A dynamical model of C. elegans thermal preference reveals independent excitatory and inhibitory learning pathways.

Caenorhabditis elegans is capable of learning and remembering behaviorally relevant cues such as smells, tastes, and temperature. This is an example of associative learning, a process in which behavior is modified by making associations between various stimuli. Since the mathematical theory of conditioning does not account for some of its salient aspects, such as spontaneous recovery of extinguished associations, accurate modeling of behavior of real animals during conditioning has turned out difficult. Here, we do this in the context of the dynamics of the thermal preference of C. elegans. We quantify C. elegans thermotaxis in response to various conditioning temperatures, starvation durations, and genetic perturbations using a high-resolution microfluidic droplet assay. We model these data comprehensively, within a biologically interpretable, multi-modal framework. We find that the strength of the thermal preference is composed of two independent, genetically separable contributions and requires a model with at least four dynamical variables. One pathway positively associates the experienced temperature independently of food and the other negatively associates with the temperature when food is absent. The multidimensional structure of the association strength provides an explanation for the apparent classical temperature-food association of C. elegans thermal preference and a number of longstanding questions in animal learning, including spontaneous recovery, asymmetric response to appetitive vs. aversive cues, latent inhibition, and generalization among similar cues.

Even lawyers do not like legalese.

Across modern civilization, societal norms and rules are established and communicated largely in the form of written laws. Despite their prevalence and importance, legal documents have long been widely acknowledged to be difficult to understand for those who are required to comply with them (i.e., everyone). Why? Across two preregistered experiments, we evaluated five hypotheses for why lawyers write in a complex manner. Experiment 1 revealed that lawyers, like laypeople, were less able to recall and comprehend legal content drafted in a complex "legalese" register than content of equivalent meaning drafted in a simplified register. Experiment 2 revealed that lawyers rated simplified contracts as equally enforceable as legalese contracts, and rated simplified contracts as preferable to legalese contracts on several dimensions-including overall quality, appropriateness of style, and likelihood of being signed by a client. These results suggest that lawyers who write in a convoluted manner do so as a matter of convenience and tradition as opposed to an outright preference and that simplifying legal documents would be both tractable and beneficial for lawyers and nonlawyers alike.

Exploring the impact of a fraction sense intervention in authentic school environments: An initial investigation.

A solid understanding of fractions is the cornerstone for acquiring proficiency with rational numbers and paves the way for learning advanced mathematical concepts such as algebra. Fraction difficulties limit not only students' educational and vocational opportunities but also their ability to solve everyday problems. Students who exit sixth grade with inadequate understanding of fractions may experience far-reaching repercussions that lead to lifelong avoidance of mathematics. This article presents the results of a randomized controlled trial focusing on the first two cohorts of a larger efficacy investigation aimed at building fraction sense in students with mathematics difficulties. Teachers implemented an evidence-informed fraction sense intervention (FSI) within their sixth-grade intervention classrooms. The lessons draw from research in cognitive science as well as mathematics education research. Employing random assignment at the classroom level, multilevel modeling revealed a significant effect of the intervention on posttest fractions scores after controlling for pretest fractions scores, working memory, vocabulary, proportional reasoning, and classroom attentive behavior. Students in the FSI group outperformed their counterparts in the control group, with noteworthy effect sizes on most fraction measures. Challenges associated with carrying out school-based intervention research are addressed.

The challenge of transfer to clinical practice: The illusion of learning and illusion of teaching.

There is increasing pressure to accelerate health professions education programs and educators have the challenge of ensuring that students can effectively transfer their learning into clinical practice. In this personal view, we discuss how insights from cognitive science can inform the redesign of current curricula and highlight the challenge of implementing these new approaches for instructional design and assessment. We also recommend that educators disseminate the important lessons learned from their endeavors.

Unshared Minds, Decaying Worlds: Towards a Pathology of Chronic Loneliness.

The moment when a person's actual relationships fall short of desired relationships is commonly identified as the etiological moment of chronic loneliness, which can lead to physical and psychological effects like depression, worse recovery from illness and increased mortality. But, this etiology fails to explain the nature and severe impact of loneliness. Here, we use philosophical analysis and neuroscience to show that human beings develop and maintain our world-picture (our sense of what is true, important, and good) through joint attention and action, motivated by friendship, in the Aristotelian sense of "other selves" who share a sense of the true and the good, and desire the good for each other as much as for themselves. The true etiological event of loneliness is the moment one's world-picture becomes unshared. The pathogenesis is a resultant decay of our world-picture, with brain and behavior changes following as sequelae.

Can incorrect artificial intelligence (AI) results impact radiologists, and if so, what can we do about it? A multi-reader pilot study of lung cancer detection with chest radiography.

OBJECTIVE: To examine whether incorrect AI results impact radiologist performance, and if so, whether human factors can be optimized to reduce error. METHODS: Multi-reader design, 6 radiologists interpreted 90 identical chest radiographs (follow-up CT needed: yes/no) on four occasions (09/20-01/22). No AI result was provided for session 1. Sham AI results were provided for sessions 2-4, and AI for 12 cases were manipulated to be incorrect (8 false positives (FP), 4 false negatives (FN)) (0.87 ROC-AUC). In the Delete AI (No Box) condition, radiologists were told AI results would not be saved for the evaluation. In Keep AI (No Box) and Keep AI (Box), radiologists were told results would be saved. In Keep AI (Box), the ostensible AI program visually outlined the region of suspicion. AI results were constant between conditions. RESULTS: Relative to the No AI condition (FN = 2.7%, FP = 51.4%), FN and FPs were higher in the Keep AI (No Box) (FN = 33.0%, FP = 86.0%), Delete AI (No Box) (FN = 26.7%, FP = 80.5%), and Keep AI (Box) (FN = to 20.7%, FP = 80.5%) conditions (all ps < 0.05). FNs were higher in the Keep AI (No Box) condition (33.0%) than in the Keep AI (Box) condition (20.7%) (p = 0.04). FPs were higher in the Keep AI (No Box) (86.0%) condition than in the Delete AI (No Box) condition (80.5%) (p = 0.03). CONCLUSION: Incorrect AI causes radiologists to make incorrect follow-up decisions when they were correct without AI. This effect is mitigated when radiologists believe AI will be deleted from the patient's file or a box is provided around the region of interest. CLINICAL RELEVANCE STATEMENT: When AI is wrong, radiologists make more errors than they would have without AI. Based on human factors psychology, our manuscript provides evidence for two AI implementation strategies that reduce the deleterious effects of incorrect AI. KEY POINTS: • When AI provided incorrect results, false negative and false positive rates among the radiologists increased. • False positives decreased when AI results were deleted, versus kept, in the patient's record. • False negatives and false positives decreased when AI visually outlined the region of suspicion.

Symbols and grounding in large language models.

Large language models (LLMs) are one of the most impressive achievements of artificial intelligence in recent years. However, their relevance to the study of language more broadly remains unclear. This article considers the potential of LLMs to serve as models of language understanding in humans. While debate on this question typically centres around models' performance on challenging language understanding tasks, this article argues that the answer depends on models' underlying competence, and thus that the focus of the debate should be on empirical work which seeks to characterize the representations and processing algorithms that underlie model behaviour. From this perspective, the article offers counterarguments to two commonly cited reasons why LLMs cannot serve as plausible models of language in humans: their lack of symbolic structure and their lack of grounding. For each, a case is made that recent empirical trends undermine the common assumptions about LLMs, and thus that it is premature to draw conclusions about LLMs' ability (or lack thereof) to offer insights on human language representation and understanding. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.

Socially intelligent machines that learn from humans and help humans learn.

A hallmark of human intelligence is the ability to understand and influence other minds. Humans engage in inferential social learning (ISL) by using commonsense psychology to learn from others and help others learn. Recent advances in artificial intelligence (AI) are raising new questions about the feasibility of human-machine interactions that support such powerful modes of social learning. Here, we envision what it means to develop socially intelligent machines that can learn, teach, and communicate in ways that are characteristic of ISL. Rather than machines that simply predict human behaviours or recapitulate superficial aspects of human sociality (e.g. smiling, imitating), we should aim to build machines that can learn from human inputs and generate outputs for humans by proactively considering human values, intentions and beliefs. While such machines can inspire next-generation AI systems that learn more effectively from humans (as learners) and even help humans acquire new knowledge (as teachers), achieving these goals will also require scientific studies of its counterpart: how humans reason about machine minds and behaviours. We close by discussing the need for closer collaborations between the AI/ML and cognitive science communities to advance a science of both natural and artificial intelligence. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.

Measuring the cognitive effort associated with task switching in routine EHR-based tasks.

OBJECTIVE: Clinical work involves performing overlapping, time-sensitive tasks that frequently require clinicians to switch their attention between multiple tasks. We developed a methodological approach using EHR-based audit logs to determine switch costs-the cognitive burden associated with task switching-and assessed its magnitude during routine EHR-based clinical tasks. METHOD: Physician trainees (N = 75) participated in a longitudinal study where they provided access to their EHR-based audit logs. Physicians' audit log actions were used to create a taxonomy of EHR tasks. These tasks were transformed into task sequences and the time spent on each task in a sequence was computed. Within these task sequences, instances of task switching (i.e., switching from one task to the next) and non-switching were identified. The primary outcome of interest was the time spent on a post-switch task. Using a mixed-effects regression model, we compared the durations of post-switch and non-switch tasks. RESULTS: 2,781,679 audit log events over 117,822 sessions from 75 physicians were analyzed. Physicians spent most time on chart review (Median (IQR) = 5,439 (2,492-8,336) seconds), note review (1,936 (827-3,321) seconds), and navigating the EHR interface (1,048 (365.5-2,006) seconds) daily. Post task switch activity times were greater for documentation (Median increase = 5 s), order entry (Median increase = 3 s) and results review (Median increase = 3 s). Mixed-effects regression showed that time spent on tasks were longer following a task switch (ß = 0.03; 95% CIlower = 0.027, CIupper = 0.034), with greater post-swtich task times for imaging, order entry, note review, handoff, note entry, chart review and best practice advisory tasks. DISCUSSION: Increased task switching time-an indicator of the cognitive burden associated with switching between tasks-is prevalent in routine EHR-based tasks. We discuss the cumulative impact of incremental switch costs have on overall EHR workload, wellness, and error rates. Relying on theoretical cognitive foundations, we suggest pragmatic design considerations for mitigating the effects of cognitive burden associated with task switching.

Recollecting What We Once Knew: My Life in Psycholinguistics.

The mid-twentieth century brought a radical change in how the linguistics community formulated its major goal, moving from a largely taxonomic science to Chomsky's revolution, which conceptualized language as a higher-order cognitive function. This article reviews the paths (not always direct) that brought Lila Gleitman into contact with that revolution, her contributions to it, and the evolution in her thinking about how language is learned by every child, regardless of extreme variation in the input received. To understand how that occurs, we need to discover what must be learned by the child and what is already there to guide that learning-what must be, in Plato's terms, "recollected." The growing picture shows a learner equipped with information-processing mechanisms that extract evidence about word meanings using various evidential sources. Chief among these are the observational and linguistic-syntactic contexts in which words occur. The former is supported by a mechanism Gleitman and her collaborators call "propose but verify," and the latter by a mechanism known as "syntactic boot-strapping."

Transforming task representations to perform novel tasks.

An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero shot), based on its relationship to previous tasks. Humans can exhibit this cognitive flexibility. By contrast, models that achieve superhuman performance in specific tasks often fail to adapt to even slight task alterations. To address this, we propose a general computational framework for adapting to novel tasks based on their relationship to prior tasks. We begin by learning vector representations of tasks. To adapt to new tasks, we propose metamappings, higher-order tasks that transform basic task representations. We demonstrate the effectiveness of this framework across a wide variety of tasks and computational paradigms, ranging from regression to image classification and reinforcement learning. We compare to both human adaptability and language-based approaches to zero-shot learning. Across these domains, metamapping is successful, often achieving 80 to 90% performance, without any data, on a novel task, even when the new task directly contradicts prior experience. We further show that metamapping can not only generalize to new tasks via learned relationships, but can also generalize using novel relationships unseen during training. Finally, using metamapping as a starting point can dramatically accelerate later learning on a new task and reduce learning time and cumulative error substantially. Our results provide insight into a possible computational basis of intelligent adaptability and offer a possible framework for modeling cognitive flexibility and building more flexible artificial intelligence systems.

Using insights from cognitive science for the teaching of clinical skills: AMEE Guide No. 155.

Students have to develop a wide variety of clinical skills, from cannulation to advanced life support, prior to entering clinical practice. An important challenge for health professions' educators is the implementation of strategies for effectively supporting students in their acquisition of different types of clinical skills and also to minimize skill decay over time. Cognitive science provides a unified approach that can inform how to maximize clinical skill acquisition and also minimize skill decay. The Guide discusses the nature of expertise and mastery development, the key insights from cognitive science for clinical skill development and skill retention, how these insights can be practically applied and integrated with current approaches used in clinical skills teaching.

A new methodological tool for research on supernatural concepts.

We introduce the first set of stimuli designed to resolve methodological and theoretical issues that have muddled the interpretation of results on the memorability of supernatural concepts (e.g., ghosts, souls, spirits), an important line of research in the cognitive science of religion (Barrett, 2007). We focus here on Boyer's (1994), Boyer, 2001) pioneering minimally counterintuitive (MCI) hypothesis according to which supernatural concepts tap a special memory-enhancing mechanism linked to violations of default intuitive inferences. Empirical tests of the MCI account have given rise to a vexed picture that renders meaningful interpretation difficult. The lack of a common standard of comparison among different studies, coupled with the presence of uncontrolled variables independently known to affect memorability, lie at the heart of these problems. We show that our new stimuli offer the hope of resolving these issues, thereby establishing a more secure foundation for the study of the memorability of supernatural concepts.

Post-Processing Automatic Transcriptions with Machine Learning for Verbal Fluency Scoring.

Objective: To compare verbal fluency scores derived from manual transcriptions to those obtained using automatic speech recognition enhanced with machine learning classifiers. Methods: Using Amazon Web Services, we automatically transcribed verbal fluency recordings from 1400 individuals who performed both animal and letter F verbal fluency tasks. We manually adjusted timings and contents of the automatic transcriptions to obtain "gold standard" transcriptions. To make automatic scoring possible, we trained machine learning classifiers to discern between valid and invalid utterances. We then calculated and compared verbal fluency scores from the manual and automatic transcriptions. Results: For both animal and letter fluency tasks, we achieved good separation of valid versus invalid utterances. Verbal fluency scores calculated based on automatic transcriptions showed high correlation with those calculated after manual correction. Conclusion: Many techniques for scoring verbal fluency word lists require accurate transcriptions with word timings. We show that machine learning methods can be applied to improve off-the-shelf ASR for this purpose. These automatically derived scores may be satisfactory for some applications. Low correlations among some of the scores indicate the need for improvement in automatic speech recognition before a fully automatic approach can be reliably implemented.

Informational Resilience in the Human Cognitive Ecology.

Resilience is a basic trait of cognitive systems and fundamentally connected to their autopoietic organization. It plays a vital role in maintaining the identity of cognitive systems in the face of external threats and perturbances. However, when examining resilience in the context of autopoiesis, an overlooked issue arises: the autopoietic theory formulated by Maturana and Varela (1980) renders traditional Shannon information obsolete, highlighting that information should not be ascribed a role in cognitive systems in a general sense. This paper examines the current situation and suggests a possible way forward by exploring an affordance-based view on information, derived from radical cognitive science, which is exempted from Maturana and Varela's critique. Specifically, it argues that the impact of social influence on affordance use is crucial when considering how resilience can manifest in informational relations pertaining to the human cognitive ecology.

Changes in Hungarian academic psychology after the end of "people's democracy".

The paper surveys the last 30 years of Hungarian academic psychology. Around 1989-1990, the time of the great social changes Hungarian psychology was rather Westernized, but still a relatively small scientific field and applied profession. The opening and liberalization of politics made psychology in Hungary a booming profession and a rich research field. Education of psychologists was spreading, and becoming more Westernized in textbook usage and reading materials. Entrance numbers at two universities with 80 students were replaced by 2010 by 6 university programs and about 8000 incoming students. The training system is a Bologna type BA + MA + PhD system, The educational booming has its own problems. As all university subjects, psychology training is also underfinanced, with high teaching loads and a move by university management towards applied areas, neglecting basic research. The research activity is characterized by a fivefold increase of English language publications coming from Hungary over a 20 years period. University research was strengthened, and competitive grant systems were introduced, whth good success aretes by psychologists. Here again, managerial thinking questions many aspects of basic research and liberalized science management. These factors are peculiar to psychology, but they do have an impact on it. The paper gives some details about one chapter of academic psychology, cognitive psychology. Institutionally, support by the Soros foundation in the 90s for the university cognitive programs had as one consequence that three departments of cognition are active in Budapest today. Another aspect of insitutional development was the series of multidisciplinary conferences in Hungary (MAKOG), and Hungarian involvement in international graduate training programs in cognitive science. The most successful cognitive group, at Central European University (5 ERC grants, publications in leading journals) is recently chased out of Hungary by anti-Western and antiliberal legal moves. This would certainly have a detrimental effect on Hungarian cognitive psychology for quite a time.

Optimized virtual reality-based Method of Loci memorization techniques through increased immersion and effective memory palace designs: a feasibility study.

For most, an improvement in memory would always be desirable, whether from the point of view of an aging individual with declining memory, or from the perspective of someone seeking to memorize large amounts of information in the shortest period of time. One way for people to improve upon their memory performance is by using the Method of Loci (MoL), a famously complex, ancient memorization technique for non-spatial information recall. With the use of virtual reality technology, this technique can finally be easily taught to individuals for use in their daily lives. In this paper, we present an exploration into this avenue of using MoL in virtual reality and report on the design and evaluation of our new virtual memory palace that aims to prove the feasibility of improving upon designs from other studies to optimize memory recall performance. An experiment was conducted to evaluate our VR MoL environment. The results from week 1 on the pre-test (M = 62.55, SD = 24.01) and post-test (M = 82.91, SD = 15.99) memory task showed an increase in the number of words remembered was statistically significant, t(20) = -2.34, p = 0.014 where participants were able to remember approximately 20.4% more non-spatial information, when compared to traditional memorization techniques. After a second use, participants improved, remembering 22.2% more non-spatial information on the pre-test (M = 63.44, SD = 26.64) and post-test (M = 85.67, SD = 16.10) memory task, indicating that the increase in number of words remembered was statistically significant, t(16) = -2.142, p = 0.024. The results suggest that the virtual memory palace experience could be optimized to help participants learn the MoL technique with very little training time and potentially produce significant improvements in recall performance as a result.

Inductive biases in theory-based reinforcement learning.

Understanding the inductive biases that allow humans to learn in complex environments has been an important goal of cognitive science. Yet, while we have discovered much about human biases in specific learning domains, much of this research has focused on simple tasks that lack the complexity of the real world. In contrast, video games involving agents and objects embedded in richly structured systems provide an experimentally tractable proxy for real-world complexity. Recent work has suggested that key aspects of human learning in domains like video games can be captured by model-based reinforcement learning (RL) with object-oriented relational models-what we term theory-based RL. Restricting the model class in this way provides an inductive bias that dramatically increases learning efficiency, but in this paper we show that humans employ a stronger set of biases in addition to syntactic constraints on the structure of theories. In particular, we catalog a set of semantic biases that constrain the content of theories. Building these semantic biases into a theory-based RL system produces more human-like learning in video game environments.

The cognitive science of COVID-19: Acceptance, denial, and belief change.

Because the spread of pandemics depends heavily on human choices and behaviors, dealing with COVID-19 requires insights from cognitive science which integrates psychology, neuroscience, computer modeling, philosophy, anthropology, and linguistics. Cognitive models can explain why scientists adopt hypotheses about the causes and treatments of disease based on explanatory coherence. Irrational deviations from good reasoning are explained by motivated inference in which conclusions are influenced by personal goals that contribute to emotional coherence. Decisions about COVID-19 can also be distorted by well-known psychological and neural mechanisms. Cognitive science provides advice about how to improve human behavior in pandemics by changing beliefs and by improving behaviors that result from intention-action gaps.

Conviction Narrative Theory: A theory of choice under radical uncertainty.

Conviction Narrative Theory (CNT) is a theory of choice under radical uncertainty - situations where outcomes cannot be enumerated and probabilities cannot be assigned. Whereas most theories of choice assume that people rely on (potentially biased) probabilistic judgments, such theories cannot account for adaptive decision-making when probabilities cannot be assigned. CNT proposes that people use narratives - structured representations of causal, temporal, analogical, and valence relationships - rather than probabilities, as the currency of thought that unifies our sense-making and decision-making faculties. According to CNT, narratives arise from the interplay between individual cognition and the social environment, with reasoners adopting a narrative that feels "right" to explain the available data; using that narrative to imagine plausible futures; and affectively evaluating those imagined futures to make a choice. Evidence from many areas of the cognitive, behavioral, and social sciences supports this basic model, including lab experiments, interview studies, and econometric analyses. We identify 12 propositions to explain how the mental representations (narratives) interact with four inter-related processes (explanation, simulation, affective evaluation, and communication), examining the theoretical and empirical basis for each. We conclude by discussing how CNT can provide a common vocabulary for researchers studying everyday choices across areas of the decision sciences.