Individual differences in the perception of cue-outcome contingencies: A signal detection analysis

In a signal detection theory (SDT) approach to associative learning, one assumes that, when a subject is exposed to a flow of stimuli, an association is created between the internal representations of a cue and of an outcome, allowing the representation of the cue to activate the representation of the outcome. The outcome activation is a random variable drawn from a Gaussian distribution with mean m (sensitivity to the contingency) and standard deviation d (variability in outcome activation). Depending on whether the outcome activation is above or below various decision thresholds, the participant perceives either a negative, a null, or a positive contingency between the cue and the outcome. This study presents a detailed SDT analysis of the performance of four participants on whom data in a contingency assessment task were collected almost daily during several months. Parameters from the SDT model proved relatively stable over time, except if feedback was provided to the subject. In that case, for some participants but not all, the sensitivity increased. The decision criteria were also affected. Some of these changes endured despite the discontinuation of feedback. The variability in outcome activation was not affected by the feedback.

Mistaking imagination for reality: Congruent mental imagery leads to more liberal perceptual detection

Visual experiences can be triggered externally, by signals coming from the outside world during perception; or internally, by signals from memory during mental imagery. Imagery and perception activate similar neural codes in sensory areas, suggesting that they might sometimes be confused. In the current study, we investigated whether imagery influences perception by instructing participants to imagine gratings while externally detecting these same gratings at threshold. In a series of three experiments, we showed that imagery led to a more liberal criterion for reporting stimulus presence, and that this effect was both independent of expectation and stimulus-specific. Furthermore, participants with more vivid imagery were generally more likely to report the presence of external stimuli, independent of condition. The results can be explained as either a low-level sensory or a high-level decision-making effect. We discuss that the most likely explanation is that during imagery, internally generated sensory signals are sometimes confused for perception and suggest how the underlying mechanisms can be further characterized in future research. Our findings show that imagery and perception interact and emphasize that internally and externally generated signals are combined in complex ways to determine conscious perception.

Statistical approaches to identifying lapses in psychometric response data

Psychometric curve fits relate physical stimuli to an observer's performance. In experiments an observer may "lapse" and respond with a random guess, which may negatively impact (e.g., bias) the psychometric fit parameters. A lapse-rate model has been popularized by Wichmann and Hill, which reduces the impact of lapses on other estimated parameters by adding a parameter to model the lapse rate. Since lapses are discrete events, we developed a discrete lapse theory and tested a "lapse identification" algorithm to identify individual outlier trials (i.e., potential lapses) based upon an approximate statistical criterion and discard these trials. Specifically, we focused on stimuli sampled using an adaptive staircase for a one-interval, direction-recognition task (i.e., psychometric function ranging from 0 to 1 and the spread of the curve corresponds to the threshold, which is often a parameter of interest for many fitted psychometric functions). Through simulations, we found that as the lapse rate increased the threshold became substantially overestimated, consistent with earlier analyses. While the lapse-rate model reduced the overestimation of threshold with many lapses, with lower lapse rates it yielded substantial threshold underestimation, though less so when fitting many (e.g., 1,000) trials. In comparison, the lapse-identification algorithm yielded accurate threshold estimates across a wide range of lapse rates (from 0 to 5%), which is critical since the lapse rate is seldom known. We further demonstrate the performance of the lapse-identification algorithm to be suitable for a variety of experimental conditions and conclude with some considerations of its use. In particular, we suggest using the lapse-identification algorithm unless the experiment has many trials (e.g., >500) or if somehow the lapse rate is known to be high (e.g., ≥5%), for which the lapse-rate model approaches remain preferred.

Muscarinic M1, but not M4, receptor antagonism impairs divided attention in male rats

Divided attention may be more important than ever to comprehend, given ubiquitous distractors in modern living. In humans, concern has been expressed about the negative impact of distraction in education, the home, and the workplace. While acetylcholine supports divided attention, in part via muscarinic receptors, little is known about the specific muscarinic subtypes that may contribute. We designed a novel, high-response rate test of auditory sustained attention, in which rats complete variable-ratio runs on one of two levers, rather than emitting a single response. By doing this, we can present a secondary visual distractor task during some trials, for which a correct nosepoke response is reinforced with a more palatable food pellet. The nonspecific muscarinic antagonist scopolamine impaired performance, and slowed and reduced lever press activity. We then explored antagonists that preferentially block the M1 and M4 subtypes, because these receptors are potential therapeutic targets for cognitive enhancers. Telenzepine, an M1-preferring antagonist, impaired divided attention performance, but not performance of the attention task without distraction. Telenzepine also had fewer nonspecific effects than scopolamine. In contrast, the M4-preferring antagonist tropicamide had no effects. Analysis of overall behavior also indicated that accuracy in the main attention task decreased as a function of engagement with the distractor task. These results implicate the M1 receptor in divided attention.

Disentangling prevalence induced biases in medical image decision-making

Many important real-world decision tasks involve the detection of rarely occurring targets (e.g., weapons in luggage, potentially cancerous abnormalities in radiographs). Over the past decade, it has been repeatedly demonstrated that extreme prevalence (both high and low) leads to an increase in errors. While this "prevalence effect" is well established, the cognitive and/or perceptual mechanisms responsible for it are not. One reason for this is that the most common tool for analyzing prevalence effects, Signal Detection Theory, cannot distinguish between different biases that might be present. Through an application to pathology image-based decision-making, we illustrate that an evidence accumulation modeling framework can be used to disentangle different types of biases. Importantly, our results show that prevalence influences both response expectancy and stimulus evaluation biases, with novices (students, N = 96) showing a more pronounced response expectancy bias and experts (medical laboratory professionals, N = 19) showing a more pronounced stimulus evaluation bias.

Are children's judgments of another's accuracy linked to their metacognitive confidence judgments?

The world can be a confusing place, which leads to a significant challenge: how do we figure out what is true? To accomplish this, children possess two relevant skills: reasoning about the likelihood of their own accuracy (metacognitive confidence) and reasoning about the likelihood of others' accuracy (mindreading). Guided by Signal Detection Theory and Simulation Theory, we examine whether these two self- and other-oriented skills are one in the same, relying on a single cognitive process. Specifically, Signal Detection Theory proposes that confidence in a decision is purely derived from the imprecision of that decision, predicting a tight correlation between decision accuracy and confidence. Simulation Theory further proposes that children attribute their own cognitive experience to others when reasoning socially. Together, these theories predict that children's self and other reasoning should be highly correlated and dependent on decision accuracy. In four studies (N = 374), children aged 4-7 completed a confidence reasoning task and selective social learning task each designed to eliminate confounding language and response biases, enabling us to isolate the unique correlation between self and other reasoning. However, in three of the four studies, we did not find that individual differences on the two tasks correlated, nor that decision accuracy explained performance. These findings suggest self and other reasoning are either independent in childhood, or the result of a single process that operates differently for self and others.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11409-021-09263-x.

The distance effect on discrimination ability and response bias during magnitude comparison in a go/no-go task

The distance effect is the change in the performance during numerical magnitude comparison, depending on the numerical distance between the compared numbers (Moyer & Landauer, Nature, 215[5109], 1519-1520, 1967). This effect is generally accepted as evidence for the mental number line (MNL) hypothesis, which proposes that the mental representation of the numbers align in an increasing linear (or monotone) order. The majority of studies investigating the distance effect are focused on the reaction time (RT) findings, which show slower responses for closer numbers. In the present study, we examined the distance effect by applying signal detection theory (SDT) to a magnitude comparison task. We aimed to reveal whether discrimination ability and the response bias measures were affected by the location of numbers on the MNL. To accomplish this, we developed a magnitude comparison task using a go/no-go procedure in which participants performed a magnitude comparison based on a reference number (i.e., 5). Results revealed a substantial distance effect in both sensitivity and response bias measures-a better discrimination performance for far numbers, and a larger response bias for close numbers. In addition, an RT distribution analysis revealed that the distance effect seems to originate mainly from slower responses. Based on the current data, we suggest that sensitivity and response bias measures could offer comprehensive information in the understanding of number-based decisions.

Informal versus formal judgment of statistical models: The case of normality assumptions

Researchers sometimes use informal judgment for statistical model diagnostics and assumption checking. Informal judgment might seem more desirable than formal judgment because of a paradox: Formal hypothesis tests of assumptions appear to become less useful as sample size increases. We suggest that this paradox can be resolved by evaluating both formal and informal statistical judgment via a simplified signal detection framework. In 4 studies, we used this approach to compare informal judgments of normality diagnostic graphs (histograms, Q-Q plots, and P-P plots) to the performance of several formal tests (Shapiro-Wilk test, Kolmogorov-Smirnov test, etc.). Participants judged whether or not graphs of sample data came from a normal population (Experiments 1-2) or whether or not from a population close enough to normal for a parametric test to be more powerful than a nonparametric one (Experiments 3-4). Across all experiments, participants' informal judgments showed lower discriminability than did formal hypothesis tests. This pattern occurred even after participants were given 400 training trials with feedback, a financial incentive, and ecologically valid distribution shapes. The discriminability advantage of formal normality tests led to slightly more powerful follow-up tests (parametric vs. nonparametric). Overall, the framework used here suggests that formal model diagnostics may be more desirable than informal ones.

A procedural analogue of prey detection and applied signal detection

Researchers have employed a variety of laboratory analogues of cryptic prey detection and applied signal detection to study factors influencing learning and performance in these ethological and applied scenarios. However, these procedural analogues do not appear to map closely onto their "real-world" counterparts, particularly with respect to the role of the "yes" (i.e., "attack") response and the payoff for this response (or its absence) on signal-present and signal-absent trials. Using domestic hens, we developed a procedural analogue in which a "yes" response requires some time to emit; such responses were reinforced only in the presence of a signal. In Experiment 1, we evaluated the influence of the "yes" response requirement by manipulating the number of responses required to qualify as a "yes" response. As the "yes" response requirement was increased, bias toward responding "no" increased, revealing that this is a critical factor controlling accuracy in this procedure. In Experiment 2, we evaluated the influence of signal probability and reinforcement rate on signal detection accuracy and found that neither of these factors significantly influenced accuracy or bias. These findings suggest that this procedural analogue may represent a valuable alternative for studying behaviour in relevant signal detection scenarios.

A novel, unbiased approach to evaluating subsequent search misses in dual target visual search

Research in radiology and visual cognition suggest that finding one target during visual search may result in increased misses for a second target, an effect known as subsequent search misses (SSM). Here, we demonstrate that the common method of calculating second-target detection performance is biased and could produce spurious SSM effects. We describe the source of that bias and document factors that influence its magnitude. We use a modification of signal-detection theory to develop a novel, unbiased method of calculating the expected value for dual-target performance under the null hypothesis. We then apply our novel method to two of our data sets that showed modest SSM effects when calculated in the traditional manner. Our correction reduced the effect size to the point that there was no longer a significant SSM effect. We then applied our method to a published data set that had a larger effect size when calculated using the traditional calculation as well as when using an alternative calculation that was recently proposed to account for biases in the traditional method. We find that both the traditional method and the recently proposed alternative substantially overestimate the magnitude of the SSM effect in these data, but a significant SSM effect persisted even with our calculation. We recommend that future SSM studies use our method to ensure accurate effect-size estimates, and suggest that the method be applied to reanalyze published results, particularly those with small effect sizes, to rule out the possibility that they were spurious.

Surgical face masks impair human face matching performance for familiar and unfamiliar faces

In response to the COVID-19 pandemic, many governments around the world now recommend, or require, that their citizens cover the lower half of their face in public. Consequently, many people now wear surgical face masks in public. We investigated whether surgical face masks affected the performance of human observers, and a state-of-the-art face recognition system, on tasks of perceptual face matching. Participants judged whether two simultaneously presented face photographs showed the same person or two different people. We superimposed images of surgical masks over the faces, creating three different mask conditions: control (no masks), mixed (one face wearing a mask), and masked (both faces wearing masks). We found that surgical face masks have a large detrimental effect on human face matching performance, and that the degree of impairment is the same regardless of whether one or both faces in each pair are masked. Surprisingly, this impairment is similar in size for both familiar and unfamiliar faces. When matching masked faces, human observers are biased to reject unfamiliar faces as "mismatches" and to accept familiar faces as "matches". Finally, the face recognition system showed very high classification accuracy for control and masked stimuli, even though it had not been trained to recognise masked faces. However, accuracy fell markedly when one face was masked and the other was not. Our findings demonstrate that surgical face masks impair the ability of humans, and naïve face recognition systems, to perform perceptual face matching tasks. Identification decisions for masked faces should be treated with caution.

Optimal templates for signal extraction by noisy ideal detectors and human observers

The optimal template for signal detection in white additive noise is the signal itself: the ideal observer matches each stimulus against this template and selects the stimulus associated with largest match. In the noisy ideal observer, internal noise is added to the decision variable returned by the template. While the ideal observer represents an unrealistic approximation to the human visual process, the noisy ideal observer may be applicable under certain experimental conditions. For template values constrained to lie within a specified range, theory predicts that the template associated with a noisy ideal observer should be a clipped image of the signal, a result which we demonstrate analytically using variational calculus. It is currently unknown whether the human process conforms to theory. We report a targeted analysis of the theoretical prediction for an experimental protocol that maximizes template-matching on the part of human participants. We find indicative evidence to support the theoretical expectation when internal noise is compared across participants, but not within each participant. Our results indicate that implicit knowledge about internal variability in different individuals is reflected by their detection templates; no implicit knowledge is retained for internal-noise fluctuations experienced by a given participant during data collection. The results also indicate that template encoding is constrained by the dynamic range of weight specification, rather than the range of output values transduced by the template-matching process.

Discrete-state versus continuous models of the confidence-accuracy relationship in recognition memory

The relationship between confidence and accuracy in recognition memory is important in real-world settings (e.g., eyewitness identification) and is also important to understand at a theoretical level. Signal detection theory assumes that recognition decisions are based on continuous underlying memory signals and therefore inherently predicts that the relationship between confidence and accuracy will be continuous. Almost invariably, the empirical data accord with this prediction. Threshold models instead assume that recognition decisions are based on discrete-state memory signals. As a result, these models do not inherently predict a continuous confidence-accuracy relationship. However, they can accommodate that result by adding hypothetical mapping relationships between discrete states and the confidence rating scale. These mapping relationships are thought to arise from a variety of factors, including demand characteristics (e.g., instructing participants to distribute their responses across the confidence scale). However, until such possibilities are experimentally investigated in the context of a recognition memory experiment, there is no sense in which threshold models adequately explain confidence ratings at a theoretical level. Here, we tested whether demand characteristics might account for the mapping relationships required by threshold models and found that confidence was continuously related to accuracy (almost identically so) both in the presence of strong experimenter demands and in their absence. We conclude that confidence ratings likely reflect the strength of a continuous underlying memory signal, not an attempt to use the confidence scale in a manner that accords with the perceived expectations of the experimenter.

Thinking in a foreign language distorts allocation of cognitive effort: Evidence from reasoning

Bilinguals, in their foreign language, are spared from several decision-making biases. We examined this "Foreign Language Effect" in the context of logical reasoning, in which reasoners are required to track the logical status of a syllogism, ignoring its believability. Across three experiments, we found the reverse Foreign Language Effect; foreign language reasoners are less able to evaluate the logical structure of syllogisms, but no less biased by their believability. One path to succeeding in reasoning tasks is always engaging in reflective processing. A more efficient strategy is metacognitively tracking whether belief-based intuitions conflict with logic-based intuitions and only reflecting when such conflict is present. We provide evidence that foreign language reasoners are less accurate because they struggle to detect belief-logic conflict, and in turn fail to engage in reflective processing when necessary to override the incorrect, intuitive response. We propose that foreign language reasoners are less able to detect belief-logic conflict either due to weakened intuitions or due to a more conservative threshold for the detection of conflict between multiple competing intuitions. Data for the experiments can be accessed publicly at https://osf.io/phbuq/.

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

BACKGROUND

The majority of eyewitness lineup studies are laboratory-based. How well the conclusions of these studies, including the relationship between confidence and accuracy, generalize to real-world police lineups is an open question. Signal detection theory (SDT) has emerged as a powerful framework for analyzing lineups that allows comparison of witnesses' memory accuracy under different types of identification procedures. Because the guilt or innocence of a real-world suspect is generally not known, however, it is further unknown precisely how the identification of a suspect should change our belief in their guilt. The probability of guilt after the suspect has been identified, the posterior probability of guilt (PPG), can only be meaningfully estimated if we know the proportion of lineups that include a guilty suspect, P(guilty). Recent work used SDT to estimate P(guilty) on a single empirical data set that shared an important property with real-world data; that is, no information about the guilt or innocence of the suspects was provided. Here we test the ability of the SDT model to recover P(guilty) on a wide range of pre-existing empirical data from more than 10,000 identification decisions. We then use simulations of the SDT model to determine the conditions under which the model succeeds and, where applicable, why it fails.

RESULTS

For both empirical and simulated studies, the model was able to accurately estimate P(guilty) when the lineups were fair (the guilty and innocent suspects did not stand out) and identifications of both suspects and fillers occurred with a range of confidence levels. Simulations showed that the model can accurately recover P(guilty) given data that matches the model assumptions. The model failed to accurately estimate P(guilty) under conditions that violated its assumptions; for example, when the effective size of the lineup was reduced, either because the fillers were selected to be poor matches to the suspect or because the innocent suspect was more familiar than the guilty suspect. The model also underestimated P(guilty) when a weapon was shown.

CONCLUSIONS

Depending on lineup quality, estimation of P(guilty) and, relatedly, PPG, from the SDT model can range from poor to excellent. These results highlight the need to carefully consider how the similarity relations between fillers and suspects influence identifications.

Oxytocin alters the effect of payoff but not base rate in emotion perception

Emotion perception, inferring the emotional state of another person, can be formalized as decision under uncertainty: another person's scowling face may indicate anger or concentration and the optimal inference is contingent on the decision consequences (payoff) and how likely real anger is encountered (base rate). Although emerging evidence suggests that the neuropeptide oxytocin influences human perception of emotional facial expressions, whether such effect relates to the alternated process of payoff or base rate still remains unclear. In addition, little is known about oxytocin's effect on metacognitive process involved in emotion perception. One hundred and twenty-two healthy male adults (sixty-two in Experiment 1 and sixty in Experiment 2, respectively) received 24 international units (IU) of intranasal oxytocin or placebo (between-subjects) in a randomized and double-blind study. We independently and systematically manipulated the payoff and base rate levels in an emotion categorization task and measured participants' response bias via categorization choice and metacognitive sensitivity via confidence report. Compared to the placebo group, oxytocin specifically induced a categorization bias under the payoff, but not base rate manipulation. In contrast, oxytocin had no effect on subjects' confidence rating, indicating that the metacognitive sensitivity can be dissociated from emotion perception. Our results pinpoint the specific role of oxytocin in payoff evaluation, but not target likelihood estimation and provide a potential theoretical framework to bridge oxytocin research in emotion perception, social cognition and value-based decisions.

The bhsdtr package: a general-purpose method of Bayesian inference for signal detection theory models

We describe a novel method of Bayesian inference for hierarchical or non-hierarchical equal variance normal signal detection theory models with one or more criteria. The method is implemented as an open-source R package that uses the state-of-the-art Stan platform for sampling from posterior distributions. Our method can accommodate binary responses as well as additional ratings and an arbitrary number of nested or crossed random grouping factors. The SDT parameters can be regressed on additional predictors within the same model via intermediate unconstrained parameters, and the model can be extended by using automatically generated human-readable Stan code as a template. In the paper, we explain how our method improves on other similar available methods, give an overview of the package, demonstrate its use by providing a real-study data analysis walk-through, and show that the model successfully recovers known parameter values when fitted to simulated data. We also demonstrate that ignoring a hierarchical data structure may lead to severely biased estimates when fitting signal detection theory models.

A test of two processes: The effect of training on deductive and inductive reasoning

Dual-process theories posit that separate kinds of intuitive (Type 1) and reflective (Type 2) processes contribute to reasoning. Under this view, inductive judgments are more heavily influenced by Type 1 processing, and deductive judgments are more strongly influenced by Type 2 processing. Alternatively, single-process theories propose that both types of judgments are based on a common form of assessment. The competing accounts were respectively instantiated as two-dimensional and one-dimensional signal detection models, and their predictions were tested against specifically targeted novel data using signed difference analysis. In two experiments, participants evaluated valid and invalid arguments, under induction or deduction instructions. Arguments varied in believability and type of conditional argument structure. Additionally, we used logic training to strengthen Type 2 processing in deduction (Experiments 1 & 2) and belief training to strengthen Type 1 processing in induction (Experiment 2). The logic training successfully improved validity-discrimination, and differential effects on induction and deduction judgments were evident in Experiment 2. While such effects are consistent with popular dual-process accounts, crucially, a one-dimensional model successfully accounted for the results. We also demonstrate that the one-dimensional model is psychologically interpretable, with the model parameters varying sensibly across conditions. We argue that single-process accounts have been prematurely discounted, and formal modeling approaches are important for theoretical progress in the reasoning field.

Targeting separate specific learning parameters underlying cognitive behavioral therapy can improve perceptual judgments of anger

BACKGROUND AND OBJECTIVES

Anxiety disorders are characterized by biased perceptual judgment. An experimental model using simple verbal instruction to target specific decision parameters that influence perceptual judgment was developed to test if it could influence anger perception, and to examine differences between individuals with social anxiety disorder (SAD) relative to generalized anxiety disorder (GAD) or non-psychiatric controls.

METHODS

Anger perception was decomposed into three decision parameters (perceptual similarity of angry vs. not-angry facial expressions, base rate of encountering angry vs. not-angry expressions, payoff for correct vs. incorrect categorization of face stimuli) using a signal detection framework. Participants with SAD (n = 97), GAD (n = 90), and controls (n = 98) were assigned an instruction condition emphasizing one of the three decision parameters. Anger perception pre-vs. post-instruction and its interaction with diagnosis were examined.

RESULTS

For all participants, base rate instructions impacted response bias over and above practice effects, supporting the validity of this instructional task-based approach to altering response bias. We failed to find a similarity or payoff instruction effect, nor a diagnosis interaction.

LIMITATIONS

Future instructional tasks may need to more closely target core cognitive and perceptual biases in anxiety disorders to identify specific deficits and how to optimally influence them.

CONCLUSIONS

This study demonstrates that specific decision parameters underlying perceptual judgment can be experimentally manipulated. Although our study failed to show diagnosis specific effects, it suggests that individual parameter "estimation" deficits may be experimentally isolated and potentially targeted, with the ultimate goal of developing an objective approach to personalized intervention targeting biased perceptual judgments in anxiety disorders.

Perceptual sensitivity is modulated by what others can see

Previous work has established that social cues such as the direction of others' gaze or their perspective on a scene may influence one's own perceptual judgments. However, up until now it has remained unclear whether such influences are exerted at a perceptual or decisional locus, as most previous studies have used response times as their primary dependent measure. Here, we asked whether perceptual sensitivity is also dependent on social cognition. To test this hypothesis, we asked participants to evaluate whether low-contrast Gabor patterns embedded in noise were visible from either their own or an avatar's perspective. Across three experiments, we found that observers' detection performance was increased if an avatar also shared perception of the stimulus location. By leveraging signal detection modelling, we show that this effect is driven by a change in perceptual sensitivity (d'), independent of decisional or response interference. Furthermore, by "blindfolding" the avatar, we show that the boosting effect of shared perception on detection sensitivity is only obtained when the participant believes the avatar can also see the stimulus, ruling out an influence of low-level directional cues. We interpret these results within a framework in which the avatar's perspective boosts top-down spatial attention by prioritising particular spatial locations at which perception is shared. In summary, we reveal that perceptual sensitivity is modulated by the perspective of others.

Detection measures for visual inspection of X-ray images of passenger baggage

In visual inspection tasks, such as airport security and medical screening, researchers often use the detection measures d' or A' to analyze detection performance independent of response tendency. However, recent studies that manipulated the frequency of targets (target prevalence) indicate that da with a slope parameter of 0.6 is more valid for such tasks than d' or A'. We investigated the validity of detection measures (d', A', and da) using two experiments. In the first experiment, 31 security officers completed a simulated X-ray baggage inspection task while response tendency was manipulated directly through instruction. The participants knew half of the prohibited items used in the study from training, whereas the other half were novel, thereby establishing two levels of task difficulty. The results demonstrated that for both levels, d' and A' decreased when the criterion became more liberal, whereas da with a slope parameter of 0.6 remained constant. Eye-tracking data indicated that manipulating response tendency affected the decision component of the inspection task rather than search errors. In the second experiment, 124 security officers completed another simulated X-ray baggage inspection task. Receiver operating characteristic (ROC) curves based on confidence ratings provided further support for da, and the estimated slope parameter was 0.5. Consistent with previous findings, our results imply that d' and A' are not valid measures of detection performance in X-ray image inspection. We recommend always calculating da with a slope parameter of 0.5 in addition to d' to avoid potentially wrong conclusions if ROC curves are not available.

Predictive cues reduce but do not eliminate intrinsic response bias

Predictive cues induce large changes in people's choices by biasing responses towards the expected stimulus category. At the same time, even in the absence of predictive cues, humans often exhibit substantial intrinsic response biases. Despite the ubiquity of both of these biasing effects, it remains unclear how predictive cues interact with intrinsic bias. To understand the nature of this interaction, we examined data across three previous experiments that featured a combination of neutral cues (revealing intrinsic biases) and predictive cues. We found that predictive cues decreased the intrinsic bias to about half of its original size. This result held both when bias was quantified as the criterion location estimated using signal detection theory and as the probability of choosing a particular stimulus category. Our findings demonstrate that predictive cues reduce but do not eliminate intrinsic response bias, testifying to both the malleability and rigidity of intrinsic biases.

Parsimonious estimation of signal detection models from confidence ratings

Signal detection theory (SDT) is used to quantify people’s ability and bias in discriminating stimuli. The ability to detect a stimulus is often measured through confidence ratings. In SDT models, the use of confidence ratings necessitates the estimation of confidence category thresholds, a requirement that can easily result in models that are overly complex. As a parsimonious alternative, we propose a threshold SDT model that estimates these category thresholds using only two parameters. We fit the model to data from Pratte et al. (Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 224–232 2010) and illustrate its benefits over previous threshold SDT models.

Deficits in auditory sensory discrimination among children with attention-deficit/hyperactivity disorder

Research into children with attention-deficit/hyperactivity disorder (ADHD) has focused on complex cognitive dysfunction, but less attention has been paid to sensory perception processes underlying the symptoms of ADHD. Based on signal detection theory, the present study compared the sensory discrimination ability and decision bias of children with and without ADHD. It also investigated the differences between ADHD with predominantly inattentive (ADHDi) and combined presentations (ADHDc). The sample of 75 children and adolescents with ADHD (24 ADHDi, 51 ADHDc) (16 females and 59 males) and 22 typical developing controls (TD) (8 females and 14 males) completed an auditory signal detection task. Participants were asked to detect signals against levels of transient background noise (35, 45, 55, and 65 dB). The results showed that with the increase of noise levels, both the ADHD and TD groups demonstrated decreased sensory discrimination. Although both groups successfully detected signal against noise levels from 35 to 55 dB, the ADHD group showed lower discrimination ability than that of the TD group. For decision bias, no group difference was found. Further comparisons regarding the predominant symptom presentation of ADHD sub-groups showed no differences. Current research has suggested that the deficit in ADHD people's signal detection performance can be attributed to sensory discrimination rather than decision bias. We suggest that background noise should be taken into account when using auditory stimuli to investigate cognitive functions in people with ADHD.

Endogenous visuospatial attention increases visual awareness independent of visual discrimination sensitivity

Visuospatial attention often improves task performance by increasing signal gain at attended locations and decreasing noise at unattended locations. Attention is also believed to be the mechanism that allows information to enter awareness. In this experiment, we assessed whether orienting endogenous visuospatial attention with cues differentially affects visual discrimination sensitivity (an objective task performance) and visual awareness (the subjective feeling of perceiving) during the same discrimination task. Gabor patch targets were presented laterally, either at low contrast (contrast stimuli) or at high contrast embedded in noise (noise stimuli). Participants reported their orientation either in a 3-alternative choice task (clockwise, counterclockwise, unknown) that allowed for both objective and subjective reports, or in a 2-alternative choice task (clockwise, counterclockwise) that provided a control for objective reports. Signal detection theory models were fit to the experimental data: estimated perceptual sensitivity reflected objective performance; decision criteria, or subjective biases, were a proxy for visual awareness. Attention increased sensitivity (i.e., improved objective performance) for the contrast, but not for the noise stimuli. Indeed, with the latter, attention did not further enhance the already high target signal or reduce the already low uncertainty on its position. Interestingly, for both contrast and noise stimuli, attention resulted in more liberal criteria, i.e., awareness increased. The noise condition is thus an experimental configuration where people think they see the targets they attend to better, even if they do not. This could be explained by an internal representation of their attentional state, which influences awareness independent of objective visual signals.