Discovering rules for rule-based machine learning with the help of novelty search

Automated prediction systems based on machine learning (ML) are employed in practical applications with increasing frequency and stakeholders demand explanations of their decisions. ML algorithms that learn accurate sets of rules, such as learning classifier systems (LCSs), produce transparent and human-readable models by design. However, whether such models can be effectively used, both for predictions and analyses, strongly relies on the optimal placement and selection of rules (in ML this task is known as model selection). In this article, we broaden a previous analysis on a variety of techniques to efficiently place good rules within the search space based on their local prediction errors as well as their generality. This investigation is done within a specific pre-existing LCS, named SupRB, where the placement of rules and the selection of good subsets of rules are strictly separated—in contrast to other LCSs where these tasks sometimes blend. We compare two baselines, random search and -evolution strategy (ES), with six novelty search variants: three novelty-/fitness weighing variants and for each of those two differing approaches on the usage of the archiving mechanism. We find that random search is not sufficient and sensible criteria, i.e., error and generality, are indeed needed. However, we cannot confirm that the more complicated-to-explain novelty search variants would provide better results than -ES which allows a good balance between low error and low complexity in the resulting models

Exploring the causes of adverse events in hospitals and potential prevention strategies

Objectives To examine the causes of adverse events (AEs) and potential prevention strategies to minimise the occurrence of AEs in hospitalised patients. Methods For the 744 AEs identified in the patient record review study in 21 Dutch hospitals, trained reviewers were asked to select all causal factors that contributed to the AE. The results were analysed together with data on preventability and consequences of AEs. In addition, the reviewers selected one or more prevention strategies for each preventable AE. The recommended prevention strategies were analysed together with four general causal categories: technical, human, organisational and patient-related factors. Results Human causes were predominantly involved in the causation of AEs (in 61% of the AEs), 61% of those being preventable and 13% leading to permanent disability. In 39% of the AEs, patient-related factors were involved, in 14% organisational factors and in 4% technical factors. Organisational causes contributed relatively often to preventable AEs (93%) and AEs resulting in permanent disability (20%). Recommended strategies to prevent AEs were quality assurance/peer review, evaluation of safety behaviour, training and procedures. For the AEs with human and patient-related causes, reviewers predominantly recommended quality assurance/peer review. AEs caused by organisational factors were considered preventable by improving procedures. Discussion Healthcare interventions directed at human causes are recommended because these play a large role in AE causation. In addition, it seems worthwhile to direct interventions on organisational causes because the AEs they cause are nearly always believed to be preventable. Organisational factors are thus relatively easy to tackle. Future research designs should allow researchers to interview healthcare providers that were involved in the event, as an additional source of information on contributing factors.

Active Learning: Effects of Core Training Design Elements on Self-Regulatory Processes, Learning, and Adaptability

This research describes a comprehensive examination of the cognitive, motivational, and emotional processes underlying active learning approaches, their effects on learning and transfer, and the core training design elements (exploration, training frame, emotion-control) and individual differences (cognitive ability, trait goal orientation, trait anxiety) that shape these processes. Participants (N = 350) were trained to operate a complex computer-based simulation. Exploratory learning and error-encouragement framing had a positive effect on adaptive transfer performance and interacted with cognitive ability and dispositional goal orientation to influence trainees’ metacognition and state goal orientation. Trainees who received the emotion-control strategy had lower levels of state anxiety. Implications for developing an integrated theory of active learning, learner-centered design, and research extensions are discussed

Ten simple rules for reporting voxel-based morphometry studies

Voxel-based morphometry [Ashburner, J. and Friston, K.J., 2000. Voxel-based morphometry—the methods. NeuroImage 11(6 Pt 1), 805–821] is a commonly used tool for studying patterns of brain change in development or disease and neuroanatomical correlates of subject characteristics. In performing a VBM study, many methodological options are available; if the study is to be easily interpretable and repeatable, the processing steps and decisions must be clearly described. Similarly, unusual methods and parameter choices should be justified in order to aid readers in judging the importance of such options or in comparing the work with other studies. This editorial suggests core principles that should be followed and information that should be included when reporting a VBM study in order to make it transparent, replicable and useful

More than one way to see it: Individual heuristics in avian visual computation

Comparative pattern learning experiments investigate how different species find regularities in sensory input, providing insights into cognitive processing in humans and other animals. Past research has focused either on one species’ ability to process pattern classes or different species’ performance in recognizing the same pattern, with little attention to individual and species-specific heuristics and decision strategies. We trained and tested two bird species, pigeons (Columba livia) and kea (Nestor notabilis, a parrot species), on visual patterns using touch-screen technology. Patterns were composed of several abstract elements and had varying degrees of structural complexity. We developed a model selection paradigm, based on regular expressions, that allowed us to reconstruct the specific decision strategies and cognitive heuristics adopted by a given individual in our task. Individual birds showed considerable differences in the number, type and heterogeneity of heuristic strategies adopted. Birds’ choices also exhibited consistent species-level differences. Kea adopted effective heuristic strategies, based on matching learned bigrams to stimulus edges. Individual pigeons, in contrast, adopted an idiosyncratic mix of strategies that included local transition probabilities and global string similarity. Although performance was above chance and quite high for kea, no individual of either species provided clear evidence of learning exactly the rule used to generate the training stimuli. Our results show that similar behavioral outcomes can be achieved using dramatically different strategies and highlight the dangers of combining multiple individuals in a group analysis. These findings, and our general approach, have implications for the design of future pattern learning experiments, and the interpretation of comparative cognition research more generally

Contributions of anterior cingulate cortex and basolateral amygdala to decision confidence and learning under uncertainty.

The subjective sense of certainty, or confidence, in ambiguous sensory cues can alter the interpretation of reward feedback and facilitate learning. We trained rats to report the orientation of ambiguous visual stimuli according to a spatial stimulus-response rule that must be learned. Following choice, rats could wait a self-timed delay for reward or initiate a new trial. Waiting times increase with discrimination accuracy, demonstrating that this measure can be used as a proxy for confidence. Chemogenetic silencing of BLA shortens waiting times overall whereas ACC inhibition renders waiting times insensitive to confidence-modulating attributes of visual stimuli, suggesting contribution of ACC but not BLA to confidence computations. Subsequent reversal learning is enhanced by confidence. Both ACC and BLA inhibition block this enhancement but via differential adjustments in learning strategies and consistent use of learned rules. Altogether, we demonstrate dissociable roles for ACC and BLA in transmitting confidence and learning under uncertainty

Estimates of prevalence, demographic characteristics and social factors among people with disabilities in the USA: a cross-survey comparison

Objective: A national priority for disability research in the USA is the standardised identification of people with disabilities in surveillance efforts. Mandated by federal statute, six dichotomous difficulty-focused questions were implemented in national surveys to identify people with disabilities. The aim of this study was to assess the prevalence, demographic characteristics and social factors among people with disabilities based on these six questions using multiple national surveys in the USA. Setting: American Community Survey (ACS), Current Population Survey Annual Social and Economic Supplement (CPS-ASEC), National Health Interview Survey (NHIS) and the Survey of Income and Program Participation (SIPP). Participants: Civilian, non-institutionalised US residents aged 18 and over from the 2009 to 2014 ACS, 2009 to 2014 CPS-ASEC, 2009 to 2014 NHIS and 2008 SIPP waves 3, 7 and 10. Primary and secondary outcome measures: Disability was assessed using six standardised questions asking people about hearing, vision, cognition, ambulatory, self-care and independent living disabilities. Social factors were assessed with questions asking people to report their education, employment status, family size, health and marital status, health insurance and income. Results: Risk ratios and demographic distributions for people with disabilities were consistent across survey. People with disabilities were at decreased risk of having college education, employment, families with three or more people, excellent or very good self-reported health and a spouse. People with disabilities were also consistently at greater risk of having health insurance and living below the poverty line. Estimates of disability prevalence varied between surveys from 2009 to 2014 (range 11.76%–17.08%). Conclusion: Replicating the existing literature, we found the estimation of disparities and inequity people with disabilities experience to be consistent across survey. Although there was a range of prevalence estimates, demographic factors for people with disabilities were consistent across surveys. Variations in prevalence estimates can be explained by survey context effects