Testing the limits of long-distance learning: learning beyond a three-segment window

Traditional flat-structured bigram and trigram models of phonotactics are useful because they capture a large number of facts about phonological processes. Additionally, these models predict that local interactions should be easier to learn than long-distance ones because long-distance dependencies are difficult to capture with these models. Long-distance phonotactic patterns have been observed by linguists in many languages, who have proposed different kinds of models, including feature-based bigram and trigram models, as well as precedence models. Contrary to flat-structured bigram and trigram models, these alternatives capture unbounded dependencies because at an abstract level of representation, the relevant elements are locally dependent, even if they are not adjacent at the observable level. Using an artificial grammar learning paradigm, we provide additional support for these alternative models of phonotactics. Participants in two experiments were exposed to a long-distance consonant-harmony pattern in which the first consonant of a five-syllable word was [s] or [∫] ("sh") and triggered a suffix that was either [-su] or [-∫u] depending on the sibilant quality of this first consonant. Participants learned this pattern, despite the large distance between the trigger and the target, suggesting that when participants learn long-distance phonological patterns, that pattern is learned without specific reference to distance.     

Perhaps Unidimensional Is Not Unidimensional

Miller (1956) identified his famous limit of 7 ± 2 items based in part on absolute identification—the ability to identify stimuli that differ on a single physical dimension, such as lines of different length. An important aspect of this limit is its independence from perceptual effects and its application across all stimulus types. Recent research, however, has identified several exceptions. We investigate an explanation for these results that reconciles them with Miller’s work. We find support for the hypothesis that the exceptional stimulus types have more complex psychological representations, which can therefore support better identification. Our investigation uses data sets with thousands of observations for each participant, which allows the application of a new technique for identifying psychological representations: the structural forms algorithm of Kemp and Tenenbaum (2008) . This algorithm supports inferences not possible with previous techniques, such as multidimensional scaling.     

Theory learning as stochastic search in the language of thought

We present an algorithmic model for the development of children's intuitive theories within a hierarchical Bayesian framework, where theories are described as sets of logical laws generated by a probabilistic context-free grammar. We contrast our approach with connectionist and other emergentist approaches to modeling cognitive development. While their subsymbolic representations provide a smooth error surface that supports efficient gradient-based learning, our symbolic representations are better suited to capturing children's intuitive theories but give rise to a harder learning problem, which can only be solved by exploratory search. Our algorithm attempts to discover the theory that best explains a set of observed data by performing stochastic search at two levels of abstraction: an outer loop in the space of theories and an inner loop in the space of explanations or models generated by each theory given a particular dataset. We show that this stochastic search is capable of learning appropriate theories in several everyday domains and discuss its dynamics in the context of empirical studies of children's learning.     

Dealing with Dynamic Decision Problems when Knowledge of the Environment Is Limited: An Approach Based on Goal Systems

We experimentally analyzed decision procedures for dealing with a dynamic decision‐making problem in which only qualitative information about the deterministic dynamics of the environment was available to participants. A participant's task was to maximize long‐term profit in a computer‐simulated monopoly market featuring delays and inertia. The design enabled a goal‐system‐based procedure, whereby a participant could select one or several short‐term variables to be controlled (goal variables) and chose target values (aspiration levels) for each of them over a total of 50 periods. We report results based on a sample of 63 participants on the formation of goal systems and the process of aspiration adaptation. Our main findings are, first, that more frequently selecting goal systems that adequately reflect the causal structure of the underlying model is positively correlated with long‐term profit; second, that goal persistence, a measure of a participant's tendency to stick to the current goal system, is positively correlated with long‐term profit; and third, that aspiration levels tend to be adapted in strong agreement with certain basic principles of a benchmark model of aspiration adaptation. Our study thus suggests and provides empirical foundation for an approach to dealing with complex dynamic decision problems based on neither optimization nor learning. Copyright © 2011 John Wiley & Sons, Ltd.     

More important and surprising actions of a moral exemplar trigger stronger admiration and inspiration

ABSTRACT

Admiring a moral role model has been found to inspire people to become better persons themselves. But what are the antecedents that trigger admiration and thus make inspiration more likely? In three studies, we tested the effect of perceived importance and perceived surprisingness of the moral action on resulting admiration and inspiration. Study 1 finds that perceived importance, and to a lesser extent, the perceived surprisingness of a moral action, are related to stronger admiration. Manipulating the perceived importance of the same moral action by only providing a little more detail about the moral action, could increase the admiration and inspiration the role models elicit (Studies 2 and 3). Our findings help the understanding of how moral exemplars trigger inspiration and provide valuable insights into further investigation toward the causes of admiration.     

Multidimensional Latent Classification of ‘Street Robbery’ Offences

In a recent study of personal robbery, commissioned by the Home Office in the UK, a qualitative typology of robbery offences was proposed based on the approach used by the offender to commit the crime, consisting of four approach types: Blitz, Confrontation, Con, and Snatch. Conceptual inspection of the typology reveals that these proposed types may be hypothetically demarcated as the product of two latent dimensions: interaction (between the offender and the victim) and violence (used to threaten/harm the victim). The current paper utilises crime scene information from 72 incarcerated male offenders convicted of ‘street’ robbery to test this hypothesis. Convergent statistical analysis was utilised to test the structure of Smith's typology first using multidimensional scaling (MDS) and then principal component analysis (PCA). MDS and PCA analyses provided convergent support for the existence of the four robbery styles and the latent dimensions of interaction and violence. Implications of Smith's typological structure and latent behavioural dimensions on the conceptualisation and classification of robbery offences are discussed within the existing literature on ‘street’ robbery. Copyright © 2012 John Wiley & Sons, Ltd.     

Performance evaluation of recent information criteria for selecting multilevel models in Behavioral and Social Sciences

This study was designed to find the best strategy for selecting the correct multilevel model among several alternatives taking into account variables such as intraclass correlation, number of groups (m), group size (n), or others as parameter values and intercept-slope covariance. First, we examine this question in a simulation study and second, to illustrate the behavior of the criteria and to explore the generalizability of the findings, a previously published educational dataset is analyzed. The results showed that none of the selection criteria behaved correctly under all the conditions or was consistently better than the others. The intraclass correlation somewhat affects the performance of all selection criteria, but the extent of this influence is relatively minor compared to sample size, parameter values, and correlation between random effects. A large number of groups appears more important than a large number of individuals per group in selecting the best model (m ≥ 50 and n ≥ 20 is suggested). Finally, model selection tools such as Akaike's Information Criterion (AIC) or the conditional AIC are recommend when it is assumed that random effects are correlated, whereas use of the Schwarz's Bayesian Information Criterion or the consistent AIC are advantageous for uncorrelated random effects.     

Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning

In this paper, it is argued that single function dual process theory is a more credible psychological account of non-monotonicity in human conditional reasoning than recent attempts to apply logic programming (LP) approaches in artificial intelligence to these data. LP is introduced and among other critiques, it is argued that it is psychologically unrealistic in a similar way to hash coding in the classicism vs. connectionism debate. Second, it is argued that causal Bayes nets provide a framework for modelling probabilistic conditional inference in System 2 that can deal with patterns of inference LP cannot. Third, we offer some speculations on how the cognitive system may avoid problems for System 1 identified by Fodor in 1983. We conclude that while many problems remain, the probabilistic single function dual processing theory is to be preferred over LP as an account of the non-monotonicity of human reasoning.