Testing theory of mind in large language models and humans.

At the core of what defines us as humans is the concept of theory of mind: the ability to track other people's mental states. The recent development of large language models (LLMs) such as ChatGPT has led to intense debate about the possibility that these models exhibit behaviour that is indistinguishable from human behaviour in theory of mind tasks. Here we compare human and LLM performance on a comprehensive battery of measurements that aim to measure different theory of mind abilities, from understanding false beliefs to interpreting indirect requests and recognizing irony and faux pas. We tested two families of LLMs (GPT and LLaMA2) repeatedly against these measures and compared their performance with those from a sample of 1,907 human participants. Across the battery of theory of mind tests, we found that GPT-4 models performed at, or even sometimes above, human levels at identifying indirect requests, false beliefs and misdirection, but struggled with detecting faux pas. Faux pas, however, was the only test where LLaMA2 outperformed humans. Follow-up manipulations of the belief likelihood revealed that the superiority of LLaMA2 was illusory, possibly reflecting a bias towards attributing ignorance. By contrast, the poor performance of GPT originated from a hyperconservative approach towards committing to conclusions rather than from a genuine failure of inference. These findings not only demonstrate that LLMs exhibit behaviour that is consistent with the outputs of mentalistic inference in humans but also highlight the importance of systematic testing to ensure a non-superficial comparison between human and artificial intelligences.

Ascribing consciousness to artificial intelligence: human-AI interaction and its carry-over effects on human-human interaction.

The question of whether artificial intelligence (AI) can be considered conscious and therefore should be evaluated through a moral lens has surfaced in recent years. In this paper, we argue that whether AI is conscious is less of a concern than the fact that AI can be considered conscious by users during human-AI interaction, because this ascription of consciousness can lead to carry-over effects on human-human interaction. When AI is viewed as conscious like a human, then how people treat AI appears to carry over into how they treat other people due to activating schemas that are congruent to those activated during interactions with humans. In light of this potential, we might consider regulating how we treat AI, or how we build AI to evoke certain kinds of treatment from users, but not because AI is inherently sentient. This argument focuses on humanlike, social actor AI such as chatbots, digital voice assistants, and social robots. In the first part of the paper, we provide evidence for carry-over effects between perceptions of AI consciousness and behavior toward humans through literature on human-computer interaction, human-AI interaction, and the psychology of artificial agents. In the second part of the paper, we detail how the mechanism of schema activation can allow us to test consciousness perception as a driver of carry-over effects between human-AI interaction and human-human interaction. In essence, perceiving AI as conscious like a human, thereby activating congruent mind schemas during interaction, is a driver for behaviors and perceptions of AI that can carry over into how we treat humans. Therefore, the fact that people can ascribe humanlike consciousness to AI is worth considering, and moral protection for AI is also worth considering, regardless of AI's inherent conscious or moral status.

Segregation of formulated powders in direct compression process and evaluations by small bench-scale testers.

Powder segregation can cause severe issues in processes of pharmaceutical drugs for control of content uniformity if the powder is likely to be free or easy flowing. Assessing segregation intensity of formulated powders in a process is challenging at the formulation stage because of the limited availability of samples. An advanced segregation evaluation using small bench-scale testers can be useful for formulation decisions and suggestions of operation conditions in the process, which has not been practically investigated before. In this study, eight formulations (two co-processed excipients blended with one active pharmaceutical ingredient at different ratios) were used for the segregation study on two types of bench-scale testers (air-induced and surface rolling segregation tester), and a pilot simulation process rig as a comparative study. The results show that segregation measured on the bench-scale testers can give a good indication of the segregation intensity of a blend if the segregation intensity is not more than 20%. The comparison also shows that both the bench-scale testers have a good correlation to the process rig, respectively, which means either segregation tester can be used independently for the evaluation. A linear regression model was explored for prediction of segregation in the process.

Predicting the attention of others.

As social animals, people are highly sensitive to the attention of others. Seeing someone else gaze at an object automatically draws one's own attention to that object. Monitoring the attention of others aids in reconstructing their emotions, beliefs, and intentions and may play a crucial role in social alignment. Recently, however, it has been suggested that the human brain constructs a predictive model of other people's attention that is far more involved than a moment-by-moment monitoring of gaze direction. The hypothesized model learns the statistical patterns in other people's attention and extrapolates how attention is likely to move. Here, we tested the hypothesis of a predictive model of attention. Subjects saw movies of attention displayed as a bright spot shifting around a scene. Subjects were able to correctly distinguish natural attention sequences (based on eye tracking of prior participants) from altered sequences (e.g., played backward or in a scrambled order). Even when the attention spot moved around a blank background, subjects could distinguish natural from scrambled sequences, suggesting a sensitivity to the spatial-temporal statistics of attention. Subjects also showed an ability to recognize the attention patterns of different individuals. These results suggest that people possess a sophisticated model of the normal statistics of attention and can identify deviations from the model. Monitoring attention is therefore more than simply registering where someone else's eyes are pointing. It involves predictive modeling, which may contribute to our remarkable social ability to predict the mind states and behavior of others.

Design, validation and implementation of an automated e-alert for acute kidney injury: 6-month pilot study shows increased awareness.

BACKGROUND: Acute kidney injury (AKI) is defined as a sudden episode of kidney failure but is known to be under-recognized by healthcare professionals. The Kidney Disease Improving Global Outcome (KDIGO) guidelines have formulated criteria to facilitate AKI diagnosis by comparing changes in plasma creatinine measurements (PCr). To improve AKI awareness, we implemented these criteria as an electronic alert (e-alert), in our electronic health record (EHR) system. METHODS: For every new PCr measurement measured in the University Medical Center Utrecht that triggered the e-alert, we provided the physician with actionable insights in the form of a memo, to improve or stabilize kidney function. Since e-alerts qualify for software as a medical device (SaMD), we designed, implemented and validated the e-alert according to the European Union In Vitro Diagnostic Regulation (IVDR). RESULTS: We evaluated the impact of the e-alert using pilot data six months before and after implementation. 2,053 e-alerts of 866 patients were triggered in the before implementation, and 1,970 e-alerts of 853 patients were triggered after implementation. We found improvements in AKI awareness as measured by (1) 2 days PCr follow up (56.6-65.8%, p-value: 0.003), and (2) stop of nephrotoxic medication within 7 days of the e-alert (59.2-63.2%, p-value: 0.002). CONCLUSION: Here, we describe the design and implementation of the e-alert in line with the IVDR, leveraging a multi-disciplinary team consisting of physicians, clinical chemists, data managers and data scientists, and share our firsts results that indicate an improved awareness among treating physicians.

Fundamental principles of cortical organization reflected in a new study.

In an exciting new finding by Gordon et al., the human motor cortex does not show a traditional body map. Instead, body-part-specific zones are separated by integrative zones, possibly reflecting the statistical structure of the human motor repertoire.

Added diagnostic value of routinely measured hematology variables in diagnosing immune checkpoint inhibitor mediated toxicity in the emergency department.

BACKGROUND: Immune checkpoint inhibitors (ICI) show remarkable results in cancer treatment, but at the cost of immune-related adverse events (irAE). irAE can be difficult to differentiate from infections or tumor progression, thereby challenging treatment, especially in the emergency department (ED) where time and clinical information are limited. As infections are traceable in blood, we were interested in the added diagnostic value of routinely measured hematological blood cell characteristics in addition to standard diagnostic practice in the ED to aid irAE assessment. METHODS: Hematological variables routinely measured with our hematological analyzer (Abbott CELL-DYN Sapphire) were retrieved from Utrecht Patient Oriented Database (UPOD) for all patients treated with ICI who visited the ED between 2013 and 2020. To assess the added diagnostic value, we developed and compared two models; a base logistic regression model trained on the preliminary diagnosis at the ED, sex, and gender, and an extended model trained with lasso that also assessed the hematology variables. RESULTS: A total of 413 ED visits were used in this analysis. The extended model showed an improvement in performance (area under the receiver operator characteristic curve) over the base model, 0.79 (95% CI 0.75-0.84), and 0.67 (95% CI 0.60-0.73), respectively. Two standard blood count variables (eosinophil granulocyte count and red blood cell count) and two advanced variables (coefficient of variance of neutrophil depolarization and red blood cell distribution width) were associated with irAE. CONCLUSION: Hematological variables are a valuable and inexpensive aid for irAE diagnosis in the ED. Further exploration of the predictive hematological variables could yield new insights into the pathophysiology underlying irAE and in distinguishing irAE from other inflammatory conditions.

A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department.

Accurate sepsis diagnosis is paramount for treatment decisions, especially at the emergency department (ED). To improve diagnosis, clinical decision support (CDS) tools are being developed with machine learning (ML) algorithms, using a wide range of variable groups. ML models can find patterns in Electronic Health Record (EHR) data that are unseen by the human eye. A prerequisite for a good model is the use of high-quality labels. Sepsis gold-standard labels are hard to define due to a lack of reliable diagnostic tools for sepsis at the ED. Therefore, standard clinical tools, such as clinical prediction scores (e.g. modified early warning score and quick sequential organ failure assessment), and claims-based methods (e.g. ICD-10) are used to generate suboptimal labels. As a consequence, models trained with these "silver" labels result in ill-trained models. In this study, we trained ML models for sepsis diagnosis at the ED with labels of 375 ED visits assigned by an endpoint adjudication committee (EAC) that consisted of 18 independent experts. Our objective was to evaluate which routinely measured variables show diagnostic value for sepsis. We performed univariate testing and trained multiple ML models with 95 routinely measured variables of three variable groups; demographic and vital, laboratory and advanced haematological variables. Apart from known diagnostic variables, we identified added diagnostic value for less conventional variables such as eosinophil count and platelet distribution width. In this explorative study, we show that the use of an EAC together with ML can identify new targets for future sepsis diagnosis research.

A Wolf in Sheep's Clothing: Reuse of Routinely Obtained Laboratory Data in Research.

Electronic health records (EHRs) contain valuable data for reuse in science, quality evaluations, and clinical decision support. Because routinely obtained laboratory data are abundantly present, often numeric, generated by certified laboratories, and stored in a structured way, one may assume that they are immediately fit for (re)use in research. However, behind each test result lies an extensive context of choices and considerations, made by both humans and machines, that introduces hidden patterns in the data. If they are unaware, researchers reusing routine laboratory data may eventually draw incorrect conclusions. In this paper, after discussing health care system characteristics on both the macro and micro level, we introduce the reader to hidden aspects of generating structured routine laboratory data in 4 steps (ordering, preanalysis, analysis, and postanalysis) and explain how each of these steps may interfere with the reuse of routine laboratory data. As researchers reusing these data, we underline the importance of domain knowledge of the health care professional, laboratory specialist, data manager, and patient to turn routine laboratory data into meaningful data sets to help obtain relevant insights that create value for clinical care.

Comparative studies of powder flow predictions using milligrams of powder for identifying powder flow issues.

Characterising powder flowability can be challenging when sample quantity is insufficient for a conventional shear cell test, especially in the pharmaceutical industry, where the cost of the active pharmaceutical ingredient (API) used is expensive at an early stage in the drug product development. A previous study demonstrated that powder flowability could be predicted based on powder physical properties and cohesiveness using a small quantity of powder samples (50 mg), but it remained an open question regarding the accuracy of the prediction compared to that measured using industry-standard shear cell testers and its potential to substitute the existing testers. In this study, 16 pharmaceutical powders were selected for a detailed comparative study of the predictive model. The flowability of the powders was predicted using a Bond number and given consolidation stresses, σ1, coupled with the model, where the Bond number represents powder cohesiveness. Compared to the measurements using a Powder Flow Tester (Brookfield) and an FT4 (Freeman Technology) Powder Rheometer shear cell tester, the results showed a good agreement between the predictions and the measurements (<22 % difference) from the two shear cell testers with different consolidation stresses, especially for cohesive materials. The model correctly predicts the class of flowability for 14 and 12 of the 16 powders for the PFT and the FT4, respectively. The study demonstrated that the prediction method of powder flowability using a small sample (50 mg) could substitute a standard shear cell test (>15 g) if the available amount of sample is small.

Conscious intention: New data on where and how in the brain.

How do we decide to act, and how do those decisions relate to conscious choice? A new study helps dissociate the neuronal mechanisms that choose, prepare, and trigger movement from our explicit reports of conscious intention.

Right temporoparietal junction encodes inferred visual knowledge of others.

When people make inferences about other people's minds, called theory of mind (ToM), a cortical network becomes active. The right temporoparietal junction (TPJ) is one of the most consistently responsive nodes in that network. Here we used a pictorial, reaction-time, ToM task to study brain activity in the TPJ and other cortical areas. Subjects were asked to take the perspective of a cartoon character and judge its knowledge of a visual display in front of it. The right TPJ showed evidence of encoding information about the implied visual knowledge of the cartoon head. When the subject was led to believe that the head could see a visual change take place, activity in the right TPJ significantly reflected that change. When the head could apparently not see the same visual change take place, activity in the right TPJ no longer significantly reflected that change. The subject could see the change in all cases; the critical factor that affected TPJ activity was whether the subject was led to think the cartoon character could see the change. We also found that whether the beliefs attributed to the cartoon head were true or false did not significantly affect activity in the present paradigm. These results suggest that the right TPJ may play a role in modeling the contents of the minds of others, perhaps more than it participates in evaluating the truth or falsity of that content.

A conceptual framework for consciousness.

This article argues that consciousness has a logically sound, explanatory framework, different from typical accounts that suffer from hidden mysticism. The article has three main parts. The first describes background principles concerning information processing in the brain, from which one can deduce a general, rational framework for explaining consciousness. The second part describes a specific theory that embodies those background principles, the Attention Schema Theory. In the past several years, a growing body of experimental evidence-behavioral evidence, brain imaging evidence, and computational modeling-has addressed aspects of the theory. The final part discusses the evolution of consciousness. By emphasizing the specific role of consciousness in cognition and behavior, the present approach leads to a proposed account of how consciousness may have evolved over millions of years, from fish to humans. The goal of this article is to present a comprehensive, overarching framework in which we can understand scientifically what consciousness is and what key adaptive roles it plays in brain function.

Consciousness is already solved: The continued debate is not about science.

A logical explanation of consciousness has been known for decades. The brain must construct a specific set of information about conscious feeling (theory-of-mind information), causing people to believe, think, and claim to have consciousness. Theories that propose an actual, intangible feeling are non-explanatory. They add a magical red herring while leaving unexplained the objective phenomena: the believing, thinking, and claiming.

The origin of smiling, laughing, and crying: The defensive mimic theory.

Why do we leak lubricant from the eyes to solicit comfort from others? Why do we bare our teeth and crinkle our faces to express non-aggression? The defensive mimic theory proposes that a broad range of human emotional expressions evolved originally as exaggerated, temporally extended mimics of the fast, defensive reflexes that normally protect the body surface. Defensive reflexes are so important to survival that they cannot be safely suppressed; yet they also broadcast information about an animal's internal state, information that can potentially be exploited by other animals. Once others can observe and exploit an animal's defensive reflexes, it may be advantageous to the animal to run interference by creating mimic defensive actions, thereby manipulating the behaviour of others. Through this interaction over millions of years, many human emotional expressions may have evolved. Here, human social signals including smiling, laughing and crying, are compared component-by-component with the known, well-studied features of primate defensive reflexes. It is suggested that the defensive mimic theory can adequately account for the physical form of not all, but a large range of, human emotional expression.

Paired corticospinal-motoneuronal stimulation and exercise after spinal cord injury.

CONTEXT: Rehabilitation after spinal cord injury (SCI) relies on the use of exercise training, which has limited functional gains. There is a need to develop more efficient approaches to facilitate recovery after SCI. METHODS: This review focuses on a neuromodulation method where transcranial magnetic stimulation (TMS) over the primary motor cortex is paired with transcutaneous electrical stimulation over a peripheral nerve to induce plasticity at corticospinal-motoneuronal synapses. These two stimuli are applied at precise inter-stimulus intervals to reinforce corticospinal synaptic transmission using principles of spike-timing-dependent plasticity applied alone or in combination with exercise training. RESULTS: Transmission in residual corticospinal axons, assessed using TMS and maximal voluntary motor output, increased after stimulation combined with exercise training in persons with SCI. There were also significant improvements in functional outcomes, including walking speed and grasping function, which persisted after 6-9 months post stimulation. Moreover, the data suggested that the effects of the stimulation protocol can be augmented with a higher number of sessions and with multiple stimulation sites in the spinal cord. CONCLUSIONS: Voluntary movement is enhanced in people with SCI through the strengthening of corticospinal-motoneuronal synapses using paired stimulation. This neuromodulation technique represents a novel powerful strategy to facilitate functional recovery after SCI.

The spectrum of skin diseases seen in a Jamaican tertiary academic medical center.

BACKGROUND: The diversity of skin diseases seen in a dermatology clinic varies with the composition of the population. OBJECTIVE: The aim was to document the spectrum of cutaneous disorders seen and the variation with sex, age, and seasons. METHODS: This was a retrospective study on new patients attending an academic dermatology clinic in Jamaica during 2018. Disease frequencies and prevalence by sex, seasons, and age group were recorded. RESULTS: There were 547 new patients with 329 females (60%) and 218 males (40%). The mean age was 36.8 years, ranging from 2 weeks old to 103 years old. The largest number of patients were in the third decade (20-29 years) (n = 139). More patients presented in the dry season and in spring and summer. The most common diagnoses were: seborrheic dermatitis (n = 65, 11.9%), acne (n = 56, 10.2%), and contact dermatitis (n = 38, 6.9%). The most common disease groups were dermatitis (n = 161, 29.4%), infections (n = 130, 23.8%), and inflammatory disorders (n = 129, 23.6%). LIMITATIONS: The generalizability of our findings may be limited, and selection bias may play a role in patients choosing to attend an academic dermatology clinic. CONCLUSIONS: Skin diseases varied with age, sex, and season with seborrheic dermatitis being most common.

The attention schema theory in a neural network agent: Controlling visuospatial attention using a descriptive model of attention.

In the attention schema theory (AST), the brain constructs a model of attention, the attention schema, to aid in the endogenous control of attention. Growing behavioral evidence appears to support the presence of a model of attention. However, a central question remains: does a controller of attention actually benefit by having access to an attention schema? We constructed an artificial deep Q-learning neural network agent that was trained to control a simple form of visuospatial attention, tracking a stimulus with an attention spotlight in order to solve a catch task. The agent was tested with and without access to an attention schema. In both conditions, the agent received sufficient information such that it should, theoretically, be able to learn the task. We found that with an attention schema present, the agent learned to control its attention spotlight and learned the catch task. Once the agent learned, if the attention schema was then disabled, the agent's performance was greatly reduced. If the attention schema was removed before learning began, the agent was impaired at learning. The results show how the presence of even a simple attention schema can provide a profound benefit to a controller of attention. We interpret these results as supporting the central argument of AST: the brain contains an attention schema because of its practical benefit in the endogenous control of attention.