Does Artificial Intelligence Worm its Way into the Study of Cognition? (Pun Intended)

Citing evidence from AI/AL/Robotics to gain insight into cognitive mechanisms, Poirier et al. (2005) clearly share the sentiment that

„a measure of understanding will be gained by studying simple and superficial models of complete agents.” (p. 762)

Although they state that to fully understand the principles governing cognition, complete models of situated embodied agents engaged in brain-body-world-interaction (or their AI-counterparts) will prove essential, they hold that

“simple models like these can help us understand some general principles governing categorization” (p. 762).

There is, of course, a bigger question looming behind this assertion:

"Can robots make good models of biological behaviour?“ (Webb 2001a)

Barbara Webb (who you can hear talk about her work on robotic crickets here) proposes that models can indeed tell us a lot about biological systems, if only the dimensions of the simulation are made explicit. She proposes the following variables:

“1. Relevance: whether the model tests and generates hypotheses applicable to biology.
2. Level: the elemental units of the model in the hierarchy from atoms to societies.
3. Generality: the range of biological systems the model can represent.
4. Abstraction: the complexity, relative to the target, or amount of detail included in the model.
5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour.
6. Performance match: to what extent the model behaviour matches the target behaviour.
7. Medium: the physical basis by which the model is implemented.“ (p. 1033)

Another problem is the confusion over the term model, which is defined in so many different ways that it is sometimes hard to find out whether two people mean the same thing when talking about models.
According to Webb (2001a)

“modelling aims to make the process of producing predictions from hypotheses more effective by enlisting the aid of an analogical mechanism” by symbolically simulating the properties assumed to perform certain functions. (p. 1035)

Biological systems and biorobotics share the property that they are

“physically instantiated and have unmediated contact with the external environment” (p. 1037).

Poirier et al. (2005) show that we can already draw much insight from this correlation, given that even simple models show how

"categorization capacities that are quite sophisticated can emerge from very simple embodied and situated systems” (p. 762).

These observations clearly speak for the importance of embodied properties when studying cognitive mechanisms. On the other hand, they give a practical example of how robotics can support specific hypotheses regarding cognition. They too show that robots that are models if animal behavior can be seen as “as a simulation technology to test hypotheses in biology” (Webb 2001a: 1049).
Many of the peer commentaries on Webb’s Behavioral and Brain Sciences Article are not that optimistic. One general criticism is that of underdetermination,

"that is, having a robot behave like an animal is no guarantee that the animal works the same way“ (Webb 2001b: 1083).

But, as Poirer et al. (2005) argue, artificial systems give us major clues about what kind of and which quantities of structure are able to perform certain functions.
Another criticism aimed at biorobotics is that, although they are inspired by biological systems, as of yet the haven’t done much to inform biology. But as Webb’s (2001a) impressive sample of biorobotics research – 78 articles from 1992-2001 ranging from bat sonar and frog snapping to simulations of insect wings, paper wasp nest construction and ant/bee landmark homing – as well as Poirier et al.’s (2005) review show, this complaint is clearly mistaken.
Another interesting test case for the ability of artificial systems to simulate biological behavior are neural networks employed to simulate properties of Caenorhabditis elegans, a roundworm that is about 1mm in length. I will discuss some of these attempts in my next post.


Reference:

Poirier, Pierre, Benoit Hardy-Vallée and Jean-Frédéric Depasquale. 2005. “Embodied Categorization.” Handbook of Categorization in Cognitive Science. Eds. Henri Cohen and Claire Lefebvre. Amsterdam: Elsevier.

Webb, Barbara. 2001a. “Can robots make good models of biological behaviour?” Behavioral and Brain Sciences 24.6: 1033–1050.

Webb, Barbara. 2001b. “Robots can be (good) models.“ Behavioral and Brain Sciences 24.6:1081-1087