Three ideal observer models for rule learning in simple languages

Children learning the inflections of their native language show the ability to generalize beyond the perceptual particulars of the examples they are exposed to. The phenomenon of “rule learning”—quick learning of abstract regularities from exposure to a limited set of stimuli—has become an important model system for understanding generalization in infancy. Experiments with adults and children have revealed differences in performance across domains and types of rules. To understand the representational and inferential assumptions necessary to capture this broad set of results, we introduce three ideal observer models for rule learning. Each model builds on the next, allowing us to test the consequences of individual assumptions. Model 1 learns a single rule, Model 2 learns a single rule from noisy input, and Model 3 learns multiple rules from noisy input. These models capture a wide range of experimental results—including several that have been used to argue for domain-specificity or limits on the kinds of generalizations learners can make—suggesting that these ideal observers may be a useful baseline for future work on rule learning.     

Young children's selective learning of rule games from reliable and unreliable models

We investigated preschoolers’ selective learning from models that had previously appeared to be reliable or unreliable. Replicating previous research, children from 4 years selectively learned novel words from reliable over unreliable speakers. Extending previous research, children also selectively learned other kinds of acts – novel games – from reliable actors. More important, – and novel to this study, this selective learning was not just based on a preference for one model or one kind of act, but had a normative dimension to it. Children understood the way a reliable actor demonstrated an act not only as the better one, but as the normatively appropriate or correct one, as indicated in both their explicit verbal comments and their spontaneous normative interventions (e.g., protest, critique) in response to third-party acts deviating from the one demonstrated. These findings are discussed in the broader context of the development of children's social cognition and cultural learning.     

Cognitive biases in human causal learning

The main aim of this work was to look for cognitive biases in human inference of causal relationships in order to emphasize the psychological processes that modulate causal learning. From the effect of the judgment frequency, this work presents subsequent research on cue competition (overshadowing, blocking, and super-conditioning effects) showing that the strength of prior beliefs and new evidence based upon covariation computation contributes additively to predict causal judgments, whereas the balance between the reliability of both, beliefs and covariation knowledge, modulates their relative weight. New findings also showed "inattentional blindness" for negative or preventative causal relationships but not for positive or generative ones, due to failure in codifying and retrieving the necessary information for its computation. Overall results unveil the need of three hierarchical levels of a whole architecture for human causal learning: the lower one, responsible for codifying the events during the task; the second one, computing the retrieved information; finally, the higher level, integrating this evidence with previous causal knowledge. In summary, whereas current theoretical frameworks on causal inference and decision-making usually focused either on causal beliefs or covariation information, the present work shows how both are required to be able to explain the complexity and flexibility involved in human causal learning.     

Compositional inductive biases in function learning

How do people recognize and learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is achieved by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels, and compare this approach with other structure learning approaches. Participants consistently chose compositional (over non-compositional) extrapolations and interpolations of functions. Experiments designed to elicit priors over functional patterns revealed an inductive bias for compositional structure. Compositional functions were perceived as subjectively more predictable than non-compositional functions, and exhibited other signatures of predictability, such as enhanced memorability and reduced numerosity. Taken together, these results support the view that the human intuitive theory of functions is inherently compositional.     

Optimistic biases in observational learning of value

Action-outcome contingencies can be learnt either by active trial-and-error, or vicariously, by observing the outcomes of actions performed by others. The extant literature is ambiguous as to which of these modes of learning is more effective, as controlled comparisons of operant and observational learning are rare. Here, we contrasted human operant and observational value learning, assessing implicit and explicit measures of learning from positive and negative reinforcement. Compared to direct operant learning, we show observational learning is associated with an optimistic over-valuation of low-value options, a pattern apparent both in participants’ choice preferences and their explicit post-hoc estimates of value. Learning of higher value options showed no such bias. We suggest that such a bias can be explained as a tendency for optimistic underestimation of the chance of experiencing negative events, an optimism repressed when information is gathered through direct operant learning.     

The conceptual basis of function learning and extrapolation: Comparison of rule-based and associative-based models

The purpose of this article is to provide a foundation for a more formal, systematic, and integrative approach to function learning that parallels the existing progress in category learning. First, we note limitations of existing formal theories. Next, we develop several potential formal models of function learning, which include expansion of classic rule-based approaches and associative-based models. We specify for the first time psychologically based learning mechanisms for the rule models. We then present new, rigorous tests of these competing models that take into account order of difficulty for learning different function forms and extrapolation performance. Critically, detailed learning performance was also used to conduct the model evaluations. The results favor a hybrid model that combines associative learning of trained input—prediction pairs with a rule-based output response for extrapolation (EXAM).     

A review of computational models of basic rule learning: The neural-symbolic debate and beyond

Psychonomic Bulletin & Review - We present a critical review of computational models of generalization of simple grammar-like rules, such as ABA and ABB. In particular, we focus on models...     

A developmental and cross-cultural study of the influences of rule structure and problem composition on the learning of conceptual classifications

Five experiments were conducted permitting direct comparisons in the way Ss of different ages and cultures solved conceptual problems. One group of problems (Traditional) could be solved using a reference rule of conjunction or disjunction. Another group of problems (Intradimensional) could be solved either by one of the reference rules, or by a structurally simpler rule. Analysis of American performance confirmed that older Ss not only solved using the simpler rule more often than younger Ss, but when they did so they solved faster. For both American age groups, solution using the simpler rule led to no difference in performance between conjunctive and disjunctive Intradimensional problems. Conjunction proved easier than disjunction only for the Traditional problems. Unlike Americans, the Kpelle of Liberia did not solve using the simpler rule, and found conjunction easier than disjunction for both Traditional and Intradimensional problems. When a memory aid was provided the Kpelle showed a pattern of performance exactly like Americans.     

Pavlovian conditioning and rule learning

The experiment reviewed here was an attempt to show that two differential Pavlovian conditioning designs, namely positive and negative patterning, can best be understood as rule learning. First, it is shown that positive patterning is equivalent to the logical rule of conjunction (AND) and that negative patterning is equivalent to the logical rule of exclusive disjunction (XOR). It is assumed that in order to learn both kinds of discrimination subjects learn to use the according rule. If this is the case, the observed differentiation should be independent of the number of reinforcements for each individual stimulus. Second, subjects should be able to transfer the rule to new stimuli. Forty human subjects were randomly divided into four groups (N=10 each). Two factors were manipulated independently between subjects: (1) positive vs negative patterning, and (2) 2 vs 4 pairs of trained stimuli. Second interval skin conductance responses were measured. During initial acquisition positive as well as negative patterning occurred independently of number of pairs of trained stimuli (with total amount of training kept constant). Furthermore, AND as well as XOR could be transfered to new stimuli.     

Patient variable biases in clinical judgment: conceptual overview and methodological considerations

Specific conceptual factors are presented as having contributed to the negative findings of past psychotherapy bias research. Among these factors are the restrictive nature of both the definition of bias and the model of the biased clinician. A review of published studies of several patient variable biases (e.g., gender, race/ethnicity, and age) indicated that bias may best be defined in broader terms and that its occurrence may be more circumscribed than originally conceived. In addition, the lack of a theoretical perspective in this area of research is noted and the applicability of some social cognitive processes is discussed. Methodological factors are also considered, and recommendations for future research are offered.     

Executive functions predict conceptual learning of science

We examined the relationship between executive functions and both factual and conceptual learning of science, specifically chemistry, in early adolescence. Sixty‐three pupils in their second year of secondary school (aged 12–13 years) participated. Pupils completed tasks of working memory (Spatial Working Memory), inhibition (Stop‐Signal), attention set‐shifting (ID/ED), and planning (Stockings of Cambridge), from the CANTAB. They also participated in a chemistry teaching session, practical, and assessment on the topic of acids and alkalis designed specifically for this study. Executive function data were related to (1) the chemistry assessment which included aspects of factual and conceptual learning and (2) a recent school science exam. Correlational analyses between executive functions and both the chemistry assessment and science grades revealed that science achievements were significantly correlated with working memory. Linear regression analysis revealed that visuospatial working memory ability was predictive of chemistry performance. Interestingly, this relationship was observed solely in relation to the conceptual learning condition of the assessment highlighting the role of executive functions in understanding and applying knowledge about what is learned within science teaching.     

Probabilistic models of cognition: conceptual foundations

Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, 'sophisticated' probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, explore how the approach relates to studies of explicit probabilistic reasoning, and give a brief overview of the field as it stands today.     

A conceptual overview of selected consultation models

Because of their current popularity and utility 4 consultation models have been selected for analysis: consultee centered, group process, social action, and ecological (exemplified by Gerald Caplan, Ronald Lippitt, Saul Alinsky, and James Kelly, respectively). The process of consultation as outlined by these models is described along the following dimensions: definitions of consultation, self-perceptions of the consultant, target populations, motivation of the client system, entry, goals, diagnosis, techniques, termination, and evaluation. The discussion focuses on criteria for selecting an appropriate consultation model.     

Iterated learning: Intergenerational knowledge transmission reveals inductive biases

Cultural transmission of information plays a central role in shaping human knowledge. Some of the most complex knowledge that people acquire, such as languages or cultural norms, can only be learned from other people, who themselves learned from previous generations. The prevalence of this process of “iterated learning” as a mode of cultural transmission raises the question of how it affects the information being transmitted. Analyses of iterated learning utilizing the assumption that the learners are Bayesian agents predict that this process should converge to an equilibrium that reflects the inductive biases of the learners. An experiment in iterated function learning with human participants confirmed this prediction, providing insight into the consequences of intergenerational knowledge transmission and a method for discovering the inductive biases that guide human inferences.     

Infant rule learning facilitated by speech

Sequences of speech sounds play a central role in human cognitive life, and the principles that govern such sequences are crucial in determining the syntax and semantics of natural languages. Infants are capable of extracting both simple transitional probabilities and simple algebraic rules from sequences of speech, as demonstrated by studies using ABB grammars (la ta ta, gai mu mu, etc.). Here, we report a striking finding: Infants are better able to extract rules from sequences of nonspeech--such as sequences of musical tones, animal sounds, or varying timbres--if they first hear those rules instantiated in sequences of speech.     

Category learning with minimal prior knowledge

In 6 experiments, the authors examined the use of prior knowledge in category learning. Previous studies of the effects of knowledge on category learning have used categories in which knowledge was related to all of the category's features. However, people's knowledge of real-world categories often consists of many "rote" features that are not related to their prior knowledge. Five experiments found that even minimal prior knowledge (1 knowledge-relevant feature and 5 rote features per exemplar) can facilitate category learning. Posttests revealed that although the knowledge aided learning, subjects also acquired the rote features that were not related to knowledge, contradicting predictions of an attentional explanation of the knowledge effect. The results of Experiment 6 suggested that subjects attempt to link even rote features to their knowledge.     

Incorporating prior theory in covariance structure analysis: A bayesian approach

A Bayesian approach to the testing of competing covariance structures is developed. The method provides approximate posterior probablities for each model under consideration without prior specification of individual parameter distributions. The method is based on ayesian updating using cross-validated pseudo-likelihoods. Given that the observed variables are the samefor all competing models, the approximate posterior probabilities may be obtained easily from the chi square values and other known constants, using only a hand calculator. The approach is illustrated using and example which illustrates how the prior probabilities can alter the results concerning which model specification is preferred.     

Sensorimotor simulations underlie conceptual representations: Modality-specific effects of prior activation

According to the perceptual symbols theory (Barsalou, 1999), sensorimotor simulations underlie the representation of concepts. Simulations are componential in the sense that they vary with the context in which the concept is presented. In the present study, we investigated whether representations are affected by recent experiences with a concept. Concept names (e.g., APPLE) were presented twice in a property verification task with a different property on each occasion. The two properties were either from the same perceptual modality (e.g.,green, shiny) or from different modalities (e.g.,tart, shiny). All stimuli were words. There was a lag of several intervening trials between the first and second presentation. Verification times and error rates for the second presentation of the concept were higher if the properties were from different modalities than if they were from the same modality.