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.

Do modals identify better models? A comparison of signal detection and probabilistic models of inductive reasoning.

The nature of the relationship between deductive and inductive reasoning is a hotly debated topic. A key question is whether there is a single dimension of evidence underlying both deductive and inductive judgments. Following Rips (2001), Rotello and Heit (2009) and Heit and Rotello (2010) implemented one- and two-dimensional models grounded in signal detection theory to assess predictions for receiver operating characteristic data (ROCs), and concluded in favor of the two-dimensional model. Recently, Lassiter and Goodman (2015) proposed a different type of one-dimensional model, the Probability Threshold Model (PTM), that they concluded offered a good account of data collected over a range of decision modals (e.g., How likely, possible, or necessary is the argument conclusion?). Here, we apply the PTM and the signal detection models to ROCs from 3 large experiments in which participants made judgments about arguments varying in terms of modals introduced by Lassiter and Goodman (2015). Two independent variables that are theoretically important for the study of inductive reasoning, namely premise-conclusion similarity (as utilized in Heit & Rotello, 2010) and number of premises (as utilized in Rotello & Heit, 2009), are also varied in Experiments 1 and 2, respectively. In all cases, the PTM provides the poorest fit both quantitatively and qualitatively; the two-dimensional signal detection model is preferred.

Putting replication in its place.

Direct replication is valuable but should not be elevated over other worthwhile research practices, including conceptual replication and checking of statistical assumptions. As noted by Rotello et al. (2015), replicating studies without checking the statistical assumptions can lead to increased confidence in incorrect conclusions. Finally, successful replications should not be elevated over failed replications, given that both are informative.

Cancer Cell Discrimination Using Host-Guest "Doubled" Arrays.

We report a nanosensor that uses cell lysates to rapidly profile the tumorigenicity of cancer cells. This sensing platform uses host-guest interactions between cucurbit[7]uril and the cationic headgroup of a gold nanoparticle to non-covalently modify the binding of three fluorescent proteins of a multi-channel sensor in situ. This approach doubles the number of output channels to six, providing single-well identification of cell lysates with 100% accuracy. Significantly, this classification could be extended beyond the training set, determining the invasiveness of novel cell lines. The unique fingerprint of these cell lysates required minimal sample quantity (200 ng, ∼1000 cells), making the methodology compatible with microbiopsy technology.

Memory bias for negative emotional words in recognition memory is driven by effects of category membership.

Recognition memory studies often find that emotional items are more likely than neutral items to be labelled as studied. Previous work suggests this bias is driven by increased memory strength/familiarity for emotional items. We explored strength and bias interpretations of this effect with the conjecture that emotional stimuli might seem more familiar because they share features with studied items from the same category. Categorical effects were manipulated in a recognition task by presenting lists with a small, medium or large proportion of emotional words. The liberal memory bias for emotional words was only observed when a medium or large proportion of categorised words were presented in the lists. Similar, though weaker, effects were observed with categorised words that were not emotional (animal names). These results suggest that liberal memory bias for emotional items may be largely driven by effects of category membership.

Paying attention to attention in recognition memory: insights from models and electrophysiology.

Reliance on remembered facts or events requires memory for their sources, that is, the contexts in which those facts or events were embedded. Understanding of source retrieval has been stymied by the fact that uncontrolled fluctuations of attention during encoding can cloud results of key importance to theoretical development. To address this issue, we combined electrophysiology (high-density electroencephalogram, EEG, recordings) with computational modeling of behavioral results. We manipulated subjects' attention to an auditory attribute, whether the source of individual study words was a male or female speaker. Posterior alpha-band (8-14 Hz) power in subjects' EEG increased after a cue to ignore the voice of the person who was about to speak. Receiver-operating-characteristic analysis validated our interpretation of oscillatory dynamics as a marker of attention to source information. With attention under experimental control, computational modeling showed unequivocally that memory for source (male or female speaker) reflected a continuous signal detection process rather than a threshold recollection process.

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).

Conjunction errors and semantic transparency.

Memory conjunction errors occur when aspects of two different events are falsely recognized or recalled as having occurred as parts of the same event. One theoretical account of conjunction errors is rooted in traditional dual-process models of recognition judgments, in which responses are based on an item's familiarity or the retrieval of recollected details associated with the encoding of that item. We manipulated the familiarity of test probes by varying their semantic overlap with studied items, taking advantage of the inherent semantic transparency of compound words. Transparent compounds are those whose component parts (lexemes) are semantically related to the meaning of the entire word. In contrast, opaque compounds' lexemes do not contribute directly to the meaning of the compound. We showed that the familiarity of semantically transparent assembly lures created from their lexemes (study dog and house, test on doghouse) is greater than the familiarity of opaque assembly lures (study back and draw, test on drawback). A response-signal experiment revealed no evidence for the use of a recall-to-reject process for either semantically transparent or opaque lures.

Positive and negative remember judgments and ROCs in the plurals paradigm: evidence for alternative decision strategies.

Using old-new ratings and remember-know judgments we explored the plurals paradigm, in which studied words must be distinguished from plurality-changed lures. The paradigm allowed us to investigate negative remembering--that is, the remembering of a plural-altered study item; capacity for this judgment was found to be poorer than or equivalent to the conventional positive remembering. A response-bias manipulation affected positive but not negative remembering. The ratings were used to construct ROC curves and test the prediction of the most common dual-process theory of recognition memory (Yonelinas, 2001) that the amount of recollection can be independently estimated from ROC curves and from remember judgments. By fitting the individual data with pure signal detection theory (SDT) models and dual-process models that combined SDT and high-threshold components (HTSDT), we identified two types of subjects. For those who were better described by HTSDT, the predicted convergence of remember-know and ROC measures was observed. For those who were better described by SDT, the ROC intercept could not predict the remember rate. The data are consistent with the idea that all subjects rely on the same representation but base their decisions on different partitions of a decision space.

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.

Modeling the effects of argument length and validity on inductive and deductive reasoning.

In an effort to assess models of inductive reasoning and deductive reasoning, the authors, in 3 experiments, examined the effects of argument length and logical validity on evaluation of arguments. In Experiments 1a and 1b, participants were given either induction or deduction instructions for a common set of stimuli. Two distinct effects were observed: Induction judgments were more affected by argument length, and deduction judgments were more affected by validity. In Experiment 2, fluency was manipulated by displaying the materials in a low-contrast font, leading to increased sensitivity to logical validity. Several variants of 1-process and 2-process models of reasoning were assessed against the results. A 1-process model that assumed the same scale of argument strength underlies induction and deduction was not successful. A 2-process model that assumed separate, continuous informational dimensions of apparent deductive validity and associative strength gave the more successful account.

Sources of bias in the Goodman-Kruskal gamma coefficient measure of association: implications for studies of metacognitive processes.

In many cognitive, metacognitive, and perceptual tasks, measurement of performance or prediction accuracy may be influenced by response bias. Signal detection theory provides a means of assessing discrimination accuracy independent of such bias, but its application crucially depends on distributional assumptions. The Goodman-Kruskal gamma coefficient, G, has been proposed as an alternative means of measuring accuracy that is free of distributional assumptions. This measure is widely used with tasks that assess metamemory or metacognition performance. The authors demonstrate that the empirically determined value of G systematically deviates from its actual value under realistic conditions. A distribution-specific variant of G, called G-sub(c), is introduced to show why this bias arises. The findings imply that caution is needed when using G as a measure of accuracy, and alternative measures are recommended.

A new argument for co-active parses during language comprehension.

One perennially important question for theories of sentence comprehension is whether the human sentence processing mechanism is parallel (i.e., it simultaneously represents multiple syntactic analyses of linguistic input) or serial (i.e., it constructs only a single analysis at a time). Despite its centrality, this question has proven difficult to address for both theoretical and methodological reasons (Gibson & Pearlmutter, 2000; Lewis, 2000). In the present study, we reassess this question from a novel perspective. We investigated the well-known ambiguity advantage effect (Traxler, Pickering, & Clifton, 1998) in a speeded acceptability judgment task. We adopted a signal detection theoretic approach to these data, with the goal of determining whether speeded judgment responses were conditioned on one or multiple syntactic analyses. To link these results to incremental parsing models, we developed formal models to quantitatively evaluate how serial and parallel parsing models should impact perceived sentence acceptability in our task. Our results suggest that speeded acceptability judgments are jointly conditioned on multiple parses of the input, a finding that is overall more consistent with parallel parsing models than serial models. Our study thus provides a new, psychophysical argument for coactive parses during language comprehension. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Response bias in "remembering" emotional stimuli: a new perspective on age differences.

Older adults sometimes show a recall advantage for emotionally positive, rather than neutral or negative, stimuli (S. T. Charles, M. Mather, & L. L. Carstensen, 2003). In contrast, younger adults respond "old" and "remember" more often to negative materials in recognition tests. For younger adults, both effects are due to response bias changes rather than to enhanced memory accuracy (S. Dougal & C. M. Rotello, 2007). We presented older and younger adults with emotional and neutral stimuli in a remember-know paradigm. Signal-detection and model-based analyses showed that memory accuracy did not differ for the neutral, negative, and positive stimuli, and that "remember" responses did not reflect the use of recollection. However, both age groups showed large and significant response bias effects of emotion: Younger adults tended to say "old" and "remember" more often in response to negative words than to positive and neutral words, whereas older adults responded "old" and "remember" more often to both positive and negative words than to neutral stimuli.

Analysis of RT distributions in the remember-know paradigm.

Do remembering and knowing differ qualitatively (reflecting distinct underlying processes) or quantitatively (reflecting different levels of strength)? Broadly speaking, models of remember-know judgments based on these alternatives have been tested by examining the proportion of remember and know responses that are made across conditions or levels of confidence. Here, we consider reaction time (RT) data. We replicate Dewhurst and Conway's (1994) observation that old judgments followed by remember responses are faster, on average, than those followed by know decisions, but show that this effect is largely due to differing distributions of remember and know responses across confidence levels. In addition, fits of ex-Gaussian distributions of hit RTs followed by either remember or know judgments yield similar parameter values when confidence level is controlled. Thus, these RT data do not provide strong support for the idea that remembering and knowing depend on different processes.

Evaluating models of remember-know judgments: complexity, mimicry, and discriminability.

Remember-know judgments provide additional information in recognition memory tests, but the nature of this information and the attendant decision process are in dispute. Competing models have proposed that remember judgments reflect a sum of familiarity and recollective information (the one-dimensional model), are based on a difference between these strengths (STREAK), or are purely recollective (the dual-process model). A choice among these accounts is sometimes made by comparing the precision of their fits to data, but this strategy may be muddied by differences in model complexity: Some models that appear to provide good fits may simply be better able to mimic the data produced by other models. To evaluate this possibility, we simulated data with each of the models in each of three popular remember-know paradigms, then fit those data to each of the models. We found that the one-dimensional model is generally less complex than the others, but despite this handicap, it dominates the others as the best-fitting model. For both reasons, the one-dimensional model should be preferred. In addition, we found that some empirical paradigms are ill-suited for distinguishing among models. For example, data collected by soliciting remember/know/new judgments--that is, the trinary task--provide a particularly weak ground for distinguishing models. Additional tables and figures may be downloaded from the Psychonomic Society's Archive of Norms, Stimuli, and Data, at www.psychonomic.org/archive.

Toward a complete decision model of item and source recognition.

In a recognition memory test, subjects may be asked to decide whether a test item is old or new (item recognition) or to decide among alternative sources from which it might have been drawn for study (source recognition). Confidence-rating-based receiver operating characteristic (ROC) curves for these superficially similar tasks are quite different, leading to the inference of correspondingly different decision processes. A complete account of source and item recognition will require a single model that can be fit to the entire data set. We postulated a detection-theoretic decision space whose dimensions, in the style of Banks (2000), are item strength and the relative strengths of the two sources. A model that assumes decision boundaries of constant likelihood ratios, source guessing for unrecognized items, and nonoptimal allocation of attention can account for data from three canonical data sets without assuming any processes specifically devoted to recollection. Observed and predicted ROCs for one of these data sets are given in the article, and ROCs for the other two may be downloaded from the Psychonomic Society's Archive of Norms, Stimuli, and Data, www.psychonomic.org/archive.

"Remembering" emotional words is based on response bias, not recollection.

Recent studies have demonstrated that emotional stimuli result in a higher proportion of recognized items that are "remembered" (e.g., Kensinger & Corkin, 2003; Ochsner, 2000), leading to greater estimates of recollection by the dual-process model (Yonelinas, 1994). This result suggests that recognition judgments to emotional stimuli depend on a recollection process. We challenge this conclusion with receiver operating characteristic (ROC) curve data from two experiments. In both experiments, subjects studied neutral and emotional words. During the recognition test, subjects made old-new confidence ratings as well as remember-know judgments. Four models of remember-know judgments were fit to individual subjects' data: two versions of a one-dimensional signal-detection-based model (Donaldson, 1996; Wixted & Stretch, 2004), the dual-process model (Yonelinas, 1994), and the two-dimensional signal-detection-based model known as STREAK (Rotello, Macmillan, & Reeder, 2004). Consistent with the literature, we found that emotion increases subjective reports of "remembering:" However, our ROC analyses and modeling work reveal that the effect is due to response bias differences rather than sensitivity change or use of a high-threshold recollection process.

Fingerprinting antibiotics with PAE-based fluorescent sensor arrays.

We outline an evolution process for tongue elements composed of poly(p-aryleneethynylene)s (PAE) and detergents, resulting in a chemical tongue (24 elements) that discerns antibiotics. Cross-breeding of this new tongue with tongue elements that consist of simple poly(p-phenyleneethynylene)s (PPE) at different pH-values leads to an enlarged sensor array, composed of 30 elements. This tongue was pruned, employing principal component analysis. We find that a filial tongue featuring three elements from each original array (i.e. a six element tongue) is superior to either of the prior tongues and the composite tongue in the discrimination of structurally different antibiotics. Such a selection process should be general and give an idea how to successfully generate powerful low-selectivity sensor elements and configure them into discriminative chemical tongues.