Nature Search

Revealing the multidimensional mental representations of natural objects underlying human similarity judgements

Hebart et al. developed a computational model of similarity judgements for 1,854 natural objects. The model accurately predicted similarity and revealed 49 interpretable dimensions that reflect both perceptual and conceptual object properties.

Martin N. Hebart
Charles Y. Zheng
Chris I. Baker
Research12 Oct 2020
Nature Human Behaviour

Volume: 4, P: 1173-1185
Dimensions underlying the representational alignment of deep neural networks with humans

An interpretability framework that compares how humans and deep neural networks process images has been presented. Their findings reveal that, unlike humans, deep neural networks focus more on visual properties than semantic ones, highlighting divergent representational strategies.

Florian P. Mahner
Lukas Muttenthaler
Martin N. Hebart
ResearchOpen Access23 Jun 2025
Nature Machine Intelligence

Volume: 7, P: 848-859
Odronextamab monotherapy in patients with relapsed/refractory diffuse large B cell lymphoma: primary efficacy and safety analysis in phase 2 ELM-2 trial

Kim and colleagues perform a phase 2 clinical trial (ELM-2) of odronextamab monotherapy in patients with relapsed/refractory diffuse large B cell lymphoma and report on the primary analyses of the efficacy and safety profile.

Won Seog Kim
Tae Min Kim
Umar Farooq
ResearchOpen Access17 Mar 2025
Nature Cancer

Volume: 6, P: 528-539
Facing up to stereotypes

Our understanding of faces reflects both our perception of their facial features and our social knowledge. This interaction of stereotypes and vision can be observed in brain signals in fusiform gyrus and orbitofrontal cortex.

Martin N Hebart
Chris I Baker
News & Views26 May 2016
Nature Neuroscience

Volume: 19, P: 763-764
Distributed representations of behaviour-derived object dimensions in the human visual system

Contier et al. show that dimensions are superior to categories at predicting brain responses to visual objects.

Oliver Contier
Chris I. Baker
Martin N. Hebart
ResearchOpen Access09 Sept 2024
Nature Human Behaviour

Volume: 8, P: 2179-2193
What comparing deep neural networks can teach us about human vision

Recent work has demonstrated important parallels between human visual representations and those found in deep neural networks. A new study comparing functional MRI data to deep neural network models highlights factors that may determine this similarity.

Katja Seeliger
Martin N. Hebart
News & Views06 Feb 2024
Nature Machine Intelligence

Volume: 6, P: 122-123
Allogeneic transplantation in multiple myeloma: long-term follow-up and cytogenetic subgroup analysis

Stefan Knop
Monika Engelhardt
M. Svaldi
Research28 Aug 2019
Leukemia

Volume: 33, P: 2710-2719
Preliminary experience in external quality control of RT-PCR PML-RARα detection in promyelocytic leukemia

P Bolufer
E Barragán
M Briz
Research08 Dec 1998
Leukemia

Volume: 12, P: 2024-2028
Ten principles for reliable, efficient, and adaptable coding in psychology and cognitive neuroscience

Programming is essential for modern research in neuroscience and psychology, but it can quickly become a source of frustration and error. This Primer introduces ten practical principles guiding researchers toward writing clear, adaptable, and easily shareable code, ultimately supporting reproducible science.

Johannes Roth
Yunyan Duan
Martin N. Hebart
ReviewsOpen Access15 Apr 2025
Communications Psychology

Volume: 3, P: 1-15
Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams

Tijl Grootswagers
Ivy Zhou
Thomas A. Carlson
ResearchOpen Access10 Jan 2022
Scientific Data

Volume: 9, P: 1-7
Mechanisms of offline motor learning at a microscale of seconds in large-scale crowdsourced data

Marlene Bönstrup
Iñaki Iturrate
Leonardo G. Cohen
ResearchOpen Access04 Jun 2020
npj Science of Learning

Volume: 5, P: 1-10
A data-driven investigation of human action representations

Diana C. Dima
Martin N. Hebart
Leyla Isik
ResearchOpen Access30 Mar 2023
Scientific Reports

Volume: 13, P: 1-11

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