@article {172, title = {An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models}, journal = {bioRxiv}, year = {2023}, month = {2023/01/01}, pages = {2022.12.31.522402}, type = {preprint}, abstract = {

How humans learn new visual objects is a longstanding scientific problem. Previous work has led to a diverse collection of models for how it is accomplished, but a current limitation in the field is a lack of empirical benchmarks which can be used to evaluate and compare specific models against each other. Here, we use online psychophysics to measure human behavioral learning trajectories over a set of tasks involving novel 3D objects. Consistent with intuition, these results show that humans generally require very few images (\≈ 6) to approach their asymptotic accuracy, find some object discriminations more easy to learn than others, and generalize quite well over a range of image transformations after even one view of each object. We then use those data to develop benchmarks that may be used to evaluate a learning model\’s similarity to humans. We make these data and benchmarks publicly available [GitHub], and, to our knowledge, they are currently the largest publicly-available collection of learning-related psychophysics data in humans. Additionally, to serve as baselines for those benchmarks, we implement and test a large number of baseline models (n=1,932), each based on a standard cognitive theory of learning: that humans re-represent images in a fixed, Euclidean space, then learn linear decision boundaries in that space to identify objects in future images. We find some of these baseline models make surprisingly accurate predictions. However, we also find reliable prediction gaps between all baseline models and humans, particularly in the few-shot learning setting.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {https://www.biorxiv.org/content/10.1101/2022.12.31.522402v1}, url = {https://www.biorxiv.org/content/biorxiv/early/2023/01/02/2022.12.31.522402.full.pdf}, author = {Lee, Michael J. and DiCarlo, James J.} } @article {183, title = {How well do rudimentary plasticity rules predict adult visual object learning?}, journal = {PLOS Computational Biology}, volume = {19}, year = {2023}, month = {Nov-12-2023}, pages = {e1011713}, abstract = {

A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images.

One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming retinal images as points in a fixed, multidimensional neural space, and 2) the optimization of linear decision boundaries in that space, via simple plasticity rules applied to a single downstream layer.

Though this scheme is biologically plausible, the extent to which it explains learning behavior in humans has been unclear\—in part because of a historical lack of image-computable models of the putative neural space, and in part because of a lack of measurements of human learning behaviors in difficult, naturalistic settings.

Here, we addressed these gaps by 1) drawing from contemporary, image-computable models of the primate ventral visual stream to create a large set of testable learning models (n = 2,408 models), and 2) using online psychophysics to measure human learning trajectories over a varied set of tasks involving novel 3D objects (n = 371,000 trials), which we then used to develop (and\ publicly release) empirical benchmarks for comparing learning models to humans.

We evaluated each learning model on these benchmarks, and found those based on deep, high-level representations from neural networks were surprisingly aligned with human behavior. While no tested model explained the entirety of replicable human behavior, these results establish that rudimentary plasticity rules, when combined with appropriate visual representations, have high explanatory power in predicting human behavior with respect to this core object learning problem.

}, issn = {1553-734X}, doi = {10.1371/journal.pcbi.1011713}, url = {https://dx.plos.org/10.1371/journal.pcbi.1011713}, author = {Lee, Michael J. and DiCarlo, James J.}, editor = {Kietzmann, Tim Christian} } @article {184, title = {How well do rudimentary plasticity rules predict adult visual object learning?}, journal = {PLOS Computational Biology}, volume = {19}, year = {2023}, month = {Nov-12-2023}, pages = {e1011713}, abstract = {

A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images.

One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming retinal images as points in a fixed, multidimensional neural space, and 2) the optimization of linear decision boundaries in that space, via simple plasticity rules applied to a single downstream layer.

Though this scheme is biologically plausible, the extent to which it explains learning behavior in humans has been unclear\—in part because of a historical lack of image-computable models of the putative neural space, and in part because of a lack of measurements of human learning behaviors in difficult, naturalistic settings.

Here, we addressed these gaps by 1) drawing from contemporary, image-computable models of the primate ventral visual stream to create a large set of testable learning models (n = 2,408 models), and 2) using online psychophysics to measure human learning trajectories over a varied set of tasks involving novel 3D objects (n = 371,000 trials), which we then used to develop (and\ publicly release) empirical benchmarks for comparing learning models to humans.

We evaluated each learning model on these benchmarks, and found those based on deep, high-level representations from neural networks were surprisingly aligned with human behavior. While no tested model explained the entirety of replicable human behavior, these results establish that rudimentary plasticity rules, when combined with appropriate visual representations, have high explanatory power in predicting human behavior with respect to this core object learning problem.

}, issn = {1553-734X}, doi = {10.1371/journal.pcbi.1011713}, url = {https://dx.plos.org/10.1371/journal.pcbi.1011713}, author = {Lee, Michael J. and DiCarlo, James J.}, editor = {Kietzmann, Tim Christian} } @article {147, title = {Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence}, journal = {Neuron}, year = {2020}, month = {Jan-09-2020}, abstract = {

A potentially organizing goal of the brain and cognitive sciences is to accurately explain domains of human intelligence as executable, neurally mechanistic models. Years of research have led to models that capture experimental results in individual behavioral tasks and individual brain regions. We here advocate for taking the next step: integrating experimental results from many laboratories into suites of benchmarks that, when considered together, push mechanistic models toward explaining entire domains of intelligence, such as vision, language, and motor control. Given recent successes of neurally mechanistic models and the surging availability of neural, anatomical, and behavioral data, we believe that now is the time to create integrative benchmarking platforms that incentivize ambitious, unified models. This perspective discusses the advantages and the challenges of this approach and proposes specific steps to achieve this goal in the domain of visual intelligence with the case study of an integrative benchmarking platform called Brain-Score.

}, issn = {08966273}, doi = {10.1016/j.neuron.2020.07.040}, url = {https://linkinghub.elsevier.com/retrieve/pii/S089662732030605X}, author = {Schrimpf, Martin and Kubilius, Jonas and Lee, Michael J. and Murty, NAR and Ajemian, Robert and DiCarlo, James J.} }