Black boxes: Hypothesis testing via indirect perceptual evidence

Black boxes: Hypothesis testing via indirect perceptual evidence Max H. Siegel, Rachel Magid, Joshua B. Tenenbaum, and Laura E. Schulz {maxs, rwmagid, jbt, lschulz}@mit.edu Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139 USA Abstract Studies of children’s causal learning typically provide learners with clear evidence for direct causal relations, e.g., a machine that activates when a toy is placed upon it. But causal systems in the real world often present indirect perceptual evidence generated by interactions between hidden variables: Consider a child trying to figure out what’s inside a box by shaking it. We propose that effective learning and exploration depend on being able to interpret evidence through the lens of intuitive theories – theories of both the physical world and one’s own perceptual apparatus – to imagine how one’s actions might change the state of the world and what kinds of changes would be most perceptually discriminable. We present three studies exploring these capacities in young children, and suggest how they could support powerful and sophisticated inferences about hidden causes. In science, much of the evidence we get for causal rela- tionships is indirect: we cannot observe the activation of neu- rons or the presence of microorganisms directly so we de- velop sophisticated technologies to detect neural activation from blood flow, or the presence of microorganisms from the amount of dissolved oxygen. This kind of inference is not re- stricted to scientific practice. Even in everyday life, there are many cases where we have to infer unobserved causes from indirect evidence. We see a curtain move and infer the cat be- hind it, we hear dripping and worry that the faucet is leaking. These are very ordinary examples, ones that might be acces- sible even to small children, but they suggest an extraordinary capacity: the ability to reason backwards from new evidence to probable unobserved causes of the data. While there is a wealth of research on children’s causal reasoning, the majority of studies has focused on childrens inferences about direct causal relationships: observed blocks that do (or do not) light up machines, levers that do (or do not) activate toys, etc (Gopnik, Sobel, Schulz, & Glymour, 2001; Gopnik & Sobel, 2000). Moreover, although there is some work outlining childrens ability to infer unobserved causes from patterns of evidence (Kushnir & Gopnik, 2005; Schulz & Sommerville, 2006; Schulz, Goodman, Tenenbaum, & Jenkins, 2008), in such cases children were asked about arbi- trary causes and what is at stake is simply childrens ability to detect conditions under which a latent variable might explain the pattern of evidence. Here we ask a different question: Do children’s intuitive theories support these inferences in cases where the mapping from causes to effects is unspecified and complex? One way of investigating unfamiliar scenarios is to take actions designed to reduce uncertainty. We can pull back the curtain, we can use more powerful tools as they’re de- veloped. Cook, Goodman, and Schulz (2011) demonstrated that children preferentially select more informative interven- tions when exploring a new causal domain; and there is more generally a rich literature on active learning and information selection in adult human cognition (Oaksford & Chater, 1994; Tsividis, Gershman, Tenenbaum, & Schulz, 2014). We aim to investigate a complementary question. People (scientists or otherwise) are never able to directly observe data that most informs the questions they actually want an- swered, for good reasons: the equipment is too expensive, the cat (or is it? ) runs away as we approach. What scientists (and other people too) actually do is more pragmatic. We know our tools, eyes, ears, or electron microscopes; and we know how they sense and transform the objects we’re interested in studying. Answering questions thus requires us to select not only the right tests, but data that would yield informative re- sults given the right test. We are especially interested in what learners decide to in- vestigate in situations where evidence is richly perceptual and interpretation involves intuitive theories of the physical world, because these are closest to the ways young children most naturally explore their surroundings. Our focus here is on physical interactions between objects that generate sound, and on what we need to know, both about these physical events and our abilities to perceive them, in order to learn about objects from the sounds they produce. We take sound as a case study because children have expectations about how objects interact to produce auditory signals, because these in- teractions produce nontrivial evidence (so that there is clearly some work to be done in interpretation), and finally because of the intuitive, everyday quality of perceptual inferences. A rubber ball shaken inside a closed box will sound differ- ent than a glass marble. Two rubber balls will sound different from either; five glass marbles will sound more different still. A novel object that looks pointy or feels hard to the touch will sound different from one that looks rounded or feels soft. How do we know these things? One might think that these examples can be explained as consequences of cross-modal perception and memory. We have experienced seeing a faucet drip while also hearing it. We have all shaken a wrapped present to find out what might be inside. However, we do not always have access to information in multiple modalities, and in these cases we are still able to reason about the causes of various sounds. Indeed, the combinatorial explosion that accompanies real-world perception suggests that reasoning from indirect evidence requires sophisticated and subtle ca- pabilities which are far more powerful than those commonly assumed. We follow in a research tradition that highlights how people use mental models and theories about the world to generate predictions, which are used to interpret new data (Tenenbaum, Kemp, Griffiths, & Goodman, 2011). But we