Computing reaction time (RT) metrics

There is a deep but still poorly understood link between sensory uncertainty, evidence accumulation, and reaction times. In this project (Goetschalckx* et al., 2024), we develop a stimulus-computable, task-optimized model of visual processing dynamics that lets us build computational accounts of reaction times: a key behavioral marker of decision-making.

Our approach connects recurrent vision models with evidence accumulation theory, offering a general framework to explore how model and human visual strategies unfold through time. This opens the door to testing hypotheses about the temporal alignment of neural and behavioral computations in the brain.

For more details, check out the dedicated project webpage.

References

2024

NeurIPS

Computing a human-like reaction time metric from stable recurrent vision models

Lore Goetschalckx^*, Lakshmi Narasimhan Govindarajan^*, Alekh Karkada Ashok, Aarit Ahuja, David Sheinberg, and 1 more author

Advances in Neural Information Processing Systems, 2024

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@article{goetschalckx2024computing,
  title = {Computing a human-like reaction time metric from stable recurrent vision models},
  author = {Goetschalckx, Lore and Govindarajan, Lakshmi Narasimhan and Karkada Ashok, Alekh and Ahuja, Aarit and Sheinberg, David and Serre, Thomas},
  journal = {Advances in Neural Information Processing Systems},
  volume = {36},
  year = {2024},
}