Linguistic Markers of Inherently False AI Communication and Intentionally False Human Communication: Evidence From Hotel Reviews
David Markowitz, Jeffrey Hancock & Jeremy Bailenson
Journal of Language and Social Psychology, forthcoming
To the human eye, AI-generated outputs of large language models have increasingly become indistinguishable from human-generated outputs. Therefore, to determine the linguistic properties that separate AI-generated text from human-generated text, we used a state-of-the-art chatbot, ChatGPT, and compared how it wrote hotel reviews to human-generated counterparts across content (emotion), style (analytic writing, adjectives), and structural features (readability). Results suggested AI-generated text had a more analytic style and was more affective, more descriptive, and less readable than human-generated text. Classification accuracies of AI-generated versus human-generated texts were over 80%, far exceeding chance (∼50%). Here, we argue AI-generated text is inherently false when communicating about personal experiences that are typical of humans and differs from intentionally false human-generated text at the language level. Implications for AI-mediated communication and deception research are discussed.
Who's afraid of the GOATs? - Shadow Effects of Tennis Superstars
Christian Deutscher, Lena Neuberg & Stefan Thiem
Journal of Economic Psychology, December 2023
In multi-stage tournaments, anticipated competition in future stages might affect the outcome of competition in the current stage. In particular, the presence of super- stars might demotivate the next-best competitors from seeking to advance to later rounds, where they ultimately are likely to face a superstar. Data from men’s professional tennis tournaments held between 2004 and 2019 affirm that the participation of superstars (Djokovic, Nadal, Federer, and Murray) reduces the probability that the remaining Top 20 players win their matches. Such shadow effects arise even in very early tournament stages, in which favoured players lose more often than expected, given their ability. The effects are more pronounced when multiple superstars compete in the tournament and disappear once all superstars have been eliminated from competition. Furthermore, shadow effects increase the probability of retirement of strong but non-superstar competitors and disappear once superstar performance is not dominant.
The Improvement Default: People Presume Improvement When Lacking Information
James Hillman, Jillian Antoun & David Hauser
Personality and Social Psychology Bulletin, forthcoming
People erroneously think that things they know little about improve over time. We propose that, due to salient cultural narratives, improvement is a highly accessible expectation that leads people to presume improvement in the absence of diagnostic information. Five studies investigated an improvement default: a general tendency to presume improvement even in self-irrelevant domains. Participants erroneously presumed improvement over esoteric historical time periods associated with decline (Study 1). Participants arranged a stranger’s experiences to produce trends of improvement (Study 2). Participants presumed improvement for a fictional city when given no diagnostic information about it (Study 3). Finally, participants who perceived more past improvement were less supportive of policies that may precipitate further improvement (Study 4). Implications for consequences, such as complacency toward improving inequality, are discussed.
An illusion of predictability in scientific results: Even experts confuse inferential uncertainty and outcome variability
Sam Zhang et al.
Proceedings of the National Academy of Sciences, 15 August 2023
Traditionally, scientists have placed more emphasis on communicating inferential uncertainty (i.e., the precision of statistical estimates) compared to outcome variability (i.e., the predictability of individual outcomes). Here, we show that this can lead to sizable misperceptions about the implications of scientific results. Specifically, we present three preregistered, randomized experiments where participants saw the same scientific findings visualized as showing only inferential uncertainty, only outcome variability, or both and answered questions about the size and importance of findings they were shown. Our results, composed of responses from medical professionals, professional data scientists, and tenure-track faculty, show that the prevalent form of visualizing only inferential uncertainty can lead to significant overestimates of treatment effects, even among highly trained experts. In contrast, we find that depicting both inferential uncertainty and outcome variability leads to more accurate perceptions of results while appearing to leave other subjective impressions of the results unchanged, on average.
Managing Mental Accounts: Payment Cards and Consumption Expenditures
Michael Gelman & Nikolai Roussanov
NBER Working Paper, August 2023
Does mental accounting matter for total consumption expenditures? We exploit a unique setting in which individuals exogenously received a new credit card, without requesting one. Using random variation in the time of receipt we show that individuals temporarily increase total consumption expenditure by making purchases with the new card without reducing spending on the others. We do not observe a corresponding increase in indebtedness. Total consumption expenditure rises even for the least liquidity-constrained individuals. The evidence is consistent with consumers treating methods of payment as nonfungible budget categories, as suggested by models of mental accounting and narrow bracketing.
Acetylcholine and noradrenaline enhance foraging optimality in humans
Nick Sidorenko et al.
Proceedings of the National Academy of Sciences, 5 September 2023
Foraging theory prescribes when optimal foragers should leave the current option for more rewarding alternatives. Actual foragers often exploit options longer than prescribed by the theory, but it is unclear how this foraging suboptimality arises. We investigated whether the upregulation of cholinergic, noradrenergic, and dopaminergic systems increases foraging optimality. In a double-blind, between-subject design, participants (N = 160) received placebo, the nicotinic acetylcholine receptor agonist nicotine, a noradrenaline reuptake inhibitor reboxetine, or a preferential dopamine reuptake inhibitor methylphenidate, and played the role of a farmer who collected milk from patches with different yield. Across all groups, participants on average overharvested. While methylphenidate had no effects on this bias, nicotine, and to some extent also reboxetine, significantly reduced deviation from foraging optimality, which resulted in better performance compared to placebo. Concurring with amplified goal-directedness and excluding heuristic explanations, nicotine independently also improved trial initiation and time perception. Our findings elucidate the neurochemical basis of behavioral flexibility and decision optimality and open unique perspectives on psychiatric disorders affecting these functions.
Champion-level drone racing using deep reinforcement learning
Elia Kaufmann et al.
Nature, 31 August 2023, Pages 982-987
First-person view (FPV) drone racing is a televised sport in which professional competitors pilot high-speed aircraft through a 3D circuit. Each pilot sees the environment from the perspective of their drone by means of video streamed from an onboard camera. Reaching the level of professional pilots with an autonomous drone is challenging because the robot needs to fly at its physical limits while estimating its speed and location in the circuit exclusively from onboard sensors. Here we introduce Swift, an autonomous system that can race physical vehicles at the level of the human world champions. The system combines deep reinforcement learning (RL) in simulation with data collected in the physical world. Swift competed against three human champions, including the world champions of two international leagues, in real-world head-to-head races. Swift won several races against each of the human champions and demonstrated the fastest recorded race time. This work represents a milestone for mobile robotics and machine intelligence, which may inspire the deployment of hybrid learning-based solutions in other physical systems.