Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT-R

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a "model fixed, data variable" paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood-based, "data fixed, model variable" paradigm in which models are treated as stochastic processes in experiments with participant-to-participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT-R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof-of-concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data.     

Using FMRI to test models of complex cognition

This article investigates the potential of fMRI to test assumptions about different components in models of complex cognitive tasks. If the components of a model can be associated with specific brain regions, one can make predictions for the temporal course of the BOLD response in these regions. An event-locked procedure is described for dealing with temporal variability and bringing model runs and individual data trials into alignment. Statistical methods for testing the model are described that deal with the scan-to-scan correlations in the errors of measurement of the BOLD signal. This approach is illustrated using a "sacrificial" ACT-R model that involves mapping 6 modules onto 6 brain regions in an experiment from Ravizza, Anderson, and Carter (in press) concerned with equation solving. The model's visual encoding predicted the BOLD response in the fusiform gyrus, its controlled retrieval predicted the BOLD response in the lateral inferior prefrontal cortex, and its subgoal setting predicted the BOLD response in the anterior cingulate cortex. On the other hand, its motor programming failed to predict anticipatory activation in the motor cortex, its representational changes failed to predicted the pattern of activity in the posterior parietal cortex, and its procedural component failed to predict an initial spike in caudate. The results illustrate the power of such data to direct the development of a theory of complex problem solving, both at the level of a specific task model as well as at the level of the cognitive architecture.     

Using category structures to test iterated learning as a method for identifying inductive biases

Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases—assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed data. This article explores a novel experimental method for identifying the biases that guide human inductive inferences. The idea behind this method is simple: This article uses the responses produced by a participant on one trial to generate the stimuli that either they or another participant will see on the next. A formal analysis of this "iterated learning" procedure, based on the assumption that the learners are Bayesian agents, predicts that it should reveal the inductive biases of these learners, as expressed in a prior probability distribution over hypotheses. This article presents a series of experiments using stimuli based on a well-studied set of category structures, demonstrating that iterated learning can be used to reveal the inductive biases of human learners.     

The tractable cognition thesis

The recognition that human minds/brains are finite systems with limited resources for computation has led some researchers to advance the Tractable Cognition thesis : Human cognitive capacities are constrained by computational tractability. This thesis, if true, serves cognitive psychology by constraining the space of computational-level theories of cognition. To utilize this constraint, a precise and workable definition of "computational tractability" is needed. Following computer science tradition, many cognitive scientists and psychologists define computational tractability as polynomial-time computability, leading to the P-Cognition thesis . This article explains how and why the P-Cognition thesis may be overly restrictive, risking the exclusion of veridical computational-level theories from scientific investigation. An argument is made to replace the P-Cognition thesis by the FPT-Cognition thesis as an alternative formalization of the Tractable Cognition thesis (here, FPT stands for fixed-parameter tractable). Possible objections to the Tractable Cognition thesis, and its proposed formalization, are discussed, and existing misconceptions are clarified.     

齐国陶瓷工艺与造型装饰特色

齐国的陶瓷是齐文化的重要组成部分,在中国陶瓷发展史上占有重要地位。由先齐地区大汶口文化的白陶、龙山文化的黑陶、特别是极具地域特征的齐国日常生活类陶器与各种反映社会现实生活的艺术品、建筑陶构件等,都充分体现出豪放与沉稳的双重造型特色以及丰富多彩的装饰艺术风格。这不仅构成了齐国独特的陶瓷文化体系,也对丰富和发展我国传统陶瓷文化起了重要作用,并影响后世陶瓷发展长达数千年。     

Chaos breeds autonomy: connectionist design between bias and baby-sitting

In connectionism and its offshoots, models acquire functionality through externally controlled learning schedules. This undermines the claim of these models to autonomy. Providing these models with intrinsic biases is not a solution, as it makes their function dependent on design assumptions. Between these two alternatives, there is room for approaches based on spontaneous self-organization. Structural reorganization in adaptation to spontaneous activity is a well-known phenomenon in neural development. It is proposed here as a way to prepare connectionist models for learning and enhance the autonomy of these models.     

A context-free language for binary multinomial processing tree models

This paper provides a new formalization for the class of binary multinomial processing tree (BMPT) models, and theorems for the class are developed using the formalism. MPT models are a popular class of information processing models for categorical data in specific cognitive paradigms. They have a recursive structure that is productively described with the tools of formal language and computation theory. We provide an axiomatization that characterizes BMPT models as strings in a context-free language, and then we add model-theoretic axioms and definitions to interpret the strings as parameterized probabilistic models for categorical data. The language for BMPT models is related to the Full Binary Tree language, a well-studied context-free language. Once BMPT models are viewed from the perspective of the Full Binary Tree language, a number of theoretical and computational results can be developed. In particular, we have a number of results concerning the enumerations of BMPT models as well as the identifiability of subclasses of these models.     

Social learning,sexual and physical abuse,and adult crime

This research examines the relationship between childhood physical and sexual abuse and the types of crimes committed by male adult offenders. We use the method of discriminant prediction to determine whether independent and dependent variables are related in ways that theories predict. Our analyses of data from the Survey of Inmates in State and Federal Correctional Facilities suggest that offenders model specific behaviors to which they have been exposed. Male offenders who were sexually abused as a child are more likely to commit sexual offenses, particularly sexual offenses against children, than nonsexual offenses. Offenders who were physically abused are more likely to engage in violent offenses than nonviolent offenses. Further analyses show that sexual offenders, and to a lesser extent violent offenders, are likely to specialize in those offenses. Our results are consistent with a social learning approach. They address a heretofore neglected issue: what exactly do children model when they are mistreated. Aggr. Behav. 35:489–501, 2009. © 2009 Wiley‐Liss, Inc.     

Components of the Behavioral Activation System and Functional Impulsivity: A test of discriminant hypotheses

It has recently been suggested that the concept of Functional Impulsivity bears some similarity to the sensitivity of the Behavioral Activation System (BAS; Smillie & Jackson, 2006). In the present research we take a closer look at this idea, testing more specific hypotheses on the associations of different BAS-related components with Functional and Dysfunctional Impulsivity. Analyzing a sample of 719, we found that the Drive component was uniquely connected with Functional Impulsivity, and Fun Seeking was linked to both impulsivities, but more weakly so to Functional Impulsivity compared with Drive. Reward Responsiveness was unrelated to Impulsivity. Implications for the conceptualization of Functional Impulsivity and for a complex view of BAS-related functions are addressed.     

Thinking as the control of imagination: a conceptual framework for goal-directed systems

This paper offers a conceptual framework which (re)integrates goal-directed control, motivational processes, and executive functions, and suggests a developmental pathway from situated action to higher level cognition. We first illustrate a basic computational (control-theoretic) model of goal-directed action that makes use of internal modeling. We then show that by adding the problem of selection among multiple action alternatives motivation enters the scene, and that the basic mechanisms of executive functions such as inhibition, the monitoring of progresses, and working memory, are required for this system to work. Further, we elaborate on the idea that the off-line re-enactment of anticipatory mechanisms used for action control gives rise to (embodied) mental simulations, and propose that thinking consists essentially in controlling mental simulations rather than directly controlling behavior and perceptions. We conclude by sketching an evolutionary perspective of this process, proposing that anticipation leveraged cognition, and by highlighting specific predictions of our model.