sdtlu: An R package for the signal detection analysis of eyewitness lineup data.

In a standard eyewitness lineup scenario, a witness observes a culprit commit a crime and is later asked to identify the culprit from a set of faces, the lineup. Signal detection theory (SDT), a powerful modeling framework for analyzing data, has recently become a common way to analyze lineup data. The goal of this paper is to introduce a new R package, sdtlu (Signal Detection Theory - LineUp), that streamlines and automates the SDT analysis of lineup data. sdtlu provides functions to process lineup data, determine the best-fitting SDT parameters, compute model-based performance measures such as area under the curve (AUC) and diagnosticity, use bootstrapping to determine uncertainty intervals around these parameters and measures, and compare parameters across two different data sets. The package incorporates closed-form solutions for both simultaneous and sequential lineups that allow for model-based analyses without Monte Carlo simulation. Show-ups are also supported. The package can estimate the base-rate of lineups that include a guilty suspect when the guilt or innocence of each suspect in the data set is unknown, as in "real-world" lineups. The package can also produce a full set of graphs, including data and model-based ROC curves and the underlying SDT model.

Frontostriatal and Dopamine Markers of Individual Differences in Reinforcement Learning: A Multi-modal Investigation.

Prior studies have shown that dopamine (DA) functioning in frontostriatal circuits supports reinforcement learning (RL), as phasic DA activity in ventral striatum signals unexpected reward and may drive coordinated activity of striatal and orbitofrontal regions that support updating of action plans. However, the nature of DA functioning in RL is complex, in particular regarding the role of DA clearance in RL behavior. Here, in a multi-modal neuroimaging study with healthy adults, we took an individual differences approach to the examination of RL behavior and DA clearance mechanisms in frontostriatal learning networks. We predicted that better RL would be associated with decreased striatal DA transporter (DAT) availability and increased intrinsic functional connectivity among DA-rich frontostriatal regions. In support of these predictions, individual differences in RL behavior were related to DAT binding potential in ventral striatum and resting-state functional connectivity between ventral striatum and orbitofrontal cortex. Critically, DAT binding potential had an indirect effect on reinforcement learning behavior through frontostriatal connectivity, suggesting potential causal relationships across levels of neurocognitive functioning. These data suggest that individual differences in DA clearance and frontostriatal coordination may serve as markers for RL, and suggest directions for research on psychopathologies characterized by altered RL.

Multi-attribute, multi-alternative models of choice: Choice, reaction time, and process tracing.

The first aim of this research is to compare computational models of multi-alternative, multi-attribute choice when attribute values are explicit. The choice predictions of utility (standard random utility & weighted valuation), heuristic (elimination-by-aspects, lexicographic, & maximum attribute value), and dynamic (multi-alternative decision field theory, MDFT, & a version of the multi-attribute linear ballistic accumulator, MLBA) models are contrasted on both preferential and risky choice data. Using both maximum likelihood and cross-validation fit measures on choice data, the utility and dynamic models are preferred over the heuristic models for risky choice, with a slight overall advantage for the MLBA for preferential choice. The response time predictions of these models (except the MDFT) are then tested. Although the MLBA accurately predicts response time distributions, it only weakly accounts for stimulus-level differences. The other models completely fail to account for stimulus-level differences. Process tracing measures, i.e., eye and mouse tracking, were also collected. None of the qualitative predictions of the models are completely supported by that data. These results suggest that the models may not appropriately represent the interaction of attention and preference formation. To overcome this potential shortcoming, the second aim of this research is to test preference-formation assumptions, independently of attention, by developing the models of attentional sampling (MAS) model family which incorporates the empirical gaze patterns into a sequential sampling framework. An MAS variant that includes attribute values, but only updates the currently viewed alternative and does not contrast values across alternatives, performs well in both experiments. Overall, the results support the dynamic models, but point to the need to incorporate a framework that more accurately reflects the relationship between attention and the preference-formation process.

Punishment Learning in U.S. Veterans With Posttraumatic Stress Disorder.

Learning processes have been implicated in the development and course of posttraumatic stress disorder (PTSD); however, little is currently known about punishment-based learning in PTSD. The current study investigated impairments in punishment-based learning in U.S. veterans. We expected that veterans with PTSD would demonstrate greater punishment-based learning compared to a non-PTSD control group. We compared a PTSD group with and without co-occurring depression (n = 27) to a control group (with and without trauma exposure) without PTSD or depression (n = 29). Participants completed a computerized probabilistic punishment-based learning task. Compared to the non-PTSD control group, veterans with PTSD showed significantly greater punishment-based learning. Specifically, there was a significant Block × Group interaction, F(1, 54) = 4.12, p = .047, η(2) = .07. Veterans with PTSD demonstrated greater change in response bias for responding toward a less frequently punished stimulus across blocks. The observed hypersensitivity to punishment in individuals with PTSD may contribute to avoidant responses that are not specific to trauma cues.

Within-subject consistency and between-subject variability in Bayesian reasoning strategies.

It is well known that people tend to perform poorly when asked to determine a posterior probability on the basis of a base rate, true positive rate, and false positive rate. The present experiments assessed the extent to which individual participants nevertheless adopt consistent strategies in these Bayesian reasoning problems, and investigated the nature of these strategies. In two experiments, one laboratory-based and one internet-based, each participant completed 36 problems with factorially manipulated probabilities. Many participants applied consistent strategies involving use of only one of the three probabilities provided in the problem, or additive combination of two of the probabilities. There was, however, substantial variability across participants in which probabilities were taken into account. In the laboratory experiment, participants' eye movements were tracked as they read the problems. There was evidence of a relationship between information use and attention to a source of information. Participants' self-assessments of their performance, however, revealed little confidence that the strategies they applied were actually correct. These results suggest that the hypothesis of base rate neglect actually underestimates people's difficulty with Bayesian reasoning, but also suggest that participants are aware of their ignorance.

Potentiated processing of negative feedback in depression is attenuated by anhedonia.

BACKGROUND: Although cognitive theories of depression have postulated enhanced processing of negatively valenced information, previous EEG studies have shown both increased and reduced sensitivity for negative performance feedback in MDD. To reconcile these paradoxical findings, it has been speculated that sensitivity for negative feedback is potentiated in moderate MDD, but reduced in highly anhedonic subjects. The goal of this study was to test this hypothesis by analyzing the feedback-related negativity (FRN), frontomedial theta power (FMT), and source-localized anterior midcingulate cortex (aMCC) activity after negative feedback. METHODS: Fourteen unmedicated participants with Major Depressive Disorder (MDD) and 15 control participants performed a reinforcement learning task while 128-channel Electroencephalogram (EEG) was recorded. FRN, FMT, and LORETA source-localized aMCC activity after negative and positive feedback were compared between groups. RESULTS: The MDD group showed higher FRN amplitudes and aMCC activation to negative feedback than controls. Moreover, aMCC activation to negative feedback was inversely related to self-reported anhedonia. In contrast, self-reported anxiety correlated with feedback-evoked frontomedial theta (FMT) within the depression group. CONCLUSIONS: The present findings suggest that, among depressed and anxious individuals, enhanced processing of negative feedback occurs relatively early in the information processing stream. These results extend prior work and indicate that although moderate depression is associated with elevated sensitivity for negative feedback, high levels of anhedonia may attenuate this effect.

The Rational Adolescent: Strategic Information Processing during Decision Making Revealed by Eye Tracking.

Adolescence is often viewed as a time of irrational, risky decision-making - despite adolescents' competence in other cognitive domains. In this study, we examined the strategies used by adolescents (N=30) and young adults (N=47) to resolve complex, multi-outcome economic gambles. Compared to adults, adolescents were more likely to make conservative, loss-minimizing choices consistent with economic models. Eye-tracking data showed that prior to decisions, adolescents acquired more information in a more thorough manner; that is, they engaged in a more analytic processing strategy indicative of trade-offs between decision variables. In contrast, young adults' decisions were more consistent with heuristics that simplified the decision problem, at the expense of analytic precision. Collectively, these results demonstrate a counter-intuitive developmental transition in economic decision making: adolescents' decisions are more consistent with rational-choice models, while young adults more readily engage task-appropriate heuristics.

Borderline personality disorder and learning: The influences of emotional state and social versus nonsocial feedback.

BACKGROUND: Borderline personality disorder (BPD) has been associated with decision-making deficits, yet such deficits may be context dependent, particularly emotional state and social context. Reinforcement learning models offer an avenue to pinpoint decision-making impairments. The current study used reinforcement learning models to examine whether feedback type (social vs. nonsocial) or emotional state (neutral vs. negative) influence the association between BPD and decision making. METHOD: Adults (N = 131) with a range of BPD symptoms completed a diagnostic interview and a computerized learning task after neutral and negative emotion inductions. We examined accuracy, learning rate, and exploration. RESULTS: We conducted linear models to examine the association between BPD criteria, feedback type, and emotional state on learning parameters and learning accuracy. We found that the negative emotion condition was associated with greater exploration, particularly for those with elevated BPD features. Furthermore, elevated BPD features were associated with impaired accuracy when aiming to avoid loss. A 3-way interaction between BPD, emotion, and feedback indicated that, for people with higher BPD features, learning performance was further impaired when receiving social feedback in the negative emotion condition. LIMITATIONS: Several limitations warrant mention, including a relatively homogenous sample, possible co-occurring diagnoses, and methodological consideration with the learning task. CONCLUSIONS: The present study underscored the link between BPD and learning impairments. Amplified learning alterations under negative social contexts have important implications for identifying optimal venues to teach new skills (of relevance to treatment) for those with BPD.

Software for the automatic correction of recorded eye fixation locations in reading experiments.

Because the recorded location of an eyetracking fixation is not a perfect measure of the actual fixated location, the recorded fixation locations must be adjusted before analysis. Fixations are typically corrected manually. Making such changes, however, is time-consuming and necessarily involves a subjective component. The goal of this article is to introduce software to automate parts of the correction process. The initial focus is on the correction of vertical locations and the removal of outliers and ambiguous fixations in reading experiments. The basic idea behind the algorithm is to use linear regression to assign each fixation to a text line and to identify outliers. The freely available software is implemented as a function, , written in R.

A complete method for assessing the effectiveness of eyewitness identification procedures: Expected information gain.

We present a method for measuring the efficacy of eyewitness identification procedures by applying fundamental principles of information theory. The resulting measure evaluates the expected information gain (EIG) for an identification attempt, a single value that summarizes an identification procedure's overall potential for reducing uncertainty about guilt or innocence across all possible witness responses. In a series of demonstrations, we show that EIG often disagrees with existing measures (e.g., diagnosticity ratios or area under the receiver operating characteristic) about the relative effectiveness of different identification procedures. Each demonstration is designed to highlight key distinctions between existing measures and EIG. An overarching theme is that EIG provides a complete measure of evidentiary value, in the sense that it factors in all aspects of identification performance. Collectively, these demonstrations show that EIG has substantial potential to inspire new discoveries in eyewitness research and provide a new perspective on policy recommendations for the use of identifications in real investigations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Bayesian statistical analysis of circadian oscillations in fibroblasts.

Precise determination of a noisy biological oscillator's period from limited experimental data can be challenging. The common practice is to calculate a single number (a point estimate) for the period of a particular time course. Uncertainty is inherent in any statistical estimator applied to noisy data, so our confidence in such point estimates depends on the quality and quantity of the data. Ideally, a period estimation method should both produce an accurate point estimate of the period and measure the uncertainty in that point estimate. A variety of period estimation methods are known, but few assess the uncertainty of the estimates, and a measure of uncertainty is rarely reported in the experimental literature. We compare the accuracy of point estimates using six common methods, only one of which can also produce uncertainty measures. We then illustrate the advantages of a new Bayesian method for estimating period, which outperforms the other six methods in accuracy of point estimates for simulated data and also provides a measure of uncertainty. We apply this method to analyze circadian oscillations of gene expression in individual mouse fibroblast cells and compute the number of cells and sampling duration required to reduce the uncertainty in period estimates to a desired level. This analysis indicates that, due to the stochastic variability of noisy intracellular oscillators, achieving a narrow margin of error can require an impractically large number of cells. In addition, we use a hierarchical model to determine the distribution of intrinsic cell periods, thereby separating the variability due to stochastic gene expression within each cell from the variability in period across the population of cells.

A 'compensatory selection' effect with standardized tests: Lack of correlation between test scores and success is evidence that test scores are predictive of success.

We introduce the statistical concept of 'compensatory selection', which arises when selecting a subset of applicants based on multiple predictors, such as when standardized test scores are used in combination with other predictors required in a school application (e.g., previous grades, references letters, and personal statements). Post-hoc analyses often fail to find a positive correlation between test scores and subsequent success, and this failure is sometimes taken as evidence against the predictive validity of the standardized test. The present analysis reveals that the failure to find a negative correlation indicates that the standardized test is in fact a valid predictor of success. This is due to compensation between predictors during selection: Some students are admitted despite a low test score because their application is exceptional in other respects, while other students are admitted primarily based on a high test score despite weakness in the rest of their application. This compensatory selection process introduces a negative correlation between test scores and other predictors among those admitted (a 'collider bias' or 'Berkson's paradox' effect). If test scores are valid predictors of success, this negative correlation between the predictors counteracts the positive correlation between test scores and success that would have been observed if all applicants were admitted. If test scores are not predictive of success, but were nevertheless used in a compensatory selection process, there would be a spurious negative correlation between test scores and success (i.e., an admitted student with a weak application except for a high test score would be unlikely to succeed). The selection effect that is described here is fundamentally different from the well-known 'restricted range' problem and can powerfully alter results even in situations that accept most applicants.

Learning from gain and loss: Links to suicide risk.

Despite preliminary evidence that people with suicide attempt histories demonstrate deficits in processing feedback, no studies have examined the interrelations of learning from feedback and emotional state on suicide risk. This study examined the influence of suicide risk and negative emotions on learning accuracy and rates among individuals with a range of borderline personality features (N = 145). Participants completed a reinforcement learning task after neutral and negative emotion inductions. Results revealed interactions between suicide risk and emotion condition, with elevated risk linked to greater increases in loss learning rate (training phase models) and gain learning rate (test phase models) post-negative emotion induction. Emotion-dependent fluctuations in learning performance may be markers of decision-making that are associated with greater suicide risk. This line of work has the potential to identify the contexts that confer greater risk for suicidal behaviors.

Framing context effects with reference points.

Research on reference points highlights how alternatives outside the choice set can alter the perceived value of available alternatives, arguably framing the choice scenario. The present work utilizes reference points to study the effects of framing in preferential choice, using the similarity and attraction context effects as performance measures. We specifically test the predictions of Multialternative Decision by Sampling (MDbS; Noguchi & Stewart, 2018), a recent preferential choice model that can account for both reference points and context effects. In Experiment 1, consistent with predictions by MDbS, we find a standard similarity effect when no reference point is given that increases when both dimensions are framed negatively and decreases when both dimensions are framed positively. Contrary to predictions by MDbS, when the two dimensions are framed as tradeoffs, participants prefer whichever alternative performs best in the negatively framed dimension. Performance of MDbS was improved by the addition of a frame-based global attention allocation mechanism. Experiment 2 extends these results to a "by-dimension" presentation format in an attempt to bring participant behavior in line with MDbS assumptions. The empirical and modeling results replicated those of Experiment 1. Experiment 3 used the attraction effect to test the effects of framing when the best-performing alternative on each dimension was identical across target conditions, therefore reducing the potential effects of a global attention allocation mechanism. The effects of framing were indeed greatly reduced, and the performance of MDbS was markedly improved. The results extend framing to the context effects literature, provide new benchmarks for models and theories of context effects, and point to the need for a global attention mechanism.

Estimating the proportion of guilty suspects and posterior probability of guilt in lineups using signal-detection models.

BACKGROUND: The majority of eyewitness lineup studies are laboratory-based. How well the conclusions of these studies, including the relationship between confidence and accuracy, generalize to real-world police lineups is an open question. Signal detection theory (SDT) has emerged as a powerful framework for analyzing lineups that allows comparison of witnesses' memory accuracy under different types of identification procedures. Because the guilt or innocence of a real-world suspect is generally not known, however, it is further unknown precisely how the identification of a suspect should change our belief in their guilt. The probability of guilt after the suspect has been identified, the posterior probability of guilt (PPG), can only be meaningfully estimated if we know the proportion of lineups that include a guilty suspect, P(guilty). Recent work used SDT to estimate P(guilty) on a single empirical data set that shared an important property with real-world data; that is, no information about the guilt or innocence of the suspects was provided. Here we test the ability of the SDT model to recover P(guilty) on a wide range of pre-existing empirical data from more than 10,000 identification decisions. We then use simulations of the SDT model to determine the conditions under which the model succeeds and, where applicable, why it fails. RESULTS: For both empirical and simulated studies, the model was able to accurately estimate P(guilty) when the lineups were fair (the guilty and innocent suspects did not stand out) and identifications of both suspects and fillers occurred with a range of confidence levels. Simulations showed that the model can accurately recover P(guilty) given data that matches the model assumptions. The model failed to accurately estimate P(guilty) under conditions that violated its assumptions; for example, when the effective size of the lineup was reduced, either because the fillers were selected to be poor matches to the suspect or because the innocent suspect was more familiar than the guilty suspect. The model also underestimated P(guilty) when a weapon was shown. CONCLUSIONS: Depending on lineup quality, estimation of P(guilty) and, relatedly, PPG, from the SDT model can range from poor to excellent. These results highlight the need to carefully consider how the similarity relations between fillers and suspects influence identifications.

Decision-tree models of categorization response times, choice proportions, and typicality judgments.

The authors present 3 decision-tree models of categorization adapted from T. Trabasso, H. Rollins, and E. Shaughnessy (1971) and use them to provide a quantitative account of categorization response times, choice proportions, and typicality judgments at the individual-participant level. In Experiment 1, the decision-tree models were fit to reaction time and choice proportion data from a study reported by A. L. Cohen and R. M. Nosofsky (2003). In Experiment 2, participants were also asked to provide typicality ratings for each stimulus. A process-tracing method called the "4-questions game" (Y. Sayeki, 1969) was used in a posttest phase to identify a decision tree for each participant. In both experiments, the decision-tree models explained a very high proportion of variance in the data and compared favorably with 2 leading exemplar models.

Using graphical models to infer multiple visual classification features.

This paper describes a new model for human visual classification that enables the recovery of image features that explain performance on different visual classification tasks. Unlike some common methods, this algorithm does not explain performance with a single linear classifier operating on raw image pixels. Instead, it models classification as the result of combining the output of multiple feature detectors. This approach extracts more information about human visual classification than has been previously possible with other methods and provides a foundation for further exploration.

Failure to detect function word repetitions and omissions in reading: Are eye movements to blame?

We tested whether failure to notice repetitions of function words during reading (e.g., Amanda jumped off the the swing and landed on her feet.) is due to the eyes' tendency to skip one of the instances of the word. Eye movements were recorded during reading of sentences with repetitions of the word the or repetitions of a noun, after which readers were asked whether an error was present. A repeated the was detected on 46% of trials overall. On trials on which both instances of the were fixated, detection was still only 66%. A repeated noun was detected on 90% of trials, with no significant effect of eye movement patterns. Detecting an omitted the also proved difficult, with eye movement patterns having only a small effect. Readers frequently overlook function word errors even when their eye movements provide maximal opportunity for noticing such errors, but they notice content word repetitions regardless of eye movement patterns. We propose that readers overlook function word errors because they attribute the apparent error to noise in the eye movement control system.

The comparison process as an account of variation in the attraction, compromise, and similarity effects.

Context effects are changes in preference that occur when alternatives are added to a choice set. Models that account for context effects typically assume a within-dimension comparison process; however, the presentation format of a choice set can influence comparison strategies. The present study jointly tests the influence of presentation format on the attraction, compromise, and similarity effects in a within-subjects design. Participants completed a series of choices designed to elicit each of the three context effects, with either a by-alternative or by-dimension format. Whereas the by-alternative format elicited a standard similarity effect, but null attraction and reverse compromise effects, the by-dimension format elicited standard attraction and compromise effects, but a reverse similarity effect. These novel results are supported by a re-analysis of the eye-tracking data collected by Noguchi and Stewart (Cognition, 132(1), 44-56, 2014) and demonstrate that flexibility in the comparison process should be incorporated into theories of preferential choice.

Model evaluation using grouped or individual data.

Analyzing the data of individuals has several advantages over analyzing the data combined across the individuals (the latter we term group analysis): Grouping can distort the form of data, and different individuals might perform the task using different processes and parameters. These factors notwithstanding, we demonstrate conditions in which group analysis outperforms individual analysis. Such conditions include those in which there are relatively few trials per subject per condition, a situation that sometimes introduces distortions and biases when models are fit and parameters are estimated. We employed a simulation technique in which data were generated from each of two known models, each with parameter variation across simulated individuals. We examined how well the generating model and its competitor each fared in fitting (both sets of) the data, using both individual and group analysis. We examined the accuracy of model selection (the probability that the correct model would be selected by the analysis method). Trials per condition and individuals per experiment were varied systematically. Three pairs of cognitive models were compared: exponential versus power models of forgetting, generalized context versus prototype models of categorization, and the fuzzy logical model of perception versus the linear integration model of information integration. We show that there are situations in which small numbers of trials per condition cause group analysis to outperform individual analysis. Additional tables and figures may be downloaded from the Psychonomic Society Archive of Norms, Stimuli, and Data, www.psychonomic.org/archive.