Meta-learning is even more broadly relevant to the study of inductive biases than Binz et al. suggest: Its implications go beyond the extensions to rational analysis that they discuss. One noteworthy example is that meta-learning can act as a bridge between the vector representations of neural networks and the symbolic hypothesis spaces used in many Bayesian models.

Commentaries on the target article offer diverse perspectives on integrative experiment design. Our responses engage three themes: (1) Disputes of our characterization of the problem, (2) skepticism toward our proposed solution, and (3) endorsement of the solution, with accompanying discussions of its implementation in existing work and its potential for other domains. Collectively, the commentaries enhance our confidence in the promise and viability of integrative experiment design, while highlighting important considerations about how it is used.

The success of models of human behavior based on Bayesian inference over logical formulas or programs is taken as evidence that people employ a “language-of-thought” that has similarly discrete and compositional structure. We argue that this conclusion problematically crosses levels of analysis, identifying representations at the algorithmic level based on inductive biases at the computational level.

The dominant paradigm of experiments in the social and behavioral sciences views an experiment as a test of a theory, where the theory is assumed to generalize beyond the experiment's specific conditions. According to this view, which Alan Newell once characterized as “playing twenty questions with nature,” theory is advanced one experiment at a time, and the integration of disparate findings is assumed to happen via the scientific publishing process. In this article, we argue that the process of integration is at best inefficient, and at worst it does not, in fact, occur. We further show that the challenge of integration cannot be adequately addressed by recently proposed reforms that focus on the reliability and replicability of individual findings, nor simply by conducting more or larger experiments. Rather, the problem arises from the imprecise nature of social and behavioral theories and, consequently, a lack of commensurability across experiments conducted under different conditions. Therefore, researchers must fundamentally rethink how they design experiments and how the experiments relate to theory. We specifically describe an alternative framework, integrative experiment design, which intrinsically promotes commensurability and continuous integration of knowledge. In this paradigm, researchers explicitly map the design space of possible experiments associated with a given research question, embracing many potentially relevant theories rather than focusing on just one. Researchers then iteratively generate theories and test them with experiments explicitly sampled from the design space, allowing results to be integrated across experiments. Given recent methodological and technological developments, we conclude that this approach is feasible and would generate more-reliable, more-cumulative empirical and theoretical knowledge than the current paradigm – and with far greater efficiency.

The commentaries raised questions about normativity, human rationality, cognitive architectures, cognitive constraints, and the scope or resource rational analysis (RRA). We respond to these questions and clarify that RRA is a methodological advance that extends the scope of rational modeling to understanding cognitive processes, why they differ between people, why they change over time, and how they could be improved.

Modeling human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the human mind can and cannot do, while others constrain it through principles of rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis. The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about human rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irrationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.

The aim of this review paper is to consider how the principles of clinical audit could be applied to the development of an audit of nutritional care in hospitals and care homes, based on criteria derived from the Essence of Care: Food and Drink. A literature review identified fifteen key papers that included guidance or standards for nutritional care in hospitals or care homes. These were used to supplement the ten factors suggested by the Essence of Care to develop a set of potential audit criteria covering all aspects of the nutritional care pathway including the identification of risk of malnutrition, implementation of nutritional care plans, referral to healthcare professionals for further nutritional assessment and nutritional support strategies. A series of audit tools have been developed, including an organisational level audit tool, a staff questionnaire, a patients' and residents' records audit tool and a patients' and residents' experiences questionnaire. Further issues to consider in designing a national nutritional audit include the potential role of direct observation of care, the use of trained auditors and the scope for including the results of pre-existing local audits. In conclusion, a national audit would need to encompass a very large number of health and care organisations of widely varying sizes and types and a diverse range of people.

If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.

In summer 2008, we investigated an outbreak of diarrhoeal illness in participants of a mountain-bike event in Wales (UK) which had been affected by heavy rain. We conducted a retrospective cohort study to investigate the cause using an internet-based questionnaire. Fifty-three percent of those contacted responded, and 161 (46·5%) out of the 347 responders, reported gastrointestinal symptoms. Median day of onset was 3 days following the event. Ten riders reported receiving a laboratory-confirmed diagnosis of Campylobacter. Multivariate logistic regression analysis identified the inadvertent ingestion of mud (OR 2·5, 95% CI 1·5–4·2, P<0·001) and eating ‘other’ food during the event (OR 2·1, 95% CI 1·2–3·6, P=0·01) as significant risk factors for illness. We concluded that the outbreak was caused by Campylobacter, spread to the riders by the inadvertent ingestion of mud which had been contaminated with sheep faeces from the rural course. Mountain-bike race organizers should consider microbiological hazards when risk-assessing potential race courses. The internet is an efficient tool for the investigation of outbreaks in computer-literate populations.

Bayesian Rationality (Oaksford & Chater 2007) illustrates the strengths of Bayesian models of cognition: the systematicity of rational explanations, transparent assumptions about human learners, and combining structured symbolic representation with statistics. However, the book also highlights some of the challenges this approach faces: of providing psychological mechanisms, explaining the origins of the knowledge that guides human learning, and accounting for how people make genuinely new discoveries.

European beech (Fagus sylvatica L.) is an important forest tree species which was always considered being nonproblematic with regard to its susceptibility to pathogens and insects. However during the past decade, in Europe and the north-eastern USA an increasing number of trees and stands were showing symptoms typical for Phytophthora diseases: increased crown transparency, abnormally small and often yellowish foliage, a dieback of the crown, tongue-shaped necroses of the inner bark and the cambium with tarry or rusty spots on the surface of the bark either extending up to 7m from the stem base (collar rot) or occurring isolated higher up the stem (aerial bleeding cankers), fine root destructions and necrotic lesions on suberized roots. Large-scale investigations on both continents using specific isolation methods and ELISA kits for detection, and morphological and physiological parameters as well as ITS-DNA sequence analysis for identification demonstrated that several Phytophthora species were regularly involved as inciting agents of the decline. The most important species were Phytophthora citricola, P. cambivora and P. cactorum in Europe, and P. inflata in the USA. Their pathogenicity to beech was shown by stem inoculation and soil infestation tests performed by various groups. A small-scale nursery survey in Germany revealed that beech fields are regularly infested with various Phytophthora species. Options for disease management and control are discussed, and an emphasis is put on the prevention of disease spread via infested nursery stock.

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models.

We address two kinds of criticisms of our Bayesian framework for generalization: those that question the correctness or the coverage of our analysis, and those that question its intrinsic value. Speaking to the first set, we clarify the origins and scope of our size principle for weighting hypotheses or features, focusing on its potential status as a cognitive universal; outline several variants of our framework to address additional phenomena of generalization raised in the commentaries; and discuss the subtleties of our claims about the relationship between similarity and generalization. Speaking to the second set, we identify the unique contributions that a rational statistical approach to generalization offers over traditional models that focus on mental representation and cognitive processes.