nils-herrmann/clean_scirepeval huggingface dataset:
Structure and strength in causal induction q

We present a framework for the rational analysis of elemental causal induction—learning about the existence of a relationship between a single cause and effect—based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relationship might be. We show that two leading rational models of elemental causal induction, DP and causal power, both estimate causal strength, and we introduce a new rational model, causal support, that assesses causal structure. Causal support predicts several key phenomena of causal induction that cannot be accounted for by other rational models, which we explore through a series of experiments. These phenomena include the complex interaction between DP and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal 0010-0285/$ see front matter 2005  q We thank Russ Burnett, David Lagnado, Tania Lombrozo, Brad Love, Doug Medin, Kevin Murphy, David Shanks, Steven Sloman, and Sean Stromsten for helpful comments on previous drafts of this paper, and Liz Baraff, Onny Chatterjee, Danny Oppenheimer, and Davie Yoon for their assistance in data collection. Klaus Melcher and David Shanks generously provided their data for our analyses. Initial results from Experiment 1 were presented at the Neural Information Processing Systems conference, December 2000. TLG was supported by a Hackett Studentship and a Stanford Graduate Fellowship. JBT was supported by grants from NTT Communication Science Laboratories, Mitsubishi Electric Research Laboratories, and the Paul E. Newton chair.  Corresponding author. Present address: Department of Cognitive and Linguistic Sciences, Brown University, Box 1978, Providence RI 02912, USA. E-mail address: Tom_Griffiths@brown.edu (T.L. Griffiths). T.L. Griffiths, J.B. Tenenbaum / Cognitive Psychology 51 (2005) 334–384 335 learning from rates. Causal support also provides a better account of a number of existing datasets than either DP or causal power.
Discovering Latent Classes in Relational Data

We present a framework for learning abstract relational knowledge, with the aim of explaining how people acquire intuitive theories of physical, biological, or social systems. Our algorithm infers a generative relational model with latent classes, simultaneously determining the kinds of entities that exist in a domain, the number of these latent classes, and the relations between classes that are possible or likely. This model goes beyond previous category-learning models in psychology, which consider the attributes associated with individual categories but not the relationships that can exist between categories. We apply this domain-general framework in two specific domains: learning the structure of kinship systems and learning causal theories.
Arengiya Anyainya Aidmeniya Aburliya Adardiya Agngiya Aweniya Amaidya Abmarliya Awaadya Anguriya Adiaya Angeliya Algyeliya Adniadya Aleriya Umbaidya Anowadya Muriya Angenduriya Amburniya Andungiya Aneriya Aiyenga Undyaidya Gnaldena

2 0 0 4 m a s s a c h u s e t t s i n s t i t u t e o f t e c h n o l o g y, c a m b r i d g e , m a 0 213 9 u s a — w w w. c s a i l. m i t. e d u massachusetts institute of technology — computer science and artificial intelligence laboratory We present a framework for learning abstract relational knowledge with the aim of explaining how people acquire intuitive theories of physical, biological, or social systems. Our approach is based on a generative relational model with latent classes, and simultaneously determines the kinds of entities that exist in a domain, the number of these latent classes, and the relations between classes that are possible or likely. This model goes beyond previous psychological models of category learning, which consider attributes associated with individual categories but not relationships between categories. We apply this domain-general framework to two specific problems: learning the structure of kinship systems and learning causal theories.
Elemental causal induction 1 Running head: ELEMENTAL CAUSAL INDUCTION Elemental causal induction

We present a framework for the rational analysis of elemental causal induction – learning about the existence of a relationship between a single cause and effect – based upon causal graphical models. This framework makes precise the intuitive distinction between causal structure and causal strength: the difference between asking whether or not a causal relationship exists, and asking how strong that causal relationship might be. We show that the two leading rational models of elemental causal induction, ∆P and causal power, both estimate causal strength, and introduce a new rational model, causal support, that assesses causal structure. Causal support provides a better account of a large number of existing datasets than either ∆P or causal power. It also predicts several phenomena that cannot be accounted for by other models, which we explore through a series of experiments. These phenomena include the complex interaction between ∆P and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates. Elemental causal induction 3 Elemental causal induction The contagion spread rapidly and before its progress could be arrested, sixteen persons were affected of which two died. Of these sixteen, eight were under my care. On this occasion I used for the first time the affusion of cold water, in the manner described by Dr. Wright. It was first tried in two cases. . . The effects corresponded exactly with those mentioned by him to have occurred in his own case and thus encouraged the remedy was employed in five other cases. It was repeated daily, and of these seven patients, the whole recovered. James Currie (1798/1960, p. 430)
Causal Structure in Conditional Reasoning

Structure in Conditional Reasoning Tevye R. Krynski (tevye@mit.edu) Joshua B. Tenenbaum (jbt@mit.edu) Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology 77 Massachusetts Ave., Cambridge, MA 02139 The Wason selection task presents subjects with a conditional statement of the form “if p then q”, and asks subjects to choose evidence to determine whether the statement is true. Prior accounts of people’s responses on the selection task have emphasized logical reasoning (Wason, 1966; Ahn & Graham, 1999), probabilistic reasoning (Oaksford & Chater, 1994), or social reasoning (Cosmides, 1989), as well as others. In contrast, we argue that the selection task often engages causal reasoning: for conditional statements in which p and q are causally related, people choose cards that will be most useful to determine which of several candidate causal structures is correct for a given situation. We have developed a model that extends Oaksford & Chater’s (1994) probabilistic information gain framework to handle causal hypotheses. The information gain framework of OC Glymour & Cheng, 1998), category learning (Rehder, 2003; Ahn, 1999), and judgment under uncertainty (Krynski & Tenenbaum, 2003). In this paper we analyze the role of causal structure in conditional reasoning (Over & Jessop, 1998), and argue that people’s responses on the Wason selection task reflect sophisticated abilities to induce causal structure. An important open question in causal reasoning is how people’s background knowledge interacts with observations when inferring causal structure. Causal domain knowledge places important constraints on which cause-effect relationships exist and how the effects depend functionally on the causes (Pearl, 2000; Krynski & Tenenbaum, 2003; Ahn, Kalish, Medin, & Gelman, 1995). This effectively specifies a hypothesis space of candidate causal structures, which we model using causal Bayes nets (Pearl, 2000). Observational evidence can then be used to determine which causal structure is most likely. We propose that this interplay of causal domain knowledge and observational evidence underlies people’s judgments on the Wason selection task. Why interpret conditionals causally? In contrast to OC i.e., p causally influences q, q causally influences p, or they have some common cause. The term “causally influences” does not necessarily mean
V1 Neurons Signal Acquisition of an Internal Representation of Stimulus Location

A fundamental aspect of visuomotor behavior is deciding where to look or move next. Under certain conditions, the brain constructs an internal representation of stimulus location on the basis of previous knowledge and uses it to move the eyes or to make other movements. Neuronal responses in primary visual cortex were modulated when such an internal representation was acquired: Responses to a stimulus were affected progressively by sequential presentation of the stimulus at one location but not when the location was varied randomly. Responses of individual neurons were spatially tuned for gaze direction and tracked the Bayesian probability of stimulus appearance. We propose that the representation arises in a distributed cortical network and is associated with systematic changes in response selectivity and dynamics at the earliest stages of cortical visual processing.
The Role of Causal Models in Reasoning Under Uncertainty

Numerous studies of how people reason with statistical data suggest that human judgment often fails to approximate rational probabilistic (Bayesian) inference. We argue that a major source of error in these experiments may be misunderstanding causal structure. Most laboratory studies demonstrating probabilistic reasoning deficits fail to explain the causal relationships behind the statistics presented, or they suggest causal mechanisms that are not compatible with people’s prior theories. We propose that human reasoning under uncertainty naturally operates over causal mental models, rather than pure statistical representations, and that statistical data typically support correct Bayesian inference only when they can be incorporated into a causal model consistent with people’s theory of the relevant domain. We show that presenting people with questions that clearly explain an intuitively natural causal structure responsible for a set of statistical data significantly improves their performance. In particular, we describe two modifications to the standard medical diagnosis scenario that each eliminates the phenomenon of base-rate neglect, merely by clarifying the causal structure behind false-positive test results.
Semi-Supervised Learning with Trees

We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure to the domain. The tree (or a distribution over trees) may be inferred using the unlabeled data. A prior over concepts generated by a mutation process on the inferred tree(s) allows efficient computation of the optimal Bayesian classification function from the labeled examples. We test our approach on eight real-world datasets.
Learning Causal Laws

Attempts to characterize people’s causal knowledge of a domain in terms of causal network structures miss a key level of abstraction: the laws that allow people to formulate meaningful causal network hypotheses, and thereby learn and reason about novel causal systems so e ectively. We outline a preliminary framework for modeling causal laws in terms of generative grammars for causal networks. We then present an experiment showing that causal grammars can be learned rapidly in a novel domain and used to support one-shot inferences about the unobserved causal properties of new objects. Finally, we give a Bayesian analysis explaining how causal grammars may be induced from the limited data available in our experiments.
Hierarchical Topic Models and the Nested Chinese Restaurant Process

We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting—which of the large collection of possible trees to use? We take a Bayesian approach, generating an appropriate prior via a distribution on partitions that we refer to as the nested Chinese restaurant process. This nonparametric prior allows arbitrarily large branching factors and readily accommodates growing data collections. We build a hierarchical topic model by combining this prior with a likelihood that is based on a hierarchical variant of latent Dirichlet allocation. We illustrate our approach on simulated data and with an application to the modeling of NIPS abstracts.
Structure and strength in causal judgments

Several recent theories have attempted to account for the judgments people make about causal relationships. We argue that questions about causal structure, such as whether or not a causal relationship actually exists, make an important contribution to these judgments. We use graphical models, a formal tool for describing causality developed in computer science, to illustrate that two leading rational accounts of causal judgment assume that a causal relationship exists and estimate the strength of that relationship. The important question of whether or not a causal relationship actually exists can be modeled as a Bayesian inference, and we present a measure of the evidence in favor of this conclusion that we term “causal support”. We show that causal support is consistent with previous results, and test its predictions with three experiments. The first and second experiments explore a novel effect predicted by this structural account, and use this effect to demonstrate that structure learning and parameter estimation can be dissociated in human judgments. The third experiment shows that learning from the rates of different events also reflects causal structure. Together, these results illustrate the importance of structural considerations in causal induction.
The Role of Causal Models in Statistical Reasoning

When making judgments based on statistical data, people have been shown to be poor at probabilistic reasoning, specifically Bayesian inference. While recent research shows people perform better when information is provided in a natural frequency format, we find this result’s explanatory reach limited, both for explaining people’s judgment failures and as a theory of human reasoning under uncertainty. Most prior studies demonstrating probabilistic reasoning deficits gave their subjects probabilistic inference problems that did not explain the causal mechanisms behind the provided statistics. Our research shows that when questions are posed that explain the causal structure of the domain, subjects perform significantly better. Specifically, base rate neglect can be made to virtually disappear when the content of a question reflects the true causal structure of the domain. We propose that causality is essential to probabilistic reasoning, and that without the opportunity to incorporate statistical data into a consistent theory of the causal structure of a domain, the typical person will have trouble performing normatively correct Bayesian inference.
Unsupervised Learning of Curved Manifolds

We describe a variant of the Isomap manifold learning algorithm , called ‘C-Isomap’. Isomap was designed to learn non-linear mappings which are isometric embeddings of a flat, convex data set. C-Isomap is designed to recover mappings in the larger class of conformal embeddings, provided that the original sampling density is reasonably uniform. We compare the performance of both versions of Isomap and other algorithms for manifold learning (MDS, LLE, GTM) on a range of data sets.
From Algorithmic to Subjective Randomness

We explore the phenomena of subjective randomness as a case study in understanding how people discover structure embedded in noise. We present a rational account of randomness perception based on the statistical problem of model selection: given a stimulus, inferring whether the process that generated it was random or regular. Inspired by the mathematical definition of randomness given by Kolmogorov complexity, we characterize regularity in terms of a hierarchy of automata that augment a finite controller with different forms of memory. We find that the regularities detected in binary sequences depend upon presentation format, and that the kinds of automata that can identify these regularities are informative about the cognitive processes engaged by different formats.
The Isomap Algorithm and Topological Stability

Topological Stability Tenenbaum et al. (1) presented an algorithm, Isomap, for computing a quasi-isometric, low-dimensional embedding of a set of high-dimensional data points. Two issues need to be raised concerning this work. First, the basic approach presented by Tenenbaum et al. is not new, having been described in the context of flattening cortical surfaces using geodesic distances and multidimensional scaling (2, 3) and in later work that used Dijkstra’s algorithm to approximate geodesic distances (4, 5). These ideas generalize to arbitrary dimensionality if the connectivity and metric information of the manifold are correctly supplied. Second, and more important, this approach is topologically unstable and can only be used after careful preprocessing of the data (6). In the application domain of cortical flattening, it is necessary to check manually for connectivity errors, so that points nearby in 3-space (for example, on opposite banks of a cortical sulcus) are not taken to be nearby in the cortical surface. If such care is taken, this method represents the preferred method for quasi-isometric cortical flattening. What is new about the Isomap algorithm is how it defines the connectivity of each data point via its nearest Euclidean neighbors in the high-dimensional space. This step is vulnerable to short-circuit errors if the neighborhood is too large with respect to folds in the manifold on which the data points lie or if noise in the data moves the points slightly off the manifold. Even a single short-circuit error can alter many entries in the geodesic distance matrix, which in turn can lead to a drastically different (and incorrect) low-dimensional embedding. We illustrate this failure in Fig. 1, using the MATLAB code published by Tenenbaum et al. along with their “Swiss roll” data, to which we have added a small amount of noise (7). Clearly, the algorithm is topologically unstable: Small errors in the data connectivity (topology) can lead to large errors in the solution. Choosing a very small neighborhood is not a satisfactory solution, as this can fragment the manifold into a large number of disconnected regions. Choosing the neighborhood “just right” requires a priori information about the global geometry of the highdimensional data manifold (8), but, presumably, it is exactly in the absence of such information that one would need to use an algorithm to find “meaningful low-dimensional structures hidden in high-dimensional observations” (1). In summary, the basic idea of Isomap has long been known, and the new component introduced by Tenenbaum et al. provides an unreliable estimate of surface connectivity, which can lead to failure of the algorithm to perform as claimed.  Eric L. Schwartz Department of Cognitive and Neural Systems and Department of Electrical and Computer Systems Boston University Boston, MA 02215, USA
Bayesian Models of Inductive Generalization

We argue that human inductive generalization is best explained in a Bayesian framework, rather than by traditional models based on similarity computations. We go beyond previous work on Bayesian concept learning by introducing an unsupervised method for constructing flexible hypothesis spaces, and we propose a version of the Bayesian Occam's razor that trades off priors and likelihoods to prevent under- or over-generalization in these flexible spaces. We analyze two published data sets on inductive reasoning as well as the results of a new behavioral study that we have carried out.
Theory-Based Causal Inference

People routinely make sophisticated causal inferences unconsciously, effortlessly, and from very little data - often from just one or a few observations. We argue that these inferences can be explained as Bayesian computations over a hypothesis space of causal graphical models, shaped by strong top-down prior knowledge in the form of intuitive theories. We present two case studies of our approach, including quantitative models of human causal judgments and brief comparisons with traditional bottom-up models of inference.
Global Versus Local Methods in Nonlinear Dimensionality Reduction

Recently proposed algorithms for nonlinear dimensionality reduction fall broadly into two categories which have different advantages and disadvantages: global (Isomap ), and local (Locally Linear Embedding , Laplacian Eigenmaps ). We present two variants of Isomap which combine the advantages of the global approach with what have previously been exclusive advantages of local methods: computational sparsity and the ability to invert conformal maps.
Using Vocabulary Knowledge in Bayesian Multinomial Estimation

Estimating the parameters of sparse multinomial distributions is an important component of many statistical learning tasks. Recent approaches have used uncertainty over the vocabulary of symbols in a multinomial distribution as a means of accounting for sparsity. We present a Bayesian approach that allows weak prior knowledge, in the form of a small set of approximate candidate vocabularies, to be used to dramatically improve the resulting estimates. We demonstrate these improvements in applications to text compression and estimating distributions over words in newsgroup data.
Some specifics about generalization

We address two kinds of criticisms of our Bayesian framework for generalization: those that question the correctness or the coverage of our analysis, and those that question its intrinsic value. Speaking to the first set, we clarify the origins and scope of our size principle for weighting hypotheses or features, focusing on its potential status as a cognitive universal; outline several variants of our framework to address additional phenomena of generalization raised in the commentaries; and discuss the subtleties of our claims about the relationship between similarity and generalization. Speaking to the second set, we identify the unique contributions that a rational statistical approach to generalization offers over traditional models that focus on mental representation and cognitive processes.