What can associative learning do for planning?

• Main Author: Johan Lind
• Format: Article
• Language: English
• Published: The Royal Society 2018-01-01
• Series: Royal Society Open Science
• Subjects: planning; associative learning; reinforcement learning; animal intelligence; flexible behaviour
• Online Access: https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.180778

There is a new associative learning paradox. The power of associative learning for producing flexible behaviour in non-human animals is downplayed or ignored by researchers in animal cognition, whereas artificial intelligence research shows that associative learning models can beat humans in chess. One phenomenon in which associative learning often is ruled out as an explanation for animal behaviour is flexible planning. However, planning studies have been criticized and questions have been raised regarding both methodological validity and interpretations of results. Due to the power of associative learning and the uncertainty of what causes planning behaviour in non-human animals, I explored what associative learning can do for planning. A previously published sequence learning model which combines Pavlovian and instrumental conditioning was used to simulate two planning studies, namely Mulcahy & Call 2006 ‘Apes save tools for future use.’ Science 312, 1038–1040 and Kabadayi & Osvath 2017 ‘Ravens parallel great apes in flexible planning for tool-use and bartering.’ Science 357, 202–204. Simulations show that behaviour matching current definitions of flexible planning can emerge through associative learning. Through conditioned reinforcement, the learning model gives rise to planning behaviour by learning that a behaviour towards a current stimulus will produce high value food at a later stage; it can make decisions about future states not within current sensory scope. The simulations tracked key patterns both between and within studies. It is concluded that one cannot rule out that these studies of flexible planning in apes and corvids can be completely accounted for by associative learning. Future empirical studies of flexible planning in non-human animals can benefit from theoretical developments within artificial intelligence and animal learning.