Causal discounting in the presence of a stronger cue is due to bias

People use information about the covariation between a putative cause and an outcome to determine whether a causal relationship obtains. When there are two candidate causes and one is more strongly related to the effect than is the other, the influence of the second is underestimated. This phenomenon is called causal discounting. In two experiments, we adapted paradigms for studying causal learning in order to apply signal detection analysis to this phenomenon. We investigated whether the presence of a stronger alternative makes the task more difficult (indexed by differences in d′) or whether people change the standard by which they assess causality (measured by β). Our results indicate that the effect is due to bias.     

Analyzing the Rate at Which Languages Lose the Influence of a Common Ancestor

Analyzing the rate at which languages change can clarify whether similarities across languages are solely the result of cognitive biases or might be partially due to descent from a common ancestor. To demonstrate this approach, we use a simple model of language evolution to mathematically determine how long it should take for the distribution over languages to lose the influence of a common ancestor and converge to a form that is determined by constraints on language learning. We show that modeling language learning as Bayesian inference of n binary parameters or the ordering of n constraints results in convergence in a number of generations that is on the order of n log n. We relax some of the simplifying assumptions of this model to explore how different assumptions about language evolution affect predictions about the time to convergence; in general, convergence time increases as the model becomes more realistic. This allows us to characterize the assumptions about language learning (given the models that we consider) that are sufficient for convergence to have taken place on a timescale that is consistent with the origin of human languages. These results clearly identify the consequences of a set of simple models of language evolution and show how analysis of convergence rates provides a tool that can be used to explore questions about the relationship between accounts of language learning and the origins of similarities across languages.     

Learning in Community for STEM Undergraduates: Connecting a Learning Sciences and a Learning Humanities Approach in Higher Education

Abstract

Previous research demonstrates that social and interpersonal factors, more than academic preparation, affect decisions by under-represented students to stay in or to leave STEM fields. Yet, much of the theorizing about STEM learning in higher education begins with conceptual and epistemological dimensions. We make the case for a new theoretical framework, a learning humanities, that begins with relationships. From this relational starting point, we locate STEM knowers as actors in relationships who become answerable for their STEM knowledge and take wise actions from this place. We then use this framework to analyze learning for STEM undergraduates involved in the STUDIO: Build Our World program, an afterschool mentoring program for low-income, immigrant, and refugee youth of color. Drawing on narrative and ethnographic analyses of data from 12 focal mentors, we found that mentors developed 3 focal practices identified by Edwards, relational expertise, common knowledge, and relational agency through their efforts to create the best possible program for youth. This built mentors’ sense of answerability and a capacity for wise action within and outside of STUDIO. We argue that this theoretical stance provides new ways to conceptualize the nature, purpose, and outcomes of STEM learning for historically non-dominant STEM undergraduates’ learning.     

Integrating Power to Advance the Study of Connective and Productive Disciplinary Engagement in Mathematics and Science

Abstract

Engle and Conant’s productive disciplinary engagement (PDE) framework has significantly advanced the study of learning in mathematics and science. This artilce revisits PDE through the lens of critical education research. Our analysis synthesizes two themes of power: epistemic diversity, and historicity and identity. We argue that these themes, when integrated into PDE, strengthen it as a tool for design and analysis of disciplinary learning in relation to power and personhood, and describe the broadened framework of connective and productive disciplinary engagement (CPDE). By comparing and contrasting the use of PDE and CPDE in relation to two cases of classroom learning—for science, Warren et al.’s metamorphosis and for mathematics, Godfrey and O’Connor’s measurement—we demonstrate how CPDE surfaces issues of history, power, and culture that may otherwise be overlooked by PDE alone. In particular, we analyze how CPDE makes visible unseen identities and generative resources of disciplinary knowing and doing among minoritized students. We discuss how the revised framework redresses epistemic injustice experienced by minoritized learners held to the narrow rubric of western epistemologies and compels close attention to the diversity of human activity in mathematics and science. Further, we elaborate how it provides a structure for teachers, teacher educators, and researchers to design and analyze learning environments as safeguarding the rightful presence of minoritized learners in STEM classrooms and beyond.     

The detection model of recognition using know and remember judgments

The signal detection model forknow andremember recognition judgments was tested in two experiments. In Experiment 1, two predictions of the model were tested: (1) that measures of memory sensitivity,A′, are equivalent in value when based on either the recognition (know or remember) criterion or on the remember criterion; and (2) that there is a positive correlation between recognition bias and the proportion of know judgments that are hits, but no correlation between recognition bias and proportion of remember hits (Donaldson, 1996). Both predictions were supported by the data. In Experiment 2, the context of test items was manipulated to make it more or less similar to learning context. The detection model requires that memory sensitivity be the same for both recognition and remember judgments, regardless of test context. Alternatively, if remember judgments reflect only the retrieval of episodic information from memory, the two measures of memory sensitivity should become more disparate in value as learning and test context are made more similar. Memory sensitivity was generally the same in value for recognition and remember criteria but different across context conditions, thus supporting the detection model. The nature of the memory continuum used in detection theory is also discussed.     

Indirect measures as a signal for evaluative change

Implicit and explicit attitudes can be changed by using evaluative learning procedures. In this contribution we investigated an asymmetric effect of order of administration of indirect and direct measures on the detection of evaluative change: A change in explicit attitudes is more likely detected if they are measured after implicit attitudes, whereas these latter change regardless of the order. This effect was demonstrated in two studies (n=270; n=138) using the self-referencing task whereas it was not found in a third study (n=151) that used a supraliminal sequential evaluative conditioning paradigm. In all studies evaluative change was present only for contingency aware participants. We discuss a potential explanation underlying the order of measure effect entailing that, in some circumstances, an indirect measure is not only a measure but also a signal that can be detected through self-perception processes and further elaborated at the propositional level.     

Problematic effects of aggregation in z ROC analysis and a hierarchical modeling solution

The confidence-rating recognition memory paradigm has been used extensively for testing theories of memory. Current methods for analyzing confidence-rating data require that data be aggregated over participants, items, or both. We show that this aggregation threatens claims of curvature in zROCs and distorts estimates of signal-distribution variance—the very findings that are used to make fine-grained distinctions between competing models. We develop a hierarchical signal detection model that accounts for variability from the sampling of participants and items in addition to variability from the underlying psychological processes. The model provides for accurate signal detection parameter estimates. With accurate estimates, the validity of benchmark findings in recognition memory may be assessed.     

Dissociable perceptual-learning mechanisms revealed by diffusion-model analysis

Performance on perceptual tasks improves with practice. Most theories address only accuracy data and tacitly assume that perceptual learning is a monolithic phenomenon. The present study pioneers the use of response time distributions in perceptual learning research. The 27 observers practiced a visual motion-direction discrimination task with filtered-noise textures for four sessions with feedback. Session 5 tested whether the learning effects transferred to the orthogonal direction. The diffusion model (Ratcliff, Psychological Review, 85, 59–108, 1978) achieved good fits to the individual response time distributions from each session and identified two distinct learning mechanisms with markedly different specificities. A stimulus-specific increase in the drift-rate parameter indicated improved sensory input to the decision process, and a stimulus-general decrease in nondecision time variability suggested improved timing of the decision process onset relative to stimulus onset (which was preceded by a beep). A traditional d’ analysis would miss the latter effect, but the diffusion-model analysis identified it in the response time data.     

A simple computational algorithm of model-based choice preference

A broadly used computational framework posits that two learning systems operate in parallel during the learning of choice preferences—namely, the model-free and model-based reinforcement-learning systems. In this study, we examined another possibility, through which model-free learning is the basic system and model-based information is its modulator. Accordingly, we proposed several modified versions of a temporal-difference learning model to explain the choice-learning process. Using the two-stage decision task developed by Daw, Gershman, Seymour, Dayan, and Dolan (2011), we compared their original computational model, which assumes a parallel learning process, and our proposed models, which assume a sequential learning process. Choice data from 23 participants showed a better fit with the proposed models. More specifically, the proposed eligibility adjustment model, which assumes that the environmental model can weight the degree of the eligibility trace, can explain choices better under both model-free and model-based controls and has a simpler computational algorithm than the original model. In addition, the forgetting learning model and its variation, which assume changes in the values of unchosen actions, substantially improved the fits to the data. Overall, we show that a hybrid computational model best fits the data. The parameters used in this model succeed in capturing individual tendencies with respect to both model use in learning and exploration behavior. This computational model provides novel insights into learning with interacting model-free and model-based components.     

Performance and learning goals for emotion regulation

Goal orientation theory is concerned with performance and learning goals in academic, athletic, and other ability areas. Here we examine performance and learning goals for emotion regulation. We define performance goals for emotion regulation as seeking to prove one’s ability to manage emotions; learning goals for emotion regulation are defined as seeking to improve one’s ability to manage emotions. In two studies, we tested the hypothesis that performance goals for emotion regulation would be associated with greater use of defensive emotion regulation strategies and depressive symptoms. Results from both studies showed that individuals with greater performance goals for emotion regulation reported higher levels of rumination and thought suppression and greater depressive symptoms, while individuals with greater learning goals reported greater use of cognitive reappraisal. The findings suggest that goals for emotion regulation may help explain individual differences in use of defensive versus constructive emotion regulation strategies.     

Testing curvatures of learning functions on individual trial and block average data

Many models offer different explanations of learning processes, some of them predicting equal learning rates between conditions. The simplest method by which to assess this equality is to evaluate the curvature parameter for each condition, followed by a statistical test. However, this approach is highly dependent on the fitting procedure, which may come with built-in biases difficult to identify. Averaging the data per block of training would help reduce the noise present in the trial data, but averaging introduces a severe distortion on the curve, which can no longer be fitted by the original function. In this article, we first demonstrate what is the distortion resulting from block averaging. Theblock average learning function, once known, can be used to extract parameters when the performance is averaged over blocks or sessions. The use of averages eliminates an important part of the noise present in the data and allows good recovery of the learning curve parameters. Equality of curvatures can be tested with a test of linear hypothesis. This method can be performed on trial data or block average data, but it is more powerful with block average data.     

Facial Affect Recognition and Psychopathy: A Signal Detection Theory Perspective

Though emotional processing deficits are often conceptualized as a core feature of psychopathy, the common assessment of these deficits using the percentage correct (or hit rate) on affect recognition tasks may not provide a full or accurate picture of facial affect recognition in psychopathic individuals. Signal detection theory (SDT) provides a more informative statistical approach by providing independent measures of perceptual sensitivity (d’) and willingness to report perceiving a signal or response criterion (c). The current study employed signal detection methods to test the predictions of the integrated emotion systems and hostile attribution bias perspectives, two theoretical perspectives that make specific predictions regarding facial affect recognition. These perspectives were tested in a sample of 280 adult male incarcerated offenders who were assessed for psychopathy using the Psychopathy Checklist-Revised and who completed a novel test of facial affect recognition presenting 324 digital morphs of faces reflecting systematic combinations of pixels from neutral and affective face images (displaying six different types of emotion) as expressed by four different actors. The findings were generally not consistent with either of these perspectives. Psychopathy was negatively associated with d` for anger. Results also indicated an effect of psychopathy on response criterion for fear and effects of psychopathy on response criterion for anger and surprise that were evident only for some actors. The implications of these findings are considered through the lenses of several theoretical perspectives, and theoretical and methodological limitations of the current study are considered.

     

Thurstonian Scaling of Compositional Questionnaire Data

To prevent response bias, personality questionnaires may use comparative response formats. These include forced choice, where respondents choose among a number of items, and quantitative comparisons, where respondents indicate the extent to which items are preferred to each other. The present article extends Thurstonian modeling of binary choice data to “proportion-of-total” (compositional) formats. Following the seminal work of Aitchison, compositional item data are transformed into log ratios, conceptualized as differences of latent item utilities. The mean and covariance structure of the log ratios is modeled using confirmatory factor analysis (CFA), where the item utilities are first-order factors, and personal attributes measured by a questionnaire are second-order factors. A simulation study with two sample sizes, N = 300 and N = 1,000, shows that the method provides very good recovery of true parameters and near-nominal rejection rates. The approach is illustrated with empirical data from N = 317 students, comparing model parameters obtained with compositional and Likert-scale versions of a Big Five measure. The results show that the proposed model successfully captures the latent structures and person scores on the measured traits.     

Discriminability and criterion differences between extraverts and introverts during vigilance

In a signal detection task introverts were better able than extraverts to distinguish between the signal and noise distribution (p < .001) and also set a higher criterion point for their responses (p < .01). The results show that superior vigilance in introverts reported by other investigators is due to two processes.     

The ROC Toolbox: A toolbox for analyzing receiver-operating characteristics derived from confidence ratings

Signal-detection theory, and the analysis of receiver-operating characteristics (ROCs), has played a critical role in the development of theories of episodic memory and perception. The purpose of the current paper is to present the ROC Toolbox. This toolbox is a set of functions written in the Matlab programming language that can be used to fit various common signal detection models to ROC data obtained from confidence rating experiments. The goals for developing the ROC Toolbox were to create a tool (1) that is easy to use and easy for researchers to implement with their own data, (2) that can flexibly define models based on varying study parameters, such as the number of response options (e.g., confidence ratings) and experimental conditions, and (3) that provides optimal routines (e.g., Maximum Likelihood estimation) to obtain parameter estimates and numerous goodness-of-fit measures.The ROC toolbox allows for various different confidence scales and currently includes the models commonly used in recognition memory and perception: (1) the unequal variance signal detection (UVSD) model, (2) the dual process signal detection (DPSD) model, and (3) the mixture signal detection (MSD) model. For each model fit to a given data set the ROC toolbox plots summary information about the best fitting model parameters and various goodness-of-fit measures. Here, we present an overview of the ROC Toolbox, illustrate how it can be used to input and analyse real data, and finish with a brief discussion on features that can be added to the toolbox.     

When increasing distraction helps learning: Distractor number and content interact in their effects on memory

Previous work has demonstrated that increasing the number of distractors in a search array can reduce interference from distractor content during target processing. However, it is unclear how this reduced interference influences learning of target information. Here, we investigated how varying the amount and content of distraction present in a learning environment affects visual search and subsequent memory for target items. In two experiments, we demonstrate that the number and content of competing distractors interact in their influence on target selection and memory. Specifically, while increasing the number of distractors present in a search array made target detection more effortful, it did not impair learning and memory for target content. Instead, when the distractors contained category information that conflicted with the target, increasing the number of distractors from one to three actually benefitted learning and memory. These data suggest that increasing numbers of distractors may reduce interference from conflicting conceptual information during encoding.

     

Assessing sensitivity in a multidimensional space: some problems and a definition of a general d'

This article provides a formal definition for a sensivity measure,d′ _g , between two multivariate stimuli. In recent attempts to assess perceptual representations using qualitative tests on response probabilities, the concept of ad′ between two multidimensional stimuli has played a central role. For example, Kadlec and Townsend (1992a, 1992b) proposed several tests based on multidimensional signal detection theory that allow conclusions concerning the perceptual and/or decisional interactions of stimulus dimensions. One proposition, referred to as thediagonal d′test, relies on specific stimulus subsets of a feature-complete factorial identification task to infer perceptual separability. Also, Ashby and Townsend (1986), in a similar manner, attempted to relate perceptual independence to dimensional orthogonality in Tanner’s (1956) model, which also involvesd′ between two multivariate signals. An analysis of the proposedd′ _g reveals shortcomings in the diagonald′ test and also demonstrates that the assumptions behind equating perceptual independence to dimensional orthogonality are too weak. Thisd′ _g can be related to a common measure of statistical distance, Mahalanobis distance, in the special case of equal covariance matrices.     

Perceptual learning in contrast detection: presence and cost of shifts in response criteria

Contemporary theoretical accounts of perceptual learning typically assume that observers are either unbiased or stably biased across the course of learning. However, standard methods for estimating thresholds, as they are typically used, do not allow this assumption to be tested. We present an approach that allows for this test specific to perceptual learning for contrast detection. We show that reliable decreases in detection thresholds and increases in hit rates are not uniformly accompanied by reliable increases in sensitivity (d′), but are regularly accompanied by reliable liberal shifts in response criteria (c). In addition, we estimate the extent to which sensitivity could have increased in the absence of these liberal shifts. The results pose a challenge to the assumption that perceptual learning has limited or no impact on response criteria.     

Are students' beliefs about knowledge and learning associated with their reported use of learning strategies?

Background. Although considerable research has examined beliefs and learning outcomes (e.g. Schommer, 1990, 1993a, 1993b ; Schommer & Dunnell, 1997 ), little has looked at the relationship between beliefs and the actual learning process. Aims. This research examines the relationship between beliefs about learning and knowledge, and reports of learning strategy‐use relevant for successful text comprehension. Sample. Participants were 81 Norwegian university students who had studied from 1 to 4 years in a range of disciplines. Method. Students' beliefs about knowledge and learning were measured with the Schommer Epistemological Questionnaire (SEQ; Schommer, 1998b ). Learning strategies particularly useful for text‐based learning were measured with the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich, Smith, Garcia, & McKeachie, 1991 ). A correlational analysis between measures and full regression analyses of how beliefs influence strategy selection were performed. Results. Beliefs about how thoroughly knowledge is integrated in networks (simple) and how fixed the ability to learn is from birth (fixed) contributed significantly to reported strategy use: Simple to rehearsal and organizational strategies, fixed to elaboration and critical thinking strategies, and a combination of simple and fixed to strategies relevant to the thoughtful monitoring of learning tasks. Beliefs about how certain knowledge is (certain) and how quickly learning can be expected to occur (quick) were not found to contribute to reported learning‐ strategy use in any significant way. Conclusion. Some, but not all, beliefs about knowledge and learning offer insight into students' reported use of learning strategies relevant for reading course literature.     

Intersensory facilitation in the motor component?

Summary In the bimodal detection task the observer must respond as soon as a signal is presented in either of two modalities (e.g., a tone or a flash). A typical finding is a facilitation of reaction time for redundant signal trials, that is, when both signals are presented simultaneously or with a short delay. Models advanced for this effect imply either statistical facilitation (separate activation) or intersensory facilitation (coactivation). This paper reports a study investigating whether part of the facilitation can be accounted for by coactivation in the motor component. An analysis of the distributions of reaction time differences between left and right hand responses from a double response paradigm gave some evidence in favor of this hypothesis. In particular, our data suggest a u-shaped functional dependence of the amount of facilitation in the motor component on the interstimulus interval.