Generative connectionist networks and constructivist cognitive development

This article presents a novel computational framework for modeling cognitive development. The new modeling paradigm provides a language with which to compare and contrast radically different facets of children's knowledge. Concepts from the study of machine learning are used to explore the power of connectionist networks that construct their own architectures during learning. These so-called generative algorithms are shown to escape from Fodor's (1980) critique of Constructivist development. We describe one generative connectionist algorithm (cascade-correlation) in detail. We report on the successful use of the algorithm to model cognitive development on balance scale phenomena; seriation; the integration of velocity, time, and distance cues; prediction of effect sizes from magnitudes of causal potencies and effect resistances; and the acquisition of English personal pronouns. The article demonstrates that computer models are invaluable for illuminating otherwise obscure discussions.     

Monkeys display classic signatures of human symbolic arithmetic

Non-human primates compare quantities in a crude manner, by approximating their values. Less is known about the mental transformations that non-humans can perform over approximate quantities, such as arithmetic transformations. There is evidence that human symbolic arithmetic has a deep psychological connection with the primitive, approximate forms of quantification of non-human animals. Here, we ask whether the subtle performance signatures that humans exhibit during symbolic arithmetic also bear a connection to primitive arithmetic. Specifically, we examined the problem size effect, the tie effect, and the practice effect—effects which are commonly observed in children’s math performance in school. We show that, like humans, monkeys exhibited the problem size and tie effects, indicating commonalities in arithmetic algorithms with humans. Unlike humans, however, monkeys did not exhibit a practice effect. Together, these findings provide new evidence for a cognitive relation between non-symbolic and symbolic arithmetic.     

SARA: a cognitive process model to simulate the anchoring effect and hindsight bias

The cognitive process model "SARA" aims to explain the anchoring effect and hindsight bias by making detailed assumptions about the representation and alteration of item-specific knowledge. The model assumes that all processes, namely generating an estimate, encoding new information (i.e., the "anchor"), and reconstructing a previously generated estimate, are based on a probabilistic sampling process. Sampling probes long-term memory in order to retrieve information into working memory. Retrieval depends on the associative strength between this information and the currently active retrieval cues. Encoding the anchor may alter this associative pattern ("selective activation") or the anchor may serve as a retrieval cue, thus directing memory search ("biased reconstruction"). Both processes lead to systematically changed retrieval probabilities, thus causing the anchoring effect or hindsight bias. The model is completely formalised and implemented as a computer program. A series of simulations demonstrates the power of SARA to reproduce empirical findings and to predict new ones.     

An attractor network model of serial recall

We present a neural network model of verbal working memory which attempts to illustrate how a few simple assumptions about neural computation can shed light on cognitive phenomena associated with the serial recall of verbal material. We assume that neural representations are distributed, that neural connectivity is massively recurrent, and that synaptic efficacy is modified based on the correlation between pre- and post-synaptic activity (Hebbian learning). Together these assumptions give rise to emergent computational properties that are relevant to working memory, including short-term maintenance of information, time-based decay, and similarity-based interference. We instantiate these principles in a specific model of serial recall and show how it can both simulate and explain a number of standard cognitive phenomena associated with the task, including the effects of serial position, word length, articulatory suppression (and its interaction with word length), and phonological similarity.     

SARA: A cognitive process model to simulate the anchoring effect and hindsight bias

The cognitive process model "SARA" aims to explain the anchoring effect and hindsight bias by making detailed assumptions about the representation and alteration of item-specific knowledge. The model assumes that all processes, namely generating an estimate, encoding new information (i.e., the "anchor"), and reconstructing a previously generated estimate, are based on a probabilistic sampling process. Sampling probes long-term memory in order to retrieve information into working memory. Retrieval depends on the associative strength between this information and the currently active retrieval cues. Encoding the anchor may alter this associative pattern ("selective activation") or the anchor may serve as a retrieval cue, thus directing memory search ("biased reconstruction"). Both processes lead to systematically changed retrieval probabilities, thus causing the anchoring effect or hindsight bias. The model is completely formalised and implemented as a computer program. A series of simulations demonstrates the power of SARA to reproduce empirical findings and to predict new ones.     

Determining the Dimensionality of Multidimensional Scaling Representations for Cognitive Modeling

Multidimensional scaling models of stimulus domains are widely used as a representational basis for cognitive modeling. These representations associate stimuli with points in a coordinate space that has some predetermined number of dimensions. Although the choice of dimensionality can significantly influence cognitive modeling, it is often made on the basis of unsatisfactory heuristics. To address this problem, a Bayesian approach to dimensionality determination, based on the Bayesian Information Criterion (BIC), is developed using a probabilistic formulation of multidimensional scaling. The BIC approach formalizes the trade-off between data-fit and model complexity implicit in the problem of dimensionality determination and allows for the explicit introduction of information regarding data precision. Monte Carlo simulations are presented that indicate, by using this approach, the determined dimensionality is likely to be accurate if either a significant number of stimuli are considered or a reasonable estimate of precision is available. The approach is demonstrated using an established data set involving the judged pairwise similarities between a set of geometric stimuli. Copyright 2001 Academic Press.     

新世纪20年国内心理统计方法研究回顾

新世纪头20年, 国内心理学11本专业期刊一共发表了213篇统计方法研究论文。研究范围主要包括以下10类(按论文篇数排序)：结构方程模型、测验信度、中介效应、效应量与检验力、纵向研究、调节效应、探索性因子分析、潜在类别模型、共同方法偏差和多层线性模型。对各类做了简单的回顾与梳理。结果发现, 国内心理统计方法研究的广度和深度都不断增加, 研究热点在相互融合中共同发展; 但综述类论文比例较大, 原创性研究论文比例有待提高, 研究力量也有待加强。     

The anchoring bias reflects rational use of cognitive resources

Cognitive biases, such as the anchoring bias, pose a serious challenge to rational accounts of human cognition. We investigate whether rational theories can meet this challenge by taking into account the mind’s bounded cognitive resources. We asked what reasoning under uncertainty would look like if people made rational use of their finite time and limited cognitive resources. To answer this question, we applied a mathematical theory of bounded rationality to the problem of numerical estimation. Our analysis led to a rational process model that can be interpreted in terms of anchoring-and-adjustment. This model provided a unifying explanation for ten anchoring phenomena including the differential effect of accuracy motivation on the bias towards provided versus self-generated anchors. Our results illustrate the potential of resource-rational analysis to provide formal theories that can unify a wide range of empirical results and reconcile the impressive capacities of the human mind with its apparently irrational cognitive biases.     

Determining informative priors for cognitive models

The development of cognitive models involves the creative scientific formalization of assumptions, based on theory, observation, and other relevant information. In the Bayesian approach to implementing, testing, and using cognitive models, assumptions can influence both the likelihood function of the model, usually corresponding to assumptions about psychological processes, and the prior distribution over model parameters, usually corresponding to assumptions about the psychological variables that influence those processes. The specification of the prior is unique to the Bayesian context, but often raises concerns that lead to the use of vague or non-informative priors in cognitive modeling. Sometimes the concerns stem from philosophical objections, but more often practical difficulties with how priors should be determined are the stumbling block. We survey several sources of information that can help to specify priors for cognitive models, discuss some of the methods by which this information can be formalized in a prior distribution, and identify a number of benefits of including informative priors in cognitive modeling. Our discussion is based on three illustrative cognitive models, involving memory retention, categorization, and decision making.     

Verbal transformation: habituation or spreading activation?

Experiment 1 tested the habituation hypothesis of the Verbal Transformation Effect, an auditory illusion in which a repeating verbal stimulus undergoes perceptual transformation, by varying stimulus dimensions which might be expected to retard habituation. Transformations were found to increase as a function of imagery value and word length, failing to support the habituation hypothesis. Experiment 2, in which transformations were found to vary as a function of number of activated semantic representations of a physically invariant homophone stimulus, provided support for a new dual-process explanation of the Transformation effect, based on spreading activation between cognitive representations.     

Constraints on Constraints: Surveying the Epigenetic Landscape

It is now relatively commonplace to advocate the need for some sorts of constraints on learning and knowledge acquisition. The critical issues to cognitive science concern the sorts of constraints that are able to best model various phenomena of learning and development. Four types of constraints on learning are proposed to be used as an interpretative framework within which to: 1. Better understand the nature of current research: 2. Allow the exploration of alternative models of learning related phenomena, and 3. See more clearly needs for further research. Superficially similar learning phenomena can be modeled by very different configurations of underlying constraints with strong implications for the sorts of representational states that are involved. Each of the five papers in this issue (Brown, Gelman, Markman, Newport, and Spelke) is considered in terms of the configuration of constraints after which each author intends to model their phenomena and in terms of alternate configurations. The papers are construed as illustrating a diverse set of models of how constraints might guide learning, and while the evidence generally favors the configurations suggested by the authors, in each case alternative models are possible and motivate quite specific future research questions. More broadly, it is suggested that asking detailed questions about the sorts of Constraints types that could potentially model complex cases of natural knowledge acquisition helps motivate fundamental questions about learning and the nature of knowledge and that the five papers in this issue are superb examples of how adopting this kind of perspective has been fruitful research orientation.     

Addressing the “Replication Crisis”: Using Original Studies to Design Replication Studies with Appropriate Statistical Power

Psychology is undergoing a replication crisis. The discussion surrounding this crisis has centered on mistrust of previous findings. Researchers planning replication studies often use the original study sample effect size as the basis for sample size planning. However, this strategy ignores uncertainty and publication bias in estimated effect sizes, resulting in overly optimistic calculations. A psychologist who intends to obtain power of .80 in the replication study, and performs calculations accordingly, may have an actual power lower than .80. We performed simulations to reveal the magnitude of the difference between actual and intended power based on common sample size planning strategies and assessed the performance of methods that aim to correct for effect size uncertainty and/or bias. Our results imply that even if original studies reflect actual phenomena and were conducted in the absence of questionable research practices, popular approaches to designing replication studies may result in a low success rate, especially if the original study is underpowered. Methods correcting for bias and/or uncertainty generally had higher actual power, but were not a panacea for an underpowered original study. Thus, it becomes imperative that 1) original studies are adequately powered and 2) replication studies are designed with methods that are more likely to yield the intended level of power.     

Error management theory: a new perspective on biases in cross-sex mind reading

A new theory of cognitive biases, called error management theory (EMT), proposes that psychological mechanisms are designed to be predictably biased when the costs of false-positive and false-negative errors were asymmetrical over evolutionary history. This theory explains known phenomena such as men's overperception of women's sexual intent, and it predicts new biases in social inference such as women's underestimation of men's commitment. In Study 1 (N = 217), the authors documented the commitment underperception effect predicted by EMT. In Study 2 (N = 289), the authors replicated the commitment bias and documented a condition in which men's sexual overperception bias is corrected. Discussion contrasts EMT with the heuristics and biases approach and suggests additional testable hypotheses based on EMT.     

Word centre is misperceived

Normal readers were asked to mark the middle of visually presented words. They made systematic errors toward the left, indicating an overestimation of the length of the beginning of a word. The number of characters determines the size of this error. The bias extended to pseudowords, letter strings, and symbols, but not to blocks, dashes, and lines. Finally, the bias was sensitive to typographical errors but not to colour cuing. These findings suggest that special cognitive operations determine the perceived spatial extent of words. Implications for our understanding of perceptual and cognitive processes in reading are discussed.     

Attention and cognition

Forty 4-year-old children were subjects in an experimental designed to determine whether learning to attend to relevant cues is a sufficient condition for acquisition of length and number conservation. Three groups of non-conservers were trained by means of an oddity-problem procedure to attend to the cues specifying either length, number, or both length and number. They were subsequently tested along with an untrained control group on tasks of length, number, mass and continuous quantity conservation. Some improvement in conservation was found, but it was neither impressive in magnitude nor specific to the cues of training. For example, the group trained to attend to length cues conserved length about 25% of the time, the same rate at which this group also conserved number, mass, and liquid quantity. This non-specificity is contrary to an attention hypothesis and suggests instead that training, to the extent it was effective, induced a general, abstract quantitative knowledge.     

Network Connectivity Dynamics,Cognitive Biases,and the Evolution of Cultural Diversity in Round-Robin Interactive Micro-Societies

The distribution of cultural variants in a population is shaped by both neutral evolutionary dynamics and by selection pressures. The temporal dynamics of social network connectivity, that is, the order in which individuals in a population interact with each other, has been largely unexplored. In this paper, we investigate how, in a fully connected social network, connectivity dynamics, alone and in interaction with different cognitive biases, affect the evolution of cultural variants. Using agent-based computer simulations, we manipulate population connectivity dynamics (early, mid, and late full-population connectivity); content bias, or a preference for high-quality variants; coordination bias, or whether agents tend to use self-produced variants (egocentric bias), or to switch to variants observed in others (allocentric bias); and memory size, or the number of items that agents can store in their memory. We show that connectivity dynamics affect the time-course of variant spread, with lower connectivity slowing down convergence of the population onto a single cultural variant. We also show that, compared to a neutral evolutionary model, content bias accelerates convergence and amplifies the effects of connectivity dynamics, while larger memory size and coordination bias, especially egocentric bias, slow down convergence. Furthermore, connectivity dynamics affect the frequency of high-quality variants (adaptiveness), with late connectivity populations showing bursts of rapid change in adaptiveness followed by periods of relatively slower change, and early connectivity populations following a single-peak evolutionary dynamic. We evaluate our simulations against existing data collected from previous experiments and show how our model reproduces the empirical patterns of convergence.     

Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predicts differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior.     

Connecting neural coding to number cognition: a computational account

The current study presents a series of computational simulations that demonstrate how the neural coding of numerical magnitude may influence number cognition and development. This includes behavioral phenomena cataloged in cognitive literature such as the development of numerical estimation and operational momentum. Though neural research has begun to describe neural coding of number, it is unclear how specific characteristics of the neural coding may relate to the expansive list of behavioral phenomena in the development of number cognition. The following study considers several possibilities.