, Matthias</td>
<td>2017</td>
<td>Advances in Neural Information Processing Systems</td>
<td></td>
<td>brain_imaging; rodent; visual</td>
</tr>
<tr>
<td>Image memorability is predicted at different stages of a convolutional neural network</td>
<td>Koch, Griffin E.; Akpan, Essang; Coutanche, Marc N.</td>
<td>2020</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/834796" target= "_blank">10.1101/834796</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Time-resolved correspondences between deep neural network layers and EEG measurements in object processing</td>
<td>Kong, Nathan C. L.; Kaneshiro, Blair; Yamins, Daniel L. K.; Norcia, Anthony M.</td>
<td>2020</td>
<td>Vision Research</td>
<td><a href="https://dx.doi.org/10.1016/j.visres.2020.04.005" target= "_blank">10.1016/j.visres.2020.04.005</a</td>
<td>brain_imaging; EEG; human; visual</td>
</tr>
<tr>
<td>Beyond category-supervision: instance-level contrastive learning models predict human visual system responses to objects</td>
<td>Konkle, Talia; Alvarez, George A.</td>
<td>2021</td>
<td>BioRxiv</td>
<td><a href="https://dx.doi.org/10.1101/2021.05.28.446118" target= "_blank">10.1101/2021.05.28.446118</a</td>
<td>brain_imaging; fMRI; human; visual</td>
</tr>
<tr>
<td>Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing</td>
<td>Kriegeskorte, Nikolaus</td>
<td>2015</td>
<td>Annual Review of Vision Science</td>
<td><a href="https://dx.doi.org/10.1146/annurev-vision-082114-035447" target= "_blank">10.1146/annurev-vision-082114-035447</a</td>
<td>review; visual</td>
</tr>
<tr>
<td>Cognitive computational neuroscience</td>
<td>Kriegeskorte, Nikolaus; Douglas, Pamela K.</td>
<td>2018</td>
<td>Nature Neuroscience</td>
<td><a href="https://dx.doi.org/10.1038/s41593-018-0210-5" target= "_blank">10.1038/s41593-018-0210-5</a</td>
<td>review</td>
</tr>
<tr>
<td>Neural network models and deep learning</td>
<td>Kriegeskorte, Nikolaus; Golan, Tal</td>
<td>2019</td>
<td>Current Biology</td>
<td><a href="https://dx.doi.org/10.1016/j.cub.2019.02.034" target= "_blank">10.1016/j.cub.2019.02.034</a</td>
<td>backpropagation</td>
</tr>
<tr>
<td>Deep Neural Networks as a Computational Model for Human Shape Sensitivity</td>
<td>Kubilius, Jonas; Bracci, Stefania; Op de Beeck, Hans</td>
<td>2016</td>
<td>PLoS computational biology</td>
<td><a href="https://dx.doi.org/10.1371/journal.pcbi.1004896" target= "_blank">10.1371/journal.pcbi.1004896</a</td>
<td>behavior; human; visual</td>
</tr>
<tr>
<td>Can deep neural networks rival human ability to generalize in core object recognition</td>
<td>Kubilius, Jonas; Kar, Kohitij; Schmidt, Kailyn; DiCarlo, James J.</td>
<td>2018</td>
<td>Cognitive Computational Neuroscience</td>
<td></td>
<td>brain_imaging; human; visual</td>
</tr>
<tr>
<td>Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs</td>
<td>Kubilius, Jonas; Schrimpf, Martin; Kar, Kohitij; Hong, Ha; Majaj, Najib J.; Rajalingham, Rishi; Issa, Elias B.; Bashivan, Pouya; Prescott-Roy, Jonathan; Schmidt, Kailyn; Nayebi, Aran; Bear, Daniel; Yamins, Daniel L. K.; DiCarlo, James J.</td>
<td>2019</td>
<td>arXiv</td>
<td><a href="http://arxiv.org/abs/1909.06161v2" target= "_blank">1909.06161v2</a</td>
<td>brain_imaging; electrophysiology; human; monkey; visual</td>