Embodied and embedded ecological rationality: A common vertebrate mechanism for action selection underlies cognition and heuristic decision-making in humans.

The last common ancestor shared by humans and other vertebrates lived over half a billion years ago. In the time since that ancestral line diverged, evolution by natural selection has produced an impressive diversity-from fish to birds to elephants-of vertebrate morphology; yet despite the great species-level differences that otherwise exist across the brains of many animals, the neural circuitry that underlies motor control features a functional architecture that is virtually unchanged in every living species of vertebrate. In this article, we review how that circuitry facilitates motor control, trial-and-error-based procedural learning, and habit formation; we then develop a model that describes how this circuitry (embodied in an agent) works to build and refine sequences of goal-directed actions that are molded to fit the structure of the environment (in which the agent is embedded). We subsequently review evidence suggesting that this same functional circuitry became further adapted to regulate cognitive control in humans as well as motor control; then, using examples of heuristic decision-making from the ecological rationality tradition, we show how the model can be used to understand how that circuitry operates analogously in both cognitive and motor domains. We conclude with a discussion of how the model encourages a shift in perspective regarding ecological rationality's "adaptive toolbox"-namely, to one that views heuristic processes and other forms of goal-directed cognition as likely being implemented by the same neural circuitry (and in the same fashion) as goal-directed action in the motor domain-and how this change of perspective can be useful.

Modeling fast-and-frugal heuristics.

Heuristics are simple rules that experts and laypeople rely on to make decisions under uncertainty as opposed to situations with calculable risk. The research program on fast-and-frugal heuristics studies formal models of heuristics and is motivated by Herbert Simon's seminal work on bounded rationality and satisficing. In this article, we first introduce the major theoretical principles (e.g., ecological rationality) and research approaches (e.g., competitive testing) that have been adopted in this research program, and then illustrate these principles and approaches with two heuristics: take-the-best and fast-and-frugal trees. We describe conditions under which simple heuristics predict as accurately as or better than more complex models, despite requiring less effort. We close by pointing out several issues that need to be further studied and better understood in the research on fast-and-frugal heuristics.

A Generative View of Rationality and Growing Awareness†.

In this paper we contrast bounded and ecological rationality with a proposed alternative, generative rationality. Ecological approaches to rationality build on the idea of humans as "intuitive statisticians" while we argue for a more generative conception of humans as "probing organisms." We first highlight how ecological rationality's focus on cues and statistics is problematic for two reasons: (a) the problem of cue salience, and (b) the problem of cue uncertainty. We highlight these problems by revisiting the statistical and cue-based logic that underlies ecological rationality, which originate from the misapplication of concepts in psychophysics (e.g., signal detection, just-noticeable-differences). We then work through the most popular experimental task in the ecological rationality literature-the city size task-to illustrate how psychophysical assumptions have informally been linked to ecological rationality. After highlighting these problems, we contrast ecological rationality with a proposed alternative, generative rationality. Generative rationality builds on biology-in contrast to ecological rationality's focus on statistics. We argue that in uncertain environments cues are rarely given or available for statistical processing. Therefore we focus on the psychogenesis of awareness rather than psychophysics of cues. For any agent or organism, environments "teem" with indefinite cues, meanings and potential objects, the salience or relevance of which is scarcely obvious based on their statistical or physical properties. We focus on organism-specificity and the organism-directed probing that shapes awareness and perception. Cues in teeming environments are noticed when they serve as cues-for-something, requiring what might be called a "cue-to-clue" transformation. In this sense, awareness toward a cue or cues is actively "grown." We thus argue that perception might more productively be seen as the presentation of cues and objects rather than their representation. This generative approach not only applies to relatively mundane organism (including human) interactions with their environments-as well as organism-object relationships and their embodied nature-but also has significant implications for understanding the emergence of novelty in economic settings. We conclude with a discussion of how our arguments link with-but modify-Herbert Simon's popular "scissors" metaphor, as it applies to bounded rationality and its implications for decision making in uncertain, teeming environments.

The evolution of cognitive biases in human learning.

Cognitive biases like underinference, the hard-easy effect, and recurrently non-monotonic confidence are evolutionarily puzzling when viewed as persistent flaws in how people learn from environmental feedback. To explain these empirically robust cognitive biases from an evolutionary perspective, we propose a model of ancestral human learning based on the cultural-evolutionary-theoretic hypothesis that the primary selection pressure acting on ancestral human cognition pertained not to learning individually from environmental feedback, but to socially learning task-specific knowledge. In our model-which is inspired by classical Bayesian models-an ancestral human learner (the student) attempts to learn task-specific knowledge from a role model, with the option of switching between different tasks and role models. Suppose that the student's method of learning from their role model is a priori uncertain-in that it can either be successful imitation learning or de facto innovation learning-and the ecological fitness costs of meaningfully retaining environmental feedback are high. Then, the student's fitness-maximizing strategy does not retain their environmental feedback and-depending on the choice of model parameters-can be characterized by all of the aforementioned cognitive biases. Specifically, in order for the evolutionarily optimal estimate of confidence in this learning environment to be recurrently non-monotonic, it is necessary (as long as the environment's marginal payoff function satisfies a plausible quantitative condition) that a positive proportion of ancestral humans' attempted imitation learning was unknowingly implemented as de facto innovation learning. Moreover, an ecologically rational strategy of selective social learning can plausibly cause the evolutionarily optimal estimate of confidence to be recurrently non-monotonic in the empirically documented way: general increase with an intermediate period of decrease.

Studies in Ecological Rationality.

Ecological rationality represents an alternative to classic frameworks of rationality. Extending on Herbert Simon's concept of bounded rationality, it holds that cognitive processes, including simple heuristics, are not per se rational or irrational, but that their success rests on their degree of fit to relevant environmental structures. The key is therefore to understand how cognitive and environmental structures slot together. In recent years, a growing set of analyses of heuristic-environment systems has deepened the understanding of the human mind and how boundedly rational heuristics can result in successful decision making. This article is concerned with three conceptual challenges in the study of ecological rationality. First, do heuristics also succeed in dynamic contexts involving competitive agents? Second, can the mind adapt to environmental structures via an unsupervised learning process? Third, how can research go beyond mere descriptions of environmental structures to develop theories of the mechanisms that give rise to those structures? In addressing these questions, we illustrate that a successful theory of rationality will focus on the adaptive aspects of the mind and will need to account for three components: the mind's information processing, the environment to which the mind adapts, and the intersection between the environment and the mind.

Naturalism, tractability and the adaptive toolbox.

Many compelling examples have recently been provided in which people can achieve impressive epistemic success, e.g. draw highly accurate inferences, by using simple heuristics and very little information. This is possible by taking advantage of the features of the environment. The examples suggest an easy and appealing naturalization of rationality: on the one hand, people clearly can apply simple heuristics, and on the other hand, they intuitively ought do so when this brings them high accuracy at little cost.. The 'ought-can' principle is satisfied, and rationality is meaningfully normative. We show, however, that this naturalization program is endangered by a computational wrinkle in the adaptation process taken to be responsible for this heuristics-based ('ecological') rationality: for the adaptation process to guarantee even minimal rationality, it requires astronomical computational resources, making the problem intractable. We consider various plausible auxiliary assumptions in attempt to remove this obstacle, and show that they do not succeed; intractability is a robust property of adaptation. We discuss the implications of our findings for the project of naturalizing rationality.

How Category Selection Impacts Inference Reliability: Inheritance Inference From an Ecological Perspective.

This article presents results from a simulation-based study of inheritance inference, that is, inference from the typicality of a property among a "base" class to its typicality among a subclass of the class. The study aims to ascertain which kinds of inheritance inferences are reliable, with attention to the dependence of their reliability upon the type of environment in which inferences are made. For example, the study addresses whether inheritance inference is reliable in the case of "exceptional subclasses" (i.e., subclasses that are known to be atypical in some respect) and attends to variations in reliability that result from variations in the entropy level of the environment. A further goal of the study is to show that the reliability of inheritance inference depends crucially on which sorts of base classes are used in making inferences. One approach to inheritance inference treats the extension of any atomic predicate as a suitable base class. A second approach identifies suitable base classes with the cells of a partition (of a preselected size k) of the domain of objects that satisfies the condition of maximizing the similarity of objects that are assigned to the same class. In addition to permitting more inferences, our study shows that the second approach results in inheritance inferences that are far more reliable, particularly in the case of exceptional subclasses.

Economic behaviours among non-human primates.

Do we have any valid reasons to affirm that non-human primates display economic behaviour in a sufficiently rich and precise sense of the phrase? To address this question, we have to develop a set of criteria to assess the vast array of experimental studies and field observations on individual cognitive and behavioural competences as well as the collective organization of non-human primates. We review a sample of these studies and assess how they answer to the following four main challenges. (i) Do we see any economic organization or institutions emerge among groups of non-human primates? (ii) Are the cognitive abilities, and often biases, that have been evidenced as underlying typical economic decision-making among humans, also present among non-human primates? (iii) Can we draw positive lessons from performance comparisons among primate species, humans and non-humans but also across non-human primate species, as elicited by canonical game-theoretical experimental paradigms, especially as far as economic cooperation and coordination are concerned? And (iv) in which way should we improve models and paradigms to obtain more ecological data and conclusions? Articles discussed in this paper most often bring about positive answers and promising perspectives to support the existence and prevalence of economic behaviours among non-human primates. This article is part of the theme issue 'Existence and prevalence of economic behaviours among non-human primates'.

Which cognitive individual differences predict good Bayesian reasoning? Concurrent comparisons of underlying abilities.

We know a lot about how to present Bayesian reasoning tasks in order to aid performance, but less about underlying individual differences that can account for interindividual variability on the same tasks. Such information would be useful for both theoretical and practical reasons. Two theoretical positions, ecological rationality and nested set views, generate multiple hypotheses about which individual difference traits should be most relevant as underlying Bayesian reasoning performance. However, because many of these traits are somewhat overlapping, testing variables in isolation can yield misleading results. The present research assesses Bayesian reasoning abilities in conjunction with multiple individual different measures. Across three experiments, Bayesian reasoning was best predicted by measures of numerical literacy and visuospatial ability, as opposed to several different measures of cognitive thinking dispositions/styles, ability to conceptually model set-theoretic relationships, or cognitive processing ability (working memory span). These results support an ecological rationality view of Bayesian reasoning, rather than nested sets views. There also was some predictive ability for the Cognitive Reflection Task, which was only partially due to the numeracy aspects of that instrument, and further work is needed to clarify if this is a distinct factor. We are now beginning to understand not only how to build Bayesian reasoning tasks, but also how to build good Bayesian reasoners.

Honeybees solve a multi-comparison ranking task by probability matching.

Honeybees forage on diverse flowers which vary in the amount and type of rewards they offer, and bees are challenged with maximizing the resources they gather for their colony. That bees are effective foragers is clear, but how bees solve this type of complex multi-choice task is unknown. Here, we set bees a five-comparison choice task in which five colours differed in their probability of offering reward and punishment. The colours were ranked such that high ranked colours were more likely to offer reward, and the ranking was unambiguous. Bees' choices in unrewarded tests matched their individual experiences of reward and punishment of each colour, indicating bees solved this test not by comparing or ranking colours but by basing their colour choices on their history of reinforcement for each colour. Computational modelling suggests a structure like the honeybee mushroom body with reinforcement-related plasticity at both input and output can be sufficient for this cognitive strategy. We discuss how probability matching enables effective choices to be made without a need to compare any stimuli directly, and the use and limitations of this simple cognitive strategy for foraging animals.

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Understanding the treatment and influencing factors of straw is important to improve the utilization efficiency of straw resources and alleviate the negative external effects of the environment. Here, we proposed an analysis framework of farmers' straw disposal behavior based on ecological rationality. The Logit model was used to analyze the farmers' willingness and influencing factors for the selection of straw burning and feed utilization with a dataset of 424 valid questionnaires in dry farming areas of Gansu Province. The results showed that the straw disposal behavior of farmers was the result of decision-making cognition formed in the process of long-term adaptation and co-evolution between farmers and the surrounding environment. In dry farming area, the straw treatment methods were diversified, with feed utilization and fuel as the two main forms and straw incineration and discard being ubiquitous. Among the factors that affect farmers' straw burning behavior, householder age (P＜0.1), education level (P＜0.01), the scale of livestock raising (P＜0.05), the proportion of agricultural income (P＜0.1), and government policy propaganda (P＜0.01) had significant inhibitory effect. The gender of householder (P＜0.1) and cognition level (P＜0.01) helped farmers to choose the non-pro-environmental behavior, and the environmental awareness of farmers was weak. In terms of straw feed utilization behavior, householder age (P＜0.1), education level (P＜0.05), the situation of family members serving as village cadres (P＜0.05), feed proces-sing technical guidance (P＜0.01) and subsidy for prohibition of straw burning (P＜0.1) had positive effect on straw forage utilization, while the topography (P＜0.1) had a negative effect. Some policy recommendations were given to promote utilization of straw resources: constructing a combination mechanism of "prohibition of burning and subsidies", strengthening the extension of straw feed utilization technology, and accelerating the improvement of straw collection-store-transportation service system.

Bridging Ecological Rationality, Embodied Emotion, and Neuroeconomics: Insights From the Somatic Marker Hypothesis.

The somatic marker hypothesis (SMH) has been utilized to demonstrate the role of emotion and somatic state in decision-making under uncertainty over the past two decades. Despite some debate, the SMH has provided not only a neurobiological framework for understanding emotion and decision-making but also a good empirical support for ecological rationality and embodied emotion. Unlike the traditional maximizing rationality and bounded satisficing rationality, the ecological rationality stresses that emotions should be brought to the decision-making process. The embodied emotion furthermore emphasizes that emotions are embodied in the body and the brain. On the other hand, behavioral decision-making has spawned many new interdisciplines, including neuroeconomics. In this case, the SMH could act as a bridge to translate the ecological rationality and the embodied emotion into emerging neuroeconomics. Thus, this mini-review article aims to propose an integrated framework for introducing ecological rationality and embodied emotion into the field of neuroeconomics by virtue of insights from the SMH.

Global-Local Incompatibility: The Misperception of Reliability in Judgment Regarding Global Variables.

A number of important decision domains, including decisions about hiring, global warming, and weather hazards, are characterized by a global-local incompatibility. These domains involve variables that cannot be observed by a single decision maker (DM) and require the integration of observations from locally available information cues. This paper presents a new bifocal lens model that describes how the structure of the environment can lead to a unique form of overconfidence when generalizing the reliability of the local environment to a global scale. When the local environment does not reliably reflect the global environment, they are incompatible. While local perspectives vary across DMs, global-local incompatibility can be understood using the structure of classical test theory as the difference between (a) perceived estimates of the reliability derived from the local environment and (b) the true reliability of the local environment. I model global-local incompatibility as the difference between the true and estimated reliability when the assumptions of classical test theory are violated. Using a series of case studies and an empirical study, I demonstrate the widespread utility of this framework, and I conclude by discussing implications for cognitive-ecological theory, risk communication, and overconfidence.

The ecological rationality of explanatory reasoning.

There is growing evidence that explanatory considerations influence how people change their degrees of belief in light of new information. Recent studies indicate that this influence is systematic and may result from people's following a probabilistic update rule. While formally very similar to Bayes' rule, the rule or rules people appear to follow are different from, and inconsistent with, that better-known update rule. This raises the question of the normative status of those updating procedures. Is the role explanation plays in people's updating their degrees of belief a bias? Or are people right to update on the basis of explanatory considerations, in that this offers benefits that could not be had otherwise? Various philosophers have argued that any reasoning at deviance with Bayesian principles is to be rejected, and so explanatory reasoning, insofar as it deviates from Bayes' rule, can only be fallacious. We challenge this claim by showing how the kind of explanation-based update rules to which people seem to adhere make it easier to strike the best balance between being fast learners and being accurate learners. Borrowing from the literature on ecological rationality, we argue that what counts as the best balance is intrinsically context-sensitive, and that a main advantage of explanatory update rules is that, unlike Bayes' rule, they have an adjustable parameter which can be fine-tuned per context. The main methodology to be used is agent-based optimization, which also allows us to take an evolutionary perspective on explanatory reasoning.

Familiarity-Matching: An Ecologically Rational Heuristic for the Relationships-Comparison Task.

Previous studies have shown that people often use heuristics in making inferences and that subjective memory experiences, such as recognition or familiarity of objects, can be valid cues for inferences. So far, many researchers have used the binary choice task in which two objects are presented as alternatives (e.g., "Which city has the larger population, city A or city B?"). However, objects can be presented not only as alternatives but also in a question (e.g., "Which country is city X in, country A or country B?"). In such a situation, people can make inferences based on the relationship between the object in the question and each object given as an alternative. In the present study, we call this type of task a "relationships-comparison task." We modeled the three inference strategies that people could apply to solve it (familiarity-matching [FM; a new heuristic we propose in this study], familiarity heuristic [FH], and knowledge-based inference [KI]) to examine people's inference processes. Through Studies 1, 2, and 3, we found that (a) people tended to rely on heuristics, and that FM (inferences based on similarity in familiarity between objects) well explained participants' inference patterns; (b) FM could work as an ecologically rational strategy for the relationships-comparison task since it could effectively reflect environmental structures, and that the use of FM could be highly replicable and robust; and (c) people could sometimes use a decision strategy like FM, even in their daily lives (consumer behaviors). The nature of the relationships-comparison task and human cognitive processes is discussed.

Individually irrational pruning is essential for ecological rationality in a social context.

Heuristics, commonly thought to violate the full rationality assumptions, are paradoxically indispensable parts of our decision-making and learning processes. To resolve this seemingly paradox, there have been several studies in the literature that aim at finding some broad daily life conditions and situations where employing heuristics are rational. However, these researches mainly focus on non-social conditions, whereas, for human beings, social and individual processes are interwoven and it would be better to study them jointly. Here, we study the role of pruning heuristic in individual reinforcement learning in a social context, where our simulated learning agents make many of their decisions relying on others' knowledge. Our simulation results suggest that the seemingly irrational pruning heuristic leads to less cost in the social settings. That is, we have a meaningfully more social outcome in the presence of this heuristic in social contexts, and social learning helps the agents to learn better where the pruning heuristic is an obstacle in the way of finding the optimal solution in the individual setting. In sum, the synergy between the pruning behavior and social learning leads to ecological rationality.

Improving judgment accuracy by sequential adjustment.

Judging an object's value based on relevant cues can be challenging. We propose a simple method to improve judgment accuracy: Instead of estimating a value after seeing all available cues simultaneously, individuals view cues sequentially, one after another, making and adjusting their estimate at each step. The sequential procedure may alleviate computational difficulties in cue integration, leading to higher judgment accuracy. We tested this hypothesis in two real-world tasks in which participants judged either the price of diamonds or the fuel economy of cars. Two studies with professional jewelers and car salespeople show that most participants indeed judged more accurately with a sequential than with a simultaneous procedure. Another two studies with college students further support this finding and show additionally that the sequential procedure could raise the judgment accuracy of inexperienced students to the same level as that of professionals judging with the simultaneous procedure.

A Cue for Rational Reasoning: Introducing a Reference Point in Cognitive Reflection Tasks.

The dual process framework posits that we reason using the quick System 1, and the deliberate System 2, both of which are part of our "adaptive toolbox". The Cognitive Reflection Test (CRT) estimates which system was used to solve a reasoning problem. Usually, the CRT tasks are solved incorrectly by using System 1, and correctly through System 2. We have applied the reference point hypothesis to the tasks of the CRT and proposed that this change would facilitate the switch between systems, resulting in better performance on the version of the test with a reference point, compared to the CRT without one. The results confirmed our assumptions, as evidenced by a generally higher score on the CRT with a reference point, albeit with different effects between items.

Spending too little in hard times.

People's decisions to consume and save resources are critical to their wellbeing. Previous experiments find that people typically spend too much because of how they discount the future. We propose that people's motive to preserve their savings can instead cause them to spend too little in hard times. We design an economic game in which participants can store resources for the future to survive in a harsh environment. A player's income is uncertain and consumption yields diminishing returns within each day, creating tradeoffs between spending and saving. We compare participants' decisions to a heuristic that performed best in simulations. We find that participants spent too much after windfalls in income, consistent with previous research, but they also spent too little after downturns, supporting the resource preservation hypothesis. In Experiment 2, we find that by varying the income stream, the downturn effect can be isolated from the windfall effect. In Experiments 3-4, we find the same downturn effect in games with financial and political themes.

A Theory of Predictive Dissonance: Predictive Processing Presents a New Take on Cognitive Dissonance.

This article is a comparative study between predictive processing (PP, or predictive coding) and cognitive dissonance (CD) theory. The theory of CD, one of the most influential and extensively studied theories in social psychology, is shown to be highly compatible with recent developments in PP. This is particularly evident in the notion that both theories deal with strategies to reduce perceived error signals. However, reasons exist to update the theory of CD to one of "predictive dissonance." First, the hierarchical PP framework can be helpful in understanding varying nested levels of CD. If dissonance arises from a cascade of downstream and lateral predictions and consequent prediction errors, dissonance can exist at a multitude of scales, all the way up from sensory perception to higher order cognitions. This helps understand the previously problematic dichotomy between "dissonant cognitive relations" and "dissonant psychological states," which are part of the same perception-action process while still hierarchically distinct. Second, since PP is action-oriented, it can be read to support recent action-based models of CD. Third, PP can potentially help us understand the recently speculated evolutionary origins of CD. Here, the argument is that responses to CD can instill meta-learning which serves to prevent the overfitting of generative models to ephemeral local conditions. This can increase action-oriented ecological rationality and enhanced capabilities to interact with a rich landscape of affordances. The downside is that in today's world where social institutions such as science a priori separate noise from signal, some reactions to predictive dissonance might propagate ecologically unsound (underfitted, confirmation-biased) mental models such as climate denialism.