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Verschure argues that models of what he calls the mind, brain, body nexus should particularly account for data about the behavior at the system level, ie. overt behavior. He calls this convergent validation

In Verschure's concept of convergent validation, the researcher does not seek inspiration for but constraints for falsification or validation of models in nature.

Mommy, where do models come from?

Rucci et al. present a robotic system based on their neural model of audiovisual localization.

There are a number of approaches for audio-visual localization. Some with actual robots, some just as theoretical ANN or algorithmic models.

Some of the multisensory properties of the SC were known in the early seventies, to be re-discovered again much later (in lethal animal experiments).

Schenck summarizes three neurorobotic studies in which he evaluates visual prediction, and, more specifically, predictive remapping. He argues that his experiments support a claim in psychology saying that pre-saccadic activation of neurons whose receptive fields will contain the location of a salient stimulus after the saccade is not just pre-activation but actually a prediction of what the visual field will be like after the saccade.

Jasso and Triesch presented a simulated virtual reality environment for training robotic models.

Jasso and Triesch acknowledge that a simulation does not always follow exactly the laws of physics. In fact, their environment does not simulate any physics except those of human motion.

Jasso and Triesch argue that for the high-level cognition they train, lacking simulations of physics aren't a problem.

Jasso and Triesch argue that their simulated robots are not limited by the capabilities of today's robotic technology.

Santangelo and Macaluso describe typical experiments for studying visual attention.

Simulations can lead researchers to postulate unrealistically reliable sensor data or actuation.

Noise in real experiments can make dynamics more stable.

Embodied robots bring together the complexity of sensing and action the real world poses. These are not present in simple models and simulations.

Biorobotics has been used successfully to study and sometimes validate theoretical biological claims.

Testing biological hypotheses using robots is called `biorobotics'.

Using a material model instead of the actual object of study is useful in two cases:

  • that physical model is better understood,
  • it is easier to use the model than the original.

Biorobotics is a case of using a material model to understand biological organisms for both reasons given by Rosenblueth and Wiener:

  • robots are generally better understood than the real thing (because we construct them)
  • they are easier studied for technical and for ethical reasons.

For biorobotic experiments to mean something, it is necessary to identify those parts of a biorobotic model which are robotic and those which model biology.

The design of a biorobotic experiment implicitly includes parts of the hypothesis being tested—those need to be made explicit.

The degree to which neuroscientists can draw conclusions from computational models depends on biological accuracy.

If input to biologically plausible models is too dissimilar to natural input, then that can lead to non-natural behavior of the model.

Sensory noise in robotic experiments validates a model's robustness. It is always realistic (but not necessarily natural).