What do fakers actually do to fake the IAT? An investigation of faking strategies under different faking conditions

Can People With Higher Versus Lower Scores on Impression Management or Self-Monitoring Be Identified Through Different Traces Under Faking?

According to faking models, personality variables and faking are related. Most prominently, people's tendency to try to make an appropriate impression (impression management; IM) and their tendency to adjust the impression they make (self-monitoring; SM) have been suggested to be associated with faking. Nevertheless, empirical findings connecting these personality variables to faking have been contradictory, partly because different studies have given individuals different tests to fake and different faking directions (to fake low vs. high scores). Importantly, whereas past research has focused on faking by examining test scores, recent advances have suggested that the faking process could be better understood by analyzing individuals' responses at the item level (response pattern). Using machine learning (elastic net and random forest regression), we reanalyzed a data set (N = 260) to investigate whether individuals' faked response patterns on extraversion (features; i.e., input variables) could reveal their IM and SM scores. We found that individuals had similar response patterns when they faked, irrespective of their IM scores (excluding the faking of high scores when random forest regression was used). Elastic net and random forest regression converged in revealing that individuals higher on SM differed from individuals lower on SM in how they faked. Thus, response patterns were able to reveal individuals' SM, but not IM. Feature importance analyses showed that whereas some items were faked differently by individuals with higher versus lower SM scores, others were faked similarly. Our results imply that analyses of response patterns offer valuable new insights into the faking process.

Improving the reliability and validity of the IAT with a dynamic model driven by similarity

The Implicit Association Test (IAT), like many behavioral measures, seeks to quantify meaningful individual differences in cognitive processes that are difficult to assess with approaches like self-reports. However, much like other behavioral measures, many IATs appear to show low test-retest reliability and typical scoring methods fail to quantify all of the decision-making processes that generate the overt task performance. Here, we develop a new modeling approach for IATs based on the geometric similarity representation (GSR) model. This model leverages both response times and accuracy on IATs to make inferences about representational similarity between the stimuli and categories. The model disentangles processes related to response caution, stimulus encoding, similarities between concepts and categories, and response processes unrelated to the choice itself. This approach to analyzing IAT data illustrates that the unreliability in IATs is almost entirely attributable to the methods used to analyze data from the task: GSR model parameters show test-retest reliability around .80-.90, on par with reliable self-report measures. Furthermore, we demonstrate how model parameters result in greater validity compared to the IAT D-score, Quad model, and simple diffusion model contrasts, predicting outcomes related to intergroup contact and motivation. Finally, we present a simple point-and-click software tool for fitting the model, which uses a pre-trained neural network to estimate best-fit parameters of the GSR model. This approach allows easy and instantaneous fitting of IAT data with minimal demands on coding or technical expertise on the part of the user, making the new model accessible and effective.

The Firearm Implicit Association Test: A Validation Study

Firearm violence causes significant public health burden, but there is a lack of research concerning motivations for firearm access despite clear epidemiological risk. Developing robust tools to measure attitudes toward firearms and firearm-related behaviors can improve our ability to conduct firearm violence research. We aimed to develop a feasible and effective tool that could indirectly measure firearm beliefs. A total of 274 undergraduates were recruited from two southern universities and completed an implicit association test (IAT) designed to indirectly assess attitudes toward firearms (Firearm IAT). Participants also completed self-report measures, including Attitude Toward Guns Scale (ATGS) and Gun Beliefs and Behavior Scale (GBBS) to examine explicit attitudes toward firearms. Demographic and firearm-related data were also assessed. The Firearm IAT revealed an association between firearms and negatively valenced words. The Firearm IAT had a good internal consistency and construct validity with a D score that is significantly different from 0 and a reliability score of 0.84. The Firearm IAT showed significant positive correlations with ATGS and GBBS suggesting this measure could serve as an indirect assessment of firearm attitudes.

Relationships between the race implicit association test and other measures of implicit and explicit social cognition

Background

The race-based Implicit Association Test (IAT) was proposed to measure individual differences in implicit racial bias subsumed within social cognition. In recent years, researchers have debated the theoretical tenets underpinning the IAT, questioning whether performance on this task: (1) measures implicit attitudes that operate automatically outside of conscious awareness; (2) reflects individual differences in social cognition; and (3) can predict social behavior. One way to better address these research questions is to assess whether the race-IAT correlates with other implicit processes that are subsumed within social cognition.

Aims

The current study assessed whether the race-IAT was related to other commonly used individual difference measures of implicit (and explicit) social cognition. Experiment 1 assessed whether dissociable patterns of performance on the race-IAT were related to measures of implicit imitative tendencies, emotion recognition and perspective taking toward White task actors, as well as explicit measures of trait and state affective empathy and racial bias. Overcoming limitations of task conceptual correspondence, Experiment 2 assessed whether these latter tasks were sensitive in detecting racial biases by using both White and Black task actors and again examined their relationships with the race-IAT.

Method

In two lab-based experiments, 226 and 237 participants completed the race-IAT followed by an extensive battery of social cognition measures.

Results

Across both experiments, pro-White/anti-Black bias on the race-IAT was positively related to a pro-White bias on explicit measures of positive affective empathy. However, relationships between the race-IAT and implicit imitative tendencies, perspective taking, emotion recognition, and explicit trait and negative state affective empathy were statistically equivalent.

Conclusion

The race-IAT was consistently related to explicit measures of positive state affective empathy but not to other individual difference measures of implicit social cognition. These findings are discussed with regards to the theoretical underpinnings of the race-IAT as an individual difference measure of implicit social cognition, as well as alternative explanations relating to the reliability of social cognition measures and the various combinations of general-purpose (social and non-social) executive processes that underpin performance on these tasks.

IAT faking indices revisited: Aspects of replicability and differential validity

Research demonstrates that IATs are fakeable. Several indices [either slowing down or speeding up, and increasing errors or reducing errors in congruent and incongruent blocks; Combined Task Slowing (CTS); Ratio 150-10000] have been developed to detect faking. Findings on these are inconclusive, but previous studies have used small samples, suggesting they were statistically underpowered. Further, the stability of the results, the unique predictivity of the indices, the advantage of combining indices, and the dependency on how faking success is computed have yet to be examined. Therefore, we reanalyzed a large data set (N = 750) of fakers and non-fakers who completed an extraversion IAT. Results showed that faking strategies depend on the direction of faking. It was possible to detect faking of low scores due to slowing down on the congruent block, and somewhat less with CTS-both strategies led to faking success. In contrast, the strategy of increasing errors on the congruent block was observed but was not successful in altering the IAT effect in the desired direction. Fakers of high scores could be detected due to slowing down on the incongruent block, increasing errors on the incongruent block, and with CTS-all three strategies led to faking success. The results proved stable in subsamples and generally across different computations of faking success. Using regression analyses and machine learning, increasing errors had the strongest impact on the classification. Apparently, fakers use various goal-dependent strategies and not all are successful. To detect faking, we recommend combining indices depending on the context (and examining convergence).

Lying on the Dissection Table: Anatomizing Faked Responses

Research has shown that even experts cannot detect faking above chance, but recent studies have suggested that machine learning may help in this endeavor. However, faking differs between faking conditions, previous efforts have not taken these differences into account, and faking indices have yet to be integrated into such approaches. We reanalyzed seven data sets (N = 1,039) with various faking conditions (high and low scores, different constructs, naïve and informed faking, faking with and without practice, different measures [self-reports vs. implicit association tests; IATs]). We investigated the extent to which and how machine learning classifiers could detect faking under these conditions and compared different input data (response patterns, scores, faking indices) and different classifiers (logistic regression, random forest, XGBoost). We also explored the features that classifiers used for detection. Our results show that machine learning has the potential to detect faking, but detection success varies between conditions from chance levels to 100%. There were differences in detection (e.g., detecting low-score faking was better than detecting high-score faking). For self-reports, response patterns and scores were comparable with regard to faking detection, whereas for IATs, faking indices and response patterns were superior to scores. Logistic regression and random forest worked about equally well and outperformed XGBoost. In most cases, classifiers used more than one feature (faking occurred over different pathways), and the features varied in their relevance. Our research supports the assumption of different faking processes and explains why detecting faking is a complex endeavor.

Science-utility and science-trust associations and how they relate to knowledge about how science works

Knowledge about how science works, trust in scientists, and the perceived utility of science currently appear to be eroding in these times in which “alternative facts” or personal experiences and opinions are used as arguments. Yet, in many situations, it would be beneficial for the individual and all of society if scientific findings were considered in decision-making. For this to happen, people have to trust in scientists and perceive science as useful. Still, in university contexts, it might not be desirable to report negative beliefs about science. In addition, science-utility and science-trust associations may differ from explicit beliefs because associations were learned through the co-occurrence of stimuli rather than being based on propositional reasoning. We developed two IATs to measure science-utility and science-trust associations in university students and tested the psychometric properties and predictive potential of these measures. In a study of 261 university students, the IATs were found to have good psychometric properties and small correlations with their corresponding self-report scales. Science-utility and science-trust associations predicted knowledge about how science works over and above self-reported beliefs. The results suggest that indirect measures are useful for assessing beliefs about science and can be used to predict outcome measures.

Challenging response latencies in faking detection: The case of few items and no warnings

Abstract

Faking detection is an ongoing challenge in psychological assessment. A notable approach for detecting fakers involves the inspection of response latencies and is based on the congruence model of faking. According to this model, respondents who fake good will provide favorable responses (i.e., congruent answers) faster than they provide unfavorable (i.e., incongruent) responses. Although the model has been validated in various experimental faking studies, to date, research supporting the congruence model has focused on scales with large numbers of items. Furthermore, in this previous research, fakers have usually been warned that faking could be detected. In view of the trend to use increasingly shorter scales in assessment, it becomes important to investigate whether the congruence model also applies to self-report measures with small numbers of items. In addition, it is unclear whether warning participants about faking detection is necessary for a successful application of the congruence model. To address these issues, we reanalyzed data sets of two studies that investigated faking good and faking bad on extraversion (n = 255) and need for cognition (n = 146) scales. Reanalyses demonstrated that having only a few items per scale and not warning participants represent a challenge for the congruence model. The congruence model of faking was only partly confirmed under such conditions. Although faking good on extraversion was associated with the expected longer latencies for incongruent answers, all other conditions remained nonsignificant. Thus, properties of the measurement and properties of the procedure affect the successful application of the congruence model.

Trying to separate the wheat from the chaff: Construct- and faking-related variance on the Implicit Association Test (IAT)

Recent research has indicated that diffusion model analyses allow the user to decompose the traditional IAT effect (D measure) into three newly developed IAT effects: IATv, which has already been shown to be significantly related to the construct-related variance of the IAT effect, and IATa and IATt0, both of which have been assumed to provide an indication of faking. But research on the impacts of faking on IATv, IATa, and IATt0 is still warranted. By reanalyzing a data set containing both faked and unfaked IAT effects, we investigated whether diffusion model analyses could be used to separate construct-related variance from faking-related variance on the IAT. Our results revealed that this separation is not yet possible. As had already been shown for the traditional IAT effect, IATv was affected by faking. Interestingly, it was affected by faking only under more difficult faking conditions (i.e., when participants were asked to fake without being given recommended strategies for how to do so, and when they were requested to fake high scores). By contrast, IATa was affected by faking only in the comparably easy faking condition (i.e., when participants had been informed about possible faking strategies and were asked to fake low scores). IATt0 was not affected by faking at all. Our results show that although diffusion model analyses cannot yet provide a clear separation between construct- and faking-related variance, they allow us to peer into the black box of the faking process itself, and thus provide a useful tool for analyzing and interpreting IAT scores.

Uninstructed BIAT faking when ego depleted or in normal state: differential effect on brain and behavior

BACKGROUND

Deception can distort psychological tests on socially sensitive topics. Understanding the cerebral processes that are involved in such faking can be useful in detection and prevention of deception. Previous research shows that faking a brief implicit association test (BIAT) evokes a characteristic ERP response. It is not yet known whether temporarily available self-control resources moderate this response. We randomly assigned 22 participants (15 females, 24.23 ± 2.91 years old) to a counterbalanced repeated-measurements design. Participants first completed a Brief-IAT (BIAT) on doping attitudes as a baseline measure and were then instructed to fake a negative doping attitude both when self-control resources were depleted and non-depleted. Cerebral activity during BIAT performance was assessed using high-density EEG.

RESULTS

Compared to the baseline BIAT, event-related potentials showed a first interaction at the parietal P1, while significant post hoc differences were found only at the later occurring late positive potential. Here, significantly decreased amplitudes were recorded for 'normal' faking, but not in the depletion condition. In source space, enhanced activity was found for 'normal' faking in the bilateral temporoparietal junction. Behaviorally, participants were successful in faking the BIAT successfully in both conditions.

CONCLUSIONS

Results indicate that temporarily available self-control resources do not affect overt faking success on a BIAT. However, differences were found on an electrophysiological level. This indicates that while on a phenotypical level self-control resources play a negligible role in deliberate test faking the underlying cerebral processes are markedly different.

Cerebral correlates of faking: evidence from a brief implicit association test on doping attitudes

Direct assessment of attitudes toward socially sensitive topics can be affected by deception attempts. Reaction-time based indirect measures, such as the Implicit Association Test (IAT), are less susceptible to such biases. Neuroscientific evidence shows that deception can evoke characteristic ERP differences. However, the cerebral processes involved in faking an IAT are still unknown. We randomly assigned 20 university students (15 females, 24.65 ± 3.50 years of age) to a counterbalanced repeated-measurements design, requesting them to complete a Brief-IAT (BIAT) on attitudes toward doping without deception instruction, and with the instruction to fake positive and negative doping attitudes. Cerebral activity during BIAT completion was assessed using high-density EEG. Event-related potentials during faking revealed enhanced frontal and reduced occipital negativity, starting around 150 ms after stimulus presentation. Further, a decrease in the P300 and LPP components was observed. Source analyses showed enhanced activity in the right inferior frontal gyrus between 150 and 200 ms during faking, thought to reflect the suppression of automatic responses. Further, more activity was found for faking in the bilateral middle occipital gyri and the bilateral temporoparietal junction. Results indicate that faking reaction-time based tests alter brain processes from early stages of processing and reveal the cortical sources of the effects. Analyzing the EEG helps to uncover response patterns in indirect attitude tests and broadens our understanding of the neural processes involved in such faking. This knowledge might be useful for uncovering faking in socially sensitive contexts, where attitudes are likely to be concealed.

The effect of implicitly incentivized faking on explicit and implicit measures of doping attitude: when athletes want to pretend an even more negative attitude to doping

The Implicit Association Test (IAT) aims to measure participants' automatic evaluation of an attitude object and is useful especially for the measurement of attitudes related to socially sensitive subjects, e.g. doping in sports. Several studies indicate that IAT scores can be faked on instruction. But fully or semi-instructed research scenarios might not properly reflect what happens in more realistic situations, when participants secretly decide to try faking the test. The present study is the first to investigate IAT faking when there is only an implicit incentive to do so. Sixty-five athletes (22.83 years ± 2.45; 25 women) were randomly assigned to an incentive-to-fake condition or a control condition. Participants in the incentive-to-fake condition were manipulated to believe that athletes with lenient doping attitudes would be referred to a tedious 45-minute anti-doping program. Attitudes were measured with the pictorial doping brief IAT (BIAT) and with the Performance Enhancement Attitude Scale (PEAS). A one-way MANOVA revealed significant differences between conditions after the manipulation in PEAS scores, but not in the doping BIAT. In the light of our hypothesis this suggests that participants successfully faked an exceedingly negative attitude to doping when completing the PEAS, but were unsuccessful in doing so on the reaction time-based test. This study assessed BIAT faking in a setting that aimed to resemble a situation in which participants want to hide their attempts to cheat. The two measures of attitude were differentially affected by the implicit incentive. Our findings provide evidence that the pictorial doping BIAT is relatively robust against spontaneous and naïve faking attempts. (B)IATs might be less prone to faking than implied by previous studies.