Emulation

In emulation learning, subjects learn about parts of their environment and use this to achieve their own goals. First coined by child psychologist David Wood (1988 ), in 1990 “emulation” was taken up by Michael Tomasello to explain the findings of an earlier study on ape social learning (Tomasello et al., 1987 ). The meaning of the term emulation has changed gradually since.

History of the term
In the original version, emulation referred to observers understanding objects in their potential to help them achieve desired results. They gained this understanding by seeing demonstrators achieving these very results with these objects. The actions performed by the demonstrators however were not copied, so it was concluded that observers learn “from the demonstration, that the tool may be used to obtain the food” (Tomasello et al., 1987).

In 1996, Tomasello redefined the term: “The individual observing and learning some affordances of the behavior of another animal, and then using what it has learned in devising its own behavioral strategies, is what I have called emulation learning. […] an individual is not just attracted to the location of another but actually learns something about the environment as a result of its behavior”. An even later definition further clarifies: “In emulation learning, learners see the movement of the objects involved and then come to some insight about its relevance to their own problems.” (Boesch & Tomasello, 1998 ). Here animals learn some physics or causal relations of the environment. This does not necessarily involve a very complex understanding of abstract phenomena (as to what defines a “tool as a tool”). Emulation comprises a large span of cognitive complexity, from minimal cognitive complexity to very complex levels (e.g. see Custance et al. (1999) for a version they call “object movement reenactment”, with regard to the lower range of complexity). Emulation was originally invented as a “cognitivist’s alternative” to associative learning(Tomasello, 1999), spanning learning about how things function and their “affordances” (Tomasello, 1999 ) put to the use of achieving ones own goals: “Emulation learning in tool-use tasks seems to require the perception and understanding of some causal relations among objects” (Call & Tomasello, 1995 ). This necessarily involves some “insight” - a cognitive domain. To further highlight this point Call & Carpenter wrote in 2001 : “it would be a harder task to teach robots to emulate than it is already to teach them to imitate”.

Current views
Recently, Huang & Chaman (2005 ) have summarized the different connotations of the term that are currently being discussed. These versions are: "end state emulation", "goal emulation", "object movement reenactment", and "emulation via affordance learning". In their words: end state emulation "the presence of an end result motivates an observer to replicate the result without explicitly encoding it in relation to the model’s goal". In goal emulation, "an observer attributes a goal to the model while attempting to devise his or her own strategy to reproduce the end result". In object movement reenactment "when an observer sees an object or its parts move, and that movement leads to a salient outcome, seeing the object movement might motivate the observer to reproduce the outcome". Emulation via affordance learning "refers to a process whereby an observer detects stimulus consequences, such as dynamic properties and temporal–spatial causal relations of objects, through watching the object movements".