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Deconfounding Hypothesis Generation and Evaluation In Deconfounding Hypothesis Generation and Evaluation in Bayesian Models Elizabeth Baraff Bonawitz (liz [email protected]) Department of Psychology, 5427 Tolman Hall Berkeley, CA 94720 USA Thomas L. Griffiths (tom griffi[email protected]) Department of Psychology, 3210 Tolman Hall Berkeley, CA 94720 USA Abstract as data are observed. However, the assumption that learners possess all relevant hypotheses before seeing data is at odds Bayesian models of cognition are typically used to describe human learning and inference at the computational level, iden- with numerous findings suggesting that generating appropri- tifying which hypotheses people should select to explain ob- ate hypotheses can be one of the hardest parts of inductive in- served data given a particular set of inductive biases. However, ference (e.g., Kuhn, 1989; Klahr, Fay, & Dunbar, 1993). We such an analysis can be consistent with human behavior even if people are not actually carrying out exact Bayesian infer- thus consider the consequences of separating the processes of ence. We analyze a simple algorithm by which people might generating hypotheses and evaluating those hypotheses, as- be approximating Bayesian inference, in which a limited set of suming that learners perform Bayesian inference with only hypotheses are generated and then evaluated using Bayes’ rule. Our mathematical results indicate that a purely computational- the set of hypotheses they generate. level analysis of learners using this algorithm would confound To investigate this, we present a mathematical analysis of the distinct processes of hypothesis generation and hypothe- sis evaluation. We use a causal learning experiment to estab- a simple algorithm in which hypothesis generation and evalu- lish empirically that the processes of generation and evalua- ation are separated. This produces a surprising result: This tion can be distinguished in human learners, demonstrating the algorithm results in behavior that can still be analyzed in importance of recognizing this distinction when interpreting Bayesian models. terms of Bayesian inference, but with a prior that conflates the plausibility of a hypothesis with the ease of generating Keywords: Approximate Bayesian Inference; Hypothesis Generation; Hypothesis Evaluation; Causal Learning that hypothesis. This result suggests that we should be cau- tious when interpreting the priors of Bayesian models esti- Introduction mated from behavioral data. Such priors will always reflect Learning causal relationships, categories, and languages all the inductive biases of human learners – those factors that require solving challenging inductive problems, using lim- lead people to select one hypothesis over another when both ited data to assess underdetermined hypotheses. In the last are equally consistent with the data. However, human induc- decade an increasing number of papers have argued that peo- tive biases can include components that result from processes ple solving inductive problems act in ways that are consis- at the algorithmic level, such as generating hypotheses. tent with optimal Bayesian inference (e.g., Griffiths & Tenen- To demonstrate the importance of taking into account baum, 2005; Goodman, Tenenbaum, Feldman, & Griffiths, algorithmic-level factors in interpreting Bayesian models, we 2008; Xu & Tenenbaum, 2007). However, most of these present an experiment exploring the separability of hypoth- analyses operate at what Marr (1982) termed the computa- esis generation and evaluation. In the task, we conduct a tional level, using Bayesian inference to identify the hypothe- causal learning experiment in which we manipulate the hy- ses that an ideal learner with particular inductive biases would potheses that people generate: by “priming” an appropriate choose to explain the observed data. An important question hypothesis, we increase the probability of people producing for this approach is what learners are doing at the algorithmic responses consistent with that hypothesis; however, when we level: identifying the psychological processes by which learn- employ a more standard Bayesian reasoning task, providing a ers solve inductive problems, and understanding how these set of hypotheses and asking participants to evaluate them, the algorithms connect back to the computational level. effect of priming goes away. A computational-level analysis Connecting the algorithmic and computational levels in- would require postulating different prior distributions in or- volves two challenges: identifying algorithms that can pro- der to explain behavior on these two components of the task. duce behavior consistent with Bayesian inference, and deter- However, an algorithmic-level analysis shows that this differ- mining how the assumptions of a computational-level analy- ence can be explained as the result of the separate effects of sis relate to the components of these algorithms. In this pa- hypothesis generation and evaluation. Finally, we discuss the per, we take up these two challenges for one class of algo- implications of this work for future models of human cogni- rithms for inductive inference. The most na¨ıve translation of tion and for studies of developmental changes. Bayesian inference into an algorithm for inductive inference would be to assume that learners implement Bayes’ rule di- Analyzing inductive inferences rectly, having a fixed set of hypotheses and updating a proba- Bayesian inference indicates how a rational learner should bility distribution over all of those hypotheses simultaneously change his or her beliefs about a set of hypotheses in light of observed data. Let h be a hypothesis belonging to a set et al., 1993). Hypotheses can be generated in many differ- of hypotheses H . Assume that the learner has different de- ent ways, including detecting cues from context, recognizing grees of belief in the truth of these hypotheses, and that these similarities to previous experiences, and making analogies to degrees of belief are reflected in a probability distribution other domains (e.g., Gick & Holyoak, 1980; Gentner, 2002; p(h), known as the prior. Then, the degrees of belief the Nersessian, 1992; Koslowski, 1996). We will not attempt to learner should assign to each hypothesis after observing data model these processes here, but for our purposes, it is suffi- d are given by the posterior probability distribution p(hjd) cient to assume that the result of all of these processes can be obtained by applying Bayes’ rule summarized in a single probability distribution over hypoth- esis spaces. Using this probability distribution, q(H ∗), we p(djh)p(h) define the Generate-Evaluate (GE) algorithm for Bayesian p(hjd) = 0 0 (1) ∑h02H p(djh )p(h ) inference with a reduced hypothesis space: Step 1: Generate Sample a reduced hypothesis space where p(djh) indicates the probability of observing d if h ∗ ⊆ from the probability distribution q( ∗). were true, and is known as the likelihood. H H H Bayes’ rule provides a computational-level theory of in- Step 2: Evaluate Evaluate the hypotheses in the reduced ductive inference, being a component of the optimal solu- hypothesis space H ∗ by applying Bayesian inference, using tions to a variety of problems of reasoning under uncer- a prior distribution on H ∗ proportional to the prior on the full tainty (Anderson, 1990; Anderson & Schooler, 1991; Free- hypothesis space H . Using p(h) to denote the prior on the man, 1994; Geisler, Perry, Super, & Gallogly, 2001; Griffiths full hypothesis space, as in Equation 1 we obtain the reduced & Tenenbaum, 2007; Huber, Shiffrin, Lyle, & Ruys, 2001; posterior distribution Knill & Richards, 1996; Kording¨ & Wolpert, 2004; Shiffrin & Steyvers, 1997; Weiss, Simonvelli, & Adelson, 2002). As ∗ p(djh)p(h) p (hjd) = 0 0 (2) an account of inductive inference, the prior p(h) captures the ∑h02H ∗ p(djh )p(h ) inductive biases of the learner, indicating which hypothesis a learner will favor when multiple hypotheses are equally con- for h 2 H ∗, with all other hypotheses receiving a posterior sistent with the observed data (ie. which hypothesis will have probability of zero. Because we are only sampling a subset higher probability when multiple hypotheses have equal like- of hypotheses, those that are not sampled will never be con- lihood). This account is attractive in that it can potentially sidered. allow us to identify the inductive biases of human learners, comparing different Bayesian models to find an appropriate Mathematical analysis prior. However, as we show in the remainder of this section, one should be cautious in interpreting such a prior: Consid- Having defined an algorithm that takes into account the pro- ering algorithms by which people might be making inductive cess of hypothesis generation, we can now analyze the con- inferences shows that multiple processes can be reflected in a sequences of using this algorithm. We have two questions of prior estimated from behavioral data. interest. First, will a learner using the GE algorithm produce behavior that appears to be consistent with Bayesian infer- Inferences with a reduced hypothesis space ence? Second, how does the process of hypothesis generation As a computational-level theory of inductive inference, influence the interpretation of the resulting Bayesian model? Bayesian models make no commitments about the psycholog- We can answer both of these questions for a special case of ical mechanisms by which people actually learn and reason. this algorithm by exploiting its relationship to a Monte Carlo The most na¨ıve interpretation
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