Intuitive Responses Research Articles

Comparative performance analysis of large language models: ChatGPT-3.5, ChatGPT-4 and Google Gemini in glucocorticoid-induced osteoporosis

BackgroundsThe use of large language models (LLMs) in medicine can help physicians improve the quality and effectiveness of health care by increasing the efficiency of medical information management, patient care, medical research, and clinical decision-making.MethodsWe collected 34 frequently asked questions about glucocorticoid-induced osteoporosis (GIOP), covering topics related to the disease’s clinical manifestations, pathogenesis, diagnosis, treatment, prevention, and risk factors. We also generated 25 questions based on the 2022 American College of Rheumatology Guideline for the Prevention and Treatment of Glucocorticoid-Induced Osteoporosis (2022 ACR-GIOP Guideline). Each question was posed to the LLM (ChatGPT-3.5, ChatGPT-4, and Google Gemini), and three senior orthopedic surgeons independently rated the responses generated by the LLMs. Three senior orthopedic surgeons independently rated the answers based on responses ranging between 1 and 4 points. A total score (TS) > 9 indicated ‘good’ responses, 6 ≤ TS ≤ 9 indicated ‘moderate’ responses, and TS < 6 indicated ‘poor’ responses.ResultsIn response to the general questions related to GIOP and the 2022 ACR-GIOP Guidelines, Google Gemini provided more concise answers than the other LLMs. In terms of pathogenesis, ChatGPT-4 had significantly higher total scores (TSs) than ChatGPT-3.5. The TSs for answering questions related to the 2022 ACR-GIOP Guideline by ChatGPT-4 were significantly higher than those for Google Gemini. ChatGPT-3.5 and ChatGPT-4 had significantly higher self-corrected TSs than pre-corrected TSs, while Google Gemini self-corrected for responses that were not significantly different than before.ConclusionsOur study showed that Google Gemini provides more concise and intuitive responses than ChatGPT-3.5 and ChatGPT-4. ChatGPT-4 performed significantly better than ChatGPT3.5 and Google Gemini in terms of answering general questions about GIOP and the 2022 ACR-GIOP Guidelines. ChatGPT3.5 and ChatGPT-4 self-corrected better than Google Gemini.

Trusting Our Instincts: Evaluating the Evolutionary and Rational Foundations of Moral Intuitions

This paper delves into the nature and reliability of moral intuitions, building upon Jonathan Haidt’s social intuitionist model, which suggests that moral judgments stem from instinctive reactions followed by rational justifications. By examining Haidt’s experiments, which reveal the primacy of intuitive responses over rational deliberation, this paper seeks to address whether we have valid reasons to trust these moral intuitions. Drawing from evolutionary psychology, it argues that moral intuitions have historically contributed to the survival and success of the human species, thereby providing them with normative value. However, given the rapid advancements in technology and societal changes, these intuitions may sometimes lead to nonoptimal or disastrous outcomes. The paper proposes a rational test to determine the reliability of moral intuitions: we should trust an intuition if its generalized application benefits the human species. Potential counterarguments, such as the historical misuse of evolutionary logic in eugenics, are addressed to underscore the validity of this test. The conclusion emphasizes that while moral intuitions are generally trustworthy due to their evolutionary roots, rational thought must intervene when intuitions become outdated in the face of contemporary challenges.

Easy-fix attentional focus manipulation boosts the intuitive and deliberate use of base-rate information.

In addressing human reasoning biases, "easy-fix" attentional focus interventions have shown that we can prompt reasoners to align responses with logico-mathematical principles. The current study aimed to test the impact of such interventions on both intuitive and deliberate responses on base-rate items. Using a two-response paradigm, participants provided initial intuitive responses under time constraints and cognitive load, followed by deliberate responses. During the intervention, we used attentional focus manipulations with base-rate items that aimed to redirect participants' attention toward the "logical" base-rate cue (i.e., the logical intervention) or toward the "heuristic" descriptive cue (i.e., the heuristic intervention). The results indicate that the logical intervention led to improved alignment with logico-mathematical principles in both intuitive and deliberate responses, albeit with a modest effect size. Conversely, the heuristic intervention had no discernible impact on accuracy. This indicates that our attentional focus manipulation is more effective at getting reasoners to respect rather than to override base-rates.

The Concept of Religious Experience by Rudolf Otto: A Phenomenological Approach

This study investigates and analyzes the religious experiences of Rudolf Otto using a phenomenological approach. The main objective of this study is to explore Otto's interpretation of religious experience and his contribution to the field of phenomenology. The research problem focuses on understanding how Otto's concept provides a framework for examining religious experiences that go beyond ordinary or everyday experience. The study uses a literature review, document analysis, and phenomenological analysis of Otto's works, including his important text, Das Heilige (The Idea of the Holy), and his personal documents, such as letters, journals, and notes. The study reveals that Otto's concept of the "Almighty" refers to a divine presence that transcends logical understanding and evokes deep emotional and intuitive responses. These findings demonstrate the importance of Otto's contribution to the phenomenology of religion, both theoretically and methodologically. Otto's insights have provided important insights for examining the complexity and richness of religious experiences in the modern world.

Integrated Mixed Potential Gas Sensor with Efficient Structure for Discriminative Volatile Organic Compounds Detection.

Amid growing interest in the precise detection of volatile organic compounds (VOCs) in industrial field, the demand for highly effective gas sensors is at an all-time high. However, traditional sensors with their classic single-output signal, bulky and complex integrated structure when forming array often involve complicated technology and high cost, limiting their widespread adoption. Here, this study introduces a novel approach, employing an integrated YSZ-based (YSZ: yttria-stabilized zirconia) mixed potential sensor equipped with a triple-sensing electrode array, to efficiently detect and differentiate six types of VOCs gases. This innovative sensor integrates NiSb2O6, CuSb2O6, and MgSb2O6 sensing electrodes (SEs), which are sensitive to pentane, isoprene, n-propanol, acetone, acetic acid, and formaldehyde gases. Through feature engineering based on intuitive spike-based response values, it accentuates the distinct characteristics of every gas. Eventually, an average classification accuracy of 98.8% and an overall R-squared error (R2) of 99.3% for concentration regression toward six target gases can be achieved, showcasing the potential to quantitatively distinguish between industrial hazardous VOCs gases.

Dense and persistent odor representations in the olfactory bulb of awake mice.

Recording and analysis of neural activity is often biased toward detecting sparse subsets of highly active neurons, masking important signals carried in low magnitude and variable responses. To investigate the contribution of seemingly noisy activity to odor encoding, we used mesoscale calcium imaging from mice of both sexes to record odor responses from the dorsal surface of bilateral olfactory bulbs (OBs). The outer layer of the mouse OB is comprised of dendrites organized into discrete "glomeruli", which are defined by odor receptor-specific sensory neuron input. We extracted activity from a large population of glomeruli and used logistic regression to classify odors from individual trials with high accuracy. We then used add-in and drop-out analyses to determine subsets of glomeruli necessary and sufficient for odor classification. Classifiers successfully predicted odor identity even after excluding sparse, highly active glomeruli, indicating that odor information is redundantly represented across a large population of glomeruli. Additionally, we found that Random Forest feature selection informed by Gini Inequality (RFGI) reliably ranked glomeruli by their contribution to overall odor classification. RFGI provided a measure of "feature importance" for each glomerulus that correlated with intuitive features like response magnitude. Finally, in agreement with previous work, we found that odor information persists in glomerular activity after odor offset. Together, our findings support a model of olfactory bulb odor coding where sparse activity is sufficient for odor identification, but information is widely, redundantly available across a large population of glomeruli, with each glomerulus representing information about more than one odor.Significance statement This study leverages meso-scale imaging and machine learning to investigate how odor information is first represented in the brain. Typically, recordings of neuronal activity focus on active individual cells, potentially overlooking broader variations in neuronal responses across populations. Our results demonstrate that a considerable amount of olfactory information is redundantly distributed across a large proportion of olfactory bulb glomeruli. Even after excluding a majority of glomeruli, odor identification remained possible. These findings indicate that, although a few glomeruli are sufficient for odor recognition, an abundance of additional information is represented across a broad population. Understanding how the brain manages redundant olfactory information will shed light on its adaptive mechanisms for navigating diverse real-world circumstances and responding to fluctuating internal states.

Of two minds: an experiment on how time scarcity shapes risk-taking behaviour

ABSTRACT Several studies report that the brain evaluates prospects and executes decisions as the outcome of two types of mental processes: one described as slow and reflective and the other as fast and intuitive. We investigate how these two mental processes affect risk-taking behaviour in a one-shot decision by using time pressure to establish an intuitive response. We observe that time constraints do not change risk attitudes. Furthermore, it is only when subjects are given ample time to decide and instructed to reflect that they show the well-documented shift of risk preferences across the domain of losses and gains. Our results suggest that framing effects are the result of cognitive processes that become operative when individuals are granted sufficient time to undertake a more comprehensive analysis of decision tasks.

X-Phi within its proper bounds

ABSTRACT Using two decades worth of experimental philosophy (aka x-phi), Edouard Machery argues in Philosophy within its Proper Bounds (2017) that philosophers’ use of the “method of cases” is unreliable because it has a strong tendency to elicit different intuitive responses from non-philosophers. And because, as Machery argues, appealing to such cases is usually the only way for philosophers to acquire the kind of knowledge they seek, an extensive philosophical skepticism follows. I argue that Machery’s “Unreliability” argument fails because, once its premises are percisified, they are either self-defeating or without justification. This is a significant result because Machery’s arguments are the most widely cited and discussed x-phi arguments for philosophical skepticism and many hold that Machery provides the most empirically informed, convincing, and thus best case for this kind of skepticism. So, if my arguments are sound, then the best x-phi argument for philosophical skepticism fails. I further argue that this result provides strong reason to believe the more general conclusion that “negative” x-phi is likely doomed: x-phi likely can never support a substantive philosophical skepticism. Ultimately, I argue for the broad conclusion that all empirically minded arguments for philosophical skepticism are likely to fail for the same reasons that Machery’s does, i.e. they are (likely) self-defeating.

Thinking disposition education improves cognitive reflection: Experimental results from an intervention study

Cognitive reflection is a construct measuring one's ability to disengage automatic or type I processing and instead activate effortful deliberation, described as type II processing in dual process theories. Successfully overriding intuitive responses is posited to rely on, in part, trait-like attributes known as thinking dispositions, though a causal link has not been established. The present research is the first randomized controlled trial investigating the effect of a novel thinking disposition intervention (TDI)—a 7-week educational curriculum—on cognitive reflection performance. In the present study, participants (N = 338) were randomized to either the TDI or a control condition. A Bayesian modeling approach suggests positive and moderate effects. Moreover, those in the treatment were over twice as likely to make CRT gains than those in the control group. Additional mediation and moderation analyses were conducted to test whether the TDI impacts CRT via thinking dispositions such as need for cognition and intellectual humility, and/or whether such attributes meaningfully interact with the intervention. Thinking dispositions do not appear to mediate this relationship, neither do they moderate the effect of the intervention. Implications for dual process theory are discussed.

Virtual lab coats: The effects of verified source information on social media post credibility.

Social media platform's lack of control over its content made way to the fundamental problem of misinformation. As users struggle with determining the truth, social media platforms should strive to empower users to make more accurate credibility judgements. A good starting point is a more accurate perception of the credibility of the message's source. Two pre-registered online experiments (N = 525;N = 590) were conducted to investigate how verified source information affects perceptions of Tweets (study 1) and generic social media posts (study 2). In both studies, participants reviewed posts by an unknown author and rated source and message credibility, as well as likelihood of sharing. Posts varied by the information provided about the account holder: (1) none, (2) the popular method of verified source identity, or (3) verified credential of the account holder (e.g., employer, role), a novel approach. The credential was either relevant to the content of the post or not. Study 1 presented the credential as a badge, whereas study 2 included the credential as both a badge and a signature. During an initial intuitive response, the effects of these cues were generally unpredictable. Yet, after explanation how to interpret the different source cues, two prevalent reasoning errors surfaced. First, participants conflated source authenticity and message credibility. Second, messages from sources with a verified credential were perceived as more credible, regardless of whether this credential was context relevant (i.e., virtual lab coat effect). These reasoning errors are particularly concerning in the context of misinformation. In sum, credential verification as tested in this paper seems ineffective in empowering users to make more accurate credibility judgements. Yet, future research could investigate alternative implementations of this promising technology.

Do Not Risk Homicide: Abortion After 10 Weeks Gestation.

When an abortion is performed, someone dies. Are we killing a human person? Widespread disagreement exists. However, it is not necessary to establish personhood in order to establish the wrongness of abortion: a substantial chance of personhood is enough. We defend The Do Not Risk Homicide Argument: abortions are wrong after 10 weeks gestation because they substantially and unjustifiably risk homicide, the unjust killing of a human person. Why 10 weeks? Because the cumulative evidence establishes a substantial chance (a more than one in five chance) that preborn human beings are persons after 10 weeks (if not before then). We submit evidence from our bad track record, widespread disagreement about personhood (after 10 weeks gestation), problems with theories of personhood, the similarity between preborn human beings and premature newborns, miscalculations of gestational age, and the common intuitive responses of women to their pregnancies and miscarriages. Our argument is cogent because it bypasses the stalemate over preborn personhood and rests on common ground rather than contentious metaphysics. It also strongly suggests that society must do more to protect preborn human beings. We briefly discuss its practical implications for fetal pain relief, social policy, and abortion law.

A Mechanoluminescent ZnS:Cu/PDMS and Biocompatible Piezoelectric Silk Fibroin/PDMS Hybrid Sensor for Self‐Powered Sensing and Artificial Intelligence Control

AbstractMechanoluminescence (ML) is luminescence induced due to mechanical stress, providing intuitive responses to strain‐related events. Piezoelectricity is the conversion of mechanical strain into electrical signals, offering a quantitative measurement of force/deformation. Combining ML and piezoelectricity within a single device provides a comprehensive understanding of mechanical events, providing qualitative and quantitative information about strain‐related phenomena. A ZnS:Cu/polydimethylsiloxane (PDMS) composite and a biocompatible silk fibroin/PDMS composite are prepared to generate ML and electrical signals, respectively. An innovative method for obtaining powder from silk fabrics is employed. The microstructure and composition of silk fibroin powder are also examined via X‐ray diffraction and Fourier Transform Infrared (FTIR) spectroscopy. Mechanical stimuli such as pressure, stretching, twisting, bending, vibration, and rubbing are applied to the device to demonstrate optical and electrical responses. Under pressure, a voltage of 3.82 V and an output current of 201.6 nA are generated at a force of 1 N. Furthermore, a handwritten test is conducted to qualitatively visualize letters based on ML effects and explore the feasibility of using artificial intelligence to classify voltage signals generated during writing into their corresponding letters. This biocompatible, dual‐modal self‐powered sensor demonstrates broad applicability in wearable technology, biomechanics, human–machine interaction, security, and energy harvesting.

Illuminating the unseen: an action learning coach's journey

ABSTRACT This is a reflective Account of Practice paper that delves into the author's role as an Action Learning coach, emphasizing the crucial role of intuition in facilitating breakthroughs and creating an environment conducive to problem-solving. It narrates a transformative 2019 coaching session where the author used unconventional techniques to empower a team member to create a comprehensive action plan for a complex issue. The paper underscores the significance of addressing ‘invisible’ group dynamics, such as shifts in group energy and emotional responses. It emphasizes personal reflection, shared learning and heightened sensitivity to group dynamics for effective coaching. The paper discusses daily practices: mindfulness, compassion and post-session reflection; these amplify intuitive responses, resilience and self-awareness. The paper highlights the Action Learning coach's capacity to positively impact others, inspire authentic responses and unveil hidden potential.

The cognitive reflection test and students’ achievements in mathematics and physics

The cognitive reflection test (CRT) assesses an individual’s capacity to restrain impulsive and intuitive responses and to engage in critical reflection on mathematical problems. The literature indicates that several factors influence students’ performance on CRT, including gender, age, and prior knowledge of mathematics. In this study, our objective was to investigate the correlation between CRT scores and students’ achievements in both mathematics and physics. We conducted our research with a sample of 150 Italian high school students, and the findings revealed a positive predictive relationship between CRT scores and students’ performance in both mathematics and physics. Furthermore, we employed an ordinal logistic regression to evaluate the impact of CRT scores, gender, and school level on students’ achievements in mathematics and physics. The results showed that both CRT scores and school level had statistically significant effects on predicting these achievements. In contrast, gender emerged as a statistically significant factor only in predicting students’ mathematics achievements.

Adieu Bias: Debiasing Intuitions Among French Speakers.

Recent debiasing studies have shown that a short, plain-English explanation of the correct solution strategy can improve reasoning performance. However, these studies have predominantly focused on English-speaking populations, who were tested with problem contents designed for an English-speaking test environment. Here we explore whether the key findings of previous debiasing studies can be extended to native French speakers living in continental Europe (France). We ran a training session with a battery of three reasoning tasks (i.e., base-rate neglect, conjunction fallacy, and bat-and-ball) on 147 native French speakers. We used a two-response paradigm in which participants first gave an initial intuitive response, under time pressure and cognitive load, and then gave a final response after deliberation. Results showed a clear training effect, as early as the initial (intuitive) stage. Immediately after training, most participants solved the problems correctly, without the need for a deliberation process. The findings confirm that the intuitive debiasing training effect extends to native French speakers.

CNN-Based Image Analysis for EEG Signal Characterization.

This article focuses on an attempt to classify and recognize the characterized images of EEG signals directly. For EEG signals, the recognition and judgment of different signals has been the key direction of research. CNN (Convolutional Neural Network) models are usually used for recognition of EEG raw signals about movement and Imagery Dataset. However, the images of EEG raw signals are basically unreadable for researchers, so characterization is a common tool. However, direct recognition of the characterized images is a relatively empty area in the existing research because it requires much higher machine performance than the traditional raw signal recognition. However, feeding the extracted feature images into a CNN and training them can be an efficient and intuitive response to the potential of EEG for brain mapping. The main goal of this research is to examine the discriminative capabilities of traditional visual and image neural networks for pictures described by EEG data. This is not typical in contemporary brain-computer interface research. The direct recognition of the described photos uses a lot of GPU (graphics computing unit) resources, but for the characterized images are easier for people to read than the original images. This work indicates the viability of direct research on defined pictures and increases the application scenario of EEG signals.

The (Personal) Experience of Values – Scheler and Hildebrand

Abstract There are several problems in conceiving of value experience in early phenomenology. What exactly does the experience of a value consist in? How are we to determine the morality of an action that is based on a value which is, as a reality in and of itself, imposed on us from without? How is the experience of values related to the person and in what way can an intuitive value response be reconciled with the application of acquired, personal value stances? In a joint reading of the early works of Max Scheler and Dietrich von Hildebrand, I propose a systematic solution to these various problems which revolve around the non-reflective self-awareness that a value experience entails: we cannot experience the real, material value quality without changing as persons. Rather than reflecting on an object, we experience the value as ourselves, dynamically reacting to it in the context of a given situation.

A dual-process perspective on the relationship between implicit attitudes and discriminatory behaviour

Abstract The dual-process perspective (DPP), which contrasts intuitive and deliberative cognitive processes, has advanced our understanding of the conditions under which cultural orientations, such as implicit attitudes, influence overt behaviour considerably. We test a central tenet of the DPP using a choice experiment on the placement of trust in hypothetical economic transactions. According to the principle of catalyzation, the impact of implicit cultural orientations on overt behaviour should be greater if the behaviour comes about in an intuitive rather than a deliberative manner. In this study, we focus on the implicit attitudes towards class and ethnicity and measure their impact on social behaviour via estimates of the effects of corresponding attributes within the choice experiment. Using a framing technique to experimentally induce intuitive or reflective responses, we find that implicit attitudes affect the placement of trust in the intuitive framing condition but not in the reflective framing condition. Besides providing a strict test of a central tenet of the DPP in a choice-experimental set-up, our study also sheds light on different cognitive mechanisms underlying discriminatory behaviour.

Boosting debiasing: Impact of repeated training on reasoning

BackgroundRecent debiasing studies have shown that a short explanation about the correct solution to a reasoning problem can often improve performance of initially biased reasoners. Yet, with only one single training session, there is still a non-neglectable group of reasoners who remained biased. AimsWe explored whether repeated training on a battery of three reasoning tasks (i.e., bat-and-ball, base-rate neglect, and conjunction fallacy) can further boost reasoning performance. SampleWe recruited 120 adults, native English speakers, through Prolific Academic. MethodsWe ran two studies with a battery of three classic reasoning tasks (see above). We used a two-response paradigm in which participants first gave an initial intuitive response, under time pressure and cognitive load, and then gave a final response after deliberation. In Study 1, we ran two repeated training sessions within one week. In Study 2, we ran a third training session two months after the initial study. ResultsStudy 1 showed that after the first training session, most of the participants solved the problems correctly, as early as the initial intuitive stage. This training effect was further boosted by additional training, which helped almost the full sample to benefit. Study 2 indicated that these effects were robust and persisted after two months. ConclusionsThe repetition of the training can further boost performance compared to the effect of one single training. These results are consistent with the wider literature on repeated testing and can serve as a proof-of-principle for a repeated debias training approach.

Accuracy of ChatGPT on Medical Questions in the National Medical Licensing Examination in Japan: Evaluation Study.

ChatGPT (OpenAI) has gained considerable attention because of its natural and intuitive responses. ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers, as stated by OpenAI as a limitation. However, considering that ChatGPT is an interactive AI that has been trained to reduce the output of unethical sentences, the reliability of the training data is high and the usefulness of the output content is promising. Fortunately, in March 2023, a new version of ChatGPT, GPT-4, was released, which, according to internal evaluations, was expected to increase the likelihood of producing factual responses by 40% compared with its predecessor, GPT-3.5. The usefulness of this version of ChatGPT in English is widely appreciated. It is also increasingly being evaluated as a system for obtaining medical information in languages other than English. Although it does not reach a passing score on the national medical examination in Chinese, its accuracy is expected to gradually improve. Evaluation of ChatGPT with Japanese input is limited, although there have been reports on the accuracy of ChatGPT's answers to clinical questions regarding the Japanese Society of Hypertension guidelines and on the performance of the National Nursing Examination. The objective of this study is to evaluate whether ChatGPT can provide accurate diagnoses and medical knowledge for Japanese input. Questions from the National Medical Licensing Examination (NMLE) in Japan, administered by the Japanese Ministry of Health, Labour and Welfare in 2022, were used. All 400 questions were included. Exclusion criteria were figures and tables that ChatGPT could not recognize; only text questions were extracted. We instructed GPT-3.5 and GPT-4 to input the Japanese questions as they were and to output the correct answers for each question. The output of ChatGPT was verified by 2 general practice physicians. In case of discrepancies, they were checked by another physician to make a final decision. The overall performance was evaluated by calculating the percentage of correct answers output by GPT-3.5 and GPT-4. Of the 400 questions, 292 were analyzed. Questions containing charts, which are not supported by ChatGPT, were excluded. The correct response rate for GPT-4 was 81.5% (237/292), which was significantly higher than the rate for GPT-3.5, 42.8% (125/292). Moreover, GPT-4 surpassed the passing standard (>72%) for the NMLE, indicating its potential as a diagnostic and therapeutic decision aid for physicians. GPT-4 reached the passing standard for the NMLE in Japan, entered in Japanese, although it is limited to written questions. As the accelerated progress in the past few months has shown, the performance of the AI will improve as the large language model continues to learn more, and it may well become a decision support system for medical professionals by providing more accurate information.