Learning what behaviour is appropriate in a specific context by observing the actions of others and their outcomes is a key constituent of human cognition, because it saves time and energy and reduces exposure to potentially dangerous situations. Observational learning of associative rules relies on the ability to map the actions of others onto our own, process outcomes, and combine these sourc...

Learning through observation in everyday activities is widely recognized in the ethnographic literature as a central way that children learn from others. There are two well-described characteristics of learning through observation: participation in meaningful activities with people who are important in the children’s lives and a belief that children are active, motivated learners who take initi...

Observational learning is typically examined when agents have precise information about their position in the sequence of play. We present a model in which agents are uncertain about their positions. Agents are allowed to have arbitrary ex-ante beliefs about their positions: they may observe their position perfectly, imperfectly, or not at all. Agents sample the decisions of past individuals an...

In the standard model of observational learning, n agents sequentially decide between two alternatives a or b, one of which is objectively superior. Their choice is based on a stochastic private signal and the decisions of others. Assuming a rational behavior, it is known that informational cascades arise, which cause an overwhelming fraction of the population to make the same choice, either co...

Untrained Octopus vulgaris (observers) were allowed to watch conditioned Octopus(demonstrators) perform the task of selecting one of two objects that were presented simultaneously and differed only in color. After being placed in isolation, the observers, in a similar test, consistently selected the same object as did the demonstrators. This learning by observation occurred irrespective of the ...

Observational learning algorithm is an ensemble algorithm where each network is initially trained with a bootstrapped data set and virtual data are generated from the ensemble for training. Here we propose a modular OLA approach where the original training set is partitioned into clusters and then each network is instead trained with one of the clusters. Networks are combined with different wei...