The simplicity principle in perception and cognition

How Occam's razor guides human decision-making

Occam's razor is the principle that, all else being equal, simpler explanations should be preferred over more complex ones. This principle is thought to guide human decision-making, but the nature of this guidance is not known. Here we used preregistered behavioral experiments to show that people tend to prefer the simpler of two alternative explanations for uncertain data. These preferences match predictions of formal theories of model selection that penalize excessive flexibility. These penalties emerge when considering not just the best explanation but the integral over all possible, relevant explanations. We further show that these simplicity preferences persist in humans, but not in certain artificial neural networks, even when they are maladaptive. Our results imply that principled notions of statistical model selection, including integrating over possible, latent causes to avoid overfitting to noisy observations, may play a central role in human decision-making.

Drug-resistant epilepsy: Is there an overlooked association between drug resistant epilepsies and neuropsychiatric comorbidities?

Despite the introduction of several first-in-class antiseizure medications in the last 15 years and the recent generation of new hypotheses to explain the drug-resistant phenotype in epilepsy, the proportion of patients with refractory epilepsy remains apparently unchanged. Therefore, it is essential to provide new perspectives (or, perhaps, revive old perspectives) to develop more effective therapeutic interventions. Some of the complex comorbid disorders associated with epilepsy, which present similar rates of unresponsive patients and whose refractoriness is possibly mediated by similar causes, could provide keys to implement novel therapeutic interventions. In this article, based on Swanson's ABC model to develop scientific hypotheses, we establish (or rescue) some interesting connections between depression and epilepsy, focusing on the relationship between drug-resistant epilepsy and depression.

Understanding dual process cognition via the minimum description length principle

Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in domains ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to operate concurrently, one relatively high in computational complexity, the other relatively simple. Why is neural information processing organized in this way? We propose an answer to this question based on the notion of compression. The key insight is that dual-process structure can enhance adaptive behavior by allowing an agent to minimize the description length of its own behavior. We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles.

Symbolic metaprogram search improves learning efficiency and explains rule learning in humans

Throughout their lives, humans seem to learn a variety of rules for things like applying category labels, following procedures, and explaining causal relationships. These rules are often algorithmically rich but are nonetheless acquired with minimal data and computation. Symbolic models based on program learning successfully explain rule-learning in many domains, but performance degrades quickly as program complexity increases. It remains unclear how to scale symbolic rule-learning methods to model human performance in challenging domains. Here we show that symbolic search over the space of metaprograms-programs that revise programs-dramatically improves learning efficiency. On a behavioral benchmark of 100 algorithmically rich rules, this approach fits human learning more accurately than alternative models while also using orders of magnitude less search. The computation required to match median human performance is consistent with conservative estimates of human thinking time. Our results suggest that metaprogram-like representations may help human learners to efficiently acquire rules.

Pragmatic trial evaluating the impact of simulation training on high-risk prescribing to older adults by junior physicians

BACKGROUND

High-risk medications like benzodiazepines, sedative hypnotics, and antipsychotics are commonly prescribed for hospitalized older adults, despite guidelines recommending avoidance. Prior interventions have not fully addressed how physicians make such prescribing decisions, particularly when experiencing stress or cognitive overload. Simulation training may help improve prescribing decision-making but has not been evaluated for overprescribing.

METHODS

In this two-arm pragmatic trial, we randomized 40 first-year medical resident physicians (i.e., interns) on inpatient general medicine services at an academic medical center to either intervention (a 40-minute immersive simulation training) or control (online educational training) groups. The primary outcome was the number of new benzodiazepine, sedative hypnotic, or antipsychotic orders for treatment-naïve older adults during hospitalization. Secondary outcomes included the same outcome by all providers, being discharged on one of the medications, and orders for related or control medications. Outcomes were measured using electronic health record data over each intern's service period (~2 weeks). Outcomes were evaluated using generalized estimating equations, adjusting for clustering.

RESULTS

In total, 522 treatment-naïve older adult patients were included in analyses. Over follow-up, interns prescribed ≥1 high-risk medication for 13 (4.9%) intervention patients and 13 (5.0%) control patients. The intervention led to no difference in the number of new prescriptions (Rate Ratio [RR]: 0.85, 95%CI: 0.31-2.35) versus control and no difference in secondary outcomes. In secondary analyses, intervention interns wrote significantly fewer "as-needed" ("PRN") order types for the high-risk medications (RR: 0.29, 95%CI: 0.08-0.99), and instead tended to write more "one-time" orders than control interns, though this difference was not statistically significant (RR: 2.20, 95%CI: 0.60-7.99).

CONCLUSIONS

Although this simulation intervention did not impact total high-risk prescribing for hospitalized older adults, it did influence how the interns prescribed, resulting in fewer PRN orders, suggesting possibly greater ownership of care. Future interventions should consider this insight and implementation lessons raised.

TRIAL REGISTRATION

Clinicaltrials.gov(NCT04668248).

Tracking the neural codes for words and phrases during semantic composition, working-memory storage, and retrieval

The ability to compose successive words into a meaningful phrase is a characteristic feature of human cognition, yet its neural mechanisms remain incompletely understood. Here, we analyze the cortical mechanisms of semantic composition using magnetoencephalography (MEG) while participants read one-word, two-word, and five-word noun phrases and compared them with a subsequent image. Decoding of MEG signals revealed three processing stages. During phrase comprehension, the representation of individual words was sustained for a variable duration depending on phrasal context. During the delay period, the word code was replaced by a working-memory code whose activation increased with semantic complexity. Finally, the speed and accuracy of retrieval depended on semantic complexity and was faster for surface than for deep semantic properties. In conclusion, we propose that the brain initially encodes phrases using factorized dimensions for successive words but later compresses them in working memory and requires a period of decompression to access them.

A Manifesto in Defense of Pain Complexity: A Critical Review of Essential Insights in Pain Neuroscience

Chronic pain has increasingly become a significant health challenge, not just as a symptomatic manifestation but also as a pathological condition with profound socioeconomic implications. Despite the expansion of medical interventions, the prevalence of chronic pain remains remarkably persistent, prompting a turn towards non-pharmacological treatments, such as therapeutic education, exercise, and cognitive-behavioral therapy. With the advent of cognitive neuroscience, pain is often presented as a primary output derived from the brain, aligning with Engel's Biopsychosocial Model that views disease not solely from a biological perspective but also considering psychological and social factors. This paradigm shift brings forward potential misconceptions and over-simplifications. The current review delves into the intricacies of nociception and pain perception. It questions long-standing beliefs like the cerebral-centric view of pain, the forgotten role of the peripheral nervous system in pain chronification, misconceptions around central sensitization syndromes, the controversy about the existence of a dedicated pain neuromatrix, the consciousness of the pain experience, and the possible oversight of factors beyond the nervous system. In re-evaluating these aspects, the review emphasizes the critical need for understanding the complexity of pain, urging the scientific and clinical community to move beyond reductionist perspectives and consider the multifaceted nature of this phenomenon.

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.

Bringing Bayes and Shannon to the Study of Behavioural and Neurobiological Timing and Associative Learning

Bayesian parameter estimation and Shannon’s theory of information provide tools for analysing and understanding data from behavioural and neurobiological experiments on interval timing—and from experiments on Pavlovian and operant conditioning, because timing plays a fundamental role in associative learning. In this tutorial, we explain basic concepts behind these tools and show how to apply them to estimating, on a trial-by-trial, reinforcement-by-reinforcement and response-by-response basis, important parameters of timing behaviour and of the neurobiological manifestations of timing in the brain. These tools enable quantification of relevant variables in the trade-off between acting as an ideal observer should act and acting as an ideal agent should act, which is also known as the trade-off between exploration (information gathering) and exploitation (information utilization) in reinforcement learning. They enable comparing the strength of the evidence for a measurable association to the strength of the behavioural evidence that the association has been perceived. A GitHub site and an OSF site give public access to well-documented Matlab and Python code and to raw data to which these tools have been applied.

Young Single Widow, Dynamics of In-Laws Interference and Coping Mechanisms: Simplicity-Parsimony Approach

The incidence of short marital duration due to the demise of a husband that often exposes young widows to in-laws' exploitation of the asset of the deceased spouses, without regard for negative health consequences and potential vulnerability to poverty has not been exhaustively investigated, especially in sub-Saharan Africa where 16% of adult women are widows. The study examined the coping mechanisms among the young widow (aged ≤ 40) who have experienced short conjugal relationships (≤5 years) and burdensome from in-laws. The research design followed a qualitative approach with the aid of semi-structured in-depth interviews among 13 young widows selected through snowballing and informant-led approaches in the purposively selected communities. Data collected were analysed using descriptive statistics and a thematic approach. The findings, among others, shows the median age of young widow as 29 years. All participants, except one, have faced exploitation from their in-laws over their husbands' assets. All the participants desired to re-marry in order to: have a father figure for their children, have their own children or have more children. There is an absence of government support, but a few have received support from religious organisations. The author proposed attitudinal-change campaigns targeting the in-laws through accessible media and legislature that could challenge the exploitation of widows and unhealthy widowhood rites.

How Language Learning and Language Use Create Linguistic Structure

Languages persist through a cycle of learning and use: You learn a language through immersion in the language used in your linguistic community, and in using language to communicate, you produce further linguistic data, which other people might learn from in turn. Languages change over historical time as a result of errors and innovations in these processes of learning and use; this article reviews experimental and computational methods that have been developed to test the hypothesis that those same processes of learning and use are responsible for creating the fundamental structural properties shared by all human languages, including some of the design features that make language such a powerful tool for communication.

Interventions to Dispel Neuromyths in Educational Settings-A Review

Neuromyths are misconceptions about the brain and learning, for instance Tailoring instruction to students' preferred “learning styles” (e.g., visual, auditory, kinesthetic) promotes learning. Recent reviews indicate that the high prevalence of beliefs in neuromyths among educators did not decline over the past decade. Potential adverse effects of neuromyth beliefs on teaching practices prompted researchers to develop interventions to dispel these misconceptions in educational settings. This paper provides a critical review of current intervention approaches. The following questions are examined: Does neuroscience training protect against neuromyths? Are refutation-based interventions effective at dispelling neuromyths, and are corrective effects enduring in time? Why refutation-based interventions are not enough? Do reduced beliefs in neuromyths translate in the adoption of more evidence-based teaching practices? Are teacher professional development workshops and seminars on the neuroscience of learning effective at instilling neuroscience in the classroom? Challenges, issues, controversies, and research gaps in the field are highlighted, notably the so-called “backfire effect,” the social desirability bias, and the powerful intuitive thinking mode. Future directions are outlined.

Long-term priors influence visual perception through recruitment of long-range feedback

Perception results from the interplay of sensory input and prior knowledge. Despite behavioral evidence that long-term priors powerfully shape perception, the neural mechanisms underlying these interactions remain poorly understood. We obtained direct cortical recordings in neurosurgical patients as they viewed ambiguous images that elicit constant perceptual switching. We observe top-down influences from the temporal to occipital cortex, during the preferred percept that is congruent with the long-term prior. By contrast, stronger feedforward drive is observed during the non-preferred percept, consistent with a prediction error signal. A computational model based on hierarchical predictive coding and attractor networks reproduces all key experimental findings. These results suggest a pattern of large-scale information flow change underlying long-term priors' influence on perception and provide constraints on theories about long-term priors' influence on perception.

An Explainable AI-Based Fault Diagnosis Model for Bearings

In this paper, an explainable AI-based fault diagnosis model for bearings is proposed with five stages, i.e., (1) a data preprocessing method based on the Stockwell Transformation Coefficient (STC) is proposed to analyze the vibration signals for variable speed and load conditions, (2) a statistical feature extraction method is introduced to capture the significance from the invariant pattern of the analyzed data by STC, (3) an explainable feature selection process is proposed by introducing a wrapper-based feature selector-Boruta, (4) a feature filtration method is considered on the top of the feature selector to avoid the multicollinearity problem, and finally, (5) an additive Shapley explanation followed by k-NN is proposed to diagnose and to explain the individual decision of the k-NN classifier for debugging the performance of the diagnosis model. Thus, the idea of explainability is introduced for the first time in the field of bearing fault diagnosis in two steps: (a) incorporating explainability to the feature selection process, and (b) interpretation of the classifier performance with respect to the selected features. The effectiveness of the proposed model is demonstrated on two different datasets obtained from separate bearing testbeds. Lastly, an assessment of several state-of-the-art fault diagnosis algorithms in rotating machinery is included.

Preferences for mHealth Technology and Text Messaging Communication in Patients With Type 2 Diabetes: Qualitative Interview Study

Background

Individuals with diabetes need regular support to help them manage their diabetes on their own, ideally delivered via mechanisms that they already use, such as their mobile phones. One reason for the modest effectiveness of prior technology-based interventions may be that the patient perspective has been insufficiently incorporated.

Objective

This study aims to understand patients’ preferences for mobile health (mHealth) technology and how that technology can be integrated into patients’ routines, especially with regard to medication use.

Methods

We conducted semistructured qualitative individual interviews with patients with type 2 diabetes from an urban health care system to elicit and explore their perspectives on diabetes medication–taking behaviors, daily patterns of using mobile technology, use of mHealth technology for diabetes care, acceptability of text messages to support medication adherence, and preferred framing of information within text messages to support diabetes care. The interviews were digitally recorded and transcribed. The data were analyzed using codes developed by the study team to generate themes, with representative quotations selected as illustrations.

Results

We conducted interviews with 20 participants, of whom 12 (60%) were female and 9 (45%) were White; in addition, the participants’ mean glycated hemoglobin A_1c control was 7.8 (SD 1.1). Overall, 5 key themes were identified: patients try to incorporate cues into their routines to help them with consistent medication taking; many patients leverage some form of technology as a cue to support adherence to medication taking and diabetes self-management behaviors; patients value simplicity and integration of technology solutions used for diabetes care, managing medications, and communicating with health care providers; some patients express reluctance to rely on mobile technology for these diabetes care behaviors; and patients believe they prefer positively framed communication, but communication preferences are highly individualized.

Conclusions

The participants expressed some hesitation about using mobile technology in supporting diabetes self-management but have largely incorporated it or are open to incorporating it as a cue to make medication taking more automatic and less burdensome. When using technology to support diabetes self-management, participants exhibited individualized preferences, but overall, they preferred simple and positively framed communication. mHealth interventions may be improved by focusing on integrating them easily into daily routines and increasing the customization of content.

Excessive Unbalanced Meat Consumption in the First Year of Life Increases Asthma Risk in the PASTURE and LUKAS2 Birth Cohorts

A higher diversity of food items introduced in the first year of life has been inversely related to subsequent development of asthma. In the current analysis, we applied latent class analysis (LCA) to systematically assess feeding patterns and to relate them to asthma risk at school age. PASTURE (N=1133) and LUKAS2 (N=228) are prospective birth cohort studies designed to evaluate protective and risk factors for atopic diseases, including dietary patterns. Feeding practices were reported by parents in monthly diaries between the 4^th and 12^th month of life. For 17 common food items parents indicated frequency of feeding during the last 4 weeks in 4 categories. The resulting 153 ordinal variables were entered in a LCA. The intestinal microbiome was assessed at the age of 12 months by 16S rRNA sequencing. Data on feeding practice with at least one reported time point was available in 1042 of the 1133 recruited children. Best LCA model fit was achieved by the 4-class solution. One class showed an elevated risk of asthma at age 6 as compared to the other classes (adjusted odds ratio (aOR): 8.47, 95% CI 2.52–28.56, p = 0.001) and was characterized by daily meat consumption and rare consumption of milk and yoghurt. A refined LCA restricted to meat, milk, and yoghurt confirmed the asthma risk effect of a particular class in PASTURE and independently in LUKAS2, which we thus termed unbalanced meat consumption (UMC). The effect of UMC was particularly strong for non-atopic asthma and asthma irrespectively of early bronchitis (aOR: 17.0, 95% CI 5.2–56.1, p < 0.001). UMC fostered growth of iron scavenging bacteria such as Acinetobacter (aOR: 1.28, 95% CI 1.00-1.63, p = 0.048), which was also related to asthma (aOR: 1.55, 95% CI 1.18-2.03, p = 0.001). When reconstructing bacterial metabolic pathways from 16S rRNA sequencing data, biosynthesis of siderophore group nonribosomal peptides emerged as top hit (aOR: 1.58, 95% CI 1.13-2.19, p = 0.007). By a data-driven approach we found a pattern of overly meat consumption at the expense of other protein sources to confer risk of asthma. Microbiome analysis of fecal samples pointed towards overgrowth of iron-dependent bacteria and bacterial iron metabolism as a potential explanation.

Workaholism in Korea: Prevalence and Socio-Demographic Differences

This study has two objectives – to provide a Korean form of the workaholism analysis questionnaire, and to analyze workaholic tendencies in South Korea by using a nationally representative data. Using 4,242 samples (2,497 men and 1,745 women), exploratory and confirmatory factor analyses were conducted to develop a Korean form (K-WAQ). The four-factor structure of K-WAQ in this study seemed to adequately represent the underlying dimensions of work addiction in Korea. The study also analyzed the prevalence of workaholism among Koreans and its differences according to socio-demographic variables. Both mean difference analyses and logistic regressions were conducted. The overall result indicated that the prevalence of workaholism in Korea can be estimated to be 39.7% of the employees. The workaholic tendencies in Korea differ significantly according to gender, age, work hours, and voluntariness of choosing employment type. Practical as well as theoretical implications and future research directions are discussed.

How Does Explanatory Virtue Determine Probability Estimation?-Empirical Discussion on Effect of Instruction

It is important to reveal how humans evaluate an explanation of the recent development of explainable artificial intelligence. So, what makes people feel that one explanation is more likely than another? In the present study, we examine how explanatory virtues affect the process of estimating subjective posterior probability. Through systematically manipulating two virtues, Simplicity-the number of causes used to explain effects-and Scope-the number of effects predicted by causes-in three different conditions, we clarified two points in Experiment 1: (i) that Scope's effect is greater than Simplicity's; and (ii) that these virtues affect the outcome independently. In Experiment 2, we found that instruction about the explanatory structure increased the impact of both virtues' effects but especially that of Simplicity. These results suggest that Scope predominantly affects the estimation of subjective posterior probability, but that, if perspective on the explanatory structure is provided, Simplicity can also affect probability estimation.

What is optimal in optimal inference?

Inferring hidden structure from noisy observations is a problem addressed by Bayesian statistical learning, which aims to identify optimal models of the process that generated the observations given assumptions that constrain the space of potential solutions. Animals and machines face similar "model-selection" problems to infer latent properties and predict future states of the world. Here we review recent attempts to explain how intelligent agents address these challenges and how their solutions relate to Bayesian principles. We focus on how constraints on available information and resources affect inference and propose a general framework that uses benefit(accuracy) and accuracy(cost) curves to assess optimality under these constraints.

A How-to-Model Guide for Neuroscience

Within neuroscience, models have many roles, including driving hypotheses, making assumptions explicit, synthesizing knowledge, making experimental predictions, and facilitating applications to medicine. While specific modeling techniques are often taught, the process of constructing models for a given phenomenon or question is generally left opaque. Here, informed by guiding many students through modeling exercises at our summer school in CoSMo (Computational Sensory-Motor Neuroscience), we provide a practical 10-step breakdown of the modeling process. This approach makes choices and criteria more explicit and replicable. Experiment design has long been taught in neuroscience; the modeling process should receive the same attention.

Dubious Claims about Simplicity and Likelihood: Comment on Pinna and Conti (2019)

Pinna and Conti (Brain Sci., 2019, 9, 149, doi:10.3390/brainsci9060149) presented phenomena concerning the salience and role of contrast polarity in human visual perception, particularly in amodal completion. These phenomena are indeed illustrative thereof, but here, the focus is on their claims (1) that neither simplicity nor likelihood approaches can account for these phenomena; and (2) that simplicity and likelihood are equivalent. I argue that their first claim is based on incorrect assumptions, whereas their second claim is simply untrue.

Face Representations via Tensorfaces of Various Complexities

Neurons selective for faces exist in humans and monkeys. However, characteristics of face cell receptive fields are poorly understood. In this theoretical study, we explore the effects of complexity, defined as algorithmic information (Kolmogorov complexity) and logical depth, on possible ways that face cells may be organized. We use tensor decompositions to decompose faces into a set of components, called tensorfaces, and their associated weights, which can be interpreted as model face cells and their firing rates. These tensorfaces form a high-dimensional representation space in which each tensorface forms an axis of the space. A distinctive feature of the decomposition algorithm is the ability to specify tensorface complexity. We found that low-complexity tensorfaces have blob-like appearances crudely approximating faces, while high-complexity tensorfaces appear clearly face-like. Low-complexity tensorfaces require a larger population to reach a criterion face reconstruction error than medium- or high-complexity tensorfaces, and thus are inefficient by that criterion. Low-complexity tensorfaces, however, generalize better when representing statistically novel faces, which are faces falling beyond the distribution of face description parameters found in the tensorface training set. The degree to which face representations are parts based or global forms a continuum as a function of tensorface complexity, with low and medium tensorfaces being more parts based. Given the computational load imposed in creating high-complexity face cells (in the form of algorithmic information and logical depth) and in the absence of a compelling advantage to using high-complexity cells, we suggest face representations consist of a mixture of low- and medium-complexity face cells.

Machine learning approach yields epigenetic biomarkers of food allergy: A novel 13-gene signature to diagnose clinical reactivity

BACKGROUND

Current laboratory tests are less than 50% accurate in distinguishing between people who have food allergies (FA) and those who are merely sensitized to foods, resulting in the use of expensive and potentially dangerous Oral Food Challenges. This study presents a purely-computational machine learning approach, conducted using DNA Methylation (DNAm) data, to accurately diagnose food allergies and potentially find epigenetic targets for the disease.

METHODS AND RESULTS

An unbiased feature-selection pipeline was created that narrowed down 405,000+ potential CpG biomarkers to 18. Machine-learning models that utilized subsets of this 18-feature aggregate achieved perfect classification accuracy on completely hidden test cohorts (on an 8-fold hidden dataset). Ensemble classification was also shown to be effective for this High Dimension Low Sample Size (HDLSS) DNA methylation dataset. The efficacy of these machine learning classifiers and the 18 CpGs was further validated by their high accuracy on a large number of hidden data permutations, where the samples in the training, cross-validation, and hidden sets were repeatedly randomly allocated. The 18-CpG signature mapped to 13 genes, on which biological insights were collected. Notably, many of the FA-discriminating genes found in this study were strongly associated with the immune system, and seven of the 13 genes were previously associated with FA.

CONCLUSIONS

Previous studies have also created highly-accurate classifiers for this dataset, using both data-driven and a priori biological insights to construct a 96-CpG signature. This research builds on previous work because it uses a completely computational approach to obtain a perfect classification accuracy while using only 18 highly discriminating CpGs (0.005% of the total available features). In machine learning, simpler models, as used in this study, are generally preferred over more complex ones (other things being equal). Lastly, the completely data-driven methodology presented in this research eliminates the need for a priori biological information and allows for generalizability to other DNAm classification problems.

Unifying Physics and Psychophysics on the Basis of Symmetry, Least-Action ≈ Simplicity Principle, and Conservation Laws ≈ Veridicality

Psychophysics is the branch of experimental psychology that deals with the study of sensation and perception. A consensus has grown up among experts in psychophysics in the last hundred years that the human being’s percepts are inferences, which are based on a minimum, or simplicity, principle that is applied to the currently available sensory data. These educated guesses play the critical role in establishing veridical perceptual representations of the three-dimensional environment, where by “veridical” we mean that the percept agrees with what is “out there.” These veridical representations cannot be achieved without making use of symmetries, much like those known in physics, where they are essential for characterizing our physical world and deriving the conservation laws. But, unlike in physics, the important role that symmetry plays in psychophysics has been demonstrated and explained only within the last 10 years. Symmetries represent regularities in our physical world. These symmetries also serve as the source of the redundancies that are inherent in 3D objects and make vision possible. The main goal of this article is to show that the similarity between the mathematical formalisms used in physics and in psychophysics is not coincidental and that exploring this similarity can benefit the sciences of perception and cognition. This article includes a brief tutorial about symmetry groups and their relationship to transformation groups as well as to their invariants. It was included to make this material available to readers who are not familiar with these topics.

Social norm complexity and past reputations in the evolution of cooperation

Indirect reciprocity is the most elaborate and cognitively demanding of all known cooperation mechanisms, and is the most specifically human because it involves reputation and status. By helping someone, individuals may increase their reputation, which may change the predisposition of others to help them in future. The revision of an individual's reputation depends on the social norms that establish what characterizes a good or bad action and thus provide a basis for morality. Norms based on indirect reciprocity are often sufficiently complex that an individual's ability to follow subjective rules becomes important, even in models that disregard the past reputations of individuals, and reduce reputations to either 'good' or 'bad' and actions to binary decisions. Here we include past reputations in such a model and identify the key pattern in the associated norms that promotes cooperation. Of the norms that comply with this pattern, the one that leads to maximal cooperation (greater than 90 per cent) with minimum complexity does not discriminate on the basis of past reputation; the relative performance of this norm is particularly evident when we consider a 'complexity cost' in the decision process. This combination of high cooperation and low complexity suggests that simple moral principles can elicit cooperation even in complex environments.

On Bayesian Simplicity in Human Visual Perceptual Organization

A returning idea among some Bayesians in research on human visual perceptual organization is that the surprisal of something (i.e., the negative logarithm of its probability) expresses its complexity (i.e., the length of its shortest description). Bayes' rule is a powerful modeling tool and descriptive simplicity is a rich concept, but this idea is wishful thinking at best: If true, it would unify the simplicity and likelihood principles, which reflect two traditionally opposed schools of thought on perceptual organization. Some rapprochement between the two principles can certainly be discerned, but the aforementioned idea lacks formal underpinning and confounds otherwise perfectly good ideas. Here, this idea is revisited and its latest version is debunked step by step. In addition, I argue that its likely origin lies, inadvertently, in a standard Bayesian textbook: The author made (a) a pivotal mistake and (b) a compelling argument that was overinterpreted by others.