Applying Occam’s razor in modeling cognition: A Bayesian approach

In mathematical modeling of cognition, it is important to have well-justified criteria for choosing among differing explanations (i.e., models) of observed data. This paper introduces a Bayesian model selection approach that formalizes Occam’s razor, choosing the simplest model that describes the data well. The choice of a model is carried out by taking into account not only the traditional model selection criteria (i.e., a model’s fit to the data and the number of parameters) but also the extension of the parameter space, and, most importantly, the functional form of the model (i.e., the way in which the parameters are combined in the model’s equation). An advantage of the approach is that it can be applied to the comparison of non-nested models as well as nested ones. Application examples are presented and implications of the results for evaluating models of cognition are discussed.     

Flexibility versus generalizability in model selection

Which quantitative method should be used to choose among competing mathematical models of cognition? Massaro, Cohen, Campbell, and Rodriguez (2001) favor root mean squared deviation (RMSD), choosing the model that provides the best fit to the data. Their simulation results appear to legitimize its use for comparing two models of information integration because it performed just as well as Bayesian model selection (BMS), which had previously been shown by Myung and Pitt (1997) to be a superior alternative selection method because it considers a model's complexity in addition to its fit. In the present study, after contrasting the theoretical approaches to model selection espoused by Massaro et al. and Myung and Pitt, we discuss the cause of the inconsistencies by expanding on the simulations of Massaro et al. Findings demonstrate that the results from model recovery simulations can be misleading if they are not interpreted relative to the data on which they were evaluated, and that BMS is a more robust selection method.     

When a good fit can be bad

How should we select among computational models of cognition? Although it is commonplace to measure how well each model fits the data, this is insufficient. Good fits can be misleading because they can result from properties of the model that have nothing to do with it being a close approximation to the cognitive process of interest (e.g. overfitting). Selection methods are introduced that factor in these properties when measuring fit. Their success in outperforming standard goodness-of-fit measures stems from a focus on measuring the generalizability of a model's data-fitting abilities, which should be the goal of model selection.     

Explaining Emotions

Emotions and cognition are inextricably intertwined. Feelings influence thoughts and actions, which in turn can give rise to new emotional reactions. We claim that people infer emotional states in others using commonsense psychological theories of the interactions among emotions, cognition, and action. We present a situation calculus theory of emotion elicitation representing knowledge underlying commonsense causal reasoning involving emotions, and show how the theory can be used to construct explanations for emotional states. The method for constructing explanations is based on the notion of abduction. This method has been implemented in a computer program called AbMaL. The results of computational experiments using AbMaL to construct explanations of examples based on cases taken from a diary study of emotions indicate that the abductive approach to explanatory reasoning about emotions offers significant advantages. We found that the majority of the diary study examples cannot be explained using deduction alone, but they can be explained by making abjuctive inferences. These inferences provide useful information relevant to emotional states.     

CRITICAL REMARKS ON THE COGNITIVE SCIENCE OF RELIGION

Cognitive explanations of religious beliefs propose an evolutionary past in which humans had to possess certain cognitive adaptations to survive. The aim of this article is to show that some cognitive accounts may overvalue the putative role of cognition. One such cognitive idea is an assumption that cognition has been evolutionarily shaped only, or most importantly, in the Pleistocene. This idea seems common among writers on the cognitive science of religion (CSR), but is mistaken. Cognition has been shaped throughout evolution. Another idea is that components of religion could not have been produced by natural selection (the hypothesis that religion is a by-product). But the article suggests that there are some domains in the field of religion and religious components that could be acquired and transmitted despite or even against alleged cognitive biases. The aim of this article is to argue for an extended approach that combines a cognitive account with functional naturalistic approaches, including an adaptationist one. Such distinction could imply that cognition is not functional. Obviously, this is not the case since cognition is the process of knowing, and surely knowledge is functional. However, the main argument for such a distinction lies in the key idea of the cognitive account that as far as cognition is functional and adaptive, religious components are not. Functionalism or “adaptivism” concerning cognition contradicts functionalism concerning religion. Numbers of scholars who consider themselves part of CSR seem also to consider both cognition and religion adaptive. However, in regard to components of religion, their adaptive, functional power is only secondary. The article concludes that the study of religion—as the study of cultural evolution in general—should include a pluralistic methodology combining cognitive and evolutionary accounts with the specificity of cultural evolution.     

5-13岁儿童对物种起源认知的发展

本研究采用临床访谈法,采用开放式和封闭式问题情境,测查了5-13岁儿童对物种起源认知的发展。结果显示儿童对物种起源的解释越来越符合科学的解释,这种认知发展过程并不是以一种起源认知替代另一种起源认知的过程,而是多种起源认知以某种方式共存于儿童的认知系统中。本研究结果既显示了儿童对物种起源认知发展的跨文化一致性,也体现了不同文化和宗教环境影响下的差异性。     

Stimulus-response compatibility and psychological refractory period effects: implications for response selection

The purpose of this paper was to provide insight into the nature of response selection by reviewing the literature on stimulus-response compatibility (SRC) effects and the psychological refractory period (PRP) effect individually and jointly. The empirical findings and theoretical explanations of SRC effects that have been studied within a single-task context suggest that there are two response-selection routes—automatic activation and intentional translation. In contrast, all major PRP models reviewed in this paper have treated response selection as a single processing stage. In particular, the response-selection bottleneck (RSB) model assumes that the processing of Task 1 and Task 2 comprises two separate streams and that the PRP effect is due to a bottleneck located at response selection. Yet, considerable evidence from studies of SRC in the PRP paradigm shows that the processing of the two tasks is more interactive than is suggested by the RSB model and by most other models of the PRP effect. The major implication drawn from the studies of SRC effects in the PRP context is that response activation is a distinct process from final response selection. Response activation is based on both long-term and short-term task-defined S-R associations and occurs automatically and in parallel for the two tasks. The final response selection is an intentional act required even for highly compatible and practiced tasks and is restricted to processing one task at a time. Investigations of SRC effects and responseselection variables in dual-task contexts should be conducted more systematically because they provide significant insight into the nature of response-selection mechanisms.     

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.     

Mechanisms of Reference Frame Selection in Spatial Term Use: Computational and Empirical Studies

Previous studies have shown that multiple reference frames are available and compete for selection during the use of spatial terms such as “above.” However, the mechanisms that underlie the selection process are poorly understood. In the current paper we present two experiments and a comparison of three computational models of selection to shed further light on the nature of reference frame selection. The three models are drawn from different areas of human cognition, and we assess whether they may be applied to a reference frame selection by examining their ability to account for both existing and new empirical data comprising acceptance rates, response times, and response time distributions. These three models are the competitive shunting model (Schultheis, 2009 ), the leaky competing accumulator (LCA) model (Usher & McClelland, 2001 ), and a lexical selection model (Howard, Nickels, Coltheart, & Cole‐Virtue, 2006 ). Model simulations show that only the LCA model satisfactorily accounts for the empirical observations. The key properties of this model that seem to drive its success are its bounded linear activation function, its number and type of processing stages, and its use of decay. Uncovering these critical properties has important implications for our understanding not only of spatial term use, in particular, but also of conflict and selection in human cognition more generally.     

A quantum theoretical explanation for probability judgment errors

A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction and disjunction fallacies, averaging effects, unpacking effects, and order effects on inference. On the one hand, quantum theory is similar to other categorization and memory models of cognition in that it relies on vector spaces defined by features and similarities between vectors to determine probability judgments. On the other hand, quantum probability theory is a generalization of Bayesian probability theory because it is based on a set of (von Neumann) axioms that relax some of the classic (Kolmogorov) axioms. The quantum model is compared and contrasted with other competing explanations for these judgment errors, including the anchoring and adjustment model for probability judgments. In the quantum model, a new fundamental concept in cognition is advanced--the compatibility versus incompatibility of questions and the effect this can have on the sequential order of judgments. We conclude that quantum information-processing principles provide a viable and promising new way to understand human judgment and reasoning.     

Personal and Social Factors in Achievement-Related Cognition: A Study of Attribution,Appraisal, and Emotion among University Students in Delhi

In recent years, achievement-related cognition and emotion have received a lot of attention from all sections of researchers. A number of researchers have tried to empirically study the determinants and consequences of achievement-related cognition. The explanations given range between individualistic explanations and sociocultural explanations. It is believed that both personal and social factors interact together in bringing about variations in achievement-related cognition and emotion. It is widely accepted that there are variations in human behavior, which are generally believed to be the result of generation-old cultural changes. Cultural factors are believed to exert influence on human behavior and psyche. When one talks about culture, it is mostly treated as a homogeneous entity. Variations within cultures and intra-cultural differences due to various social factors are largely ignored. Researchers world over have started believing in the fact that exposure not only to varying cultural conditions but also to different social factors affects individuals’ psychological functioning. Present research is directed towards exploring variations in achievement-related cognition and emotion due to personal and social factors. Eighty students from a university in Delhi participated in the research. The personal factor of self-construal and social factors like gender, social setting, perceived social class, and family were taken into account. Findings of the study corroborated the hypothesis that both personal and social factors have an impact on achievement-related cognition and emotion.     

Illusory inferences: a novel class of erroneous deductions.

The mental model theory postulates that reasoners build models of the situations described in premises, and that these models normally make explicit only what is true. The theory has an unexpected consequence: it predicts the occurrence of inferences that are compelling but invalid. They should arise from reasoners failing to take into account what is false. Three experiments corroborated the systematic occurrence of these illusory inferences, and eliminated a number of alternative explanations for them. Their results illuminate the controversy among various current theories of reasoning.     

Reversing Arrows in Mediation Models Does Not Distinguish Plausible Models

Reversing arrows in the classic tri-variate X-M-Y mediation models as a test to check whether one mediation model is superior to another is inadmissible. Presenting evidence that one tri-variate mediation model yields a significant indirect effect, whereas one with some reversed arrows does not, is not proof or even evidence that one model should be preferred. In fact, the significance of the indirect or any other effect can never be used to infer whether one model should be preferred over another, if the models are in the same so-called equivalence class. The practice of running several mediation models with reversed arrows to decide which model to prefer should be abandoned. The only way to choose among equivalent models is through assumptions that are either fulfilled by design features or invoked based on theory. Similar arguments about reversing arrows in mediation models have been made before, but this current work is the first to derive this result analytically for the complete (Markovian) equivalence class of the tri-variate mediation model.     

Neuronal models of cognitive functions

Understanding the neural bases of cognition has become a scientifically tractable problem, and neurally plausible models are proposed to establish a causal link between biological structure and cognitive function. To this end, levels of organization have to be defined within the functional architecture of neuronal systems. Transitions from any one of these interacting levels to the next are viewed in an evolutionary perspective. They are assumed to involve: (1) the production of multiple transient variations and (2) the selection of some of them by higher levels via the interaction with the outside world. The time-scale of these "evolutions" is expected to differ from one level to the other. In the course of development and in the adult this internal evolution is epigenetic and does not require alteration of the structure of the genome. A selective stabilization (and elimination) of synaptic connections by spontaneous and/or evoked activity in developing neuronal networks is postulated to contribute to the shaping of the adult connectivity within an envelope of genetically encoded forms. At a higher level, models of mental representations, as states of activity of defined populations of neurons, are discussed in terms of statistical physics, and their storage is viewed as a process of selection among variable and transient pre-representations. Theoretical models illustrate that cognitive functions such as short-term memory and handling of temporal sequences may be constrained by "microscopic" physical parameters. Finally, speculations are offered about plausible neuronal models and selectionist implementations of intentions.     

What Sort of Taxonomy of Causation Do We Need for Language Understanding?*

A proposal is made concerning the introduction of the notions of cause and reason into a natural language understanding system. Its hypothesis is that one should prefer rational explanations of actions when dealing with human, or human-like, agents, if one can find them in what one is analyzing, but that in other, nonhuman, cases one should prefer causal explanations. The reader is reminded of the existing state of the preference semantics system, and then are described the changes that would have to be made to incorporate the above proposal and to find examples to test it. Finally, it is argued that there is no reason to think that a more complex taxonomy of causes (beyond the opposition of causes and reasons) is required, at least not if, as here. one seeks a procedural distinction corresponding to any taxonomic distinction among inference rules.     

具身心智对进化心理学的质疑与挑战

具身-生成心智倡导生命与认知的深刻连续性,它重新定义了自然选择,强调身体和生命活动对认知的关键意义,并指出每一个人都是经过文化适应的主体。它在心智进化的本质、认知的核心工作假设、人类本性与文化,以及基因、身体、生命活动与认知的关系等问题上都有着完全不同于进化心理学的理解,并对后者提出了质疑和挑战。     

Situating Emotions: From Embodied Cognition to Mindreading

In this article we analyze the strengths and weaknesses of mindreading versus embodied cognition approaches to emotion understanding. In the first part of the article we argue that mindreading explanations of how we understand the emotions of others (TT, ST or hybrid) face a version of the frame problem, i.e. the problem of how to limit the scope of the information that is relevant to mindreading. Also, we show that embodied cognition explanations are able to by-pass this problem because they provide a characterization of social understanding as being essentially situated. However, embodied cognition explanations seem to be limited in scope insofar as they do not target the more sophisticated forms of emotion understanding that have traditionally been the main focus of mindreading explanations. In the second part of the article we discuss Goldie’s account of emotion understanding as a possible way to complement embodied cognition approaches without re-introducing the frame problem. We offer two suggestions that might further the integration of Goldie’s account of emotion understanding within the framework of embodied cognition.     

Using Attributions to Understand the Effects of Explanations on Applicant Reactions: Are Reactions Consistent With the Covariation Principle?1

Research has shown that explanations for selection decisions may influence a variety of applicant perceptions and behavior, but an understanding of how and why this occurs remains largely unknown. This study attempts to understand the effects of explanations by adopting Kelley's (1967, 1972) covariation model of the attribution process. Specifically, explanations that vary on consensus, distinctiveness, and consistency covariation information should produce predictable effects on applicant perceptions and attributions. Results from 2 studies, the first a laboratory study and the second a field study with actual applicants, support the utility of the covariation model for understanding the influence of explanations for selection decisions on locus attributions, fairness, self‐perceptions, and organizational attractiveness. These results suggest that the covariation model may be a useful means to construe the explanation‐attribution‐perception relationship, and thus provide a number of theoretical and practical implications.