ALCOVE: an exemplar-based connectionist model of category learning.

ALCOVE (attention learning covering map) is a connectionist model of category learning that incorporates an exemplar-based representation (Medin & Schaffer, 1978; Nosofsky, 1986) with error-driven learning (Gluck & Bower, 1988; Rumelhart, Hinton, & Williams, 1986). Alcove selectively attends to relevant stimulus dimensions, is sensitive to correlated dimensions, can account for a form of base-rate neglect, does not suffer catastrophic forgetting, and can exhibit 3-stage (U-shaped) learning of high-frequency exceptions to rules, whereas such effects are not easily accounted for by models using other combinations of representation and learning method.     

Eye gaze and individual differences consistent with learned attention in associative blocking and highlighting

The associative learning effects called blocking and highlighting have previously been explained by covert learned attention, but evidence for learned attention has been indirect, via models of response choice. The present research reports results from eye tracking consistent with the attentional hypothesis: Gaze duration is diminished for blocked cues and augmented for highlighted cues. If degree of attentional learning varies across individuals but is relatively stable within individuals, then the magnitude of blocking and highlighting should covary across individuals. This predicted correlation is obtained for both choice and eye gaze. A connectionist model that implements attentional learning is shown to fit the data and account for individual differences by variation in its attentional parameters.     

Integrating advice and experience: learning and decision making with social and nonsocial cues

When making decisions, people typically gather information from both social and nonsocial sources, such as advice from others and direct experience. This research adapted a cognitive learning paradigm to examine the process by which people learn what sources of information are credible. When participants relied on advice alone to make decisions, their learning of source reliability proceeded in a manner analogous to traditional cue learning processes and replicated the established learning phenomena. However, when advice and nonsocial cues were encountered together as an established phenomenon, blocking (ignoring redundant information) did not occur. Our results suggest that extant cognitive learning models can accommodate either advice or nonsocial cues in isolation. However, the combination of advice and nonsocial cues (a context more typically encountered in daily life) leads to different patterns of learning, in which mutually supportive information from different types of sources is not regarded as redundant and may be particularly compelling. For these situations, cognitive learning models still constitute a promising explanatory tool but one that must be expanded. As such, these findings have important implications for social psychological theory and for cognitive models of learning.     

Posterior predictive checks can and should be Bayesian: Comment on Gelman and Shalizi, ‘Philosophy and the practice of Bayesian statistics’

Bayesian inference is conditional on the space of models assumed by the analyst. The posterior distribution indicates only which of the available parameter values are less bad than the others, without indicating whether the best available parameter values really fit the data well. A posterior predictive check is important to assess whether the posterior predictions of the least bad parameters are discrepant from the actual data in systematic ways. Gelman and Shalizi (2012a) assert that the posterior predictive check, whether done qualitatively or quantitatively, is non‐Bayesian. I suggest that the qualitative posterior predictive check might be Bayesian, and the quantitative posterior predictive check should be Bayesian. In particular, I show that the ‘Bayesian p‐value’, from which an analyst attempts to reject a model without recourse to an alternative model, is ambiguous and inconclusive. Instead, the posterior predictive check, whether qualitative or quantitative, should be consummated with Bayesian estimation of an expanded model. The conclusion agrees with Gelman and Shalizi regarding the importance of the posterior predictive check for breaking out of an initially assumed space of models. Philosophically, the conclusion allows the liberation to be completely Bayesian instead of relying on a non‐Bayesian deus ex machina. Practically, the conclusion cautions against use of the Bayesian p‐value in favour of direct model expansion and Bayesian evaluation.     

Rule-based extrapolation: A continuing challenge for exemplar models

Erickson and Kruschke (1998, 2002) demonstrated that in rule-plus-exception categorization, people generalize category knowledge by extrapolating in a rule-like fashion, even when they are presented with a novel stimulus that is most similar to a known exception. Although exemplar models have been found to be deficient in explaining rule-based extrapolation, Rodrigues and Murre (2007) offered a variation of an exemplar model that was better able to account for such performance. Here, we present the results of a new rule-plus-exception experiment that yields rule-like extrapolation similar to that of previous experiments, and yet the data are not accounted for by Rodrigues and Murre's augmented exemplar model. Further, a hybrid rule-and-exemplar model is shown to better describe the data. Thus, we maintain that rule-plus-exception categorization continues to be a challenge for exemplar-only models.     

Adaptation and communicative design.

Two theoretical frameworks that examine the nature of adaptability and mutual influence in interaction, interpersonal deception theory and interaction adaptation theory, were used to derive hypotheses concerning patterns of interaction that occur across time in truthful and deceptive conversations. Two studies were conducted in which senders were either truthful or deceptive in their interactions with a partner who increased or decreased involvement during the latter half of the conversation. Results revealed that deceivers felt more anxious and were more concerned about self‐presentation than truthtellers prior to the interaction and displayed less initial involvement than truthtellers. Patterns of interaction were also moderated by deception. Deceivers increased involvement over time but also reciprocated increases or decreases in receiver involvement. However, deceivers were less responsive than truthtellers to changes in receiver behavior. Finally, partner involvement served as feedback to senders regarding their own performance.     

Bayesian data analysis for newcomers

This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.     

An Evolutionary Analysis of Learned Attention

Humans and many other species selectively attend to stimuli or stimulus dimensions—but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it within an organism's processing capacity limitations. Instead, being able to shift attention provides adaptive benefit by allowing faster learning with fewer errors in a range of ecologically plausible environments.     

Population of linear experts: knowledge partitioning and function learning

Knowledge partitioning is a theoretical construct holding that knowledge is not always integrated and homogeneous but may be separated into independent parcels containing mutually contradictory information. Knowledge partitioning has been observed in research on expertise, categorization, and function learning. This article presents a theory of function learning (the population of linear experts model--POLE) that assumes people partition their knowledge whenever they are presented with a complex task. The authors show that POLE is a general model of function learning that accommodates both benchmark results and recent data on knowledge partitioning. POLE also makes the counterintuitive prediction that a person's distribution of responses to repeated test stimuli should be multimodal. The authors report 3 experiments that support this prediction.