Arousal and motivation interact to profoundly influence behavior. For example, experience tells us that we have some capacity to control our arousal when appropriately motivated, such as staying awake while driving a motor vehicle. However, little is known about how arousal and motivation jointly influence decision computations, including if and how animals, such as rodents, adapt their arousal state to their needs. Here, we developed and show results from an auditory, feature-based, sustained-attention task with intermittently shifting task utility. We use pupil size to estimate arousal across a wide range of states and apply tailored signal-detection theoretic, hazard function, and accumulation-to-bound modeling approaches in a large cohort of mice. We find that pupil-linked arousal and task utility both have major impacts on multiple aspects of task performance. Although substantial arousal fluctuations persist across utility conditions, mice partially stabilize their arousal near an intermediate and optimal level when task utility is high. Behavioral analyses show that multiple elements of behavior improve during high task utility and that arousal influences some, but not all, of them. Specifically, arousal influences the likelihood and timescale of sensory evidence accumulation but not the quantity of evidence accumulated per time step while attending. In sum, the results establish specific decision-computational signatures of arousal, motivation, and their interaction in attention. So doing, we provide an experimental and analysis framework for studying arousal self-regulation in neurotypical brains and in diseases such as attention-deficit/hyperactivity disorder.

Carragher and Hancock (2023) investigated how individuals performed in a one-to-one face matching task when assisted by an Automated Facial Recognition System (AFRS). Across five pre-registered experiments they found evidence of suboptimal aided performance, with AFRS-assisted individuals consistently failing to reach the level of performance the AFRS achieved alone. The current study reanalyses these data (Carragher and Hancock, 2023), to benchmark automation-aided performance against a series of statistical models of collaborative decision making, spanning a range of efficiency levels. Analyses using a Bayesian hierarchical signal detection model revealed that collaborative performance was highly inefficient, falling closest to the most suboptimal models of automation dependence tested. This pattern of results generalises previous reports of suboptimal human-automation interaction across a range of visual search, target detection, sensory discrimination, and numeric estimation decision-making tasks. The current study is the first to provide benchmarks of automation-aided performance in the one-to-one face matching task.

For decades, eyewitness memory research has had the worthy goal of minimizing the chances that an innocent suspect is falsely identified. However, this is not the only goal. Partial receiver operating characteristic (ROC) curves provide a way to identify lineup procedures that keep the false alarm rate low while also maximizing the hit rate. Recently, there have been attempts to extend the ROC curve into high false alarm rate regions that fair lineups are intentionally designed to avoid. These new full ROCs could provide a way for the police to circumvent the protections offered by fillers in a fair lineup. Moreover, these attempts to extend the ROC curve are not based on a mathematically coherent model of latent diagnostic signals. In this article, we empirically demonstrate how this lack of a solid foundation can lead to dubious conclusions, such as eyewitnesses possessing precognition and being able to reliably identify the person they will see commit a crime in the future.

Humans experience feelings of confidence in their decisions. In perception, these feelings are typically accurate - we tend to feel more confident about correct decisions. The degree of insight people have into the accuracy of their decisions is known as metacognitive sensitivity. Currently popular methods of estimating metacognitive sensitivity are subject to interpretive ambiguities because they assume people have normally shaped distributions of different experiences when they are repeatedly exposed to a single input. If this normality assumption is violated, calculations can erroneously underestimate metacognitive sensitivity. Here, we describe a means of estimating metacognitive sensitivity that is more robust to violations of the normality assumption. This improved method can easily be added to standard behavioral experiments, and the authors provide Matlab code to help researchers implement these analyses and experimental procedures.

When presented with a lineup, the witness is tasked with identifying the culprit or indicating that the culprit is not present. The witness then qualifies the decision with a confidence judgment. But how do witnesses go about making these decisions and judgments? According to absolute-judgment models, witnesses determine which lineup member provides the strongest match to memory and base their identification decision and confidence judgment on the absolute strength of this MAX lineup member. Conversely, relative-judgment models propose that witnesses determine which lineup member provides the strongest match to memory and then base their identification decision and confidence judgment on the relative strength of the MAX lineup member compared to the remaining lineup members. We took a critical test approach to test the predictions of both models. As predicted by the absolute-judgment model, but contrary to the predictions of the relative-judgment model, witnesses were more likely to correctly reject low-similarity lineups than high-similarity lineups (Experiment 1), and more likely to reject biased lineups than fair lineups (Experiment 2). Likewise, witnesses rejected low-similarity lineups with greater confidence than high-similarity lineups (Experiment 1) and rejected biased lineups with greater confidence than fair lineups (Experiment 2). Only a single pattern was consistent with the relative model and inconsistent with the absolute model: suspect identifications from biased lineups were made with greater confidence than suspect identifications from fair lineups (Experiment 2). The results suggest that absolute-judgment models better predict witness decision-making than do relative-judgment models and that pure relative-judgment models are unviable.

The discriminability measure d \' is widely used in psychology to estimate sensitivity independently of response bias. The conventional approach to estimate d \' involves a transformation from the hit rate and the false-alarm rate. When performance is perfect, correction methods must be applied to calculate d \' , but these corrections distort the estimate. In three simulation studies, we show that distortion in d \' estimation can arise from other properties of the experimental design (number of trials, sample size, sample variance, task difficulty) that, when combined with application of the correction method, make d \' distortion in any specific experiment design complex and can mislead statistical inference in the worst cases (Type I and Type II errors). To address this problem, we propose that researchers simulate d \' estimation to explore the impact of design choices, given anticipated or observed data. An R Shiny application is introduced that estimates d \' distortion, providing researchers the means to identify distortion and take steps to minimize its impact.

Researchers dispute the cause of errors in high Go, low No Go target detection tasks, like the Sustained Attention to Response Task (SART). Some researchers propose errors in the SART are due to perceptual decoupling, where a participant is unaware of stimulus identity. This lack of external awareness causes an erroneous response. Other researchers suggest the majority of the errors in the SART are instead due to response leniency, not perceptual decoupling. Response delays may enable a participant who is initially unaware of stimulus identity, perceptually decoupled, to become aware of stimulus identity, or perceptually recoupled. If, however, the stimulus presentation time is shortened to the minimum necessary for stimulus recognition and the stimulus is disrupted with a structured mask, then there should be no time to enable perception to recouple even with a response delay. From the perceptual decoupling perspective, there should be no impact of a response delay on performance in this case. Alternatively if response bias is critical, then even in this case a response delay may impact performance. In this study, we shortened stimulus presentation time and added a structured mask. We examined whether a response delay impacted performance in the SART and tasks where the SART\'s response format was reversed. We expected a response delay would only impact signal detection theory bias, c, in the SART, where response leniency is an issue. In the reverse formatted SART, since bias was not expected to be lenient, we expected no impact or minimal impact of a response delay on response bias. These predictions were verified. Response bias is more critical in understanding SART performance, than perceptual decoupling, which is rare if it occurs at all in the SART.

In many decision tasks, we have a set of alternative choices and are faced with the problem of how to use our latent beliefs and preferences about each alternative to make a single choice. Cognitive and decision models typically presume that beliefs and preferences are distilled to a scalar latent strength for each alternative, but it is also critical to model how people use these latent strengths to choose a single alternative. Most models follow one of two traditions to establish this link. Modern psychophysics and memory researchers make use of signal detection theory, assuming that latent strengths are perturbed by noise, and the highest resulting signal is selected. By contrast, many modern decision theoretic modeling and machine learning approaches use the softmax function (which is based on Luce\'s choice axiom; Luce, 1959) to give some weight to non-maximal-strength alternatives. Despite the prominence of these two theories of choice, current approaches rarely address the connection between them, and the choice of one or the other appears more motivated by the tradition in the relevant literature than by theoretical or empirical reasons to prefer one theory to the other. The goal of the current work is to revisit this topic by elucidating which of these two models provides a better characterization of latent processes in m -alternative decision tasks, with a particular focus on memory tasks. In a set of visual memory experiments, we show that, within the same experimental design, the softmax parameter β varies across m -alternatives, whereas the parameter d \' of the signal-detection model is stable. Together, our findings indicate that replacing softmax with signal-detection link models would yield more generalizable predictions across changes in task structure. More ambitiously, the invariance of signal detection model parameters across different tasks suggests that the parametric assumptions of these models may be more than just a mathematical convenience, but reflect something real about human decision-making.

Emotion recognition is central in prosocial interaction, enabling the inference of mental and affective states. Individuals who have committed sexual offenses are known to exhibit socio-affective deficits, one of the four dynamic risk assessment dimensions found in the literature. Few research focused on emotion recognition. The available literature, exclusively on individuals in prison who have committed sexual offenses, showed contrasting results. Some found a global (across all emotions) or specific (e.g., anger, fear) deficit in emotion recognition. In contrast, others found no difference between individuals in prison who have committed sexual offenses and those who have committed non-sexual offenses. In addition, no such study has been undertaken among forensic inpatients who exhibit socio-affective deficits. This study aims to investigate the recognition of dynamic facial expressions of emotion in 112 male participants divided into three groups: forensic inpatients who have committed sexual offenses (n = 37), forensic inpatients who have committed non-sexual offenses (n = 25), and community members (n = 50), using the Signal Detection Theory indices: sensitivity (d\') and response bias (c). In addition, measures related to reaction time, emotion labeling reflection time, task easiness, and easiness reflection time were also collected. Non-parametric analyses (Kruskall-Wallis\' H, followed by Mann-Whitney\'s U with Dunn-Bonferroni correction) highlighted that the two forensic inpatient groups exhibited emotion recognition deficits when compared to community members. Forensic inpatients who have committed sexual offenses were more conservative in selecting the surprise label than community members. They also took significantly more time to react to stimuli and to select an emotional label. Despite emotion recognition deficits, the two forensic inpatient groups reported more stimuli easiness than community members.

Previous research has reported diverging patterns of results with respect to discriminability and response bias when comparing the simultaneous lineup to two different lineup procedures in which items are presented sequentially, the sequential stopping rule lineup and the UK lineup. In a single large sample experiment, we compared discriminability and response bias in six-item photographic lineups presented either simultaneously, sequentially with a stopping rule, or sequentially requiring two full laps through the items before making an identification and including the ability to revisit items, analogous to the UK lineup procedure. Discriminability was greater for the simultaneous lineup compared to the sequential stopping rule lineup, despite a non-significant difference in empirical discriminability between the procedures. There was no significant difference in discriminability when comparing the simultaneous lineup to the sequential two lineup and the sequential two lap lineup to the sequential stopping rule lineup. Responding was most lenient for the sequential two lap lineup, followed by the simultaneous lineup, followed by the sequential lineup. These results imply that sequential item presentation may not exert a large effect in isolation on discriminability and response bias. Rather, discriminability and response bias in the sequential stopping rule lineup and UK lineup result from the interaction of sequential item presentation with other aspects of these procedures.