Here we apply the two-high threshold eyewitness identification model to identify the effects of lineup size on the detection-based and non-detection-based processes underlying eyewitness decisions. In Experiment 1, lineup size was manipulated by showing participants simultaneous or sequential lineups that contained either three or six persons. In Experiment 2, the lineups contained either two or five persons. In both experiments, the culprit was better detected in smaller than in larger lineups. Furthermore, participants made fewer guessing-based selections in smaller than in larger lineups. However, guessing-based selection in larger lineups was not increased to a level sufficient to offset the effect of increased protection of suspects in larger lineups due to the fact that the guessing-based selections that occur are distributed across more persons. The results show that increasing the lineup size causes several changes in the detection-based and non-detection-based processes underlying eyewitness decisions. While many structural and procedural aspects of lineups have been studied, including the lineup presentation format , the instructions given to eyewitnesses  or the characteristics of fillers , there has been relatively little research addressing the question of how the number of fillers in a lineup affects eyewitness decisions. In addition, studies to date have mainly focused on the question as to which lineup size should be preferred by relying on overall measures of lineup quality . The present study aims to complement the existing literature by providing a more detailed dissection of how lineup size affects the processes underlying eyewitness decisions in lineups. Improving our understanding of the processes underlying lineup size effects seems desirable considering the large variability in lineup size policies across jurisdictions. For instance, in the United States, a lineup most often contains five fillers8, the police in the United Kingdom typically use eight fillers9 and in Germany, the recommended lineup includes at least seven fillers10. Before considering the potential effects of lineup size on the latent processes underlying eyewitness decisions, it must be pointed out that it is plausible that increasing the lineup size diminishes the probability of suspect identifications simply due to the mathematical consequences of selecting among a larger group of persons. If participants randomly guess among the available options, the sampling probability of selecting the suspect among the fillers is inversely proportional to the size of the lineup. However, it is quite possible that variations in the number of persons that have to be considered in a lineup also affect the latent detection-based and non-detection-based processes underlying eyewitness decisions in addition to this sampling probability. Here we rely on the well-validated two-high threshold (2-HT) eyewitness identification model12 to disentangle the effects of lineup size on the detection-based and non-detection-based processes involved in eyewitness decisions.Eyewitness identification via lineup procedures is a major source of evidence in criminal investigations. In a lineup procedure, an eyewitness is presented with a suspect (who is guilty or innocent) along with a number of fillers (who are known to be innocent) and is asked to make an identification or to reject the lineup. However, human memory is unreliable and highly prone to error. According to the Innocence Project14). These models form an easily accessible class of measurement models for which both excellent tutorials15 and easy-to-use free software for parameter estimation and statistical hypothesis tests exist16. Multinomial processing tree models are based on the assumption that an observable response originates from a sequence of latent processes. These sequences are visualized as branches in a processing tree. The branches consist of links and nodes. The links terminating in nodes represent probabilities with which certain cognitive processes occur. These are the parameters of the model that can be measured based on observable data.Multinomial processing tree models, to which class the 2-HT eyewitness identification model belongs, are statistical models for categorical data that have been widely used to infer latent cognitive processes from observable behavior , the detection of culprit absence (parameter dA), guessing-based selection among the lineup members (parameter g) and biased suspect selection in unfair lineups (parameter b). The model has the further advantage that it incorporates the inverse relationship between the random-sampling probability and lineup size in terms of a fixed constant that is independent of the parameters representing the detection-based and non-detection-based processes that have to be estimated from the data. In validation studies, it has been empirically shown that the parameters of the 2-HT eyewitness identification model sensitively reflect manipulations of the processes they were intended to measure in both simultaneous and sequential lineups12. The model has already been successfully applied to examine the effects of first-yes-counts instructions on guessing-based selection17 and to measure the effects of lineup fairness18.Based on the full range of data categories in a typical lineup task , the 2-HT eyewitness identification model provides measures of the latent processes from which eyewitness decisions originate Fig. . SpecifidP, resulting in a correct identification of the culprit. This process is enhanced under conditions that facilitate memory formation and retrieval such as the better encoding of the culprit’s face12. If the presence of the culprit is not detected, which occurs with probability 1 − dP, two types of non-detection-based processes may still lead to a correct culprit identification in culprit-present lineups. With probability b, biased selection of the culprit may occur because the culprit stands out from the other persons in an unfair lineup. For instance, parameter b increases if the suspect has distinctive facial features  that make the suspect stand out from the fillers12. No biased suspect selection occurs with the complementary probability 1 − b. In this case, participants may still select one of the persons in the lineup based on guessing with probability g. Parameter g increases, for instance, if the lineup instructions insinuate that the culprit is in the lineup12. Guessing-based selection leads either to the identification of the culprit with the random-sampling probability that is equal to 1 ÷ lineup size or to the identification of one of the fillers with the complementary probability 1 − (1 ÷ lineup size). Alternatively, participants may not select a lineup member based on guessing with probability 1 − g, in which case the lineup is falsely rejected.The upper tree in Fig. dA, leading to a correct lineup rejection. This may occur, for instance, if all lineup members in a culprit-absent lineup can be ruled out as the culprit because their faces share a common feature  that the culprit did not have12. Culprit-absence detection fails with probability 1 − dA, in which case the non-detection-based processes occur in the same way as in culprit-present lineups. With probability b, biased selection of the innocent suspect occurs because the innocent suspect stands out from the other persons in the lineup. No biased suspect selection occurs with probability 1 − b. In this case, participants may still make a guessing-based selection with probability g, resulting in either the identification of the innocent suspect with probability 1 ÷ lineup size or the identification of a filler with probability 1 − (1 ÷ lineup size). If participants do not make a guessing-based selection, which occurs with probability 1 − g, then the lineup is correctly rejected. More details can be found elsewhere12.The lower tree of the 2-HT eyewitness identification model represents the processes that may occur in response to culprit-absent lineups. The absence of the culprit is detected with probability Focusing on the role of lineup size in the model immediately clarifies the mathematical consequences of larger lineups on suspect identifications: The random-sampling probability that expresses the probability with which guessing-based selection leads to the identification of the suspect decreases with increasing lineup size. For instance, in a three-person lineup, a guessing-based selection will lead to a suspect identification with a random-sampling probability of 1/3, whereas in a six-person lineup, the random-sampling probability is reduced to 1/6.19.Here we report two well-powered experiments to examine the effects of lineup size on the detection-based and non-detection-based processes underlying eyewitness decisions. Participants viewed a video of a staged crime and were subsequently presented with four lineups. In Experiment 1, lineup size was manipulated between participants by presenting lineups consisting of either three or six persons. Experiment 2 served as a conceptual replication of Experiment 1 with the only difference being that lineup size was manipulated by presenting lineups consisting of either two or five persons. In addition to simultaneous lineups (in which the lineup members are presented together), we also presented sequential lineups (in which the lineup members are presented one at a time) because sequential lineups have become the standard format for presenting lineups in countries such as the United Kingdom or Germany20. First, in most studies on lineup size, no designated innocent suspect was included in culprit-absent lineups. As a consequence, the rate of innocent-suspect identifications had to be estimated by dividing the overall number of false identifications in culprit-absent lineups by the number of persons in the lineup . However, this method places an upper limit on the rate of innocent-suspect identifications—the inverse of the size of the lineup—which may underestimate the real-world risk to innocent suspects because it is based on the assumption that the lineups are perfectly fair and biased selections cannot occur21. In the present experiments, culprit-absent lineups with a designated innocent suspect were used. This type of lineup composition is more ecologically valid than using culprit-absent lineups containing only fillers. In practice, the photograph of the suspect (whose guilt is unknown to the police) is often taken from a different source  than the photographs of the fillers  and may therefore differ to some extent from that of the fillers. Second, in some studies on lineup size, the number of persons in the lineup was confounded with the identity of the fillers such that certain fillers were only used in the larger lineups but not in the smaller lineups . In the present experiments, a random-selection procedure (see the Lineup procedures section below) guaranteed that the identity of the lineup fillers was not confounded with the lineup-size variable.The present study avoids two methodological problems in lineup size research that have been pointed out by Juncu and Fitzgerald26. However, none of these studies had a sufficiently large sample size to detect an effect of lineup size on eyewitness decisions with a high sensitivity27. The results of studies using larger sample sizes indicate that there may be a trade-off between the aims of protecting the innocent suspects on the one side and of prosecuting the culprits on the other. Adding lineup fillers decreases the proportion of false innocent-suspect identifications but also decreases the proportion of culprit identifications7. However, these response rates may hide theoretically important effects as they confound different underlying processes. The increase in culprit identifications and innocent-suspect identifications in smaller compared to larger lineups may simply be a consequence of the fact that the sampling probability with which random guessing-based selection among the lineup members leads to the identification of the suspect is inversely related to lineup size. However, it is also possible to hypothesize that the increase in culprit identifications in smaller lineups may additionally be the result of enhanced culprit-presence detection. Previous research either found that lineup size did not affect the ability to discriminate between culprits and innocent suspects7 or provided evidence for an increased ability to discriminate between culprits and innocent suspects in smaller compared to larger lineups5. Increased culprit-presence detection in smaller lineups is expected based on the assumption that “larger lineups may prove to be more difficult due to the increase in cognitive demands required by a larger lineup, that in turn may hinder identification accuracy” . In fact, the mere processing of visual information has already been demonstrated to interfere with the accuracy of memories . In the case of an increased number of filler faces in larger lineups, the additional faces represent more visual information that may require additional processing resources which may then be diverted from the to-be-detected culprit’s face, resulting in a diminished ability to detect the culprit (cf.30). Here we use the 2-HT eyewitness identification model that represents the inverse relationship between the random-sampling probability and lineup size by a fixed constant—given by 1 ÷ lineup size—which is independent of the parameters representing the detection-based and non-detection-based processes that have to be estimated from the data. Based on the literature available to date, it remains an open question as to whether culprit-presence detection, represented by the model’s parameter dP, is increased in smaller compared to larger lineups or whether dP is independent of lineup size.Among the few studies that have investigated the effects of lineup size on eyewitness decisions, some failed to find a significant difference in either the proportions of suspect identifications or the proportions of lineup rejections20. This pattern of results may be taken to suggest that the protection of suspects that is granted by large lineup sizes is only caused by the dispersion of guessing-based selections among the lineup members due to the inverse relationship to the random-sampling probability while the probability with which eyewitnesses make guessing-based selections may actually increase in larger lineups compared to smaller lineups. To test this hypothesis more directly, the model’s guessing-based-selection parameter g was compared between the larger and the smaller lineups.The second test of interest refers to guessing-based selection. Even though both culprit identifications and innocent-suspect identifications decrease with increasing lineup size—which may at first glance be taken as evidence for a decreased reliance on guessing—, a closer look at the other data categories that are available from the lineups reveals that lineup rejections decrease and filler identifications increase with increasing lineup sizehttps://www.respondi.com) who compensated participants for their participation. Of the 1821 participants who initially filled out the socio-demographic questionnaire at the beginning of the experiment, 284 had to be excluded because they either failed to complete the experiment or withdrew the consent to use their data (n = 234), saw the staged-crime video more than once due to repeated participation (n = 39) or failed the attention check . The final sample, characterized by a diverse level of education, included the data of 1537 participants  aged between 18 and 88 years . A sensitivity analysis with G*Power31 showed that, given a sample size of N = 1537, four eyewitness decisions per participant and α = β = 0.05, it was possible to detect even small effects of lineup size of size w = 0.05 on the model parameters across the lineup format conditions (df = 2). Participants were randomly assigned to one of the four lineup conditions: the three-person sequential lineup condition (n = 382), the six-person sequential lineup condition (n = 393), the three-person simultaneous lineup condition (n = 380) and the six-person simultaneous lineup condition (n = 382).Data were collected using the research panel of respondi AG based in Cologne, Germany , our participants were shown a crime video including four culprits to increase the efficiency of data collection while maintaining ecological validity given that a substantial number of real-world crimes involve multiple culprits39.Participants were shown one of two staged-crime videos (henceforth referred to as Video 1 and Video 2). In both videos, four hooligans of the German soccer club FC Bayern München (the culprits) attacked a fan of the rivaling soccer club Borussia Dortmund (the victim) at a bus station. The victim and the culprits wore fan clothing  showing their club’s color and logo. The culprits insulted the victim, made fun of him and tossed his personal belongings around. As the video progressed, the culprits became more physically aggressive. They pushed the victim around until he got knocked to the ground. The four culprits carried on with their verbal and physical abuse of the victim until they suddenly became aware of the presence of another person approaching (not depicted in the videos). At that point, they swiftly ran away. Following the lead of other researchers  to one of the four culprits of Video 2 in terms of body shape, hair color and hairstyle . Participants were randomly assigned to one of the two versions of the staged-crime video. The videos lasted about 130 s and were presented at a resolution of 885 × 500 pixels. The videos allowed participants to have a clear view on the culprits’ faces.Participants started the video by pressing the ‘Start’ button. They could not fast-forward, replay or pause the video and could only proceed to the next page after they had seen the whole video. To ensure that the video had been attended, a 10-alternatives attention-check question was displayed afterwards, asking participants to select the option that correctly indicated what type of persons had been shown in the video (correct response: “soccer fans”). Participants who failed the attention check were excluded from further analyses.Immediately after the attention-check question, participants were informed that they would see several lineups with a series of faces and that their task was to identify the FC Bayern München hooligans from the video. Participants were also told that the lineups may or may not contain a previously seen face to emphasize not only the need to identify the culprit when the culprit is present but also the need to reject the lineup when the culprit is absent.18 was used to manipulate culprit presence. Two of the culprits of the video participants had seen were randomly selected to be presented in the two culprit-present lineups alongside the fillers. In the two culprit-absent lineups, the photographs of the suspects were photographs of the actors from the video participants had not seen. For instance, if participants had viewed Video 1, then two randomly selected culprits from Video 1  served as the culprits in the culprit-present lineups and two culprits from Video 2  served as the innocent suspects in the culprit-absent lineups. This lineup construction procedure helps to ensure that the photographs of the culprits and of the innocent suspects differs to the same degree from the photographs of the fillers. This represents an ecological valid procedure. In the real world, the photograph of the suspect (whose status of being innocent or guilty is unknown to the police) is often taken from a different source  than the photographs of the fillers, which are typically obtained from face databases.Participants were then presented with four separate lineups in a randomized order, each corresponding to one of the four culprits in the video. Depending on the lineup size condition, the lineups consisted of the facial photographs of one suspect and of either two or five fillers. Two of the four lineups were culprit-present lineups, the other two were culprit-absent lineups. The crossed lineup procedure40. Fillers were chosen based on their resemblance (as determined by the authors) to the culprits in terms of body shape, hair color and hairstyle. In the three-person lineup conditions, two fillers were randomly selected for each participant from the pool of five fillers of each lineup. The randomization ensured that the critical difference between the lineup size conditions was the number of fillers and not the filler’s identity. The positions of the suspect and filler photographs were randomized in each lineup. The photographs of the suspects and the fillers showed a front view of face and neck against a black background. All faces had neutral facial expressions. All photographs were matched for lighting and face size and were displayed at a resolution of 142 × 214 pixels. Examples of the lineups used in Experiments 1 and 2 can be found at https://osf.io/ckdbr/.For each lineup, five male filler faces aged between 18 and 29 years were taken from the Center for Vital Longevity Face Database42, considering the last identification as a revision of any prior identification. Therefore, the last identification was used in the present analysis. If participants identified none of the persons in the lineup as one of the culprits, the lineup was counted as rejected. After participants had made their decision in the fourth lineup, they were debriefed and thanked for their time.Depending on the lineup format condition, the lineups were presented either simultaneously or sequentially. In simultaneous lineups, the photographs of the suspect and the fillers were shown together in one row. Participants could identify a person as the culprit by pressing the “Yes, was present” button underneath the photograph of this person or they could reject the lineup by pressing the “No, none of these persons was present” button located to the right of each lineup. After their decision, participants were asked to indicate their confidence in their response to make the procedure similar to that of a real police lineup. The participants could then initiate the presentation of the subsequent lineup by pressing the “Next” button. In sequential lineups, the photographs of the suspect and the fillers were presented, one at a time, in random succession. For each lineup member, participants decided whether or not the depicted person was one of the culprits of the video they had seen by pressing the “Yes, was present” button underneath the photograph or the “No, this person was not present” button located at the right side of the photograph. Participants were required to make a decision before they could proceed to the next photograph. After making each decision, participants were asked to indicate their confidence in their response. If participants identified more than one face in a lineup, we followed the standard police procedure in Germany and the United StatesmultiTree16. The α level was set to 0.05. The observed response frequencies and proportions for Experiments 1 and 2 are shown in Table For all analyses reported in this article, parameter estimates and goodness-of-fit tests were calculated using 17. First, given that the same sets of suspects and fillers were used in all conditions, lineup fairness must necessarily be the same across the four conditions. Therefore, the biased-suspect-selection parameter b was set to be equal across the four lineup conditions. Second, there was no a priori reason to assume that the ability to detect the absence of the culprit should differ among the conditions . Therefore, the culprit-absence-detection parameter dA was set to be equal across the four conditions. The base model incorporating these restrictions fit the data, G2(6) = 8.39, p = 0.211. The estimates of parameters b and dA were 0.07 (SE = 0.01) and 0.11 (SE = 0.02), respectively. The estimates of parameters dP and g are displayed in Fig. To analyze the results of Experiment 1, we needed four instances of the model illustrated in Fig. 15). For instance, the hypothesis that culprit-presence detection differs between the three-person lineups and the six-person lineups can be tested by setting the culprit-presence-detection parameter dP to be equal between these conditions. If the fit of the model including this restriction is significantly worse than the fit of the base model not including this restriction, then it can be concluded that culprit-presence detection differs between conditions in the direction indicated by the parameter estimates. Participants in the three-person lineup conditions were significantly more likely to detect the culprit than participants in the six-person lineup conditions, ∆G2(2) = 22.17, p < 0.001. In addition, the probability of guessing-based selection was significantly higher when the lineups contained six persons than when the lineups contained three persons, ∆G2(2) = 10.86, p = 0.004.One advantage of multinomial processing tree models is that hypothesis tests can easily be performed directly at the level of the postulated processes , saw the staged-crime video more than once due to repeated participation (n = 19) or failed the attention check (n = 19). The final sample, characterized by a diverse level of education, included the data of 1596 participants  aged between 18 and 74 years . A sensitivity analysis with G*Power31 showed that, given a sample size of N = 1596, four eyewitness decisions per participant and α = β = 0.05, it was possible to detect even small effects of lineup size of size w = 0.05 on the model parameters across the lineup format conditions (df = 2). Participants were randomly assigned to one of the four lineup conditions: the two-person sequential lineup condition (n = 408), the five-person sequential lineup condition (n = 401), the two-person simultaneous lineup condition (n = 393) and the five-person simultaneous lineup condition (n = 394).Participants were recruited and compensated as in Experiment 1. Of the 1851 participants who initially filled out the socio-demographic questionnaire at the beginning of the experiment, 255 had to be excluded because they either failed to complete the experiment or withdrew the consent to use their data  = 12.14, p = 0.059. The estimates of parameters b and dA were 0.10 (SE = 0.01) and 0.11 (SE = 0.02), respectively. The estimates of parameters dP and g are displayed in Fig. To analyze the results of Experiment 2, we needed four instances of the model depicted in Fig. G2(2) = 38.72, p < 0.001. In addition, the probability of guessing-based selection was significantly higher when the lineups contained five persons than when the lineups contained two persons, ∆G2(2) = 62.98, p < 0.001.Participants in the two-person lineup conditions were significantly more likely to detect the culprit than participants in the five-person lineup conditions, ∆The aim of Experiment 2 was to determine whether the main findings of Experiment 1 could be replicated using lineups consisting of either two or five persons. As in Experiment 1, culprit-presence detection was better in smaller compared to larger lineups and increasing the lineup size caused increased guessing-based selection among the lineup members, thus supporting the robustness of those findings.44 presumably because large lineups seem to be better than small lineups at protecting the suspect from being selected based on guessing. This protective effect may already result from the decreased sampling probability when eyewitnesses randomly select a person from a larger lineup. However, it is unclear whether having to consider a larger number of faces in a lineup may have additional effects on detection-based and non-detection-based processes. The aim of the present study was to apply the 2-HT eyewitness identification model to identify the effects of lineup size on the latent processes underlying eyewitness decisions. In Experiment 1, participants were presented with simultaneous or sequential lineups consisting of either three or six persons. In Experiment 2, participants were presented with simultaneous or sequential lineups consisting of either two or five persons. Taken together, the data of Experiments 1 and 2 provide a coherent picture by showing a decrease of culprit-presence detection and an increase in guessing-based selections with increasing lineup size.In many jurisdictions, larger minimum lineup sizes are recommended7, cannot solely be attributed to the increased sampling probability with which random guessing-based selection among the lineup members leads to suspect identifications in smaller lineups. It is also a consequence of improved culprit-presence detection in smaller compared to larger lineups. Here we can only speculate as to why this is the case. It is well established that the processing of visual information can interfere with memory of visual details . With an increasing number of fillers in a lineup, a larger portion of resources may be devoted to the processing of the visual details of more and more fillers’ faces, leaving fewer and fewer resources for processing the visual details of the suspect’s face, resulting in a diminished ability to detect the culprit. Whatever the reason, the higher probability of culprit-presence detection is an interesting feature of smaller compared to larger lineups. As a side note, in the present experiments participants saw not one but four lineups and were thus exposed to identification conditions which may perhaps have been even more resource-demanding than the identification conditions in studies with only one lineup. However, the effect of lineup size on culprit-presence detection is clearly present even when only the data of the first of the four lineups are analyzed. This is the case for the data from both Experiments 1 and 2 . This suggests that the effect of lineup size is robust regardless of whether one or more lineups are used.The model-based results thus demonstrate that the increased culprit-identification rates in the smaller compared to the larger lineups, as also previously found in other studies on lineup sizeg, then the conditional probability with which the suspect is sampled is 1/3 in a three-person lineup, but only 1/6 in a six-person lineup. The 2-HT eyewitness identification model represents this simple mathematical consequence of sampling from more options explicitly in the sampling probability given by the constant 1 ÷ lineup size. The probability with which the suspect is selected based on guessing is g · (1 ÷ lineup size). To illustrate, the probability with which the suspect is selected based on guessing is g · 1/3 in a three-person lineup and g · 1/6 in a six-person lineup. However, including more fillers also affects the probability of guessing-based selection, represented by the model parameter g. In a lineup, each face provides an opportunity for a guessing-based selection. Larger lineups provide more opportunities for guessing-based selections than smaller lineups. For instance, in a three-person lineup, the eyewitness has three opportunities to make a guessing-based selection, whereas in a six-person lineup, the eyewitness has six opportunities to make a guessing-based selection. It is thus plausible that the overall probability of making a guessing-based selection in a lineup (parameter g) increases with increasing lineup size, which is indeed what was found in the present model-based analysis. Just as in the eyewitness situation, including a larger number of plausible response options in multiple-alternative forced-choice questions provides more opportunities for guessing a false option and, as a result, impairs performance by increasing the number of false answers and by decreasing the number of correct answers . Similarly, at the level of raw response rates, larger lineups in the present experiments were associated with an increased rate of filler identifications and a decreased rate of rejections of culprit-absent lineups . However, if guessing-based selection occurs, then the selections are dispersed over a larger number of persons, including fillers that are known to be innocent . Given that the probability with which the suspect is selected based on guessing is given by g · (1 ÷ lineup size), the question is whether the increase in guessing-based selection, as represented by parameter g, is compensated or even overcompensated by a decrease of the sampling probability given by the constant 1 ÷ lineup size. In both Experiments 1 and 2, larger lineups were associated with increased guessing-based selection compared to smaller lineups, but the decrease in the sampling probability in larger lineups overcompensated this effect. As a result, larger lineups were associated with a decreased rate of suspect identifications at the level of the raw response rates in both experiments (see Table 20).To summarize, larger lineups provide more opportunities for guessing-based selection than smaller lineups, as a consequence of which guessing-based selections become more likely  = 5.47, p = 0.065; Experiment 2, ∆G2(2) = 3.24, p = 0.198) although culprit-presence detection was descriptively better for simultaneous than for sequential lineups in all conditions except in the two-person lineup condition in Experiment 2  = 209.42, p < 0.001, and Experiment 2, ∆G2(2) = 210.16, p < 0.001. This finding is in line with previous model-based analyses18 but contradicts the common assumption that sequential lineups induce more conservative responding than simultaneous lineups47. Here it seems relevant that, in contrast to most previous research, participants in the sequential lineup conditions in the present experiments were not informed that only their first positive response would count. Instead, the presentation of the sequential lineups continued after a positive identification had been made; only the participant’s last identification was counted as an identification decision . This sequential lineup protocol was used here because it corresponds to standard police protocols48 and to the original sequential lineup protocol outlined by Lindsay and Wells49. Horry et al.42 have demonstrated that a lineup is more likely to be rejected and the suspect is less likely to be selected when a first-yes-counts protocol is used, strongly suggesting that a first-yes-counts protocol discourages participants from guessing. This finding is to be expected because participants will be reluctant to use their only positive response too early in the sequence, not knowing whether better alternatives might be presented later in the sequence. The more ecologically valid sequential lineup protocol applied here gives participants the opportunity to change their mind by allowing them to select a lineup member later in the sequence even though another lineup member has already been selected earlier. In fact, there is direct evidence that the probability of guessing-based selection is significantly less likely when first-yes-counts instructions are used than when the standard police protocol is used17.In contrast to most prior work, we included both simultaneous and sequential lineups in the present experiments. Given that in some countries such as the United Kingdom or Germany, the sequential format is the standard way for presenting police lineups20, the present results indicate that there is a trade-off between the aims of providing optimal conditions for detecting the culprit on the one side and protecting the suspect from the consequences of guessing-based selections on the other. Specifically, under the circumstances realized in the present experiments, larger lineups were associated with a disadvantage in culprit-presence detection and guessing-based selection while their main advantage may be seen in a broader dispersion of those guessing-based selections among the lineup members.The purpose of the present study is to contribute to a better understanding of lineup size effects on eyewitness decisions using the 2-HT eyewitness identification model. By taking into account the full range of data categories observed in typical lineup procedures , the model provides new insights into how lineup size affects the latent detection-based and non-detection-based processes underlying eyewitness decisions. The results demonstrate that, compared to smaller lineups, larger lineups are associated with a decreased ability to detect the culprit in the lineup and an increased probability of selecting a lineup member based on guessing. However, the increase in guessing-based selection in larger lineups is overcompensated by a reduced probability of randomly selecting the suspect among the lineup members if guessing-based selection occurs. As a consequence, the rate of innocent-suspect identifications decreases with increasing lineup size, albeit to a lesser degree than the rate of culprit identifications. In line with previous research


While user-generated short videos have become very common in tourism marketing, how they affect potential tourists’ decisions has not been discussed academically. Based on the customer inspiration theory, this study explored the effects of different tourism short video contents on potential tourists’ travel intentions, as well as the mediating effect of customer inspiration and the moderating effect of consumption orientation through three experiments. The following conclusions were drawn. (1) Tourism short videos significantly increased potential tourists’ customer inspiration and travel intention; (2) The customer inspirations (“inspired-by” and “inspired-to”) chain-mediated the relationship between tourism short videos and potential tourists’ travel intentions; (3) Consumption orientation positively moderated the chained mediation effect above, and the chained mediation effect of tourism short videos on the travel intentions of tourists with hedonistic motivations through inspire-by and inspire-to is stronger than that of tourists with utilitarian motivations. The above findings could help expand the perspective of tourism short video research and provide suggestions for tourism business managers to apply short video content to marketing. Short videos are those with durations under 5 min that can be shot, edited, and beautified quickly by smart mobile devices and shared in real-time on social media platforms . TourismTourists usually conduct an information search before deciding to visit a destination . In the Research has found that visual information could help to improve the fluency of information processing, resulting in more positive evaluation results . Short vIn addition to external factors , tourists’ behavioral intentions are also influenced by their individual traits . Among them, travel intention is one of the most important prerequisites for participation in travel activities . AccordiDespite the insights from the existing literature , the exaThis study has made three main contributions. Firstly, it enriched the mechanism of tourism short video content marketing from the perspective of customer inspiration. Secondly, this study examined the mediating mechanism and boundary conditions between different tourism short video contents and potential tourists’ travel intentions and illustrated the existence of a customer inspiration triggering mechanism of tourism short videos. Finally, this study provided suggestions for tourism managers in terms of further triggering customer inspiration by stimulating different consumption orientations and, in turn, designing more effective content marketing strategies for tourism short videos.2.2.1.Tourism short videos are those in which tourists share their comments on the destinations, tourism products, and services they experienced using the destination as background and express their personal views on the experiences, services, or products . AccordiInformation-oriented short videos are designed to deliver information about product attributes, price information, service experiences, and brand events . This tyEmotion-oriented short videos are designed to evoke emotional responses from the viewers using a variety of narrative storylines, including positive or negative emotions, and subsequently obtain a persuasive effect . Unlike When applying the above classification methods to tourism short videos, this study defines information-oriented tourism short videos as those focusing on tourist destination attraction information presentation and introduction of the historical background, cultural traditions, travel arrangements, price information, etc. of the destination. Emotion-oriented tourism short videos are defined as those showing the direct emotional connections between tourists and destinations in dramatic ways and drawing the viewers into the storytelling, thus activating their positive emotions. While both types of videos mentioned above are prevalent on social networking services (SNSs) such as YouTube and TikTok, in-depth analyses of how different tourism short video contents influence the attitudes and behaviors of potential tourists. To this end, this important topic is analyzed in this paper to fill the gaps in the existing literature.2.2.Derived from the generalized concept of “inspiration” in social psychology , customeAccording to the relevant literature, a large number of studies provided empirical evidence for the sources of customer inspiration and their triggering mechanisms in tourism short videos. Customer inspiration is not spontaneous but inspired by external marketing stimuli . Utilita2.3.Although studies have demonstrated the effects of tourism short video content on tourists’ behavioral intentions, questions about which content is more compelling need to be further explored in addition to further revealing the action mechanisms . InformaBased on the above discussion, the following hypotheses are formulated.H1: Emotion-oriented tourism short video contents have a significant positive effect on potential tourists’ behavioral intentions compared to information-oriented tourism short video content.2.4.Customer inspiration is a temporary motivation state produced by customers in response to marketing stimuli that leads consumers to produce new ideas and turn them into reality . CustomeAccording to the customer inspiration theory, when potential tourists watch a tourism short video, its series of inspiring contents, such as destination scenery, destination activities and events, and fresh attempts in travel, may trigger them to form new ideas (inspire-by) about travel consumption with the spirit of self-transcendence. This temporary activation state further motivates potential tourists to urgently pursue tourism experience realization , the potential tourist may discover a new and better possibility (which has never been discovered before) and form a sense of positivity, clarity, and self-improvement, thus producing an inner motivation to turn new ideas into action . At thisFurther, customer inspiration consists of two dimensions: inspire-by and inspire-to, which are in a coherent order . There iOn this basis, we formulate the following hypothesis.H3: Compared with information-oriented tourism short video content, emotion-oriented tourism short video content positively influences potential tourists’ travel intentions through the chained mediation effect of inspire-by and inspire-to.2.5.According to the existing research on customer inspiration, individual traits  affect inspiration generation by moderating the relationship between information sources and customer inspiration . GeneralResearch has found that compared to customers with utilitarian motivations, customers with hedonistic motivations are generally more daring and radical and more active in discovering new products, trying innovations, and accepting new things . They alTherefore, we can formulate the following hypothesis.H4: Consumption orientations moderated the chained mediation effect in which tourism short video contents affect the travel intentions through inspire-by and inspire-to, i.e., the chained mediation effect above is stronger for potential tourists holding hedonistic motivations than those holding utilitarian motivations.2.6.Based on the above hypotheses, as shown in 3.Experiment 1 aimed to explore the effects of different tourism short video contents on travel intentions. Since short videos are not professionally produced anytime and anywhere, and users focus on conveying information and emotional expression, studies have focused on the emotional and informational aspects of video content . Therefo3.1.3.1.1.M = 21.068, SD = 1.713). Subjects with daily viewing time lengths of 30 min or less, 30 to 60 min, 1 to 2 h, 2 to 4 h, and over 4 h accounted for 17.74, 22.58, 29.03, 24.19, and 6.45%, respectively.Experiment 1 was conducted at a university in China from October 12 to 17, 2021, on a paid (¥1 each) sample of 124 students with undergraduate and higher degrees from that university, including 62 in the information-oriented group and 62 in the emotion-oriented group. Male students accounted for 46.8% of the experimental sample, and female students accounted for 53.2%. The average age was21 years  of this study, was selected as the tourist destination. As a famous tourist city in China with rich tourism resources and an important city for tourism short video promotion, Qingdao ranked 4th in terms of the comprehensive index in the March 2022 National Municipal Culture and Tourism New Media Communication Power Index Report released by the Culture and Tourism Industry Index Lab. The researchers searched the TikTok platform with the keyword “Qingdao tourism,” sorted the results by likes, and reviewed the top 500 Qingdao tourism short videos in detail. The tourism short videos were classified based on the existing research , and thet-test showed a significant difference between information-oriented and emotion-oriented short videos . For screening question 1, the scores of the information-oriented group were significantly higher than the emotion-oriented group . For screening question 2, the scores of the information-oriented group were significantly lower than the emotion-oriented group . Taken together, the short video experiment material was successful and effective in manipulating the independent variables, and thus a formal experimental study could be conducted on top of this.Through the pilot study, we performed a manipulation check to verify the effectiveness of short video stimuli. Specifically, 74 subjects were recruited through payment (¥1) and randomly assigned into two groups to watch one of the videos. After watching the video, subjects filled out a questionnaire to determine the type of video content (information-oriented or emotion-oriented) first and then to distinguish (1) to what extent did the video focus on presenting information about the destination and (2) to what extent did the video focus on appealing to your emotions , their familiarity with the destination was not significantly different , neither was their familiarity with the character . Therefore, the experimental manipulation was successful.According to the independent samples 3.2.2.Minf = 3.887, Memo = 4.919, F = 26.547, p < 0.000] on travel intentions (α = 0.804), indicating that emotion-oriented short videos were more likely to inspire tourists’ travel intentions than information-oriented short videos, thus, validating H1.In this study, one-way variance analysis (ANOVA) was conducted to test the differences in the effects of different types of short videos on tourists’ travel intentions. Data analysis results showed that the different types of short videos had significantly different effects  on travel intentions (α = 0.804), indicating that emotion-oriented short videos were more likely to inspire tourists’ travel intentions than information-oriented short videos, thus, again validating H1.In this study, one-way variance analysis (ANOVA) was conducted to test the differences in the effects of different types of short videos on tourists’ travel intentions. Data analysis results showed that the different types of short videos  had significantly different effects  on inspired-by customer inspirations (α = 0.859), thus validating H2a; the different types of short videos had significantly different effects  on inspired-to customer inspirations (α = 0.928), thus validating H2b.In this study, one-way variance analysis (ANOVA) was conducted to test the differences in the effects of inspired-by and inspired-to on tourists’ travel intentions. According to the results, the different types of short videos had significantly different effects . The emotion-oriented short videos have a significant and positive impact on inspired-to customer inspiration . Inspired-by customer inspirations have a significant and positive impact on inspired-to customer inspirations . Inspired-by customer inspirations have a significant and positive impact on travel intentions . Inspired-to customer inspirations have a significant and positive impact on travel intentions . After adding continuous mediating variables, the regression coefficient  of emotion-oriented short videos on travel intention is no longer significant  compared to information-oriented short videos. In terms of the three mediation paths, the “short video – inspired-by customer inspiration – travel intention” path is significant , the “short video – inspired-to customer inspiration – travel intention” path is significant , and the “short video – inspired-by customer inspiration – inspired-to customer inspiration – travel intention” path is significant . The above results validate H3.To investigate the chain mediation effect of inspired-by customer inspiration and inspired-to customer inspiration, this study used Model 6 of the PROCESS plugin provided by Thus, it is concluded that emotion-oriented tourism short videos (vs. information-oriented ones) increase inspired-by customer inspirations, which, in turn, leads to higher inspired-to customer inspirations and finally strengthens the travel intentions, thus proving H3.5.Experiment 2 verified that mediating effect of customer inspiration in the relationship between short videos and travel intentions. Experiment 3 examined whether tourist consumption orientations had a moderating effect in that relationship, where a 2 (information-oriented vs. emotion-oriented short videos) × 2 (utilitarian vs. hedonistic consumption orientations) matrix was used to validate H4.5.1.5.1.1.M = 20.120, SD = 1.402). Subjects with daily viewing time lengths of 30 min or less, 30 to 60 min, 1 to 2 h, 2 to 4 h, and over 4 h accounted for 20.34, 33.05, 33.05, 11.44, and 2.12%, respectively. Questionnaire respondents were recruited by payment, and no duplicate responses or inattentive responses were found.Experiment 3 was conducted from March 11 to 15 and May 20, 2022, on 236 undergraduates recruited by payment (¥2) from three universities in southern China. Of those, 34.3% were male, and the average age was 20 years (5.1.2.n = 60), utilitarian and emotion-oriented short videos (n = 66), hedonistic and information-oriented short videos (n = 54), and hedonistic and emotion-oriented short videos (n = 56). First, the subjects were told that they were about to prepare for a trip. Specifically, the utilitarian group was told that the aim of the trip was to save as much money as possible while visiting as many sites as possible. The hedonistic group was told that the aim of the trip was to enjoy as much freedom of mind and body as possible. After that, the experimental assistant began to play the short videos. After viewing, the subjects were asked to report their travel motivations , judge the type of the short video content, complete the customer inspiration and travel intention measurement scales . In terms of different travel motivations, the two groups of subjects differed in the purpose of spending less  and the purpose of finding freedom and pleasure . The two groups had no difference in familiarity with the destination  and the character . Therefore, the experimental manipulation was successful.The independent samples 5.2.2.Minf = 3.974, Memo = 4.457, F = 11.237, p = 0.001] on travel intentions (α = 0.813), indicating that emotion-oriented short videos were more likely to inspire tourists’ travel intentions than information-oriented short videos, thus, again validating H1.In this study, one-way variance analysis (ANOVA) was conducted to test the differences in the effects of different short video contents on potential tourists’ travel intentions. The results showed that the different short video contents had significantly different effects  on inspired-by customer inspirations (α = 0.829), thus validating H2a; the different types of short videos had significantly different effects  on inspired-to customer inspirations (α = 0.902), thus validating H2b.In this study, one-way variance analysis (ANOVA) was conducted to test the differences in the effects of inspired-by and inspired-to on tourists’ travel intentions. According to the results, the different types of short videos had significantly different effects . Under hedonistic travel motivations, the continuous mediation effects of inspired-by and inspired-to customer inspirations between short videos and travel motivations were not valid, β = 0.359, SE = 0.088, 95% confidence interval . Thus, the chained mediation effects under different travel motivations differed significantly, βhed-uti = 0.265, SE = 0.087, 95% confidence interval . Therefore, H4 was verified, i.e., travel motivation moderated the chained mediation effects of tourism short video content on travel intentions through inspire-by and inspire-to.To test hypothesis 4, we imported the independent variable of different types of short video content , the dependent variable of travel intention, the mediating variables of inspired-by and inspired-to customer inspirations, and the moderating variable of travel motivation into the Model83 in the PROCESS plugin provided by 6.6.1.In this study, three experiments were conducted to explore the impact of different types of tourism short video content on potential tourists’ travel intentions. We came to the following conclusions. Firstly, different tourism short video contents significantly affect potential tourists’ travel intentions. The results show that emotion-oriented short videos are more likely to stimulate potential tourists’ travel intentions than information-oriented short videos. Emotion-oriented short videos are conducive to facilitating potential tourists to make travel decisions. Secondly, the results show that emotion-oriented short videos induce stronger inspired-by and inspired-to customer inspirations than information-oriented short videos, prompting potential tourists to form stronger customer inspirations. Under the chained mediation effects of inspired-by and inspired-to inspirations, the direct effect of emotion-oriented short videos on potential tourists’ travel intentions is no longer significant, but the chained mediation effect is significant. These results suggest that emotion-oriented short videos indirectly increase potential tourists’ travel intentions by stimulating their customer inspiration. Finally, consumption orientation moderates the chained mediation effects of tourism short video content on travel intentions through inspire-by and inspire-to; For potential tourists holding hedonistic motivations, tourism short videos induce stronger new ideas of travel experiences in them, which, in turn, create the urge to acquire such tourism experiences, eventually reinforcing the travel intentions in the tourism decision-making process. In contrast, for customers with utilitarian motivations, inspire-by and inspire-to have no significant effect on the chain mediating effect between short videos and travel intention. The above study expands the perspective of tourism short video research  and enri6.2.As one of the few studies on tourism short videos from a customer inspiration perspective, this paper makes the following important theoretical contributions.Firstly, this study provides a promising new perspective for tourism short video research. The existing research on tourism short videos mainly focuses on destination short video marketing , tourismSecondly, this study applies the customer inspiration theory to further explore the intrinsic mechanism by which tourism short videos drive potential tourists’ travel intentions and successfully verifies the chained mediation effects of inspire-by and inspire-to in that mechanism, which enriches the research on the intrinsic mechanism of tourism short videos. Customer inspiration theory suggests that inspiration is a mediating relationship between external marketing stimuli and behavioral intentions. Based on customer inspiration theory, this study proposes a new mechanism driving the formation of potential tourists’ travel intentions:emotion-oriented tourism short videos (vs. information-oriented ones) with rich emotions and storytelling narratives promote potential tourists’ travel intentions by stimulating their customer inspirations (including the generation of new ideas and the urge to acquire travel experiences). The results of this empirical study validate the hypotheses proposed based on the framework of customer inspiration theory and demonstrate the applicability of customer inspiration theory in explaining the behavior mechanism of tourism short video users, which contributes to the application of customer inspiration theory in the field of tourism short video content marketing.Finally, this study explores the factors influencing potential tourists’ travel intention and uses consumption orientation as a moderating variable, which helps to expand the boundary conditions for the positive impact of tourism short videos. Previous studies have also found that among different consumption orientations, hedonistic motivations promote the formation of customers’ behavioral intentions . This st6.3.In addition to theoretical contributions, this study also provides some suggestions on tourism short video content marketing.Firstly, the findings in this study confirm that tourism short videos, especially emotion-oriented tourism short videos, have a facilitating effect on travel intentions from the customer inspiration perspective. On the one hand, the increasing number of entities providing tourism short video content must provide higher quality content in order to stand out from the large number of short videos and acquire the limited attention resources of potential tourists. On the other hand, high-quality tourism short video content marketing must stimulate potential tourists’ customer inspirations and effectively transform the short video content and tourism products. Thus, tourism short video content must be designed from three aspects: tourism product perspective, tourist perspective, and short video content perspective.Secondly, the findings in this study point out that customer inspiration is an important mediating variable through which tourism short videos affect potential tourists’ travel intentions. Therefore, tourism enterprise managers should first clearly understand that stimulating customer inspiration is not easy and draw the power of tourists as mobile self-media carriers by providing a large number of short video materials to facilitate their creation; For consumers, customer inspiration may stimulate the potential traveling intention. However, constraints such as travel, accommodation, and economy must be considered to reduce the failure of the tourism experience caused by impulsive decision-making. Secondly, flexible editing and choreography skills are also important ways of inspiration. Tourism agency managers can make a large number of skill templates through platforms such as TikTok for direct use by short video creators, thus promoting tourism marketing of the destination. Thirdly, tourism agency managers should further explore the cultural connotation and shape tourism branding IPs to reduce the time for tourists to produce inspiration and compress the possibility of ambiguity in the tourism decision-making process.Finally, the results showed that hedonistic motivations had a stronger impact on potential tourists’ customer inspiration. Therefore, tourism agencies should first use digital means to identify different consumer motivations using “baits” such as most beautiful attraction contests, thus segmenting potential tourists from the motivation perspective. Secondly, emotion-oriented tourism short videos should be distributed to groups with hedonistic motivations to stimulate their customer inspirations. In addition, tasks such as sharing, liking, and commenting should be assigned to enhance their stickiness with tourism agencies, including the destination and scenic spot. Detailed tour information about the destination should be distributed to groups with utilitarian motivations to save the time cost.6.4.Although this study expands the perspective to explain the relationship between tourism short videos and travel intentions, some limitations remain.Firstly, we conducted three experiments and used three different Chinese samples to test the hypotheses, which ensured the preciseness of the experimental method but limited the generalizability of the findings as these data were collected in China. Therefore, future studies may examine the relationship between the variables with a larger and more diverse population and scales of good reliability from a cross-cultural perspective.in situ. Cooperation with tourism short video platforms (such as YouTube and TikTok) can also be considered to analyze the relationship between tourism short videos and tourism consumption in different tourism consumption stages using the real consumption data available on those platforms, thus further expanding this study.Secondly, although this study used real short videos as experimental materials in the scenario-based experiments, the subjects’ contextual integration and emotional experience may not be as authentic. Therefore, future research can investigate the relationship between different types of tourism short videos and travel intention Thirdly, using questionnaire scales to measure customer inspiration, a psychological state, might lead to a certain subjective bias. In the future, a more accurate multi-channel physiological instrument could be used to obtain objective data for more precise impact effect analysis in real situations.Finally, only short video content was selected as an influencing factor on the travel intention in this study, while other factors may also affect potential tourists’ travel intentions, such as short video characters and short video usage experience. In the future, subsequent studies can further explore the mechanism by which tourism short videos promote travel intention formation and short video content marketing strategies through in-depth interviews and focus groups, thus further improving our study on this basis.The original contributions presented in the study are included in the article/GW is responsible for the research design, data collection, draft writing, and paper revision of this project. XD was responsible for part of the literature collection. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The development of automatic methods for image and video quality assessment that correlate well with the perception of human observers is a very challenging open problem in vision science, with numerous practical applications in disciplines such as image processing and computer vision, as well as in the media industry. In the past two decades, the goal of image quality research has been to improve upon classical metrics by developing models that emulate some aspects of the visual system, and while the progress has been considerable, state-of-the-art quality assessment methods still share a number of shortcomings, like their performance dropping considerably when they are tested on a database that is quite different from the one used to train them, or their significant limitations in predicting observer scores for high framerate videos. In this work we propose a novel objective method for image and video quality assessment that is based on the recently introduced Intrinsically Non-linear Receptive Field (INRF) formulation, a neural summation model that has been shown to be better at predicting neural activity and visual perception phenomena than the classical linear receptive field. Here we start by optimizing, on a classic image quality database, the four parameters of a very simple INRF-based metric, and proceed to test this metric on three other databases, showing that its performance equals or surpasses that of the state-of-the-art methods, some of them having millions of parameters. Next, we extend to the temporal domain this INRF image quality metric, and test it on several popular video quality datasets; again, the results of our proposed INRF-based video quality metric are shown to be very competitive. Image quality evaluation is of crucial importance in the media industry, where it has numerous practical applications, but also in applied disciplines such as image processing and computer vision, where it plays an important role in the development, optimization, and testing of algorithms. It may also be used to optimize any trade-offs between the components of an image/video transport system  and perceived quality, aiming for a good user experience while maximizing the efficiency of the transmission, and thus reducing the ecological footprint associated to streaming. Subjective evaluation, consisting in measuring image quality by human beings, is costly and time-consuming. Therefore, the goal of objective quality assessment is to develop automatic methods that produce quantitative measures that are consistent with the perception of human observers. But this is a very challenging open problem in vision science, given the limitations of current visual perception models and the way they are exacerbated by emerging image display technologies of ever-increasing resolution, contrast, color gamut and framerate .Image quality methods can be divided into three categories: full-reference methods, which compare an original image with a distorted version of it; reduced-reference methods, that compare some characteristics of the distorted and reference image since the complete reference image is not available; and no-reference methods , operating solely on the distorted image.In this article we will focus on full-reference methods, which constitute the vast majority of image quality approaches. A simple solution, and possibly the most widely used metric to estimate image quality, is the peak signal-to-noise ratio (PSNR), which is a non-linear transform of the mean square error (MSE) between the reference and the distorted images, another very popular metric. These metrics are simple to calculate, and they have a clear physical meaning; however, they are not very well correlated with perceived visual quality .Therefore, in the last two decades, the goal of image quality assessment (IQA) research has been to improve these metrics by developing more sophisticated methods that mimic some aspects of the visual system. For instance, the Normalized Laplacian Pyramid Distance (NLPD)  is basedFor video quality assessment (VQA), a simple option is to apply image quality metrics on a frame-by-frame basis, but this type of approach often provides a limited performance, especially in the case of high framerate (HFR) videos . TherefoDespite the notable advances in the field, it is important to point out that state-of-the-art quality assessment methods still share a number of shortcomings, like their performance dropping considerably when they are tested on a database that is quite different from the one used to train them , or theiThe main contributions of this work are as follows. Firstly, we start by optimizing, on a classic image quality database, the four parameters of a very simple INRF-based metric, and proceed to test this metric on three other databases, showing that its performance equals or surpasses that of the state-of-the-art IQA methods, some of them having millions of parameters. Secondly, we extend to the temporal domain this INRF image quality metric, and test it on several popular video quality databases; our results show that the proposed INRF-based VQA is very competitive, ranking best in several challenging scenarios like those provided by a very recent dataset for high frame rate videos. Finally, and to the best of our knowledge, the approach of using a neural summation model to create IQA and VQA methods is completely novel, and given its success it might pave the way for other neuroscience models to inform the design of new image quality assessment algorithms.The structure of this manuscript is as follows. Section “2. Proposed methods for IQA and VQA” explains the INRF model and how it is used for IQA and VQA. Section “3. Materials and methods” provides details on how the INRF parameters are optimized for IQA and VQA usage, describes the different datasets on which performance is tested and explains further points on how the assessment of INRF for IQA and VQA models is performed. Section “4. Results and comparisons” shows the performance of INRF, employed for IQA and VQA, on different datasets, and results are compared with performance of other state-of-the-art IQA and VQA algorithms on those same datasets. Finally, in Section “5. Discussion,” results and their implications are discussed.In vision science, the receptive field (RF) of a neuron is the extent of the visual field where light influences the neuron’s response. In the “standard model” of vision, the first stage is a filtering operation consisting of multiplying the intensities at each local region of an image stimulus by the values of a filter (the weights of the RF), and summing the resulting intensities ; this we•Adaptation makes the linear RF change with the input and, in fact, the linear RF has been observed to have different sizes, orientations, preferred directions, or even different polarity (ON/OFF) for different stimuli ;•a linear RF depends on the choice of basis functions used to estimate it ;•cannot be modeled as a linear RF followed by an output non-linearity  is:The INRF equation for the response of a single neuron at location mi stands for a 2D kernel m, locations yi  are neighbors of x, λ is a scalar, wi stands for a 2D kernel w, σ represents a non-linearity, and g is also a 2D kernel.where mi, and some other dendrites are non-linear and their contributions are summed with weights wi. The feedback from the soma is reflected in the shifting nature of the non-linearity σ, expressed by the term yi.The model is based on knowledge about dendritic activity: some dendritic branches act as non-linear units , a singlm,w,g have Gaussian form and the non-linearity is a power-law sigmoid, and applying it to grayscale images, where x and yi now denote pixel locations and I(⋅) are pixel values, the model response INRF(⋅) emulates the perceived image and can explain several visual perception phenomena that challenge the standard model  at the surround luminance level when the background is uniform, but the phenomenon disappears for salt and pepper background . The INR•White’s illusion under noise. The INRF output qualitatively predicts the observers’ response to White’s illusion when bandpass noise is added, while in •irradiation illusion”). This phenomenon can be reproduced with a fixed INRF formulation, while a L + NL model needs to change with the stimulus  between the processed images is computed. The underlying idea here was that the INRF model performs a sort of “perceptual linearization,” a non-linear transform that brings light-intensity images , to the space corresponding to perceptual images (which can now be compared with a simple Euclidean metric like RMSE). This metric had only five parameters that were optimized for the “crispening” brightness perception experiment mentioned above (with σ a power-law sigmoid and g a delta function) on a handful of synthetic images, and despite this fact and the simplicity of the metric, when tested on the natural image database TID2013  Given a grayscale image m,w,g are 2D Gaussians of standard deviations σm,σw,σg, respectively, λ is a scalar, S is a sigmoid that has the form of an atan function, and the symbol ⋆ denotes the 2D convolution.where the kernels I and its distorted version ID is computed as:(2) The INRF-IQA value comparing image I is the luminance channel of ID is the luminance channel of where grayscale image m,σw,σg, and λ, are chosen so as to maximize the correlation between the INRF-IQA perceptual distance of Eq. 3 and the mean opinion scores (MOS) of human observers over the large-scale natural image database TID2008 ((3) The values for the four parameters of the metric, namely σ TID2008  . For instance, if V is a 2K video of resolution 1920×1080, and given that images in TID2008 are of size 512×384, the scaling factor is where m,σw,σg, and λ, and the optimal parameters found are: σm = 1.74,σw = 25,σg = 1 and λ = 3.The parameter values of INRF-IQA (Eq. 3) are found through optimization on the TID2008 dataset  to maximWe use the LIVE , CSIQ anWe test the performance of our INRF-VQA model on four popular video quality databases, all publicly available and containing observers’ scores for a number of common spatial and temporal distortions: LIVE-YT-HFR, LIVE-MOBILE, LIVE-VQA and VQEG-HD3.The very recent LIVE-YT-HFR dataset  spans 16The LIVE-MOBILE dataset ,b,c consThe LIVE-VQA dataset ,b consisFinally, the VQEG-HD3 dataset  consistsExisting subjective image quality and video quality scores in each of the datasets are, respectively, correlated with our INRF-IQA and INRF-VQA metrics. In the case of INRF-IQA, SRCC is calculated. For INRF-VQA, SRCC and PLCC are obtained. Before calculating PLCC, predicted objective video quality scores are passed through a four-parameter sigmoid function as described in x stands for the raw INRF-VQA scores, and β1,β2,β3, and β4 are its parameters where Performance of our INRF-IQA metric is evaluated on images from the LIVE , CSIQ anWe start evaluating the performance of our INRF-VQA metric on videos from the very recent  LIVE-YT-HFR dataset . This daIt is important to note that our INRF-VQA metric, as well as many other full-reference metrics, needs reference and distorted video sequences to have the same number of frames. For this reason, when a distorted video has a lower frame rate than the reference video, either the reference video must be downsampled to match the number of frames in the distorted video or the distorted video must be upsampled to match the reference. For 60 and 30 fps, and for the four VQA methods, performance is seen to improve when a frame dropping strategy rather than a duplication one is used (for which correlations are low). For this reason, we use this approach as our preferred choice to evaluate INRF-VQA for 30 and 60 fps.The results in Next, we evaluate our INRF-VQA metric on a very popular dataset, the LIVE-MOBILE ,b,c. TabTo further test our INRF-VQA metric, we evaluate its performance on two other popular video quality datasets widely used in the VQA literature, LIVE-VQA ,b and VQAs a final note, we would like to remark that our INRF-VQA metric had its parameters optimized on an image dataset rather than a video dataset: this is also the approach followed by several of the best-performing VQA methods, like MS-SSIM, VIF, VSNR or NQM, while other excellent VQA algorithms are specifically trained on video quality datasets, like VMAF or GSTI.In this study we have taken a recent neural summation model and used it as a foundation for novel metrics for image and video quality assessment. To the best of our knowledge this is a novel approach, that might pave the way for other neuroscience models to inform the creation of IQA and VQA methods.Our validation, on popular datasets of observer scores, shows that our proposed metrics for IQA and VQA compare very well with the state-of-the-art and, very importantly, that their performance is very good and does not drop substantially for different datasets, unlike what many methods are prone to do and is often the case with those based on deep learning techniques.For the very recent, and challenging, video quality dataset LIVE-YT-HFR , our metIt is important to remark that the parameters of our proposed INRF-VQA metric were optimized for image data, i.e., INRF-VQA was not trained on any video dataset. INRF-VQA is a straightforward extension of the INRF-IQA method, in which metric values are computed on a frame-by-frame basis and then averaged over time to produce a single score for the video: the fact that this simple temporal extension of an IQA method works so remarkably well for VQA challenges a common assumption in the literature, where it is thought that the best VQA metrics must be developed specifically for video Regarding future work, we want to point out that for INRF-VQA we have resorted to very simple and very effective design choices, like mean average for temporal pooling instead of a more optimal strategy e.g., , or the https://github.com/raullunadelvalle/INRF-IQA-VQA.git.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. MATLAB code with a numerical implementation of the INRF-IQA/INRF-VQA algorithm is available at RL and MB wrote the manuscript. RL did the final version of the INRF-VQA code and carried out the INRF-VQA experiments. IZ did the INRF-IQA version of the code and carried out the INRF-IQA experiments. MB conceptualized the original idea. All authors reviewed the manuscript.


To examine trends in public awareness and knowledge of drinking guidelines in the UK since their revision in 2016, which had moved from a daily to a weekly guideline, made the guideline the same for men and women, and reduced the guideline for men by around one-third.Data were from a representative, repeat cross-sectional survey. We analysed changes in awareness and knowledge of drinking guidelines among 8168 adult drinkers between 2016 and 2022 and associations with sociodemographic characteristics, smoking status and level of alcohol consumption.The proportion of drinkers aware of guidelines declined from 86.0% (95%CI 84.0–88.0%) in 2016 to 81.7% (79.5–84.0%) in 2019, then increased during the COVID-19 pandemic, peaking at 91.6% (90.1–93.1%) in 2020. The proportion who correctly identified the guideline as a maximum of exactly 14 units/week remained at around a quarter from 2016  to 2018 , whereas the proportion who gave a figure of 14 units or fewer rose from 52.1 (49.2–55.0%) to 57.4% (54.6–60.3%). However, by 2022, guideline knowledge had worsened significantly, with these figures falling to 19.7 (17.4–21.9%) and 46.5% (43.6–49.4%), respectively. Changes over time were similar across subgroups. Odds of guideline awareness and knowledge were higher among drinkers who were aged ≥35, female, more educated and from more advantaged social grades.The majority of adult drinkers in the UK are aware of low-risk drinking guidelines. However, 6 years since their announcement, knowledge of the revised drinking guidelines remains poor. Less than a quarter know the recommended weekly limit and only around half think it is 14 units or less. Inequalities have persisted over time, such that disadvantaged groups remain less likely to know the guidelines. Alcohol use ranks among the leading risk factors for death and disability-adjusted life years globally . We restricted our sample to adults (≥18 years) who reported having drunk alcohol in the past 6 months. Our primary analysis used annual data collected in April of each year from 2016 to 2022, to limit seasonal influence on trends over time. This provided a sample of 8168 participants. We also report descriptive data from 2115 participants surveyed in November/December 2015 (combined) to provide an indication of change from before to after the change in guidelines, taking the total sample size to 10,283. Secondary analyses used data from all available waves, providing a sample size of 34,265.The explanatory variable was survey year, which ranged from 2016 to 2022, and was analysed as a categorical variable to allow for non-linear associations with our outcomes.As you may be aware, some drinks contain more alcohol than others. The amount of alcohol in a drink is measured in units. Before today, have you ever heard of there being a recommended maximum number of alcohol units people should drink in a day or a week? This is sometimes known as a “drinking guideline”.’ Response options were yes, no and don’t know. We dichotomized responses to distinguish between those who were aware of the guidelines and those who were not aware or responded don’t know.Outcome variables were awareness and knowledge of drinking guidelines. Awareness of drinking guidelines was assessed among participants who reported drinking any alcohol in the past 6 months with the question: ‘Can you tell me how many units per day or per week that drinking guideline is for men/women?’ Participants could respond in units per day or per week. We analysed responses in five categories: (i) fewer than 14 units per week (or 2 units per day), (ii) exactly 14 units, (iii) more than 14 units, (iv) aware of but did not know the guidelines and (v) not aware of the guidelines. For some analyses (as described below), we dichotomized responses to distinguish between those who knew the guidelines were equal to or fewer than 14 units per week and those who did not .Knowledge of drinking guidelines was assessed among participants who indicated being aware of drinking guidelines before the interview. They were asked about the guideline for their own gender, with the question: ‘male, female, or in another way. Data are not reported separately for the latter group because very small numbers meant we were not able to derive precise estimates of prevalence. Education was categorized as any vs. no post-16 qualifications. Social grade was categorized as ABC1  vs. C2DE . This occupational measure of social grade is a valid index of SES, widely used in research in UK populations .Analyses were done in SPSS v.27 and R Studio v.1.4.1717. The analyses were not pre-registered and should be considered exploratory. Data were weighted  to match the English population profile on age, social grade, region, tenure, ethnicity and working status within sex. The dimensions are derived monthly from a combination of the English 2011 census, Office for National Statistics mid-year estimates and an annual random probability survey conducted for the National Readership Survey. Missing cases were excluded on a per-analysis basis. We applied a false discovery rate correction  to all PWe used descriptive statistics to estimate the prevalence and 95% confidence interval (CI) of (i) awareness and (ii) knowledge of drinking guidelines (using the five-category variable), in relation to survey year. We used multivariable logistic regression to analyse associations of survey year, sociodemographic characteristics and drinking and smoking status with odds of awareness and knowledge (using the two-category variable) of drinking guidelines. Then, to explore whether changes in awareness and knowledge of drinking guidelines over time were moderated by sociodemographic characteristics, drinking status and smoking status, we ran a series of logistic regression models in which two-way interactions between survey year and (i) age (18–34 vs. 35–64 vs. ≥65), (ii) gender , (iii) education , (iv) occupational social grade (ABC1 vs. C2DE), (v) smoking status (current smoker vs. non-smoker) and (vi) level of alcohol consumption (continuous AUDIT-C score) were added. Our primary analyses focused on data collected in April of each year, to remove any seasonal influence on trends. We also present descriptive statistics on trends using data from all available waves as secondary analyses in above 14 units per week also declined significantly over time, from 27.3% [24.7–29.9%] in 2016 to 17.1% [15.0–19.3%] in 2022. There was a substantial increase in the proportion of drinkers who were aware of drinking guidelines but did not know the guideline from 2020 onwards, increasing from 6.6% [5.2–8.1%] in 2016 to 25.2% [22.7–27.7%] in 2022.In 2016, just 25.0% [22.4–27.5%] of drinkers were able to correctly identify the guideline as a maximum of exactly 14 units per week and 52.1% [49.2–55.0%] gave a figure of 14 units or fewer . AlthougTrends in the prevalence of awareness and knowledge of drinking guidelines by survey year followed a similar pattern when data from all available waves were included .Logistic regression models testing associations of survey year and sociodemographic characteristics with awareness and knowledge of drinking guidelines are shown in Survey year was independently associated with both awareness and knowledge of drinking guidelines. After adjusting for age, gender, education, social grade, smoking status and level of alcohol consumption, odds of being aware of the guidelines were lower among drinkers surveyed in 2019, relative to those surveyed in 2016, and significantly higher among those surveyed in 2020–2021. In contrast, odds of reporting the guideline to be 14 units per week or fewer were significantly higher among drinkers surveyed in 2018 than those surveyed in 2016 but were not significantly different from 2016 among those surveyed in other years.Relative to younger drinkers (18–34 years), those aged 35–64 and ≥ 65 had significantly higher odds of being aware of, and accurately recalling, the guideline. There were some significant differences in trends over time: in the first year following the announcement of the revised guidelines (2016–2017), knowledge decreased among drinkers aged 35–64 compared with an increase among younger drinkers  , there were increases in knowledge among younger drinkers (18–34 years) and those who smoked. As younger drinkers had lower levels of knowledge to begin with, this narrowed inequalities across ages—but only in the short term, as the gap in knowledge widened again post-2017. In addition, we observed a greater increase in awareness of the guidelines in 2020 among heavier drinkers. There are several possible explanations for this related to the COVID-19 pandemic as outlined above, including a change in the composition of this group , which meant we had to limit our sample size in order to remove any seasonal influence on trends (there were insufficient data points to address this by simply adjusting for month of the year). However, our trends tell a broadly consistent picture despite this, with the exception of the COVID-19 effect. Secondly, other questions about the guidelines that we had included in previous papers , such asIn conclusion, 6 years since their announcement, knowledge of the revised drinking guidelines remains poor. While the majority of adult drinkers in the UK are aware of the guidelines, less than a quarter know the recommended weekly limit and only around half think it is 14 units or fewer. These data suggest passive dissemination of guidelines remains a weak implementation approach: with little improvement in knowledge since the revised guidelines were announced, increasing public awareness of low-risk drinking guidelines warrants a more proactive approach. Inequalities in awareness and knowledge of drinking guidelines have persisted over time, such that disadvantaged groups  are less likely to know the guidelines. This suggests that, to date, activity to raise public awareness of the guidelines has not benefited those at highest risk. Additional interventions targeted at or tailored for disadvantaged groups may be required to address this disparity.Sarah Jackson , Xiaotang Guo , John Holmes , Jamie Brown .Data collection was funded by Cancer Research UK (project PRCRPG-Nov21\100002) and the National Institute for Health Research (NIHR) Public Health Research (PHR) Programme (project 15/63/01). Cancer Research UK funded SJ’s salary (PRCRPG-Nov21\100002). The views expressed are those of the authors and not necessarily those of Cancer Research UK, the NIHR, or the Department of Health and Social Care.J.B. has received unrestricted research funding from Pfizer, who manufacture smoking cessation medications. All authors declare no financial links with the alcohol industry, tobacco companies or e-cigarette manufacturers, or their representatives.Ethical approval for the STS was granted originally by the UCL Ethics Committee (ID 0498/001). The data are not collected by UCL and are anonymized when received by UCL.Data are available from the corresponding author on reasonable request.Drinking_guidelines_supplementary_material_R1_agad007Click here for additional data file.


Hydrodynamic interactions are important for diverse fluids, especially those with low Reynolds number such as microbial and particle-laden suspensions, and proteins diffusing in membranes. Unfortunately, while far-field (asymptotic) hydrodynamic interactions are fully understood in two- and three-dimensions, near-field interactions are not, and thus our understanding of motions in dense fluid suspensions is still lacking. In this contribution, we experimentally explore the hydrodynamic correlations between particles in quasi-two-dimensional colloidal fluids in the near-field. Surprisingly, the measured displacement and relaxation of particle pairs in the body frame exhibit direction-dependent dynamics that can be connected quantitatively to the measured near-field hydrodynamic interactions. These findings, in turn, suggest a mechanism for how and when hydrodynamics can lead to a breakdown of the ubiquitous Stokes-Einstein relation (SER). We observe this breakdown, and we show that the direction-dependent breakdown of the SER is ameliorated along directions where hydrodynamic correlations are smallest. In total, the work uncovers significant ramifications of near-field hydrodynamics on transport and dynamic restructuring of fluids in two-dimensions. Hydrodynamics and correlated motion of colloids in the near-field interactions are not fully understood. The authors report the motion of particles in particle-pairs is direction-dependent in the near-field and that the Stokes-Einstein relation is not applicable in this case. Generally, particle transport in such fluids is influenced by hydrodynamic interactions, which span near- and far-field length scales6, and which depend strongly on spatial confinement (dimension) and fluid boundary conditions18. In three dimensions (3D), monopole-like hydrodynamic interactions give rise to drag forces on particles in particle-pairs of the same sign in both longitudinal (L) and transverse (T) directions17. The body-frame  − axes are along and perpendicular to the line joining the particle-pair at the initial time 6. In addition, as the particle packing fraction increases, while analytic solutions to the Stokes flow are challenging to obtain, experiments have shown that near-field drag correlations exhibit oscillatory modulations with respect to particle separation that are in-phase with structural signatures such as the particle pair correlation function19. The nature of transverse antidrag coupling in the near-field, however, deviates from the far-field dipolar flow profile and remains largely unexplored; for example, phase differences between transverse and longitudinal correlations could exist and, if so, could have consequences in dense suspensions and confined geometries.The investigation of hydrodynamics in fluids at low Reynolds numbers has a venerable history and continues to yield surprises22. Interestingly, although originally derived for dilute suspensions, the SER has been found to be valid in both dilute and dense suspensions in three and higher spatial dimensions23. However, the microscopic origins of its breakdown in the reduced dimensions remains unclear26. Our experiments reveal unique features of 2D hydrodynamic interactions that lead to violation of the SER in confined geometries and identify scenarios that recover the SER. This work takes steps towards elucidating the microscopic origins of SER breakdown in reduced dimensions which to date have been unclear26.Our experiments on confined colloidal suspensions in 2D shed light on these issues, revealing direction-dependent transport of colloids in the body-frame of colloid pairs caused by the contrast in strength between longitudinal drag and transverse antidrag, as well as the phase difference between them. The influence of such anisotropic hydrodynamic interactions on the Stokes-Einstein relation (SER) for fluids in confined geometries (or otherwise) is unexplored. The SER explicitly connects the bulk viscosity of fluids to the microscopic self-diffusivity of tracers and is central to diverse processes, such as drug delivery, and physics in emulsions and biological systemsWe employ optical video microscopy to probe hydrodynamic interactions of aqueous colloidal suspensions in quasi-2D sample cells (Methods). The experiments were performed with suspensions of micron-size polystyrene latex beads  or transverse (T) direction in the body frame. Figure 6, showing co-diffusion (drag) of particles in a pair along The nature and influence of the hydrodynamic interactions between particles in a colloid-pair are revealed in other panels of Fig. mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimmentclass2pt{minimmentclass2pt{minimmentclass2pt{minimmentclass2pt{minimNotice, in Fig. mentclass2pt{minim6. Here, T) direction; the averaging, Figure mentclass2pt{minimmentclass2pt{minim SI Fig. . Specifimentclass2pt{minim19. Surprisingly, we find that the antidrag spatial modulations associated with As reported in previous studies, our measurements find that the spatial modulation of  SI Fig. 4,19. Sur SI Fig. . This efi.e., they reside in first minima of Zrel . For each reference particle x  direction. The other vectors then also rotate through x − axis (now aligned with The hydrodynamic displacement field shown in  SI Fig. . The polmentclass2pt{minimSingle particle diffusivity,  SI Fig. . \documeThe diffusion of single particles,  SI Fig. . At high24 (SI Fig. x − axis.The structural relaxation time,  SI Fig. . \documeThe structural relaxation time, Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Video 1Supplementary Video 2Supplementary Video 3Source Data