Wisdom and good decision making are within your power: just as soon as you start speaking of yourself in the third person
A revolution in our sense of self | Nick Chater
Psychologists have tried to plumb the depths of human motivation to make sense of our behaviour. But our inner mental world is a fiction, sustained by constant improvisation
How to take things less personally and avoid mind reading | Psyche Guides
Always blaming yourself or assuming others think ill of you? A CBT therapist shares ways to break these self-critical habits
The Self-Evidencing Agent: Mind, Existence, and Predictive Processing
How the concept of self-evidencing offers a philosophical principle for understanding mind and behavior, consciousness, value, wisdom, and meaning.What is
Cognitive Surrender and Tri-System Theory · Today I Learned
How AI can support, displace, or bypass human deliberation
Epistemic Vigilance
Humans massively depend on communication with others, but this leaves them open to the risk of being accidentally or intentionally misinformed. To ensure that, despite this risk, communication remain...
Why sycophantic LLMs may imperil interactive norms between humans
Interactions with conversational AI are effortless by design—instant, compliant, and largely consequence-free. Human communication norms, by contrast, evolved under conditions of reciprocity and social accountability. We propose that repeated engagement with conversational AI systems may produce norm leakage: the cross-context carryover of instrumental communicative habits acquired in human–AI exchanges into subsequent human–human interaction. Emerging experimental evidence suggests short-term spillover effects on social judgment and behavior, including harsher evaluations, reduced cooperation, and diminished perceived humanness. Preliminary longitudinal findings are consistent with the possibility that such exposure may shape communicative habits over time, although the durability and real-world magnitude of these effects remain unclear. We further propose that sycophantic alignment may amplify norm leakage by reinforcing instrumental interaction styles. At stake, then, is the possibility that repeated engagement with highly compliant artificial agents could subtly influence users’ communicative expectations and interpersonal judgments.
Talking with strangers is surprisingly informative
A meaningful amount of people’s knowledge comes from their conversations with others. The amount people expect to learn predicts their interest in having a conversation (pretests 1 and 2), suggesting that the presumed information value of conversations guides decisions of whom to talk with. The results of seven experiments, however, suggest that people may systematically underestimate the informational benefit of conversation, creating a barrier to talking with—and hence learning from—others in daily life. Participants who were asked to talk with another person expected to learn significantly less from the conversation than they actually reported learning afterward, regardless of whether they had conversation prompts and whether they had the goal to learn (experiments 1 and 2). Undervaluing conversation does not stem from having systematically poor opinions of how much others know (experiment 3) but is instead related to the inherent uncertainty involved in conversation itself. Consequently, people underestimate learning to a lesser extent when uncertainty is reduced, as in a nonsocial context (surfing the web, experiment 4); when talking to an acquainted conversation partner (experiment 5); and after knowing the content of the conversation (experiment 6). Underestimating learning in conversation is distinct from underestimating other positive qualities in conversation, such as enjoyment (experiment 7). Misunderstanding how much can be learned in conversation could keep people from learning from others in daily life.
Why I work on self-improving AI despite the risks - Jeff Clune
Why I work on self-improving AI despite the risks. Jeff Clune.
Why major life decisions should be approached with ‘epistemic humility’ | Psyche Videos
At a crossroads in life? No matter how much you deliberate, one unknowable remains: the person you will be on the other side
Shared Reality: Experiencing Commonality with others' Inner States about the World
Humans have a fundamental need to experience a shared reality with others. We present a new conceptualization of shared reality based on four conditions. We posit (a) that shared reality involves a (subjectively perceived) commonality of individuals' inner states (not just observable behaviors); (b) that shared reality is about some target referent; (c) that for a shared reality to occur, the commonality of inner states must be appropriately motivated; and (d) that shared reality involves the experience of a successful connection to other people's inner states. In reviewing relevant evidence, we emphasize research on the saying-is-believing effect, which illustrates the creation of shared reality in interpersonal communication. We discuss why shared reality provides a better explanation of the findings from saying-is-believing studies than do other formulations. Finally, we examine relations between our conceptualization of shared reality and related constructs (including empathy, perspective taking, theory of mind, common ground, embodied synchrony, and socially distributed knowledge) and indicate how our approach may promote a comprehensive and differentiated understanding of social-sharing phenomena.
Self-Distancing: What It Is and How You Can Use It to Make Better Decisions – Effectiviology
It’s hard to set your emotions aside when you’re faced with a difficult decision. This is problematic, since our emotions often cause us to make the wrong decision in important areas of life, including in our relationships, our finances, and our health.
Thinking Fast, Slow, Artificially: AI and Your Brain
AI can boost your decisions — or quietly undermine them. Wharton researchers introduce a new theory of cognition that every leader needs to understand.
Trust and reliance on AI — An experimental study on the extent and costs of overreliance on AI
Decision-making is undergoing rapid changes due to the introduction of artificial intelligence (AI), as AI recommender systems can help mitigate human flaws and increase decision accuracy and efficiency. However, AI can also commit errors or suffer from algorithmic bias. Hence, blind trust in technologies carries risks, as users may follow detrimental advice resulting in undesired consequences. Building upon research on algorithm appreciation and trust in AI, the current study investigates whether users who receive AI advice in an uncertain situation overrely on this advice — to their own detriment and that of other parties. In a domain-independent, incentivized, and interactive behavioral experiment, we find that the mere knowledge of advice being generated by an AI causes people to overrely on it, that is, to follow AI advice even when it contradicts available contextual information as well as their own assessment. Frequently, this overreliance leads not only to inefficient outcomes for the advisee, but also to undesired effects regarding third parties. The results call into question how AI is being used in assisted decision making, emphasizing the importance of AI literacy and effective trust calibration for productive deployment of such systems.
Warning labels shift perceptions of sycophantic AI, but not its influence
Recent work has raised concerns about the influence of sycophantic AI on user judgment and relationships. One proposed mitigation, which has received regulatory attention, is to warn users about potentially harmful AI behaviors such as sycophancy. In a preregistered experiment in which participants (N = 2,610) discussed real interpersonal conflicts with an AI system, we test whether warning labels mitigate sycophancy's influence. We find that a basic AI disclosure (``This chatbot is AI'') has no detectable effect. Labeling the system as sycophantic (``...may agree with you and validate you even when you are wrong...'') does shift users' perceptions, reducing perceived objectivity and trust, but it does not reliably reduce sycophancy's influence on users' self-perceived rightness or their willingness to repair the conflict. Our results reveal a gap between AI perception and AI influence: by shifting perception without reducing influence, warning-based interventions may offer a false sense of protection. Addressing the harms of sycophancy will therefore require understanding the specific mechanisms through which it shapes judgment, and improving model behavior itself.
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