How Can You Tell When Someone is Lying? | Norah Dunbar | TEDxLagunaBlancaSchool
TLDRIn a compelling discourse, deception detection researcher David DeRuwe debunks common myths about lie detection, such as liars looking up to the left or displaying easily discernible facial cues. He emphasizes that humans are poor lie detectors, often relying on biases and flawed cues. DeRuwe outlines three key aspects of deception: humans' inaccuracy in lie detection, the absence of a single tell for deception, and the subtlety of deceptive behavior. He suggests looking for clusters of cues, including uncertainty, tension, and cognitive load, which manifest in both verbal and non-verbal behaviors. DeRuwe also advocates for the use of computer algorithms to aid in detecting deception more accurately by processing multiple cues simultaneously, which humans cannot do effectively. His talk encourages a more nuanced understanding of deception and the potential of technology in enhancing our ability to detect it.
Takeaways
- 📢 The story of Brian Williams, a celebrated news anchor, illustrates the severe consequences of deception in professional settings, leading to job loss and a tarnished reputation.
- 🤔 Deception by admired figures, such as politicians, journalists, or athletes, can lead to increased cynicism, while lies from loved ones can deeply hurt and damage trust.
- 🧐 Contrary to popular myths, there are no definitive physical signs like looking up to the left for liars or up to the right for truth-tellers that can accurately indicate lying.
- 🚫 Humans are poor lie detectors, often being wrong half the time due to biases and reliance on incorrect cues.
- 👀 The myth that the eyes are the 'windows to the soul' is debunked; liars can lie while maintaining eye contact.
- 🚫 There is no single physical cue, like blinking or pupil dilation, that is a reliable indicator of deception.
- 🔍 To detect deception accurately, one should look for clusters of cues, including verbal and non-verbal signals, rather than focusing on a single behavior.
- 🤨 Uncertainty can be a sign of deception, as liars often lack detail and plausibility in their stories and show less engagement in their gestures and language.
- 😓 Tension is another cluster to observe, as liars may display increased vigilance, leading to behaviors like pursed lips, less facial animation, and unnatural gestures.
- 🧠 Cognitive load is indicated when a person's brain is working hard to maintain a lie, which can result in inconsistencies, short answers, repetition, and longer pauses.
- 💻 Computer algorithms can assist in detecting deception more accurately by analyzing multiple cues simultaneously, which is beyond human processing capabilities.
- 🤖 The use of technology for deception detection is not about replacing human judgment but enhancing it with objective, less biased analysis.
Q & A
What was the issue with Brian Williams' story about a helicopter crash in Iraq?
-The issue was that the story Brian Williams told about a helicopter crash in Iraq was not entirely true. This deception led to his termination from the network and loss of his nightly news anchor job, although he was later reinstated.
Why does deception by people we admire hurt more than others?
-Deception by people we admire hurts more because it ruins our relationships and makes us less trusting in the future. It stings because we have a higher expectation of honesty from those we respect and trust.
What is a common myth about detecting lies that the speaker refutes?
-A common myth that the speaker refutes is that liars look up to the left and truth tellers look up to the right. The speaker clarifies that this is not true and is one of the many myths perpetuated by media.
How often are humans wrong when trying to judge between truths and lies?
-Humans are wrong about half the time when trying to judge between truths and lies due to biases and the influence of personal experiences and history.
What is the speaker's main argument against relying on single cues to detect deception?
-The speaker argues against relying on single cues to detect deception because there is no one tell that works for everyone. Deception detection requires looking for clusters of cues and patterns of behavior.
What are the three clusters of cues the speaker suggests looking for to detect deception?
-The three clusters of cues the speaker suggests are uncertainty, tension, and cognitive load. These clusters involve a combination of verbal and non-verbal cues that can indicate deception.
How does the speaker describe the nature of deception?
-The speaker describes deception as subtle and beneath the surface. It involves small differences between truth and lies, often with lies being embedded with truths, making it difficult to detect.
What is the role of computer algorithms in detecting deception according to the speaker?
-Computer algorithms can assist in detecting deception more accurately by analyzing multiple verbal and non-verbal cues simultaneously, which humans cannot do as effectively. They can be less biased and more objective.
Why does the speaker suggest that relying solely on human judgment is flawed in deception detection?
-The speaker suggests that relying solely on human judgment is flawed because humans are prone to biases, lack consistent feedback on their decisions, and often look for the wrong cues, leading to inaccurate deception detection.
What are some of the tools used in the speaker's lab to detect deception?
-In the speaker's lab, they use facial tracking, gesture tracking, pupillometry, and even thermal imaging to analyze a wide range of non-verbal and verbal cues to detect deception.
How does the speaker alleviate concerns about the use of computer algorithms in deception detection?
-The speaker alleviates concerns by clarifying that the use of computer algorithms is not about replacing human decision-making with AI but rather using technology to assist in detecting deception more accurately, reducing bias, and improving objectivity.
What is the speaker's hope for the audience after the talk?
-The speaker hopes that the audience will learn to think about deception differently and gain some methods to detect deception more accurately in the future.
Outlines
📢 Myths and Realities of Deception Detection
The first paragraph discusses the story of Brian Williams, a news anchor whose false story about a helicopter crash in Iraq led to his termination and tarnished reputation. It highlights the emotional impact of deception from those we admire and trust. The speaker, a deception detection researcher, dispels common myths about detecting lies, such as liars looking up to the left or displaying certain facial expressions. The paragraph emphasizes the need to move beyond these myths to accurately detect deception and improve our lives. It introduces three key facts about deception: humans are poor lie detectors, there is no single cue for deception, and deception is subtle. The speaker suggests looking for clusters of cues, including uncertainty, tension, and cognitive load, to detect deception more accurately.
🕵️♂️ Detecting Deception: Uncertainty, Tension, and Cognitive Load
The second paragraph delves into the three clusters of cues that can help detect deception: uncertainty, tension, and cognitive load. Uncertainty is identified by a lack of detail, implausible stories, and less animated body language when individuals are lying. Tension is exhibited by liars who are vigilant, watching the listener's reactions, and adapting their story, which can manifest in increased voice pitch, pursed lips, and less natural gestures. Cognitive load occurs when the brain works overtime to maintain a lie, leading to inconsistencies, short answers, repetition, and longer pauses. The speaker advises not to rely solely on one cue but to consider a combination of verbal and non-verbal cues. Additionally, the paragraph suggests that computer algorithms can assist in detecting deception by analyzing multiple cues simultaneously, which is more objective and less biased than human judgment.
Mindmap
Keywords
💡Deception
💡Lie Detection
💡Biases
💡Uncertainty
💡Tension
💡Cognitive Load
💡Myths
💡Pinocchio's Nose
💡Clusters of Cues
💡Computer Algorithms
💡Non-verbal Cues
Highlights
Brian Williams was fired from his job as a news anchor for lying about a helicopter crash in Iraq.
Deception hurts because it ruins relationships and makes us less trusting.
Common myths about lying, like looking up to the left, are not true.
Humans are poor lie detectors, being wrong about half the time.
We are biased and look for the wrong cues when trying to detect lies.
There is no single cue like Pinocchio's nose that always indicates deception.
Deception is subtle and lies are often embedded with some truth.
Instead of one cue, look for clusters of verbal and nonverbal cues.
Liars often show uncertainty - less detail, implausible story, less animation.
Tension in a liar's voice, face and body language can be a sign of deception.
Cognitive load causes liars to give short, repetitive answers and pause more.
Computer algorithms can help detect deception more accurately by analyzing many cues.
Using technology like facial and gesture tracking can provide objective data.
AI should assist, not replace, human judgment in detecting deception.
Dispelling common myths and using a scientific approach can improve lie detection.
Detecting deception is difficult but possible with the right methods and tools.
This talk provides practical advice for thinking about and detecting deception more accurately.
Transcripts
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