Truth-default Theory Research Articles

How political consultants can leverage partisan media combativeness in the U.S.

ABSTRACT The growth of partisan media presents new challenges for political consultants. Informed by social identity theory and truth-default theory, this paper examines how U.S. voters react to politicians in combative news interviews. Experiment 1 (N = 320) establishes that an ingroup politician gains more trust among ingroup voters when interviewed by cross-partisan media accusing the politician of deception than when interviewed more congenially by co-partisan media. Experiment 2 (N = 131) finds that perceived media hostility increases Democrats’ and Republicans’ trust in a politician regardless of whether the interviewer accuses the politician of deception or is congenial. Experiment 3 (N = 126) replicates the findings when a Democrat is accused of deception on Fox News. The study tested a moderated mediation model, finding that Republican and Democratic voters do not react differently in the process. The discussion highlights the practical and theoretical implications of hostile media perceptions and ingroup–outgroup partisan bias in a polarized media environment with conflicting exposure to live fact-checking and deception detection.

The truth-default is ubiquitous, but some people experience it more than others

ABSTRACT Truth-default theory posits that most people are normatively honest and are believed by others. Do people think that others consider them honest? This paper explores how social perceptions of deception align with the truth-default. Using data from the All of Us dataset (N = 116,914 total respondents), we observed that most people feel trusted. While the predicted long-tail distribution was universal across subsamples, people who self-identified as male, are minorities in the US, had less education, and had less income were less likely to feel trusted by others. Connections to and implications for truth-default theory are discussed.

Parsing communication duration and diagnostic question effects in deception detection

ABSTRACT An experiment (N = 155) testing mere duration and diagnostic content hypotheses in deception detection is reported. To the extent that observable behavioral cues aid deception detection, longer behavioral samples may provide a greater opportunity for cues to emerge, thereby increasing accuracy relative to the observation of short snippets of interviews. In contrast, truth-default theory proposes that question-and-answer content is a moderator, and more is only better when questions prompt diagnostic communication content. A 2 × 2 experiment varied communication duration (short, long) and questioning approach (more, less diagnostic). Consistent with the diagnostic content hypothesis, the results suggest that duration only mattered within the more diagnostic question condition. In the less diagnostic question condition, the results mirrored prior meta-analysis regardless of duration. The findings align with truth-default theory and provide evidence for a theory-based approach to improving deception detection.

The spread of fake news: Disclosure willingness role

The epidemic has had a profound negative impact on individuals worldwide, leading to pervasive anxiety, fear, and mental instability. Exploiting these fears, a significant amount of fake information proliferates and spreads rapidly on social networks. This study explores the factors that cause individuals to believe fake news under stressful and fearful conditions by applying the truth-default theory. Data was collected online in Vietnam, using Smart PLS software to analyze the research model. The findings indicated that risk perception, media trust, trust in celebrity posts, and stress were factors that urge users to believe news posted on social media, and even they actively share this news on their own channels. Disclosure willingness moderated the relationship between adoption fake news and sharing it. Both theoretical and practical implications were discussed.

Lie–truth judgments: adaptive lie detector account and truth-default theory compared and contrasted

Abstract Two contemporary theoretical perspectives explain when and how people make lie–truth judgments. The adaptive lie detector account (ALIED) and truth-default theory (TDT) are described, compared, and contrasted. ALIED and TDT come from different scholarly traditions and propose very different processes and mechanisms, yet they converge on many behavioral predictions. Both views presume adaptive processes. ALIED presumes that humans are adaptive by using available information while TDT presumes that the adaptive value of efficient communication outweighs the value of real-time deception detection. ALIED proposes a Bayesian reasoning approach to lie–truth judgments that weighs information based on its perceived diagnosticity, making no distinction in the processes between reaching a lie and truth judgment. TDT alternatively proposes that the passive presumption of the truth is the default, and the presence of triggers is required to reach a lie judgment. Suggestions for future research are provided.

Who gets duped? The impact of education on fraud detection in an investment task

Many financial scandals appear to depend on a lack of skepticism on the part of their victims. Sophisticated investors trusted Bernie Madoff, for example, despite early warning signs of implausible returns. Our study investigates how education explains fraud detection in financial decision-making. In a simple survey, economics and finance students are asked to make an investment recommendation from among four hypothetical funds, including one based on Madoff’s fund. We use Truth Default Theory to explain our results. We show that education increases the likelihood that students are suspicious of Madoff’s fund, and that for students whose suspicions are aroused, education makes them less likely to choose the Madoff fund.

Generative AI Are More Truth-Biased Than Humans: A Replication and Extension of Core Truth-Default Theory Principles

Humans often display a truth-bias—the perception that others are honest independent of message veracity—but does this phenomenon extend to generative artificial intelligence (AI)? We had humans and large language models make nearly 1,000 veracity judgments across different prompts. Human detection accuracies were near chance (50%–53%) with notable truth-biases (59%–64%); AI had a substantially greater truth-bias than humans (67%–99%). GPT-4 was also truth-default, not suspecting deception when veracity assessments were unprompted. Together, people and AI judge most information to be true.

The Relative Frequency of True and False Confessions and Denials in an Experimental Paradigm Designed to Investigate Deception Detection

ABSTRACT Truth-default theory (TDT) proposes that deception is infrequent relative to honesty and that deception requires a motive. TDT further lists confessed deception as a viable method of improving deception detection accuracy. Findings from the experimental literature on false confessions conflict with TDT predictions suggesting that confessions might be an important boundary condition for the theory. Data from various versions of a cheating experiment (N = 485) were reexamined for the rates of true and false confessions and denials under different types of interrogation strategies including accusatory questioning and false evidence ploys. Frequent honest but not false confessions were observed. Demand effects, communication goals, and deception motives are proposed as potential explanations for the conflicting findings. The results are interpreted and framed within TDT. Suggestions are provided for practitioners and for future research.

The Communication Patterns of Highly Effective Lie Detectors

ABSTRACT A prior experiment involving professional interrogators using unscripted questions produced exceptionally high (98%) deception detection accuracy. Guided by truth-default theory, videotapes of those interviews were examined and coded to better understand how the unusually high degree of accuracy might have been obtained. Eighty-four videotaped interviews of potential cheaters by five professional interrogators were used to develop a coding scheme consisting of 78 potentially relevant behaviors. Consistent with truth-default theory, the experts explored communication motives that might make deceit more likely, sought contextually relevant communication, circled back in questioning with attention to inconsistencies, and persuaded honest confessions. Observed strategies for persuading honesty included pitting the interviewee against their partner, minimizing the act of cheating, normalizing cheating as just human nature, appealing to the interviewee’s character, appealing to the integrity of the research or data, and guilt appeals. The findings are discussed in terms of practical and theoretical implications.

When a journalist and politician engage in deception detection: Effects of demeanor, refutation, and partisanship in combative media interviews

ABSTRACT When journalists accuse politicians of deception and politicians return fire, how do voters decide what to believe? Grounded on truth-default theory and visual primacy theory, this paper reports experiments with stimuli of interviews in which a journalist accuses a politician of deceptive evasion. In Study 1, we manipulate whether the journalist’s allegation is accurate. Voters seem unable to tell, basing their perceptions on the politician’s demeanor. In Study 2 we test the effect of a politician honestly refuting a dishonest journalist. Voters still attend to demeanor, not verbal message content. In Study 3 partisanship, verbal refutation, and nonverbal demeanor interact. Democratic voters respond more favorably to their politician refuting a journalist and are not misled by demeanor like Republicans.

Deception detection and question effects: testing truth-default theory predictions in South Korea

Abstract Meta-analysis has shown that people are only slightly better than chance at distinguishing truths from lies in deception detection experiments. Truth-default theory (TDT), however, specifies multiple paths to lowering and increasing accuracy. The current experiment (n = 81) tested truth-default theory’s proposition 13 and diagnostic questioning module with a student sample from South Korea. The proposition and module predict that how an interviewee is questioned can affect deception detection in both directions, improving or reducing accuracy. Consistent with the original findings, questioning was found to significantly enhance (65%) and reduce (30%) deception-detection accuracy relative to the results of meta-analysis (54%). The current findings provide additional evidence consistent with TDT and replicate prior findings documenting substantial question effect on deception-detection accuracy. The implications of question effects for non-native speakers and intercultural lie detection are discussed.

Assertion and truth default

I argue that a successful assertion may consist exclusively of adding “Speaker has said that p” to the common ground. Other effects of the assertion, such as the assumption by the hearer that the speaker believes p and the addition of p to the common ground, follow from the truth-bias, a cornerstone of Truth Default Theory (Levine 2019). Empirical support for this view comes from what I call minimal assertions, a type of speech act that has the hallmark of an assertion to the extent that it elicits a commitment to the truth of the statement but does not seem to have any of the additional properties usually attached to an assertion, such as an expectation that the speaker believes or knows p. I show that minimal assertions are made in contexts in which the truth bias is inoperative because it is beside the point. I conclude that minimal assertions provide empirical support for the commitment theory of assertion (Geurts 2019).

Are Presidential Candidates Impervious to Deception Detection? A Test of Voters' Truth‐Default

This study applies truth‐default theory (TDT) to presidential candidates. TDT holds that people tend to passively believe others without consciously considering whether they are being told the truth. But do voters have a truth‐default toward presidential candidates? In an experiment, voters across the United States (N = 294) watched a news interview in which a presidential candidate was either honest or deceptive. Party affiliation was also manipulated. Consistent with TDT, thought‐listing tasks revealed that most voters did not mention deception after exposure to the presidential campaign interview. Voters largely defaulted to the truth even when sustaining outgroup partisan exposure and deception, and when asked about the candidate's demeanor. Filling out closed‐ended scales, though, voters reported distrust, suspicion, and perceiving deceptive messaging. The discussion concerns the implications of voters' perceptions of a presidential candidate's veracity varying based on how voters are prompted.

Lie judgment trigger sensitivity and truth-bias: truth default theory in intergroup communication

ABSTRACT The central premise of truth-default theory (TDT) posits that listeners are truth-biased in communication exchanges. Levine’s TDT speculates that intergroup communication impacts truth-bias. To test this notion, participants judged the veracity of videos of speakers from the U.S. and China discussing their opinions on mask mandates and personal relationships. The results show that people hold a stronger truth-bias toward in-group members when those people share the same stance on the social issue of mask mandates. However, the study findings did not show a relationship between cultural identity on truth-bias. Interestingly, all participants showed a higher lie judgment trigger sensitivity toward speakers from a different culture, and higher lie judgment trigger sensitivity was also present for those who shared differing opinions on mask mandates.

Sender and Receiver Lie Frequencies and Motives: Testing Predictions from Truth-Default Theory

ABSTRACT Lie frequency and motives were examined from the perspectives of senders and receivers. Participants (N = 294) were randomly assigned to either report on recent lies they told or on lies told to them by others. Based on truth-default theory, it was predicted that the frequency of reports of own lies told would be positively skewed, that most lies would be told by a relatively few prolific liars, and that people would report similar reasons for lying and being lied to. A key question is, do receivers report receiving fewer lies than senders reported telling? The results show that lies reported by senders and receivers did not differ in frequency. Senders, however, consider their own lies to be less important, less wrongful, and less hurtful compared to lies received.

Default to truth in information behavior: a proposed framework for understanding vulnerability to deceptive information

PurposeThis study aims to recognize the challenge of identifying deceptive information and provides a framework for thinking about how we as humans negotiate the current media environment filled with misinformation and disinformation.Design/methodology/approachThis study reviews the influence of Wilson’s (2016) General Theory of Information Behavior (IB) in the field of information science (IS) before introducing Levine’s Truth-Default Theory (TDT) as a method of deception detection. By aligning Levine’s findings with published scholarship on IB, this study illustrates the fundamental similarities between TDT and existing research in IS.FindingsThis study introduces a modification of Wilson’s work which incorporates truth-default, translating terms to apply this theory to the broader area of IB rather than Levine’s original face-to-face deception detection.Originality/valueFalse information, particularly online, continues to be an increasing problem for both individuals and society, yet existing IB models cannot not account for the necessary step of determining the truth or falsehood of consumed information. It is critical to integrate this crucial decision point in this study’s IB models (e.g. Wilson’s model) to acknowledge the human tendency to default to truth and thus providing a basis for studying the twin phenomena of misinformation and disinformation from an IS perspective. Moreover, this updated model for IB contributes the Truth Default Framework for studying how people approach the daunting task of determining truth, reliability and validity in the immense number of news items, social media posts and other sources of information they encounter daily. By understanding and recognizing our human default to truth/trust, we can start to understand more about our vulnerability to misinformation and disinformation and be more prepared to guard against it.

Fake Online Reviews: A Unified Detection Model Using Deception Theories

Online reviews influence consumers’ purchasing decisions. However, identifying fake reviews online automatically remains a complex problem, and current detection approaches are inefficient in preventing the spread of fake reviews. The literature on fake reviews detection lacks a comprehensive and interpretable theory-based model with high performance, which enables us to understand the phenomenon from a psychological perspective and analyze reviews based on user-generated content as well as consumer behavior. In this research, we synthesized ten well-founded deception theories from psychology, namely leakage theory, four-factor theory, interpersonal deception theory, self-presentational theory, reality monitoring theory, criteria-based content analysis, scientific content analysis, verifiability approach, truth-default theory, and information manipulation theory, and selected nine relevant constructs to develop a unified model for detecting fake online reviews. These constructs include specificity, quantity, non-immediacy, affect, uncertainty, informality, consistency, source credibility, and deviation in behavior. We characterized the selected constructs using verbal and non-verbal features to validate the proposed model empirically. Subsequently, we extracted features from the Yelp datasets and used them to train four machine learning algorithms, specifically Logistic Regression, Naïve Bayes, Decision Tree, and Random Forest. We demonstrated that quantity, non-immediacy, affect, informality, consistency, source credibility, and deviation in behavior are essential constructs for detecting fake reviews. To our surprise, we discovered that non-verbal features are more important than verbal features and that combining features from both types improves the prediction performance. Our theory-based model outperformed most of the state-of-the-art fake review detection models and yielded high interpretability and low complexity.

Response: Commentary: Distrust, False Cues, and Below-Chance Deception Detection Accuracy: Commentary on Stel et al. (2020) and Further Reflections on (Un)Conscious Lie Detection From the Perspective of Truth-Default Theory.

Response: Commentary: Distrust, False Cues, and Below-Chance Deception Detection Accuracy: Commentary on Stel et al. (2020) and Further Reflections on (Un)Conscious Lie Detection From the Perspective of Truth-Default Theory.

(In)Sincere Demeanor and (In)Sincere Language in Crisis Communication

Perceptions of a crisis communicator’s sincerity drive reactions to an organization’s response amidst a scandal. However, a spokesperson can nonverbally appear sincere while deceptively evading questions and can appear insincere while actually speaking sincere truths. Applying truth-default theory to crisis communication, we assess people’s reactions to a spokesperson varying in sincerity through demeanor and language. In an experiment ( N = 801), adults from across the United States were randomly assigned to view one of four versions of a news interview. The stimuli present the spokesperson replying to questions with sincere or insincere demeanor and sincere language (conveying relevance and clarity) or insincere language (evasion and obfuscation). Results indicate that sincerity in demeanor and language interact to affect (a) account acceptance, (b) negative word-of-mouth intention, and (c) attribution of responsibility. But sincerity in language largely overrides behavioral impressions. Discussion concerns considering evasion and obfuscation as demeanor cues, when violations of relevance and clarity in language undercut a spokesperson’s believability.

Unpacking variation in lie prevalence: Prolific liars, bad lie days, or both?

ABSTRACT Testing truth-default theory, individual-level variation in lie frequency was parsed from within-individual day-to-day variation (good/bad lie days) by examining 116,366 lies told by 632 participants over 91 days. As predicted and consistent with prior findings, the distribution was positively skewed. Most participants lied infrequently and most lies were told by a few prolific liars. Approximately three-quarters of participants were consistently low-frequency liars. Across participants, lying comprised 7% of total communication and almost 90% of all lies were little white lies. About 58% of the variance was explained by stable individual differences with approximately 42% of the variance attributable to within-person day-to-day variability. The data were consistent with both the existence of a few prolific liars and good/bad lie days.