Logics in animal cognition: Are they important to Brain Computer Interfaces (BCI) and aerospace missions?

Conventional wisdom is that logic and language are tightly connected to logics in human cognition. However, recent studies have revealed that, in animal cognition, there exist logics that do not depend on languages. In other words, logical behavior is not human brain specific. At least four logics: perceptual logic, technical logic, social logic, and inference logic have been studied in animal cognition. Despite the obvious differences between animals and humans in using languages, recent studies confirm that both humans and animals utilize the socalled sensor brain maps for most sensory modalities: populations of neurons are selectively tuned to different stimulus features or feature combinations (Ewert 2005, Ma and Krings 2009). This commonality suggests that the studies of animal logics should also be insightful for understanding human logics. After briefly reviewing some of the recent advances in animal logics research, we turn to a more practical research field—the Brain Computer Interface (BCI) [also known as Brain Machine Interface (BMI)] in biomedicine. BCI promises to provide non-muscular communication and control for people with severe motor disabilities. A fundamental goal of BCI is to translate thought or intent into action with brain activity only (Birbaumer 2006). If we recognize that logic is about the way of thinking and it is probably the most reliable and possibly most efficient way to understand thoughts, an interesting question could be: will the understanding of animal logics be very helpful for BCI research? The current BCI research is primarily targeted for rehabilitation applications. In this article, we also discuss the potential of using BCI techniques in aerospace systems and space explorations. One can imagine the potential that an astronaut operates a robot device by only thinking. Perhaps a revolutionary breakthrough from BCI technology can be the ‘copiloting’ of aerial vehicles by multiple pilots including some who stations at the ground. This copiloting not only reduces the stress (brain fatigue) of pilots, but also enhances the reliability and fault tolerance of aerial vehicles. 1 2

[1]  Richard W. Byrne,et al.  Machiavellian Intelligence II: The Technical Intelligence hypothesis: An additional evolutionary stimulus to intelligence? , 1997 .

[2]  Catriona M. E. Ryan,et al.  The logic of the stimulus , 2006, Animal Cognition.

[3]  N. Birbaumer Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. , 2006, Psychophysiology.

[4]  Russell D. Gray,et al.  The right tool for the job: what strategies do wild New Caledonian crows use? , 2006, Animal Cognition.

[5]  J. Edwards Genetic Epistemology , 1971 .

[6]  Marco Dadda,et al.  Do fish count? Spontaneous discrimination of quantity in female mosquitofish , 2008, Animal Cognition.

[7]  Heidi L. Marsh,et al.  The use of perceptual features in categorization by orangutans (Pongo abelli) , 2008, Animal Cognition.

[8]  Jonathan R Wolpaw,et al.  Brain–computer interfaces as new brain output pathways , 2007, The Journal of physiology.

[9]  N. Franks Animal Architecture Mike Hansell , 2005, Animal Behaviour.

[10]  Merrill Cornish The right tool for the job , 1988 .

[11]  L. Huber,et al.  Inferential reasoning by exclusion in pigeons, dogs, and humans , 2008, Animal Cognition.

[12]  M. Srinivasan,et al.  Evidence for counting in insects , 2008, Animal Cognition.

[13]  Johan J. Bolhuis,et al.  The Behavior of Animals: Mechanisms, Function, and Evolution , 2005 .

[14]  Valeria Anna Sovrano,et al.  Recognition of partly occluded objects by fish , 2007, Animal Cognition.

[15]  L. Cohen,et al.  Brain–computer interfaces: communication and restoration of movement in paralysis , 2007, The Journal of physiology.

[16]  Martin Giurfa,et al.  Categorization of visual stimuli in the honeybee Apis mellifera , 2006, Animal Cognition.

[17]  B. Heinrich,et al.  Pilfering ravens, Corvus corax, adjust their behaviour to social context and identity of competitors , 2006, Animal Cognition.

[18]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[19]  Cognitive Ecology: The Evolutionary Ecology of Information Processing and Decision Making , 1998, Trends in Cognitive Sciences.

[20]  Axel W. Krings,et al.  Insect sensory systems inspired computing and communications , 2009, Ad Hoc Networks.

[21]  I. Pepperberg Grey parrot numerical competence: a review , 2006, Animal Cognition.

[22]  E S Spelke,et al.  Core knowledge. , 2000, The American psychologist.

[23]  Heba M. Lakany,et al.  Understanding intention of movement from electroencephalograms , 2007, Expert Syst. J. Knowl. Eng..

[24]  L. Huber,et al.  Animal logics: Decisions in the absence of human language , 2006, Animal Cognition.

[25]  Á. Miklósi,et al.  Reproducing human actions and action sequences: “Do as I Do!” in a dog , 2006, Animal Cognition.

[26]  Luca Citi,et al.  Prospects of brain-machine interfaces for space system control , 2006 .

[27]  Edward O. Wilson,et al.  Success and Dominance in Ecosystems: The Case of the Social Insects , 1991 .

[28]  Thomas R. Zentall,et al.  Imitation: definitions, evidence, and mechanisms , 2006, Animal Cognition.

[29]  L. Huber,et al.  Technical intelligence in animals: the kea model , 2006, Animal Cognition.

[30]  A. Bogen Do As I Do , 2006 .

[31]  M. Giurfa Cognitive neuroethology: dissecting non-elemental learning in a honeybee brain , 2003, Current Opinion in Neurobiology.

[32]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.