| How to control for confounds in decoding analyses of neuroimaging data L Snoek, S Miletić, HS Scholte Neuroimage 184, 741-760, 2019 | 123 | 2019 |
| The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses L Snoek, MM van der Miesen, T Beemsterboer, A Van Der Leij, ... Scientific data 8 (1), 85, 2021 | 90 | 2021 |
| Choosing to view morbid information involves reward circuitry S Oosterwijk, L Snoek, J Tekoppele, LH Engelbert, HS Scholte Scientific reports 10 (1), 15291, 2020 | 45 | 2020 |
| How much intelligence is there in artificial intelligence? A 2020 update HLJ Van der Maas, L Snoek, CE Stevenson Intelligence 87, 101548, 2021 | 42 | 2021 |
| Shared states: using MVPA to test neural overlap between self-focused emotion imagery and other-focused emotion understanding S Oosterwijk, L Snoek, M Rotteveel, LF Barrett, HS Scholte Social Cognitive and Affective Neuroscience 12 (7), 1025-1035, 2017 | 28 | 2017 |
| The relationship between individual differences in gray matter volume and religiosity and mystical experiences: A preregistered voxel‐based morphometry study M van Elk, L Snoek European Journal of Neuroscience 51 (3), 850-865, 2020 | 23 | 2020 |
| Degrees of algorithmic equivalence between the brain and its DNN models PG Schyns, L Snoek, C Daube Trends in Cognitive Sciences 26 (12), 1090-1102, 2022 | 20 | 2022 |
| Porcupine: A visual pipeline tool for neuroimaging analysis T Van Mourik, L Snoek, T Knapen, DG Norris PLoS computational biology 14 (5), e1006064, 2018 | 16 | 2018 |
| Religious belief and cognitive conflict sensitivity: A preregistered fMRI study S Hoogeveen, L Snoek, M van Elk Cortex 129, 247-265, 2020 | 14 | 2020 |
| The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses. Sci Data 8, 85 L Snoek, MM van der Miesen, T Beemsterboer, A Van Der Leij, ... | 7 | 2021 |
| AOMIC-PIOP2 L Snoek, M van der Miesen, A van der Leij, T Beemsterboer, A Eigenhuis, ... OpenNeuro10 18112, 2020 | 5 | 2020 |
| A Critical Test of Deep Convolutional Neural Networks' Ability to Capture Recurrent Processing in the Brain Using Visual Masking J Loke, N Seijdel, L Snoek, M Van der Meer, R Van de Klundert, ... Journal of cognitive neuroscience 34 (12), 2390-2405, 2022 | 4 | 2022 |
| Testing, explaining, and exploring models of facial expressions of emotions L Snoek, R Jack, P Schyns, O Garrod, M Mittenbühler, C Chen, ... Science Advances 9 (6), 2023 | 3 | 2023 |
| Stimulus models test hypotheses in brains and DNNs PG Schyns, L Snoek, C Daube Trends in Cognitive Sciences 27 (3), 216-217, 2023 | 1 | 2023 |
| Dynamic face imaging: a novel analysis framework for 4D social face perception and expression L Snoek, RE Jack, PG Schyns 2023 IEEE 17th International Conference on Automatic Face and Gesture …, 2023 | 1 | 2023 |
| Action Intentions, Predictive Processing, and Mind Reading: Turning Goalkeepers Into Penalty Killers KR Ridderinkhof, L Snoek, G Savelsbergh, J Cousijn, AD van Campen Frontiers in Human Neuroscience 15, 789817, 2022 | 1 | 2022 |
| Human visual cortex and deep convolutional neural network care deeply about object background J Loke, N Seijdel, L Snoek, LKA Sörensen, R van de Klundert, ... Journal of Cognitive Neuroscience 36 (3), 551-566, 2024 | | 2024 |
| A critical test of deep convolutional neural networks’ ability to capture recurrent processing using visual masking. J Loke, N Seijdel, L Snoek, R van de Klundert, M van der Meer, E Quispel, ... Journal of Vision 22 (14), 3651-3651, 2022 | | 2022 |
HS ScholteDepartment of Brain & Cognition, University of AmsterdamVerified email at uva.nl
