The two-high threshold (2-HT) eyewitness identification model serves as a new measurement tool to measure the latent cognitive processes underlying eyewitness identification performance. By simultaneously taking into account correct culprit identifications, false innocent-suspect identifications, false filler identifications in culprit-present and culprit-absent lineups as well as correct and false lineup rejections, the model capitalizes on the full range of data categories that are observed when measuring eyewitness identification performance. Thereby, the model is able to shed light on detection-based and non-detection-based processes underlying eyewitness identification performance. Specifically, the model incorporates parameters for the detection of culprit presence and absence, biased selection of the suspect and guessing-based selection among the lineup members. Here, we provide evidence of the validity of each of the four model parameters by applying the model to eight published data sets. The data sets come from studies with experimental manipulations that target one of the underlying processes specified by the model. Manipulations of encoding difficulty, lineup fairness and pre-lineup instructions were sensitively reflected in the parameters reflecting culprit-presence detection, biased selection and guessing-based selection, respectively. Manipulations designed to facilitate the rejection of culprit-absent lineups affected the parameter for culprit-absence detection. The reanalyses of published results thus suggest that the parameters sensitively reflect the manipulations of the processes they were designed to measure, providing support of the validity of the 2-HT eyewitness identification model. When the lineup includes the culprit (culprit-present lineup), the witness may correctly identify the culprit (correct culprit identification), but there is also the risk of an incorrect response in that the witness may identify a filler  or reject the lineup . When the suspect is innocent (culprit-absent lineup), the witness may either correctly reject the lineup (correct lineup rejection) or incorrectly identify the innocent suspect  or a filler . An important goal of eyewitness identification research is to understand the latent cognitive processes underlying these decisions.The lineup procedure is an essential tool for assessing eyewitness identifications. In a lineup, a suspect is presented among fillers (known distractors that are not suspected of having committed the crime) to an eyewitness to test the hypothesis that the suspect is the culprit against the hypothesis that the suspect is innocent. Although eyewitness identifications can be a powerful and indispensable form of evidence, the problem of misidentifications has been well documented through DNA-based exonerations2. This demonstrates that understanding eyewitness identification performance in lineup procedures is complex because the selection of a suspect may not only be caused by the detection of the culprit but also by the biased selection of a suspect who stands out from the fillers, by guessing-based selection among the lineup members or by any combination of these processes. To disentangle the contributions of these different processes to eyewitness identification performance, it is useful to apply a measurement model to the eyewitness identification data. Here, we introduce a novel two-high threshold (2-HT) eyewitness identification model for measuring the processes involved in eyewitness identification decisions. The aim of the present article is to test the validity of this model by reanalyzing published data. In an accompanying validation study3, we applied the model to novel data that we had collected specifically for the purpose of testing the model’s validity. We consider these two approaches to model validation as complementary: A model should not only be able to account for novel data generated with the model in mind but also for published data of other research groups. To anticipate, both approaches support the validity of the model by showing that the model’s parameters sensitively respond to manipulations of the processes they were designed to measure, suggesting that the model parameters sensitively reflect these processes. Before we describe the 2-HT eyewitness identification model in more detail, we first provide a brief overview of the important discussion about the strengths and weaknesses of different methods for analyzing lineup data that has informed the development of the new measurement tool.When two lineup procedures are compared, the simplest case is that one procedure is clearly superior to the other by yielding both a higher rate of correct culprit identifications in culprit-present lineups and a higher rate of correct lineup rejections of culprit-absent lineups. From such a data pattern, one may conclude that the superior lineup procedure provides better conditions for the process of detecting the culprit’s face in the lineup. However, an increase in the correct culprit identifications is often accompanied by an increase in the false identifications of innocent suspectsdiagnosticity ratio , which is defined as the ratio of the proportion of correct culprit identifications to the proportion of false innocent-suspect identifications4. Larger ratios indicate a higher likelihood that an identified suspect is guilty5, which may be interpreted to suggest that a lineup procedure that consistently generates a higher diagnosticity ratio should be preferred over one that does not. However, it has been argued that this measure is affected not only by the ability to discriminate culprits from innocent suspects but also by the witness’s response bias, which reflects the overall conservative or liberal tendency to choose someone from the lineup10. More specifically, the diagnosticity ratio has been shown to increase as a function of an increasingly conservative response bias11. This discussion has resulted in the application of signal detection theory12 to eyewitness identifications. Specifically, Mickes et al.13 introduced Receiver Operating Characteristic (ROC) analyses to the field of eyewitness identification research.For some decades, eyewitness identification performance has been measured by using the 13. In the context of lineups, the term discriminability has been interpreted to refer to a witness’s ability to distinguish culprits from innocent suspects14. An ROC curve is created by plotting the hit rate  against the false alarm rate  at different levels of liberal or conservative responding, the latter of which is typically inferred from the witnesses’ post-decision confidence judgements. It has been argued that the lineup procedure associated with the higher ROC curve—indicating higher hit rates and lower false alarm rates—is associated with superior discrimination between the culprit and an innocent suspect and should therefore be preferred15.ROC analyses have the advantage of yielding a measure of discriminability which is not confounded by response bias12). Lineups differ from these simple detection tasks in that they include not only a culprit or an innocent suspect but also fillers. Therefore, in each of the two types of lineups , witnesses can make one of three responses , resulting in the 2 × 3 data structure displayed in the lower half of Table 116. In ROC analyses, filler identifications are treated like lineup rejections and the two data categories are combined to transform the 2 × 3 data structure of lineups into a 2 × 2 data structure. This is justified by noting that filler identifications and lineup rejections have the same legal consequences for the suspect. Irrespective of whether the witness identifies a filler or rejects the lineup, the suspect is not further incriminated by the eyewitness procedure. Therefore, it has been argued that, for the purpose of deciding which of two lineup procedures is superior, it is sufficient to analyze the rate of correct culprit identifications and the rate of false innocent-suspect identifications13.ROC analyses have been developed to account for simple detection tasks with a 2  × 2  data structure  models. Models from this class of formal measurement models for categorical data have been successfully applied to different areas within psychology19 such as memory23 or decision making26. Wagenaar and Boer27 have already successfully introduced MPT models to the area of eyewitness memory research in a study in which they investigated the processes underlying the misinformation effect. MPT models are based on the assumption that observed response frequencies in a finite set of response categories can arise from combinations of latent processes that can be depicted in a tree-like structure28. Each cognitive process is represented by a model parameter that serves to measure the probability with which the process occurs29. Parameter estimation is achieved by employing the expectation-maximization algorithm proposed by Hu and Batchelder30. The algorithm aims to determine a set of parameters that minimize the distance between the observed response frequencies and the response frequencies predicted by the model, as measured by the log-likelihood ratio goodness-of-fit statistic G232. If the deviation between the frequencies predicted by the model and the observed response frequencies is not statistically significant, it can be assumed that the model fits the data29. A model is called identifiable when a unique set of parameter estimates provides an optimal fit for a given set of observed response frequencies29. Under these circumstances, MPT models can be used to test hypotheses directly at the level of the model parameters. Hypotheses are tested by imposing theoretically motivated restrictions on model parameters. If the restricted model provides a significantly worse fit to the data than the model without the restriction (measured by the ∆G2 difference statistic), then the assumption implied by the restriction has to be rejected29. Thereby, this method provides insights into the latent processes underlying observable behavior, which is a major advantage of MPT models given that psychological theories often involve hypotheses about cognitive processes. Parameter estimation and statistical tests can be performed with freely available computer programs33.Here, we introduce a new measurement model for eyewitness identification performance that capitalizes on the full range of the data categories observed in typical lineup procedures within 35]. The model comprises two trees, one for each of the two possible types of lineups presented to the witnesses (culprit-present and culprit-absent lineups). If the culprit is present , which results in the correct identification of the culprit. The detection state is based on the witnesses’ memory of the culprit. If witnesses fail to detect the culprit, which occurs with the complementary probability 1 − dP, then two different types of non-detection-based processes may still lead to correct culprit identifications. First, the lineup may be unfair in that the suspect stands out from the fillers so that it can be inferred who the suspect is without relying on memory. One telling example is the case of Marvin Lamont Anderson who served fifteen years in prison for a rape that he did not commit. The selection of Anderson’s face in the police lineup was most likely due to the fact that the lineup was unfair: The witness was shown a color identification card of Anderson along with six black-and-white mug shots that served as fillers36. The false identification of Anderson as the culprit was not based on a culprit-detection process but most likely due to the biased selection of the color photo of Anderson that stood out from the black-and-white photos of the fillers. The process of biased selection of the suspect in unfair lineups is reflected in parameter b. In culprit-present lineups, the biased selection of the suspect yields a correct identification of the culprit. Second, if a biased selection of the suspect does not occur (with probability 1 − b), then witnesses may still select one of the lineup members based on guessing with probability g (for guessing-based selection). Guessing-based selection leads to the identification of the culprit with a probability that is equal to 1 ÷ lineup size. For instance, in a lineup with six persons, the probability that guessing-based selection will lead to the identification of the culprit is 1/6. With the complementary probability , the witnesses will identify a filler. Alternatively (with probability 1 − g), the witnesses may abstain from selecting a lineup member based on guessing, which leads them to incorrectly reject the culprit-present lineup.The 2-HT eyewitness identification model is illustrated in Fig. dA (for detection of the absence of the culprit), which results in the correct rejection of the lineup. This may occur, for instance, if all persons in the lineup have a birthmark and a witness then remembers that the culprit did not have a birthmark. When the absence of the culprit is not detected (with probability 1 − dA), the witnesses rely on the same non-detection-based biased and guessing-based processes as in culprit-present lineups. With probability b, biased selection may occur if the innocent suspect stands out from the fillers in an unfair lineup. Biased selection leads to the false identification of the innocent suspect. If no biased selection occurs (with probability 1 − b), the witnesses may select one of the lineup members based on guessing with probability g. The probability with which guessing-based selection leads to the identification of the innocent suspect or one of the fillers depends on the lineup size . If the witnesses abstain from selecting a lineup member based on guessing (with probability 1 − g), the culprit-absent lineup is correctly rejected.The lower tree illustrates the cognitive processes in response to lineups in which the culprit is absent. Witnesses may correctly detect that the culprit is absent and that no other person in the lineup can possibly be the culprit with probability 39]. Parameter dP indicates the probability of crossing the threshold for culprit-presence detection, whereas dA indicates the probability of crossing the threshold for detecting the absence of the culprit. To make 2-HT models in standard recognition tests identifiable, it is often assumed that the probability of detecting the presence of a signal is equal to the probability of detecting the absence of the signal . Here, we have the advantage that both detection parameters (dP and dA) can vary freely due to the increased number of independent observable data categories in lineups compared to simple old-new recognition tests [cf.40]. The 2-HT eyewitness identification model can be transformed into a one-high threshold model by setting parameter dA to zero41. The fact that dA does not have to be set to a certain value to obtain model identifiability implies that it is possible to empirically test whether witnesses are able to detect the absence of a culprit or not. To anticipate, the results of novel validation studies3 and the present reanalyses of existing data consistently indicate that witnesses do not always spontaneously succeed in detecting the absence of the culprit but that there are circumstances in which witnesses can in fact detect the absence of the culprit . When no further restrictions are employed, then the 2-HT eyewitness identification model has zero degrees of freedom, implying that there are as many free parameters as there are independent data categories to fit.The 2-HT eyewitness identification model belongs to the class of 2-HT recognition models  and staged-crime studies44 showing better detection performance following longer exposure. Here, we tested whether these effects of exposure duration are sensitively reflected in parameter dP by reanalyzing the data of Memon et al.45 who had manipulated culprit-exposure duration at encoding. We also applied the 2-HT eyewitness identification model to the data of Smith  who had manipulated the viewing conditions at encoding. When the viewing conditions at encoding are poor, the culprit’s face should provide a weaker match to memory, which should hinder culprit-presence detection.Our first aim was to validate parameter 45, participants viewed a simulated staged-crime video in which they saw the culprit’s face for either a long or a short duration. In line with previous research44, Memon et al. found that participants were better able to identify the culprit under the long than under the short exposure duration. The model-based reanalysis of these data should show that the manipulation of exposure duration affects parameter dP. More precisely, parameter dP should be significantly higher for the long compared to the short exposure condition.In the study of Memon et al.45 randomly assigned younger  and older  participants to one of the four conditions resulting from a 2 (exposure duration: short vs. long) × 2 (culprit presence: present vs. absent) between-subjects design. Participants saw a staged-crime video depicting a robbery. The video was long (100 s) or short (67 s). The long video involved a clear exposure to the face of the main culprit for 45 s. The short video provided a full-face and profile-view exposure to the face of the main culprit for only 12 s. After having completed several filler questionnaires, participants were given standard two-sided pre-lineup instructions. Specifically, participants were informed that the culprit may or may not be in the lineup. Participants were then asked to identify one of the lineup members as the culprit or to indicate that the culprit was absent. Half of the participants saw a culprit-present black-and-white photo lineup consisting of six faces in a 3 × 2 array. For the remaining half of the participants, the culprit was replaced by another filler to construct a culprit-absent lineup. All fillers matched the culprit’s general description. Both the culprit and the filler that replaced the culprit were positioned in the top right-hand corner of the array .Memon et al.multiTree32. The α level was set to 0.05. The observed response frequencies  to estimate the number of participants in each condition. Following standard practice [cf.48], the number of innocent-suspect identifications was estimated by dividing the total number of false identifications in culprit-absent lineups by the number of lineup members . Likewise, the number of filler identifications in culprit-absent lineups was estimated by subtracting the number of innocent-suspect identifications from the total number of false identifications. To test how the manipulation of exposure duration affects parameter dP, four sets of the processing trees displayed in Fig. dA and b were each set to be equal among the four conditions because there was no reason to assume that these parameters should differ as a function of the conditions. To simplify the output, we also aggregated the data for younger and older participants by imposing the equality restrictions that culprit-presence detection (dP) and guessing-based selection (g) did not differ between the two age groups in each exposure condition. These assumptions were based on the results of Memon et al. who had found no significant effect of age on lineup performance. The model incorporating these restrictions was used as a comparison standard for the subsequent nested likelihood-ratio tests, G2(10) = 8.19, p = 0.610. The estimates of parameter dP as a function of exposure duration are shown in the left panel of Fig. b, g and dA.For all analyses reported in this article, parameter estimates were obtained and likelihood-ratio tests were performed using differences between the models used as comparison standards and the more restricted, nested models that are central to the model validation are affected by this issue.As explicated above, the 2-HT eyewitness identification model is based on the full 2 × 3 structure of lineup data. To use the model for the present reanalysis and that of all other data sets without a designated innocent suspect, it is thus necessary to make the assumption that the culprit-absent lineups contained an innocent suspect and that the lineups were fair, which implies that the innocent suspects were selected with the same probability as each of the fillers. Given that the proportion of innocent-suspect identifications is determined from the proportion of filler identifications, one could argue that, technically speaking, experiments without a designated innocent suspect have one fewer independent data category than experiments with a designated innocent suspect. As noted by a reviewer during the review process, this would result in a loss of degrees of freedom in the model used as a comparison standard. However, the same applies to all nested models generated by parameter restrictions (see below). Therefore, none of the subsequent likelihood-ratio tests, all of which pertain to the dP was clearly affected by exposure duration. Participants in the long-exposure group were significantly more likely to detect the culprit than those in the short-exposure group, ∆G2(1) = 34.33, p < 0.001. Furthermore, the probability of selecting a lineup member based on guessing (parameter g) was significantly higher when exposure duration was short than when it was long, ∆G2(1) = 10.24, p = 0.001, likely reflecting the well-established phenomenon of compensatory guessing, that is, the phenomenon that participants tend to rely more on guessing when memory is poor54.The culprit-presence detection parameter 46, Experiment 1] manipulated the viewing conditions at encoding by presenting participants with either a clear or a degraded version of a simulated-crime video. In line with the results of Smith et al. , degraded viewing conditions decreased culprit detection performance. If the 2-HT eyewitness identification model is valid, the manipulation of viewing conditions should affect the culprit-presence detection parameter dP. Specifically, parameter dP should be significantly higher for the clear compared to the degraded viewing conditions.Smith  randomly assigned 615 participants to one of the four experimental conditions resulting from a 2 (viewing conditions: clear vs. degraded) × 2 (culprit presence: present vs. absent) between-subjects design. Half of the participants viewed the video of the culprit in high-resolution so that the culprit’s facial features were clearly visible. The remaining participants saw a low-resolution, overexposed version of the same video in which it was extremely difficult to perceive facial details. In both versions of the 90-s video, a scene at an airport was shown in which the culprit switched his suitcase with that of another person. After having completed a 4-min anagram task, participants were given standard two-sided pre-lineup instructions and were then presented with a simultaneous lineup. The data from the culprit-absent lineups were crucial for testing Smith’s hypothesis. Therefore, approximately two-thirds of the participants were presented with the culprit-absent lineup containing six fillers. About one-third of the participants saw a six-person culprit-present lineup that included the culprit together with five of the six fillers who were randomly selected. All fillers matched the culprit’s general description. The lineup members were presented in random order .Smith . For the model-based reanalysis, we needed two sets of the processing trees depicted in Fig. dA and b were each set to be equal between both conditions because there was no reason to assume that these parameters should differ as a function of the viewing conditions. The model incorporating these restrictions was used as a comparison standard for the subsequent nested likelihood-ratio tests, G2(2) = 1.81, p = 0.405. The estimates of culprit-presence detection parameter dP as a function of viewing conditions are shown in the right panel of Fig. b, g and dA.The observed response frequencies  = 74.73, p < 0.001. In addition, parameter g was significantly higher when viewing conditions were poor, ∆G2(1) = 32.02, p < 0.001, which can be attributed to compensatory guessing54.The model-based reanalysis confirmed that parameter 46, we first focused on parameter dP of the 2-HT eyewitness identification model. Parameter dP reflects the detection of the presence of the culprit in a lineup. Both exposure duration and viewing conditions can be predicted clearly and unambiguously to affect the ability to detect the culprit in culprit-present lineups. Parameter dP sensitively reflected the two different manipulations of culprit-presence detection. The results of the first reanalysis45 confirmed that parameter dP was significantly higher under long than short exposure to the culprit at encoding. The results of the second reanalysis  demonstrated that the culprit-presence detection parameter dP was significantly higher when viewing conditions at encoding were good than when they were poor. It can thus be concluded that parameter dP sensitively reflects manipulations of culprit-presence detection in the predicted directions, which is consistent with the theoretical interpretation of the results in the original studies46. This suggests that the validation of parameter dP was successful.In reanalyzing data obtained from the literatureg was also affected by the manipulations of encoding difficulty. Participants were more likely to select one of the lineup members based on guessing when culprit-presence detection was poor. Ideally, the procedure of model validation entails experimental manipulations that only influence the target parameter (dP in this instance) in the expected direction without affecting other parameters. However, it is often not possible to find strong manipulations that influence only a single cognitive process without side effects on other processes29. In such a case it is ideal if there is a plausible explanation for these side effects. In the present case, the side effect on the guessing-based selection parameter g can be explained by compensatory guessing, which refers to the well-established phenomenon that participants rely more on guessing when memory is poor54. The effect of compensatory guessing may thus be linked to the fact that the differences in memory between the conditions was rather strong in the reanalyzed studies of Memon et al.45 and of Smith46.In both reanalyses, the guessing-based selection parameter b, which serves to measure the probability of biased selection of the suspect from a lineup if the suspect stands out from the other lineup members. Thus, the estimate of b should primarily be determined by the degree of lineup fairness. Specifically, parameter b should be higher for unfair lineups than for fair lineups. If a lineup is perfectly fair, there is no way to tell the suspect apart from the other lineup members without memory of the culprit. Under these conditions, biased selection of the suspect should not be possible and parameter b should be indistinguishable from zero. Note that this is what happened in the reanalyses of Memon et al.45 and Smith46 in which we followed the standard procedure of dividing the total number of false identifications in culprit-absent lineups by the number of lineup members [cf.48] to obtain an estimate of the innocent-suspect identifications in culprit-absent lineups. However, it is well known that real-world lineups are often not perfectly fair and there is strong evidence of a biased selection of suspects in unfair lineups . If the 2-HT eyewitness identification model is valid, then the biased selection of suspects in unfair lineups should be sensitively reflected in parameter b. This was tested by reanalyzing data from two large studies 59 in which lineup fairness was manipulated. Specifically, Wetmore et al.59 observed an effect on lineup fairness when manipulating the similarity between the suspect and the fillers. The study of Colloff et al.48 provides a complementary approach to manipulating the biased selection of suspects by eliminating, via digital photo manipulation, distinctive features that caused the suspect to stand out from the fillers.Our second objective was to investigate the validity of parameter 59 manipulated the degree of similarity between the lineup fillers and the suspect. Good fillers (high similarity to the suspect) were used to create a fair lineup, whereas bad fillers (low similarity to the suspect) were used to create an unfair lineup. Wetmore et al. found significantly more suspect identifications when the lineup was unfair than when it was fair. The theoretical interpretation of this finding was that the higher rate of suspect identifications in unfair lineups was due to biased suspect selection. Therefore, Wetmore et al.’s lineup fairness manipulation should be reflected in the 2-HT eyewitness identification model’s parameter b. Specifically, parameter b should be significantly larger for the unfair lineup than for the fair lineup if the interpretation of parameter b in terms of biased suspect selection is valid.Wetmore et al.59 randomly assigned 1584 participants to one of the 18 conditions of a 3 (identification task: showup vs. fair lineup vs. unfair lineup) × 3 (suspect: guilty vs. Innocent 1 vs. Innocent 2) × 2 (delay: immediate vs. delayed) between-subjects design. Participants watched a 105-s video of a man stealing a woman’s purse. Participants completed a distractor task that consisted of solving 20 anagrams either immediately or 48 h after the simulated crime had been shown. Standard two-sided pre-lineup instructions were given before the participants proceeded to the identification task. Participants in the lineup condition viewed a six-person simultaneous lineup composed of two rows of three photos. The lineups included the culprit (culprit-present lineup) or an innocent suspect (culprit-absent lineup) who matched the culprit’s description. Wetmore et al. distinguished between two innocent suspects that had been taken from an earlier study by Gronlund et al.60. The innocent suspects were intended to be equally good matches to the culprit. For simplicity, we did not distinguish between the two innocent suspects. The participants saw either a fair or an unfair lineup. To create unfair lineups in which the suspect stood out, five poor fillers were selected who, apart from being white men, shared only one characteristic with the culprit. In contrast, fair lineups contained good fillers who, apart from being white men, shared five characteristics with the culprit .Wetmore et al.59 in their Table 2. For simplicity, we limited our reanalysis to the lineup data to focus on the most relevant comparison (fair vs. unfair) for testing the validity of parameter b. Thus, four sets of the trees shown in Fig. dA parameters were set to be equal among the four conditions because there was no reason to assume that the detection of culprit absence should differ as a function of the conditions. Given that Wetmore et al. had found no effect of delay on identification performance, we reduced the model complexity by assuming that biased suspect selection (b), culprit-presence detection (dP) and guessing-based selection (g) did not differ between immediate and delayed lineups. The model incorporating these restrictions was used as a comparison standard for the subsequent nested likelihood-ratio tests, G2(9) = 14.10, p = 0.119. The estimates of the biased-suspect-selection parameter b as a function of lineup fairness are shown in the left panel of Fig. dP, g and dA.The observed response frequencies  = 31.84, p < 0.001. In addition, guessing-based selection was more prevalent when the fillers matched the description of the culprit than when they did not match the description: Parameter g was decreased in the unfair in comparison to the fair-lineup condition, ∆G2(1) = 15.42, p < 0.001. This is to be expected because guessing-based selection among the lineup members should have been discouraged in the unfair lineup due to the poor match of the fillers to the description of the culprit. Parameter dP, which measures the ability to detect the presence of the culprit, was not affected by lineup fairness, ∆G2(1) = 0.19, p = 0.665.The fairness manipulation affected parameter 48 presented participants with staged-crime videos in which the culprits had distinctive facial features. To create fair lineups, Colloff et al. tested three possible techniques to prevent distinctive suspects from standing out: blocking, pixelating or replicating the feature on all faces in the lineup using digital photo-manipulation technology. Colloff et al. also included an unfair lineup in which only the suspect had the distinctive feature. The results showed that if a suspect stood out, identifications shifted from the fillers to the suspect. Thus, unfair lineups led to significantly more suspect identifications than fair lineups. If the 2-HT eyewitness identification model is valid, the biased-suspect-selection parameter b should be affected by the manipulation of fairness. Specifically, parameter b should be higher for the unfair than for the fair lineups.Colloff et al.48 randomly assigned 8925 participants to one of the eight conditions of a 4 (lineup type: block vs. pixelation vs. replication vs. unfair) × 2 (culprit presence: present vs. absent) between-subjects design. The participants viewed one of four 30-s crime videos  depicting four different culprits, each with a unique and distinctive facial feature . After an 8-min retention interval, participants were presented with a simultaneous six-person lineup composed of two rows of three photos. The lineup consisted of either one culprit and five fillers (culprit-present lineup) or six fillers (culprit-absent lineup). The fillers were randomly drawn from a pool of 40 description-matched fillers created for each culprit. Depending on the condition, the distinctive feature of the culprit was treated differently. Three treatments were intended to produce fair lineups by (a) concealing the distinctive feature with a solid black rectangle on the culprit and by covering the equivalent area on each filler (block), (b) pixelating the distinctive feature on the culprit and the equivalent area on each filler (pixelation) or (c) digitally adding the distinctive feature to each filler (replication). In the unfair condition, participants saw a lineup in which the suspect stood out due to them being the only person with a distinctive feature. For unfair culprit-present lineups, Colloff et al. left the distinctive feature on the culprit unaltered. For each unfair culprit-absent lineup, a replication filler photo with the culprit’s distinctive feature was randomly selected to create an innocent suspect with the culprit’s distinctive feature while the other five filler photos remained unedited. For all lineups, the position of the culprit (in culprit-present lineups) and innocent suspect (in unfair culprit-absent lineups) was randomly determined. Before viewing the lineup, standard two-sided pre-lineup instructions were presented .Colloff et al.48 in their Table  2 . Four sets of the model trees depicted in Fig. dA parameters were set to be equal among the four conditions because there was no reason to assume that the detection of culprit absence should differ as a function of the conditions. In Colloff et al., the three fair-lineup conditions were all associated with similar levels of performance. To keep the model as simple as possible, we therefore assumed that biased suspect selection (b), culprit-presence detection (dP) and guessing-based selection (g) did not differ among the three fair-lineup conditions. The model incorporating these restrictions was used as a comparison standard for the subsequent nested likelihood-ratio tests, G2(9) = 12.92, p = 0.166. The estimates of the biased-suspect-selection parameter b as a function of lineup fairness are shown in the right panel of Fig. dP, g and dA.The observed response frequencies  = 418.89, p < 0.001. In addition, parameter g, which represents the probability of selecting one of the lineup members based on guessing, was significantly decreased in the unfair-lineup condition in comparison to the fair-lineup condition, ∆G2(1) = 48.99, p < 0.001. This is to be expected given that guessing-based selection among the lineup members was discouraged by the fact that none of the fillers shared the culprit’s distinctive facial feature. This is parallel to what was observed in the data of Wetmore et al.59 considered in the previous section. The ability to detect the presence of the culprit, captured by parameter dP, was significantly higher in unfair than in fair lineups, ∆G2(1) = 8.37, p = 0.004, which can be explained by attention being drawn to faces with the culprit’s distinctive facial features.Parameter b representing the process of biased suspect selection. In the two experiments that were reanalyzed, unfair lineups were created either by using fillers with low similarity to the suspect59 or by using a suspect with an uncovered distinctive facial feature that makes the suspect stand out from the fillers48. Both model-based reanalyses demonstrated parameter b to be significantly higher for unfair lineups than for fair lineups. Parameter b thus sensitively reflects manipulations of biased suspect selection.The results support the validity of parameter 48 data, the culprit-presence detection parameter dP was also affected by lineup fairness. Better detection of culprit presence in unfair lineups can be easily explained by distinctive facial features drawing attention to the culprit’s face. This finding is in line with the well-established principle that the presence of highly similar distractors at test decreases memory performance [cf.62]. In both reanalyses, parameter dP was higher in unfair than in fair lineups, but this difference reached significance only in the reanalysis of the Colloff et al. data. Given that sample size was much larger in the study of Colloff et al. than in the study of Wetmore et al.59, this discrepancy is expected if the secondary effect of lineup fairness on culprit-presence detection is more subtle than the primary effect of lineup fairness on biased selection and can thus only be detected when the sample size provides the statistical power to detect a subtle effect. At first glance, better culprit detection in unfair lineups may seem unexpected given that unfair lineups were associated with an impaired ability to discriminate between culprits and innocent suspects in the analyses of Colloff et al. and Wetmore et al. However, their conclusions relied on ROC analyses and thus only on the correct culprit identifications and false innocent-suspect identifications. As Smith et al.62 have already pointed out, the finding of increased culprit-presence detection in Colloff et al.’s unfair lineups is already apparent at the surface level of the raw response frequencies when considering the full 3 × 2 matrix of lineup data. The effect of the fairness manipulation on biased selection is obvious when looking at the false responses: Participants chose the innocent suspect in culprit-absent lineups more often than a filler when the lineup was unfair but chose one of the fillers more often than the innocent suspect when the lineup was fair. In the 2-HT eyewitness identification model, this effect is reflected in the biased-suspect-selection parameter b. However, when looking at the correct responses, it is clear that culprit-presence detection was better in unfair lineups than in fair lineups: In culprit-present lineups, participants made more correct identifications when the lineups were unfair than when they were fair, while the correct rejections in unfair lineups stayed at about the same rate. This aspect of the data is captured in the culprit-presence detection parameter dP in the 2-HT eyewitness identification model. Furthermore, the results of both reanalyses59 showed a decreased guessing-based selection among the lineup members (parameter g) in unfair compared to fair lineups. Even without using a formal model of the processes underlying eyewitness performance, the surface-level data already suggest that guessing-based selection among the lineup members occurred less frequently when the lineup was unfair than when it was fair: Participants in both studies produced fewer filler identifications when lineups were unfair than when they were fair. Guessing-based selection among lineup members may have been discouraged in unfair lineups due to the poor match of the fillers to the culprit.When applying the 2-HT eyewitness identification model to Colloff et al.’s62].The 2-HT eyewitness identification model is able to capture all of these changes in performance because its parameters are based on the full 3 × 2 data structure to distinguish between culprit-presence detection, culprit-absence detection, biased selection and guessing-based selection so that the model is able to capture changes in those data categories that are often ignored when lineup performance is analyzed. Our findings thus suggest that fair lineups produce better outcomes than unfair lineups because they decrease biased selection of the suspect and not because they improve culprit-presence detection . In the context of lineups, so-called ‘biased’ pre-lineup instructions insinuate that the culprit is in the lineup and thus increase participants’ willingness to select one of the lineup members based on guessing when they are uncertain about whether or not the culprit is in the lineup66. The term ‘biased instructions’ is often used to refer to one-sided instructions that emphasize selectively the importance of selecting the culprit. The term ‘unbiased instructions’ is often used to refer to two-sided instructions that make participants aware of the fact that the culprit may or may not be in the lineup so that it is equally important to identify the culprit in culprit-present lineups and to reject culprit-absent lineups. Manipulating pre-lineup instructions therefore can be expected to affect the guessing-based selection parameter g and not the biased-suspect-selection parameter b. To avoid confusion, we therefore reserve the term ‘bias’ for the biased selection of the suspect in unfair lineups. We use the term one-sided instructions for instructions that emphasize selectively the need to identify the culprit and the term two-sided instructions for instructions that emphasize both the need to identify the culprit in culprit-present lineups and the need to reject culprit-absent lineups. One-sided instructions should encourage guessing-based selection, while two-sided instructions should discourage guessing-based selection. Here we reanalyzed datasets of Malpass and Devine 67 and of Lampinen et al.  who had used one-sided and two-sided pre-lineup instructions.The next step was to test the validity of parameter 67 influenced their participants’ guessing behavior by manipulating pre-lineup instructions that either insinuated or did not insinuate that the culprit was in the lineup. Malpass and Devine found that participants were more likely to choose one of the lineup members when one-sided instructions were given than when two-sided instructions were given, reflecting a higher prevalence of guessing-based selection after one-sided instructions. If parameter g of the 2-HT eyewitness identification model validly reflects guessing-based selection, then parameter g should be higher under one-sided than under two-sided instructions.Malpass and Devine67 randomly assigned 100 students to one of the four conditions of a 2 (lineup instruction: one-sided vs. two-sided) × 2 (culprit presence: present vs. absent) between-subjects design. The students witnessed a staged act of vandalism during a biofeedback demonstration at the university. They were exposed to a male confederate (visible for 85 s) who damaged the electrical equipment before he fled the room. On one of three evenings following the act of vandalism, participants viewed a simultaneous live lineup consisting of five persons who were lined up against the wall of a room. Half of the lineups included the culprit and four innocent fillers (culprit-present lineups), while the remaining lineups consisted of five innocent fillers (culprit-absent lineups). All fillers matched the appearance of the culprit. The position of each lineup member was counterbalanced. Before viewing the lineup, participants read either one-sided or two-sided printed lineup instructions. The one-sided instructions led participants to believe that the culprit was present. The students were instructed to choose one of five numbers (one number for each lineup member). There was no obvious option for rejecting the lineup. Instead, participants had to ask how to indicate such a response. In contrast, the two-sided instructions explicitly stated that the culprit may or may not be present and provided the participants with an option (circling number 0) to reject the lineup .Malpass and Devine67 in their Table 1.  Again, the number of innocent-suspect identifications was estimated by dividing the total number of false identifications in culprit-absent lineups by the number of lineup members [cf.48]. For the model-base d r e analysis, two sets of the trees shown in Fig. dA and b were each set to be equal between the conditions because there was no reason to assume that these parameters should differ as a function of the pre-lineup instructions. The model incorporating these restrictions was used as a compariso n standard for the subsequent nested likelihood-ratio tests, G2( 2) = 3.35, p = 0.187. The estimates of the guessing-based selection parameter g as a function of pre-lineup instructions are shown in the left panel of Fig. dP, b and dA.The observed response frequencies  = 20.95, p < 0.001. By contrast, the culprit-presence detection parameter dP remained unaffected by the instructions, ∆G2(1) = 0.88, p = 0.347.The guessing-based selection parameter was clearly affected by the manipulation of the pre-lineup instructions. Parameter 68, Experiment 1] manipulated participants’ guessing behavior in a similar fashion as Malpass and Devine67. While one-sided pre-lineup instructions were presented to encourage guessing-based selection, two versions of two-sided pre-lineup instructions were given to discourage guessing-based selection. The findings showed that the two-sided instructions significantly reduced inaccurate identifications68. Thus, in terms of the 2-HT eyewitness identification model, the guessing-based selection parameter g should be higher under one-sided instructions than under two-sided instructions.Lampinen et al.  randomly assigned 995 students to one of the six experimental conditions resulting from a 3 (lineup instruction: one-sided vs. standard two-sided vs. detailed two-sided) × 2 (culprit presence: present vs. absent) between-subjects design. Participants viewed a 15-s video showing a woman stealing a backpack. After completing a 5-min distractor task, participants were given a paper copy of one of three types of instructions to read while the experimenter simultaneously read the instructions out loud. The one-sided instructions simply required the participants to identify the culprit in the lineup. The standard two-sided instructions contained the additional statement that the culprit may or may not be in the lineup. The detailed two-sided instructions were formulated according to the recommendations of major United States and international law enforcement agencies and were supplemented by statements that (a) it is just as important to clear an innocent person as it is to identify the culprit and (b) the police will continue to investigate the crime regardless of whether an identification is made or the lineup is rejected69. Participants were subsequently shown a simultaneous lineup consisting of six faces in a 3 × 2 array in which the culprit was either present or absent. Lampinen et al. created six versions of the culprit-present lineups by replacing one of the fillers with the culprit. All fillers matched the culprit’s description .Lampinen et al. . Three sets of the trees shown in Fig. dA and b were each set to be equal among the conditions because there was no reason to assume that these parameters should differ as a function of the lineup instructions. For the sake of simplicity, the processing trees for the two types of two-sided instructions were combined by additionally assuming that guessing-based selection (g) and culprit-presence detection (dP) did not differ between more and less detailed two-sided instructions. This assumption was based on the results of Lampinen et al., who had found no differences in identification performance between the two types of two-sided lineup instructions. The model incorporating these restrictions was used as a comparison standard for the subsequent nested likelihood-ratio tests, G2(6) = 4.86, p = 0.562. The estimates of the guessing-based selection parameter g as a function of pre-lineup instructions are shown in the right panel of Fig. dP, b and dA.The observed response frequencies  = 36.39, p < 0.001. By contrast, the culprit-presence detection parameter dP did not significantly differ between the types of instructions, ∆G2(1) = 0.69, p = 0.406.The estimate of parameter 68 showed that parameter g, representing the probability of selecting one of the lineup members based on guessing, were consistently higher for one-sided than for two-sided pre-lineup instructions. The fact that the guessing-based selection parameter sensitively reflected the experimental manipulations of the pre-lineup instructions in both reanalyses further supports the validity of the 2-HT eyewitness identification model.The model-based results of both reanalysesdA, which represents the probability of detecting the absence of the culprit. In order to validate this parameter, an experimental manipulation is needed that affects the proportion of correct rejections of culprit-absent lineups. To this end, Winter et al.3 constructed culprit-absent lineups in which all members had conspicuous birthmarks, which was not the case for the culprit. Given that none of the members in the culprit-absent lineups resembled the culprit, it was relatively easy to detect the absence of the culprit and thus to reject the culprit-absent lineups. As expected, parameter dA was significantly higher when the culprit-absent lineups were easy to reject. The obvious and downright trivial manipulation of the detection of culprit absence used by Winter et al. is ideal for the purpose of the model validation. However, it seems also interesting to explore whether there are factors that facilitate culprit-absence detection in more realistic settings. In fact, it would be highly desirable to find methods that actually improve the witnesses’ ability to detect the absence of the culprit and to specifically reject culprit-absent lineups, ideally without affecting guessing-based selection. Two such methods are reported below.The final step in the model validation presented here concerns parameter 70. In the majority of studies available to date, an intriguing pattern of results has emerged: A wildcard decreases the rate of false identifications in culprit-absent lineups without increasing the rate of false rejections of culprit-present lineups73. This pattern of results suggests that the effect of using a wildcard does not affect guessing-based selection—in which case it should have increased both correct and false lineup rejections—but may specifically improve the detection of the absence of the culprit.The first method is the use of a wildcard, that is, a silhouette with a question mark that represents a ‘mystery man’ that can be chosen instead of the suspect or one of the fillers. The wildcard option was introduced to make it easier for children to reject culprit-absent lineups by providing an option to reject the lineup that is more equivalent to a positive response when choosing the suspect74 that has been introduced to facilitate the rejecting of culprit-absent lineups for older adults. Just as the wildcard, the culprit-absent practice lineup has led to an increase in correct culprit-absent lineup rejections without affecting the rate of false culprit-present lineup rejections, suggesting that the underlying process is a facilitation of the detection of the culprit absence and not a decrease in guessing-based selection.The second method is the use of a culprit-absent practice lineup70 and culprit-absent practice lineups74 should be reflected in parameter dA, which was designed to measure the detection of the absence of the culprit. We tested this assumption by reanalyzing the data obtained by Karageorge and Zajac70 and by Wilcock and Bull .If the 2-HT eyewitness identification model is valid, then the effects of wildcards75]. Karageorge and Zajac70 aimed to enhance children’s ability to reject culprit-absent lineups by inserting a wildcard within the lineup that could be chosen instead of one of the lineup members. Children were more likely to reject the culprit-absent lineup when a wildcard was provided than when no such option was provided. Interestingly, the rate of correct rejections of culprit-absent lineups increased, whereas the rate of false rejections of culprit-present lineups did not. If a wildcard would simply affect the probability of selecting one of the lineup members based on guessing, it should have affected correct and false rejections equally. The selective effect of a wildcard on correct lineup rejections thus can only be caused by an increased detection of the absence of the culprit. In the 2-HT eyewitness identification model, a wildcard manipulation thus should selectively affect the culprit-absence detection parameter dA. Specifically, parameter dA should be higher in the wildcard condition than in the control condition.The results of a number of studies have shown that children appear to have considerable difficulty rejecting lineups even if the culprit is absent .Karageorge and Zajac70 in their Table 1 which the data were already collapsed over the delay conditions (1 to 2 days vs. 2 weeks). Given that the total number of children in each age group in the final sample was not specified, it was not possible to analyze the data for younger and older children separately. Therefore, we collapsed the data across age groups. According to Karageorge and Zajac , half of the children were presented with the culprit-present lineup, while the remaining children were presented with the culprit-absent lineup. More specifically, in the control condition, 53 of the 107 children saw the culprit-present lineup and 54 children saw the culprit-absent lineup, whereas in the wildcard condition, 48 of the 97 children saw the culprit-present lineup and 49 children saw the culprit-absent lineup. For the model-based reanalysis, we needed two sets of the model trees depicted in Fig. b parameters were set to be equal between the conditions because there was no reason to assume that these parameters should differ as a function of the presence or absence of the wildcard. The model incorporating these restrictions was used as a comparison standard for the subsequent nested likelihood-ratio tests, G2(1) = 3.58, p = 0.059. The estimates of the culprit-absence detection parameter dA as a function of the wildcard condition are shown in the left panel of Fig. dP, b and g.The observed response frequencies  = 29.79, p < 0.001. The wildcard manipulation did not affect the probability of detecting the culprit as measured by parameter dP, ∆G2(1) = 0.21, p = 0.646. The guessing-based selection parameter g was not affected by the wildcard manipulation either, ∆G2(1) = 1.44, p = 0.229.The wildcard manipulation affected the culprit-absence detection parameter 76]. Wilcock and Bull  examined the effect of a culprit-absent practice lineup on correct lineup rejections. Participants in the culprit-absent practice lineup condition correctly rejected culprit-absent lineups more often than participants in the control condition without a culprit-absent practice lineup. Interestingly, the culprit-absent practice lineup did not increase false rejections of culprit-present lineups. This pattern of results suggests that guessing-based selection cannot be responsible for the effect of the culprit-absent practice lineup because guessing-based selection would have decreased the rate of rejections of both culprit-present and culprit-absent lineups. Instead, the culprit-absent practice lineup must have improved the detection of the absence of the culprit because only this explanation is consistent with a selective increase in correct rejections of culprit-absent lineups. Therefore, it can be predicted that the culprit-absent practice manipulation should affect the culprit-absence detection parameter dA. Specifically, parameter dA should be significantly higher in the culprit-absent practice lineup condition than in the control condition.Just like children, older adults are less likely to correctly reject a culprit-absent lineup than younger adults  randomly assigned 100 older participants to one of two groups (culprit-absent practice: culprit-absent practice lineup vs. control). Culprit presence (present vs. absent) was a within-subjects factor. Participants were shown a 110-s video of two men breaking into a house. After a 30-min retention interval, half of the participants were presented with a culprit-absent practice lineup consisting of six color pictures of famous women. The participants were asked to identify the Queen of England and were also informed that her face may or may not be present (there was no picture of the Queen). All participants correctly rejected the lineup. After rejecting the lineup, participants were again warned that not all police lineups include the culprit and that even the police can make mistakes. Participants were then given standard two-sided pre-lineup instructions before viewing the real lineups. Wilcock and Bull constructed a culprit-present and a culprit-absent lineup for each of the two culprits consisting of six faces in a 3 × 2 array. Participants were shown one culprit-present and one culprit-absent lineup . For the sake of simplicity, we did not distinguish between the two culprits but aggregated the data. All fillers matched the culprits’ descriptions. In the culprit-absent lineups, the culprit was replaced by the filler who was rated as most similar-looking to the culprit. The culprit and the culprit replacement were randomly placed across all six lineup positions .Wilcock and Bull  presented a culprit-absent practice lineup. Both procedures selectively increased the rate of correct rejections of culprit-absent lineups but did not affect the rate of false rejections of culprit-present lineups, suggesting that introducing these procedures did not induce decreased guessing-based selection. The latter conclusion is consistent with the fact that guessing-based selection parameter g was not affected by either the wildcard or the culprit-absent practice lineup.The two final reanalyses demonstrated that manipulations designed to facilitate culprit-absence detection selectively affected parameter 13. The 2-HT eyewitness identification model serves to disentangle the effects of manipulations on the processes underlying eyewitness identifications by yielding separate measures of these latent processes and by enabling researchers to test hypotheses directly at the level of the parameters representing these latent processes. In that way, the model can be used to tackle important research questions such as, for example, the question of whether the process of culprit-presence and culprit-absence detection, biased selection and guessing-based selection differ between simultaneous and sequential lineups. However, before a measurement model such as the 2-HT eyewitness identification model can be used to tackle new and unresolved empirical questions, it is important to demonstrate that the model parameters sensitively reflect the processes they are intended to measure29.Here, we report a validation of the novel 2-HT eyewitness identification model using published data. The model simultaneously takes into account all of the data categories that are observed in lineups, that is, correct culprit identifications, false innocent-suspect identifications, false filler identifications in culprit-present and culprit-absent lineups, false rejections of culprit-present lineups and correct rejections of culprit-absent lineups. Based on these data, the model yields measures of latent processes that underlie eyewitness identification performance. Specifically, the model is designed to distinguish between two types of detection processes—detection of the presence of the culprit and detection of the absence of the culprit—as well as two different types of non-detection-based decision processes—biased selection of a suspect that stands out from the fillers in unfair lineups and selecting a lineup member based on guessing. We hope that distinguishing between these qualitatively different latent processes helps to improve the clarity of the interpretation of lineup data. A typical approach is to try to infer the underlying processes indirectly from surface-level data by comparing the rate of correct culprit identifications and false innocent-suspect identifications between different conditions, but often the problem arises that the same manipulation can simultaneously affect qualitatively different processes such as, for example, both culprit-presence detection and guessing-based selection3 have reported fresh experiments that were designed with the sole purpose of testing whether the parameters of the 2-HT eyewitness identification model sensitively reflect specific manipulations that were carefully crafted to affect the latent processes underlying eyewitness identifications postulated by the model; these tests were successful. The analyses reported here complement the results reported by Winter et al. by demonstrating that the model parameters also sensitively reflect manipulations of the processes they were designed to measure in published studies obtained with a wide variety of different experimental manipulations, samples, paradigms and laboratory as well as real-world settings. First, it was demonstrated that parameter dP reacts sensitively to manipulations of encoding conditions that can be expected to affect the detection of the presence of the culprit. Specifically, the culprit-presence detection parameter dP was higher in response to long as opposed to short culprit exposure and under good as opposed to poor viewing conditions at encoding. Second, manipulations of lineup fairness affected the estimate of parameter b, designed to reflect the process of the biased selection of a suspect who stands out from the fillers. As expected under the assumption that this model parameter is valid, parameter b was higher for lineups with low as opposed to high suspect-filler similarity and for lineups in which the suspects had unique facial features distinguishing them from the fillers as opposed to lineups in which these distinctive features were concealed in all photos or replicated in the photos of the fillers. Third, manipulations of pre-lineup instructions had the predicted effects on the parameter representing the selection of a lineup member based on guessing. Parameter g was consistently higher when one-sided than when two-sided instructions were used. One-sided instructions implicate that the culprit is in the lineup, whereas two-sided instructions emphasize that the culprit may be present or absent. It should be uncontroversial that difficult encoding conditions should affect the process of culprit-presence detection, that biased suspect selection should be increased in unfair lineups and that participants can be discouraged to select a lineup member based on guessing by two-sided lineup instructions. Selecting manipulations that obviously affect the detection of the absence of the culprit is somewhat more difficult. Winter et al. have provided evidence showing that the detection of culprit absence is enhanced when all of the lineup members in the culprit-absent lineup can be easily ruled out based on salient perceptual features. Such a manipulation is unlikely to be found in the literature. This is so because such an experiment makes sense only in the context of model validation in which the ideal manipulation is obvious and trivial in the sense that there is broad agreement on the manipulation’s effect on certain cognitive processes. Therefore, our reanalysis-based test of the validity of the culprit-absence detection parameter concentrated on manipulations that were specifically designed to help children and older adults to reject culprit-absent lineups74. In previous studies these manipulations led to an increase in correct rejections of culprit-absent lineups while the false rejections of culprit-present lineups remained unaffected, suggesting that the affected process was the detection of the absence of the culprit and not the selection of a lineup member based on guessing. The present reanalysis confirms this conclusion and thereby provides further evidence for the process of culprit-absence detection: The use of a wildcard procedure and a culprit-absent practice lineup increased the estimates of culprit-absence detection parameter dA compared to standard lineup procedures.In a separate empirical contribution, Winter et al.g) and a process of biased selection of a suspect who stands out from the fillers (parameter b). Traditionally, eyewitness researchers have relied on the mock-witness task to determine the degree to which a lineup is unfair. This task involves presenting a lineup and a description of a culprit to participants who did not witness the crime—so called mock witnesses—and then asking the mock witnesses to identify the person who best matches the description77. Based on the answers of the mock witnesses, various lineup fairness measures can be calculated, reflecting either the lineup size  or biased selection of the suspect . However, some researchers have cautioned that these measures may suffer from low validity and reliability and have suggested to consider alternative methods for estimating lineup fairness80. The 2-HT eyewitness identification model offers such an alternative method for measuring and testing hypotheses about biased suspect selection. In fact, the 2-HT eyewitness identification model measures lineup fairness directly from the witnesses’ identification data—without relying on a separate paradigm involving mock witnesses. Based on this direct measurement, one can draw conclusions about the cognitive processes that determine the decisions of eyewitnesses that may, to some degree, differ from those of mock witnesses81.The 2-HT eyewitness identification model distinguishes between two non-detection-based judgements: a process of selecting a lineup member based on guessing (parameter dP) and the detection of the absence of the culprit (parameter dA). Traditional measures of lineup performance often provide only a single accuracy index that simultaneously accounts for the witness’s performance in culprit-present and culprit-absent lineups. However, it is possible to argue that detecting the presence of a culprit might be achieved by a different underlying process than detecting the absence of a culprit. The first piece of evidence supporting this argument is that the process of detecting the presence of the culprit varies as a function of manipulations that leave the process of detecting the absence of the culprit unaffected . Interestingly, it seems plausible that techniques developed to help children and older adults to reject culprit-absent lineups specifically affect the process of culprit-absence detection. However, without the help of a model-based analysis, Karageorge and Zajac70 and Wilcock and Bull74 had to rely on the observation that a wildcard procedure or a culprit-absent lineup practice selectively increased correct rejections of culprit-absent lineups with no effect on false rejections of culprit-present lineups, which indirectly suggests that these procedures may help to detect culprit absence and do not decrease guessing-based selection. With the 2-HT eyewitness identification model it is less indirect and more straightforward to conclude that these procedures enhance the detection of the culprit absence (parameter dA) while leaving guessing-based selection (parameter g) unaffected. We hope that by including a separate parameter for culprit-absence detection, the 2-HT eyewitness identification model will stimulate more research on techniques that specifically improve witnesses’ ability to detect the absence of a culprit, which seems highly desirable.The model also makes it possible to distinguish between two detection processes: the detection of the presence of the culprit  than the photographs of the fillers . Photographs from different sources may differ systematically in certain characteristics. It thus greatly improves the ecological validity of the results to have a designated innocent suspect whose photograph deviates from the photographs of the fillers in the culprit-absent lineups in the same way as the photograph of the culprit deviates from the photographs of the fillers in the culprit-present lineups [cf.3].A limitation of the present reanalyses is that many studies in the eyewitness literature used only fillers in culprit-absent lineups [e.g.,29. However, a more controversial property of MPT models is that they involve a threshold concept, thus assuming that recognition judgements result from discrete memory states rather than continuously distributed signal strength (as the SDT assumes). While some researchers argued that the threshold assumption is inconsistent with the available empirical evidence83, others have shown that threshold models and SDT-based models can both account for recognition memory performance85. The theoretical and practical usefulness of both approaches in the field of eyewitness identifications has to be further investigated in future research.Due to their mathematical and conceptual simplicity, the class of MPT models is ideally suited to develop simple formal measurement models to assess latent processes involved in specific empirical paradigmsdP), biased suspect selection (b), guessing-based selection among the lineup members (g) and culprit-absence detection (dA) showed that the parameters sensitively reflected experimental manipulations of the processes they were designed to measure. We conclude that the 2-HT eyewitness identification model is promising and can complement existing tools to analyze eyewitness identifications in lineups.Given the well-documented evidence illustrating the fallibility of eyewitness testimonies, it remains an important goal to advance our knowledge about the latent processes underlying eyewitness identification decisions. We hope to contribute to this advancement by presenting a multinomial model for analyzing lineup performance, the 2-HT eyewitness identification model. By incorporating the entire 2 × 3 data structure of responses in lineup identification tasks, the model enables inferences about latent cognitive processes that are not accessible when standard measures of eyewitness identification accuracy are used. Here, we tested the validity of the model by applying it to published data. A series of eight reanalyses provides evidence in support of a successful model validation. Validations of culprit-presence detection (


Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client–server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels . Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video. Adaptive media streaming through Hypertext Transfer Protocol (HTTP) is a widely used mechanism by the service provider. The main advantage is that it does not require any change in the underlying network layer to support streaming. The standard organization Moving Picture Experts Group (MPEG) and the 3rd Generation Partnership Project (3GPP) have standardized a method called Dynamic Adaptive Streaming over HTTP (DASH) to ensure interoperability . In DASHAnother development in adaptive video streaming is the study of 3G/4G cellular networks offering Internet connection. Many times, the user equipment offered by a cellular operator to support Internet services fails to deliver the desired quality for many practical reasons. For example, The objective performance of the proposed system is evaluated using standard metrics while meeting the design goal. The International Telecommunication Union Standardization Sector (ITU-T) recommends using full reference metrics when the original video is available at the receiver to test the individual system in a laboratory environment. The standard evaluation metrics can be applied to test video quality in different formats, including quarter common intermediate format (QCIF), common intermediate format (CIF), and Video Graphics Array (VGA). The proposed work in this paper tries to use these parameters with their corresponding standard values in implementation and development. The existing link bit rate assessment method at the client involves sending a ping message to the server and computing the bit rate using the time spent by the packet to come back. However, this method lacks precision because of many external factors, for example, instantaneous congestion to the router can temporarily interrupt the incoming rate of a ping message. Thus, the best practice for dealing with this issue is to evaluate the capacity of the link in terms of bit rate to the receiver by analyzing the bit stream arrival on the fly.The streaming that needs to be sampled at times is analyzed and sent a feedback message to the sender for performing remedial action on the outgoing stream so that the end user enjoys a better quality of experience during the entire viewing session. The schematic approach of the proposed architecture is shown in An analytical model is included to support the performance measure of the proposed work. The bit rate profile and performance measure are also presented in tabular form. Although the proposed system is basically developed as on-the-top of HTTP (OTT), it has incorporated the inherent behavior of streamed data over the wireless network. The dynamics of the observed bit rate are due to the burst nature of the Internet traffic  and the We devised a novel feed-forward attention-based LSTM model using reinforcement learning to successfully integrate features from several layers of the LSTM network to solve sequential bit rate dependency problems and adaptive video streaming over HTTP.We present a cost function for attention networks that maximizes video quality and minimizes re-buffering time.Experimental results have revealed that the suggested A-LSTM technique performs better than the state-of-the-art buffer filling algorithms on the standard datasets.We present a summary of our contributions:The rest of the paper is organized as follows: video streaming, video buffering, and machine learning techniques in multimedia streaming-related works are summarized in The bit rate adaptation of video streaming involves many factors, including scheduling of segmented video, bit rate selection, bandwidth estimation, etc. Many commercially available services, such as Smooth Streaming, Akamai HD, Netflix, and Adobe OSMF, implement adaptive streaming through the Internet. Appropriate modeling and analysis of the key phase include switching of the multimedia data during streaming used by the service provider, which helps to refine the design of the system for improving performance in the feedback control loop .In the HTTP based adaptive streaming (HAS), the quality of experience (QoE) depends on the appropriate selection of video segment and switching of bit stream based on client input . The undA new version of adaptive rate control algorithms  is propoTo solve the pixel intensity problem, a novel algorithm  is preseDynamic Adaptive Streaming over HTTP (DASH) in the client–server environment has attracted worldwide attention for many reasons from researchers and developers . There iFurther, a learning approach may help with streaming video through multilink. For example, the learning method can incorporate a Markov decision process with a finite state. The reward calculation in such implementations must include video quality of service (QoS) ,22. The Another focus for adaptation of DASH/HAS video in cellular mobile systems can be enforced by the network operator based on the knowledge of cell load and channel conditions to optimize the content delivery. Further, this opens an avenue for joint optimization of resource allocation in multi-user networks and controlling video streaming for the DASH client . FurtherUnderstanding the time complexities and other quality of service (QoS) requirements of live video streaming, our work considers sampling of incoming bit rate alone and the buffer state for the client’s decision-making. Furthermore, the proposed system is developed to cope with the fluctuation in bit rate due to the best-effort model of the Internet and the time-varying characteristics of a wireless channel. The combined network effect on the sampled data set (bit rate) tends to behave as random variables. Hence the Markov process-based decision-making is not suitable here . AnotherThe proposed system is demonstrated after the client–server architecture. The main function of the server side module is to receive the live or stored video for transcoding before streaming . The sysThe four unique patterns of variation in bit rate are defined  for compThe H.264 codec with VLCJ media framework captures the video and streams continuously through the HTTP port.The second module deals with listening to client feedback messages to adjust the video stream parameter, which includes resolution and frame rate.The server’s implementation procedure involves monitoring client feedback and assigning the appropriate parameter values to the ongoing video session. It consists of two main modules:The first module consists of the VLCJ framework for playing streamed media data.The implementation of module 2 forms the core of the proposed system, which estimates the client’s bit rate by applying a suitable bit pattern matching algorithm.The third module formats the feedback messages in a standard format than can be understood by the server.The client periodically samples and analyzes the streaming data to estimate the dynamically changing bit rate trend. The client side implementation has three main modules:Algorithm 1. Client algorithm.(1)Sample the data rate and put it in an array;(2)(i)(ii)(iii)Continue step 2 until (3)(i)(ii)(iii)Continue step 3 until (4)(5)(6)Find status:(2)(i)Progressive;(ii)Stabilized;(iii)Fluctuated;(iv)Degraded;(v) set Status as non-monotonic and call Find_rms (Bit rates);A. function Analyze (Bit rates):Read start data point, startp;Read end data point, endp;Find median value, medianp;If  tends to monotonic increase, return Else if  tends to monotonic decrease, return If  tends to neither monotonic increase nor decrease, return B. function Find_RMS (Bit rate):Calculate the root mean square (RMS) of the samples;Divide the N different samples into M segments (M = 3);Continue Step1 to find the RMS values of each segments:Compute the difference among the overall RMS and the RMS of the corresponding segments;Calculate Calculate Calculate If1;Else if0;Else return 2.The proposed system at the client samples the incoming bit rate periodically at patterns  to simplAlgorithm 2. Server side algorithm.and(1)a default value (2)Continue:Read feedback message If     continue with the existing setup;Else if      call A-LSTM(status), ##Increase Spatial/Temporal resolution;Else if      find Switch_time (bit rate),   call A-LSTM(status), ##Update Spatial/Temporal resolution;Else if      call A-LSTM(status), ##decrease Spatial/Temporal resolution;Else if     wait till next feedback message arrives;(3)Continue till connection is terminated;A. Function Find_Switch_Time (bit rate):Calculate time for quality switch      #where      # request, and      # switching request; Set a timer when request for quality switch is received;  Wait for the next feedback message; If     discard the request for quality switching and wait for next client      feedback message;Else,     serve the request for quality switching.Let The server side algorithm (Algorithm 2) decodes the client message and modifies the streaming video parameter accordingly. The execution time of the switching process from switching the current stream to the new stream is taken as one input parameter in the server’s decision-making. If the client’s algorithm wrongly classifies the arrival pattern , then it will lead to an improper action by the server, which may degrade the streaming video on the client side.In this paper, we model the future bit-rate prediction for higher QoS as a time series prediction problem. Time series analysis, which involves analyzing past examples of bit rate in various network qualities to infer an optimal QoS, may be utilized to predict video bit rate. This time series analysis problem is learned in this work using attention-based LSTM (A-LSTM), an advanced variant of recurrent deep neural networks. The A-LSTM algorithm, which was trained using the back propagation through time (BPTT) algorithm, is more useful for learning long-duration dependencies.tS) that maximizes the overall video quality viewed by the end user. In Equation (1), cost function Q(t) has now been created to assess the total effectiveness of a video streaming session:To improve the quality of service (QoS) for individual consumers, we use a deep neural model consisting primarily of attention-based LSTM and reinforcement learning architecture . Furthermore, during training, attention networks are trained for parameters The inputs to the proposed algorithm are bit rate bt, and the last chunk bit rate as bt, are instantaneous inputs that are fed to a fully connected layer with 128 filters, each of size 4 and stride 1. The final layer, which is fully connected, chooses the state policies’ action for state St using a softmax function. The softmax function’s output is a selection of the bit rate for the following video segment with the highest probability, ensuring that the best bit rate is chosen for a corresponding state St. In the training phase, user feedback messages are reinforced to the attention network and LSTM network to obtain optimum parameters, using policy gradient strategy. Only the attention-LSTM network is used in predicting spatial resolutions in the testing phase.The experiment uses the proposed attention-based model, which takes into account the video sequence, download times, and past (k = 16) bit rate measurements. As shown in For comparison purposes, we implemented a buffer filling algorithm based on the traditional adaptive streaming method  to analyThe streaming content can be treated as a chronological sequence of statistical data, which is sampled periodically for analysis and prediction. The error due to sampling and subsequent analysis need to be formulated and modeled to define the system’s design objective and performance evaluation. The non-parametric approach  could beA non-parametric prediction interval can be defined to include a simple maximum and minimum value in a sample set of a given population. Generally, for an exchangeable sequence of random variables, each sample qualifies as the maximum or minimum. In a bit rate sample set of For a given sample θ can be denoted as:The variance of the estimate indicates the distance from the expected value of the estimates. Sometimes, the distance between the average of the collection of estimates and the single parameter being estimated, called bias, need to be computed. The bias of Further,The mean squared error (MSE) can be expressed in terms of variance and bias as:n predicted values, and If Since the full reference (FR) methods correspond to the objective evaluation of video quality and provide the most accurate result, it was used to evaluate the performance of the proposed system. The two widely used FR metrics are PSNR and SSIM. The paramount interest of the proposed solution is that the system response to the changing network resource should result in higher PSNR and SSIM while sustaining video communication.MSE (m)) is computed as the absolute difference between the original and the decoded video in the same frame (mth), denoted as:The PSNR provides information about the degradation of decoded video quality with respect to the original content. It is calculated on luminance components of the video (ITU-T recommendation), which can be formulated on a logarithmic scale as:The PSNR observation is basically an offline process that can be carried out on a few selected frames at the end of the experiment to ascertain the quality of the streaming system. In designing and developing a higher quality streaming system, achieving a minimum average PSNR of 30 dB may be desirable.The SSIM  metrics x, y, x, y, x and y. The constants L represents the dynamic range of the pixel values given by:The SSIM ,31 metriUsing these components, the SSIM is represented as: An SSIM index of more than 0.95 represents a good decoded video, and it could be a design objective considering the requirement of the end user in high-quality video communication.The proposed system was implemented in a client–server environment, where the server uses four standard video formats, namely common intermediate format (CIF), quarter CIF (QCIF), sub quarter CIF (SQCIF), and quarter Video Graphics Array (QVGA), to map the video quality dynamically corresponding to the feedback messages. The frame rate of 10, 18, 25, 30, and 35 fps was used to alter temporal resolution. The default value of the temporal resolution (in fps), and also during the initial setup, was assigned as 30. The streaming system at the server selects either one or a combination of spatial and temporal resolutions to achieve the predicted bit rate of the network by the client.In the experimental setup, the wireless Internet connectivity was established by a 4G Internet-connect device, Reliance_Netconnect+, working on CDMA20001xRTT and 1xEV-DO Rev-A techniques. As per the specification provided by Reliance communication Ltd., the Reliance_Netconnect + device is designed to support a download and upload data rate of 3.1 Mbps and 1.8 Mbps, respectively, but a real-time measurement carried out using an online tool (SpeedOf.Me) had the average bit rate during uplink and downlink estimated at 0.54 Mbps and 0.45 Mbps, respectively, in the laboratory environment. Therefore, fluctuating throughput in data communication across the Internet connected by Reliance_Netconnect + provided us with the ideal real-time platform to test our proposed system.® Core ™ i7-3770 CPU @ 3.4 GHz processor and 8 GB RAM. The Window-7 Professional 32-bit operating system was installed to run the program for the client/server system. The streaming process was implemented over HTTP with the user datagram protocol as the transport protocol.The different modules of client and server were developed on Dell Inspiron N5010 personal computers having the IntelH.264 codec generates a variable bit rate for the input video depending on the scene change in visual contents. VLC media player is used in a Java framework (VLCJ) at the sender as well as the receiver. VLCJ provides a higher level framework to cope with the complexities of VLC libraries. The VLC framework includes a variety of media formats through libavcodec library of codecs to the media player, which seamlessly plays the H.264 bit stream. Furthermore, JPCAP provides a library for packet capturing in-network applications using Java, which helps analyze real-time network traffic.The proposed system was implemented using the open-source tool (VLCJ framework), and experimental results were obtained on live as well as stored video in wireless 4G CDMA networks. Since the intended application of this system is to support live video streaming over wireless on the top of HTTP, the result presented here corresponds to live-streamed video in the laboratory environment. Although the working of the system was successfully demonstrated many times, the numerical result analyzed here represents a single instance of experimentation.The variability of packet delay in the one-way end-to-end communication was observed while discarding packet loss during the experimentation of the proposed video streaming system. As shown in The PSNR observation  was perfThe SSIM was calculated offline using the same approach as that of PSNR. It is observed based on the data generated by three methods: default without adaptation scheme, existing buffer filling algorithm, and proposed adaptation algorithm resulting from the live streaming of video . The sysThe development of a mechanism to support adaptive streaming of video over HTTP in dealing with fluctuation in available bit rate on the Internet with last-mile connectivity as a wireless network is a rewarding approach. The proposed A-LSTM system adopted the theory of maxima–minima along with an RMS method, reinforcement techniques, along with attention-LSTM networks to compute and match the pattern of the bit stream. It also included the duration of network fluctuation as well as the time for switching excellence in effective decision-making, while switching between different video qualities. The proposed solution tackled the problem of inherent conduct of wireless and Internet traffic in a unified tactic. The link level quality of service parameters such as delay, jitter, and packet loss was not considered in the problem formulation as the system is developed on top of HTTP. Although the proposed system is targeted toward the attainment of quality of the video, it can be used in many other video streaming applications. Abrupt congestion at the router on the Internet may cause extra delay in video streaming packets, and a suitable method is required to tackle it. Another future work includes supporting video streaming to hand-held devices (smart phones) connected to the Internet through a cellular network.