The problem of Language Evolution When investigating language evolution, the obvious problem is that there is a lack of empirical data from when our species first started using language. We have: •No fossils •No time machines •No humans without language In recent years many studies have attempted to research the emergence of language using human participants (Kirby et al., 2008). However, these studies use participants who are: •Modern humans •Fully language-proficient •Adults Participants’ existing linguistic knowledge will interfere during testing. ABACUS – Advancing Behavioral And Cognitive Understanding of Speech The ABACUS project is investigating the cognitive mechanisms which allow humans to use combinatorial speech. The principal investigator is Bart de Boer. Combinatorial speech is the ability to combine a limited set of meaningless building blocks into an unlimited repertoire of meaningful utterances. As part of ABACUS, we are trying to identify the cognitive processes involved in the emergence of combinatoriality in speech using artificial language learning experiments. Hannah Little 1 & Sabine van der Ham 2 Artificial Intelligence Lab, Vrije Universiteit Brussel 1 [email protected] , 2 [email protected] Many thanks to the European Research Council for funding this research and Bart de Boer for supervising it. Artificial language learning experiments Artificial language learning experiments involve a mini artificial language which human participants learn and then use in various paradigms: •Individual learning experiments Investigates how individuals acquire the building blocks of speech and other continuous signals. •Artificial repertoire learning experiments (between category variation) •Skewed distribution learning experiments (within category variation) •Cultural learning experiments Investigates how individual level biases extrapolate to population wide processes. •Iterated learning experiments (vertical transmission) (Figure 1) •Social coordination experiments (horizontal transmission) (Figure 2) Our experiments are focusing on how, and under what pressures, combinatorial structure emerges from continuous articulatory spaces. We are looking at the effect of: •Meaning •Communication •The structure of the initial input What type of signals to use? a) Non-linguistic signals -But how much can they tell us about language? •Slide whistles •Humming •Monkey calls •Graphical representations b) Signals that are language-like -But previous linguistic knowledge will interfere. •Vowel strings •Clicks •Whistled human languages •Gestures We need to test both to see how cognitive mechanisms for learning differ when faced with differing signals. This will help address questions of whether speech is the result of domain general processes, or the result of cognitive processes which are specifically adapted for language. Whistling in the dark: The challenges of investigating the emergence of speech with artificial language learning experiments REFERENCES Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in the laboratory: an experimental approach to the origins of structure in human language. Proceedings of the National Academy of Sciences of the United States of America, 105(31), 10681–6. doi:10.1073/pnas.0707835105

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Participants’ existing linguistic knowledge will interfere during testing. ABACUS – Advancing Behavioral And Cognitive Understanding of Speech The ABACUS project is investigating the cognitive mechanisms which allow humans to use combinatorial speech. The principal investigator is Bart de Boer. Combinatorial speech is the ability to combine a limited set of meaningless building blocks into an unlimited repertoire of meaningful utterances. As part of ABACUS, we are trying to identify the cognitive processes involved in the emergence of combinatoriality in speech using artificial language learning experiments.

Hannah Little1 & Sabine van der Ham2

Artificial Intelligence Lab, Vrije Universiteit Brussel [email protected], [email protected]

Many thanks to the European Research Council for funding this research and Bart de Boer for supervising it.

Artificial language learning experiments Artificial language learning experiments involve a mini artificial language which human participants learn and then use in various paradigms: • Individual learning experiments Investigates how individuals acquire the building blocks of speech and other continuous signals. • Artificial repertoire learning experiments (between category variation) • Skewed distribution learning experiments (within category variation) • Cultural learning experiments Investigates how individual level biases extrapolate to population wide processes. • Iterated learning experiments (vertical transmission) (Figure 1) • Social coordination experiments (horizontal transmission) (Figure 2)

Our experiments are focusing on how, and under what pressures, combinatorial structure emerges from continuous articulatory spaces. We are looking at the effect of: • Meaning • Communication • The structure of the initial input

What type of signals to use? a)  Non-linguistic signals - But how much can they tell us about language?

• Slide whistles • Humming • Monkey calls • Graphical representations

b) Signals that are language-like - But previous linguistic knowledge will interfere.

• Vowel strings • Clicks • Whistled human languages • Gestures We need to test both to see how cognitive mechanisms for learning differ when faced with differing signals. This will help address questions of whether speech is the result of domain general processes, or the result of cognitive processes which are specifically adapted for language.

Whistling in the dark: The challenges of investigating the emergence of speech with artificial language learning experiments

REFERENCES Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in the laboratory: an experimental approach to the origins of structure in human language. Proceedings of the National Academy of Sciences of the United States of America, 105(31), 10681–6. doi:10.1073/pnas.0707835105