Do I know that you know what you know? Modeling testimony in causal inference

We rely on both our own observations and on others ’ testimony when making causal inferences. To integrate these sources of information we must consider an informant’s statements about the world, her expressed level of certainty, her previous accuracy, and perhaps her apparent self-knowledge – how accurately she conveys her own certainty. It can be difficult to tease apart the contributions of all these variables simply by observing people’s causal judgments. We present a computa-tional account of how these different cues contribute to a ratio-nal causal inference, and two experiments looking at adults’ inferences from causal demonstrations and informant testi-mony, focusing on cases where these sources conflict. We find that adults are able to combine social information with their own observations, and are sensitive to the reliability of each. Adults are also sensitive to the accuracy, certainty, and self-knowledge of the informant, a result confirmed by comparing predictions from models with and without these variables

Investigating the Explore/Exploit Trade-off in Adult Causal Inferences

We explore how adults learn counterintuitive causal relationships, and whether they interpret evidence and discoverhypotheses by incrementally revising beliefs. We examined how adults learned a novel, unusual causal rule when given datathat initially appeared to follow a simpler, more salient rule. Adults watched a video of blocks placed sequentially on a detectorthat activated when a block was a ”blicket”, then were asked to determine the underlying causal structure. We contrasted twocausal learning problems. In both cases, one rule could be used to determine which objects were blickets; in the first problemthis rule was complex, but could be found by making incremental improvements to a simple and salient initial hypothesis. Thesecond problem’s rule was simpler, but to adopt it, participants had to ignore initial beliefs. Our results provide some of thefirst evidence for an inference trade-off analogous to the ”explore-exploit” trade-off in active learning

Computational, experimental, and statistical analyses of social learning in humans and animals

Social learning is ubiquitous among animals and humans and is thought to be critical to the widespread success of humans and to the development and evolution of human culture. Evolutionary theory, however, suggests that social learning alone may not be adaptive but that individuals may need to be selective in who and how they copy others. One of the key findings of these evolutionary models (reviewed in Chapter 1) is that social information may be widely adaptive if individuals are able to combine social and asocial sources of information together strategically. However, up until this point the focus of theoretic models has been on the population level consequences of different social learning strategies, and not on how individuals combine social and asocial information on specific tasks. In Chapter 2 I carry out an analysis of how animal learners might incorporate social information into a reinforcement learning framework and find that even limited, low-fidelity copying of actions in an action sequence may combine with asocial learning to result in high fidelity transmission of entire action sequences. In Chapter 3 I describe a series of experiments that find that human learners flexibly use a conformity biased learning strategy to learn from multiple demonstrators depending on demonstrator accuracy, either indicated by environmental cues or past experience with these demonstrators. The chapter reveals close quantitative and qualitative matches between participant's performance and a Bayesian model of social learning. In both Chapters 2 and 3 I find, consistent with previous evolutionary findings, that by combining social and asocial sources of information together individuals are able to learn about the world effectively. Exploring how animals use social learning experimentally can be a substantially more difficult task than exploring human social learning. In Chapter 4, I develop and present a refined version of Network Based Diffusion analysis to provide a statistical framework for inferring social learning mechanisms from animal diffusion experiments. In Chapter 5 I move from examining the effects of social learning at an individual level to examining their population level outcomes and provide an analysis of how fine-grained population structure may alter the spread of novel behaviours through a population. I find that although a learner's social learning strategy and the learnability of a novel behaviour strongly impact how likely the behaviour is to spread through the population, fine grained population structure plays a much smaller role. In Chapter 6 I summarize the results of this thesis, and provide suggestions for future work to understand how individuals, humans and other animals alike, use social information