The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation

The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints generally and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We incorporate cognitive plausibility by using an age‐appropriate unit of perceptual representation, evaluating the model output in terms of its utility, and incorporating cognitive constraints into the inference process. Our more cognitively plausible model shows a beneficial effect of cognitive constraints on segmentation performance. One interpretation of this effect is as a synergy between the naive theories of language structure that infants may have and the cognitive constraints that limit the fidelity of their inference processes, where less accurate inference approximations are better when the underlying assumptions about how words are generated are less accurate. More generally, these results highlight the utility of incorporating cognitive plausibility more fully into computational models of language acquisition.     

A computational cognitive framework of spatial memory in brains and robots

Computational cognitive models of spatial memory often neglect difficulties posed by the real world, such as sensory noise, uncertainty, and high spatial complexity. On the other hand, robotics is unconcerned with understanding biological cognition. Here, we describe a computational framework for robotic architectures aiming to function in realistic environments, as well as to be cognitively plausible.We motivate and describe several mechanisms towards achieving this despite the sensory noise and spatial complexity inherent in the physical world. We tackle error accumulation during path integration by means of Bayesian localization, and loop closing with sequential gradient descent. Finally, we outline a method for structuring spatial representations using metric learning and clustering. Crucially, unlike the algorithms of traditional robotics, we show that these mechanisms can be implemented in neuronal or cognitive models.We briefly outline a concrete implementation of the proposed framework as part of the LIDA cognitive architecture, and argue that this kind of probabilistic framework is well-suited for use in cognitive robotic architectures aiming to combine spatial functionality and psychological plausibility.     

Modeling Misretrieval and Feature Substitution in Agreement Attraction: A Computational Evaluation

We present computational modeling results based on a self-paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k-fold cross-validation. We find that our data are better accounted for by an encoding-based model of agreement attraction, compared to a retrieval-based model. A novel methodological contribution of our study is the use of comprehension questions with open-ended responses, so that both misinterpretation of the number feature of the subject phrase and misassignment of the thematic subject role of the verb can be investigated at the same time. We find evidence for both types of misinterpretation in our study, sometimes in the same trial. However, the specific error patterns in our data are not fully consistent with any previously proposed model.     

Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods

The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals.     

情节记忆中的前摄干扰：神经心理药物学及计算认知建模研究

记忆障碍患者的前摄干扰敏感性显著上升, 但其认知机制仍不清楚。结合神经心理药物学实验和计算认知建模方法对这一问题进行了研究。实验为被试内、双盲设计, 由健康成年人进行两次词对任务的学习, 间隔一周, 两次测试中或注射0.03 mg/kg体重咪唑安定或注射相同浓度的生理盐水。学习过程中及学习结束后进行测试, 要求被试根据线索词回忆靶词。实验结果发现, 注射咪唑安定可引起情节记忆的短时下降, 两种注射条件下均呈现明显的前摄干扰; 与生理盐水条件相比, 注射咪唑安定时引起的前摄干扰显著较高。基于SAC (Source of Action Confusion)的计算认知建模结果较好地拟合了实验数据。这一结果提示, 编码困难可能是记忆障碍患者前摄干扰敏感性较高的主要原因。     

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.     

The Effect of Prominence and Cue Association on Retrieval Processes: A Computational Account

We present a comprehensive empirical evaluation of the ACT‐R–based model of sentence processing developed by Lewis and Vasishth (2005) (LV05). The predictions of the model are compared with the results of a recent meta‐analysis of published reading studies on retrieval interference in reflexive‐/reciprocal‐antecedent and subject–verb dependencies (Jäger, Engelmann, & Vasishth, 2017). The comparison shows that the model has only partial success in explaining the data; and we propose that its prediction space is restricted by oversimplifying assumptions. We then implement a revised model that takes into account differences between individual experimental designs in terms of the prominence of the target and the distractor in memory‐ and context‐dependent cue‐feature associations. The predictions of the original and the revised model are quantitatively compared with the results of the meta‐analysis. Our simulations show that, compared to the original LV05 model, the revised model accounts for the data better. The results suggest that effects of prominence and variable cue‐feature associations need to be considered in the interpretation of existing empirical results and in the design and planning of future experiments. With regard to retrieval interference in sentence processing and to the broader field of psycholinguistic studies, we conclude that well‐specified models in tandem with high‐powered experiments are needed in order to uncover the underlying cognitive processes.     

Scoring Depression on a Common Metric: A Comparison of EAP Estimation,Plausible Value Imputation,and Full Bayesian IRT Modeling

There are a growing number of item response theory (IRT) studies that calibrate different patient-reported outcome (PRO) measures, such as anxiety, depression, physical function, and pain, on common, instrument-independent metrics. In the case of depression, it has been reported that there are considerable mean score differences when scoring on a common metric from different, previously linked instruments. Ideally, those estimates should be the same. We investigated to what extent those differences are influenced by different scoring methods that take into account several levels of uncertainty, such as measurement error (through plausible value imputation) and item parameter uncertainty (through full Bayesian IRT modeling). Depression estimates from different instruments were more similar, and their corresponding confidence/credible intervals were larger when plausible value imputation or Bayesian modeling was used, compared to the direct use of expected a posteriori (EAP) estimates. Furthermore, we explored the use of Bayesian IRT models to update item parameters based on newly collected data.     

A Comparison of ML,WLSMV, and Bayesian Methods for Multilevel Structural Equation Models in Small Samples: A Simulation Study

Multilevel structural equation models are increasingly applied in psychological research. With increasing model complexity, estimation becomes computationally demanding, and small sample sizes pose further challenges on estimation methods relying on asymptotic theory. Recent developments of Bayesian estimation techniques may help to overcome the shortcomings of classical estimation techniques. The use of potentially inaccurate prior information may, however, have detrimental effects, especially in small samples. The present Monte Carlo simulation study compares the statistical performance of classical estimation techniques with Bayesian estimation using different prior specifications for a two-level SEM with either continuous or ordinal indicators. Using two software programs (Mplus and Stan), differential effects of between- and within-level sample sizes on estimation accuracy were investigated. Moreover, it was tested to which extent inaccurate priors may have detrimental effects on parameter estimates in categorical indicator models. For continuous indicators, Bayesian estimation did not show performance advantages over ML. For categorical indicators, Bayesian estimation outperformed WLSMV solely in case of strongly informative accurate priors. Weakly informative inaccurate priors did not deteriorate performance of the Bayesian approach, while strong informative inaccurate priors led to severely biased estimates even with large sample sizes. With diffuse priors, Stan yielded better results than Mplus in terms of parameter estimates.     

A Test by Any Other Name: P Values,Bayes Factors,and Statistical Inference

Procedures used for statistical inference are receiving increased scrutiny as the scientific community studies the factors associated with insuring reproducible research. This note addresses recent negative attention directed at p values, the relationship of confidence intervals and tests, and the role of Bayesian inference and Bayes factors, with an eye toward better understanding these different strategies for statistical inference. We argue that researchers and data analysts too often resort to binary decisions (e.g., whether to reject or accept the null hypothesis) in settings where this may not be required.     

Evidence for Mixed Processes in Normal/Mirror Discrimination of Rotated Letters: A Bayesian Model Comparison Between Single- and Mixed-Distribution Models1

When determining whether a rotated letter is normal or mirrored, an observer mentally rotates the letter to its canonical orientation. To account for patterns of response times (RTs) for the normal/mirror discrimination of rotated letters, previous research formulated a model that postulated a mixture of trials with and without mental rotation. While this model could explain the curvilinear relationship that has been found between averaged RT and letter orientations, the curved RT function is still open to alternative explanations without assuming mixed processes. To address this issue and test the mixed-process hypothesis more directly, we analyzed trial-by-trial RT data instead of averaged RTs by employing a Bayesian model comparison technique. If rotation and non-rotation trials are mixed, trial-by-trial RTs for letters in a particular orientation should not follow a single distribution but a mixed one formed from the superposition of two separate distributions, one for rotation and one for non-rotation trials. In the present study, we compared single- and mixed-distribution models. Bayes-factor analysis showed decisive support for the mixed-distribution model over the single-distribution model. In addition, using the widely applicable information criterion (WAIC), the predictive accuracy of the mixed-distribution model was found to be as high as that of the single-distribution model. These results indicated the involvement of mixed processes in normal/mirror discrimination of rotated letters. The usefulness of statistical modeling in psychological study and necessary precautions to take in the interpretation of the parameters of unconfirmed models are also discussed.     

Cognitive biases in processing infant emotion by women with depression,anxiety and post-traumatic stress disorder in pregnancy or after birth: A systematic review

Perinatal psychological problems such as post-natal depression are associated with poor mother–baby interaction, but the reason for this is not clear. One explanation is that mothers with negative mood have biased processing of infant emotion. This review aimed to synthesise research on processing of infant emotion by pregnant or post-natal women with anxiety, depression or post-traumatic stress disorder (PTSD). Systematic searches were carried out on 11 electronic databases using terms related to negative affect, childbirth and perception of emotion. Fourteen studies were identified which looked at the effect of depression, anxiety and PTSD on interpretation of infant emotional expressions (k = 10), or reaction times when asked to ignore emotional expressions (k = 4). Results suggest mothers with depression and anxiety are more likely to identify negative emotions (i.e., sadness) and less accurate at identifying positive emotions (i.e., happiness) in infant faces. Additionally, women with depression may disengage faster from positive and negative infant emotional expressions. Very few studies examined PTSD (k = 2), but results suggest biases towards specific infant emotions may be influenced by characteristics of the traumatic event. The implications of this research for mother–infant interaction are explored.