Biologists and philosophers of biology have argued that learning rules that do not lead organisms to play evolutionarily stable strategies (ESSes) in games will not be stable and thus not evolutionarily successful. This claim, however, stands at odds with the fact that learning generalization---a behavior that cannot lead to ESSes when modeled in games---is observed throughout the animal kingdom. In this paper, I use learning generalization to illustrate how previous analyses of the evolution of learning have gone wrong. It has (...) been widely argued that the function of learning generalization is to allow for swift learning about novel stimuli. I show that in evolutionary game theoretic models learning generalization, despite leading to suboptimal behavior, can indeed speed learning. I further observe that previous analyses of the evolution of learning ignored the short term success of learning rules. If one drops this assumption, I argue, it can be shown that learning generalization will be expected to evolve in these models. I also use this analysis to show how ESS methodology can be misleading, and to reject previous justifications about ESS play derived from analyses of learning. (shrink)

We present an account of semantics that is not construed as a mapping of language to the world but rather as a mapping between individual meaning spaces. The meanings of linguistic entities are established via a “meeting of minds.” The concepts in the minds of communicating individuals are modeled as convex regions in conceptual spaces. We outline a mathematical framework, based on fixpoints in continuous mappings between conceptual spaces, that can be used to model such a semantics. If concepts are (...) convex, it will in general be possible for interactors to agree on joint meaning even if they start out from different representational spaces. Language is discrete, while mental representations tend to be continuous—posing a seeming paradox. We show that the convexity assumption allows us to address this problem. Using examples, we further show that our approach helps explain the semantic processes involved in the composition of expressions. (shrink)

Vague predicates, those that exhibit borderline cases, pose a persistent problem for philosophers and logicians. Although they are ubiquitous in natural language, when used in a logical context, vague predicates lead to contradiction. This paper will address a question that is intimately related to this problem. Given their inherent imprecision, why do vague predicates arise in the first place? I discuss a variation of the signaling game where the state space is treated as contiguous, i.e., endowed with a metric that (...) captures a similarity relation over states. This added structure is manifested in payoffs that reward approximate coordination between sender and receiver as well as perfect coordination. I evolve these games using a variation of Herrnstein reinforcement learning that better reflects the generalizing learning strategies real-world actors use in situations where states of the world are similar. In these simulations, signaling can develop very quickly, and the signals are vague in much the way ordinary language predicates are vague—they each exclusively apply to certain items, but for some transition period both signals apply to varying degrees. Moreover, I show that under certain parameter values, in particular when state spaces are large and time is limited, learning generalization of this sort yields strategies with higher payoffs than standard Herrnstein reinforcement learning. These models may then help explain why the phenomenon of vagueness arises in natural language: the learning strategies that allow actors to quickly and effectively develop signaling conventions in contiguous state spaces make it unavoidable. (shrink)

Conceptual spaces are a frequently applied framework for representing concepts. One of its central aims is to find criteria for what makes a concept natural. A prominent demand is that natural concepts cover convex regions in conceptual spaces. The first aim of this paper is to analyse the convexity thesis and the arguments that have been advanced in its favour or against it. Based on this, I argue that most supporting arguments focus on single-domain concepts (e.g., colours, smells, shapes). Unfortunately, (...) these concepts are not the primary examples of natural concepts. Building on this observation, the second aim of the paper is to develop criteria for natural multi-domain concepts. The representation of such concepts has two main aspects: features that are associated with the concept and the probabilistic correlation pattern which the concept captures. Conceptual spaces, together with probabilistic considerations, provide a helpful framework to approach these aspects. With respect to feature representation, the existence of characteristic features (i.e., that apples have a specific taste) is essential. Moreover, natural concepts capture peaks of a probabilistic distribution over complex spaces. They carve up nature at its joints, that is, at areas with no or low probabilistic density. This last aspect is shown to be closely related to the convexity demand. (shrink)

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. (shrink)

Conceptual spaces have become an increasingly popular modeling tool in cognitive psychology. The core idea of the conceptual spaces approach is that concepts can be represented as regions in similarity spaces. While it is generally acknowledged that not every region in such a space represents a natural concept, it is still an open question what distinguishes those regions that represent natural concepts from those that do not. The central claim of this paper is that natural concepts are represented by the (...) cells of an optimally designed similarity space. (shrink)

In a recent article, Carlos Santana shows that in common interest signaling games when signals are costly and when receivers can observe contextual environmental cues, ambiguous signaling strategies outperform precise ones and can, as a result, evolve. I show that if one assumes a realistic structure on the state space of a common interest signaling game, ambiguous strategies can be explained without appeal to contextual cues. I conclude by arguing that there are multiple types of cases of payoff-beneficial ambiguity, some (...) of which are better explained by Santana’s models and some of which are better explained by models presented here. (shrink)

This article uses sim-max games to model perceptual categorization with the goal of answering the following question: To what degree should we expect the perceptual categories of biological actors to track properties of the world around them? I argue that an analysis of these games suggests that the relationship between real-world structure and evolved perceptual categories is mediated by successful action in the sense that organisms evolve to categorize together states of nature for which similar actions lead to similar results. (...) This conclusion indicates that both strongly realist and strongly antirealist views about perceptual categories are too simple. (shrink)

The overreliance on verbal models and theories in psychology has been criticized for hindering the development of reliable research programs (Harris, 1976; Yarkoni, 2020). We demonstrate how the conceptual space framework can be used to formalize verbal theories and improve their precision and testability. In the framework, scientific concepts are represented by means of geometric objects. As a case study, we present a formalization of an existing three-dimensional theory of emotion which was developed with a spatial metaphor in mind. Wundt (...) posits that the range of human emotion can be represented along three axes of basic emotions (pleasure/displeasure, excitement/inhibition, tension/relaxation), just as color vision can be represented using three basic colors. We use dimensions to represent basic emotions, points to represent emotional states, and regions to represent broader emotional concepts. We then compare our formalization to an existing structuralist formalization of Wundt’s theory. Further, we discuss the empirical predictions that our formalization generates, such as comparisons of similarity and intensity. We conclude by demonstrating how the tools developed in the conceptual space framework can be used to formulate a theory of emotion based on empirical observation. (shrink)

Traditional explanations for the presence of ambiguous words in natural language have focused on the cost of added complexity that would accompany unambiguous languages. In these theories, ambiguity arises because it represents the optimal trade-off between the informational benefits from precision and the costs for rich languages. In this paper, we suggest that ambiguity remains an inevitable feature of learning languages even without complexity costs. We show that ambiguous words occur more frequently and will therefore be learned more readily, thus (...) triggering more semantic activations between senses of the ambiguous word. We illustrate this through a game-theoretical example. (shrink)

According to orthodox representationalism, perceptual states have constitutive veridicality or accuracy conditions. In defense of this view, several philosophers—most notably Tyler Burge—employ a realist strategy that turns on the purported explanatory ineliminability of representational posits in perceptual science. I argue that Burge’s version of the realist strategy fails as a defense of orthodox representationalism. However, it may vindicate a different kind of representationalism.

Vague predicates, those that exhibit borderline cases, pose a persistent problem for philosophers and logicians. Although they are ubiquitous in natural language, when used in a logical context, vague predicates lead to contradiction. This paper will address a question that is intimately related to this problem. Given their inherent imprecision, why do vague predicates arise in the first place? I discuss a variation of the signaling game where the state space is treated as contiguous, i.e., endowed with a metric that (...) captures a similarity relation over states. This added structure is manifested in payoffs that reward approximate coordination between sender and receiver as well as perfect coordination. I evolve these games using a variation of Herrnstein reinforcement learning that better reflects the generalizing learning strategies real-world actors use in situations where states of the world are similar. In these simulations, signaling can develop very quickly, and the signals are vague in much the way ordinary language predicates are vague—they each exclusively apply to certain items, but for some transition period both signals apply to varying degrees. Moreover, I show that under certain parameter values, in particular when state spaces are large and time is limited, learning generalization of this sort yields strategies with higher payoffs than standard Herrnstein reinforcement learning. These models may then help explain why the phenomenon of vagueness arises in natural language: the learning strategies that allow actors to quickly and effectively develop signaling conventions in contiguous state spaces make it unavoidable. (shrink)

Recently, game theory and evolutionary game theory - mathematical frameworks from economics and biology designed to model and explain interactive behavior - have proved fruitful tools for philosophers in areas such as ethics, philosophy of language, social epistemology, and political philosophy. This methodological osmosis is part of a trend where philosophers have blurred disciplinary lines to import the best epistemic tools available. In this vein, experimental philosophers have drawn on practices from the social sciences, and especially from psychology, to expand (...) philosophy's grasp on issues from morality to consciousness. We argue that the recent prevalence of formal work on human interaction in philosophy opens the door for new methods in experimental philosophy. In particular, we discuss methods from experimental economics, focusing on a small literature we have been developing investigating signaling and communication in humans. We describe results from a novel experiment showing how environmental structure can shape signaling behavior. (shrink)

In response to those who argue for ‘property cluster’ views of natural kinds, I use evolutionary models of sim-max games to assess the claim that linguistic terms will appropriately track sets of objects that cluster in property spaces. As I show, there are two sorts of ways this can fail to happen. First, evolved terms that do respect property structure in some senses can be conventional nonetheless. Second, and more crucially, because the function of linguistic terms is to facilitate successful (...) action in the world, when such success is based on something other than property clusters, we should not expect our terms to track those clusters. The models help make this second point salient by highlighting a dubious assumption underlying some versions of the cluster kinds view—that property clusters lead to successful generalization and induction in a straightforward way. As I point out, those who support property cluster kinds as natural can revert to a promiscuous realism in response to these observations. (shrink)

Signalling games are popular models for studying the evolution of meaning, but typical approaches do not incorporate vagueness as a feature of successful signalling. Complementing recent like-minded models, we describe an aggregate population-level dynamic that describes a process of imitation of successful behaviour under imprecise perception and realization of similar stimuli. Applying this new dynamic to a generalization of Lewis’s signalling games, we show that stochastic imprecision leads to vague, yet by-and-large efficient signal use, and, moreover, that it unifies evolutionary (...) outcomes and helps avoid sub-optimal categorization. The upshot of this is that we see ‘as-if’-generalization at an aggregate level, without agents actually generalizing. _1_ Introduction _2_ Background _2.1_ Sim-max games and conceptual spaces _2.2_ Vagueness in sim-max games and conceptual spaces _2.3_ Vagueness, functional pressure, and transmission biases _3_ Imprecise Imitation _3.1_ Replicator dynamic in behavioural strategies _3.2_ Noise-perturbed conditional imitation _4_ Exploring Imprecise Imitation _4.1_ Setting the stage _4.2_ Simulation set-up _4.3_ Measures of interest _4.4_ Results _5_ Discussion _5.1_ Levels of vagueness _5.2_ Evolutionary benefits of imprecision _5.3_ Related work _6_ Conclusion Appendix. (shrink)

According to a recent proposal, natural concepts are represented in an optimally designed similarity space, adhering to principles a skilled engineer would use for creatures with our perceptual and cognitive capacities. One key principle is that natural concepts should be easily learnable. While evidence exists for parts of this optimal design proposal, there has been no direct evidence linking naturalness to learning until now. This article presents results from a computational study on perceptual color space, demonstrating that naturalness indeed facilitates (...) learning. (shrink)

This thesis studies the role of conceptual content in inference and reasoning. The first two chapters offer a theoretical and historical overview of the relation between inference and meaning in philosophy and psychology. In particular, a critical analysis of the formality thesis, i.e., the idea that rational inference is a rule-based and topic-neutral mechanism, is advanced. The origins of this idea in logic and its influence in philosophy and cognitive psychology are discussed. Chapter 3 consists of an analysis of the (...) relationship between inference and representation. It is argued that inference has to be studied from a pluralistic per- spective due to its dependence on different formats of representing information. The following four chapters apply conceptual spaces, a formal theory of concepts within cognitive semantics, to three concept-based inference-types. First, an explication of Sellars notion of material inference is advanced. Later, the model is extended to account for nonmonotonic inference by studying the role expectations in reasoning. Finally, a conceptual space-model of category-based induction is presented. This model predicts most of the empirical properties of this psychological phenomenon and subsumes some of the previous theories in psychology. It is stated that the explanatory fruitfulness of this new approach is evidence for the failure of the formality thesis and calls for a unified model of rational inference that puts semantics at center stage. The last chapter of the thesis discusses how inference and concepts interact in scientific reasoning, which makes constant use of hybrid symbolic structures for representing conceptual information. Stephen Toulmin’s notions of method of representation and inferential technique are developed and applied in a case study about the emergence of the notion of instantaneous speed during the passage from geometrical physics to analytical mechanics. It is claimed that this analysis provides support to the pluralistic perspective for theorizing about reasoning. (shrink)

In response to those who argue for ‘property cluster’ views of natural kinds, I use evolutionary models of similarity-maximizing games to assess the claim that linguistic terms appropriately track sets of objects that cluster in property spaces. As I show, there are two sorts of ways this can fail to happen. First, evolved terms that do respect property structure in some senses can be conventional nonetheless. Second, and more crucially, because the function of linguistic terms is to facilitate successful action (...) in the world, when such success is based on something other than property clusters, we should not expect our terms to track those clusters. The models help make this second point salient by highlighting a dubious assumption underlying some versions of the cluster kinds view—that property clusters lead to successful generalization and induction in a straightforward way. As I point out, those who support property cluster kinds as natural can revert to a promiscuous realism in response to these observations. 1 Introduction2Cluster Kinds3Models and Results 3.1Similarity-maximizing games3.2Voronoi languages, clusters, and categories4Conventionality and Functionality in Kind Terms 4.1Conventional categories4.2Functionality and categories5Unnatural Perceptual Categories6The Payoff Relevance of Property Space7Conclusion. (shrink)

The notion of conceptual space, proposed by Gärdenfors as a framework for the representation of concepts and knowledge, has been highly influential over the last decade or so. One of the main theses involved in this approach is that the conceptual regions associated with properties, concepts, verbs, etc. are convex. The aim of this paper is to show that such a constraint—that of the convexity of the geometry of conceptual regions—is problematic; both from a theoretical perspective and with regard to (...) the inner workings of the theory itself. On the one hand, all the arguments provided in favor of the convexity of conceptual regions rest on controversial assumptions. Additionally, his argument in support of a Euclidean metric, based on the integral character of conceptual dimensions, is weak, and under non-Euclidean metrics the structure of regions may be non-convex. Furthermore, even if the metric were Euclidean, the convexity constraint could be not satisfied if concepts were differentially weighted. On the other hand, Gärdenfors’ convexity constraint is brought into question by the own inner workings of conceptual spaces because: some of the allegedly convex properties of concepts are not convex; the conceptual regions resulting from the combination of convex properties can be non-convex; convex regions may co-vary in non-convex ways; and his definition of verbs is incompatible with a definition of properties in terms of convex regions. Therefore, the mandatory character of the convexity requirement for regions in a conceptual space theory should be reconsidered. (shrink)

This article takes a cognitive approach to natural concepts. The aim is to introduce criteria that are evaluated with respect to how they support the cognitive economy of humans when using concepts in reasoning and communicating with them. I first present the theory of conceptual spaces as a tool for expressing the criteria. Then I introduce the central idea that natural concepts correspond to _convex_ regions of a conceptual space. I argue that this criterion has far-reaching consequences as regards natural (...) concepts. Partly following earlier work, I present some other criteria that further delimit the class of natural concepts. One of these is coherence, which does not seem to have been discussed previously. Finally, I show that convexity and other criteria make it possible to ensure that people mean the same thing when they communicate using concepts. Apart from its philosophical interest, the analysis presented in the article will be relevant for tasks of conceptual engineering in artificial systems that work with concepts. (shrink)

In Lewisean signaling games with common interests, perfect signaling strategies have been shown to be optimal in terms of communicative success and evolutionary fitness. However, in signaling game models that involve contextual cues, ambiguous signaling strategies can match up to or even outperform perfect signaling. For a minimalist example of such a context signaling game, I will show that three strategy types are expected to emerge under evolutionary dynamics: perfect signaling, partial ambiguity and full ambiguity. Moreover, I will show that (...) partial ambiguity strategies are the most expected outcome and have the greatest basin of attraction among these three types when sender and receiver costs are arbitrarily small or similar. I will demonstrate that the evolutionary success of partial ambiguity is due to being risk dominant, which points to a better compatibility with other strategy types. (shrink)

It is a fact that human subjects rate sentences about typical properties such as “Ravens are black” as very likely to be true. In comparison, modified sentences such as “Feathered ravens are black” receive lower ratings, especially if the modifier is atypical for the noun, as in “Jungle ravens are black”. This is called themodifier effect. However, the likelihood of the unmodified statement influences the perceived likelihood of the modified statement: the higher the rated likelihood of the unmodified sentence, the (...) higher the rated likelihood of the modified one. That means the modifier effect does not fully blockdefault inheritanceof typical properties from nouns to modified nouns. This paper discusses this inheritance effect. In particular, I ask whether it is the direct result of composing concepts from nouns, that is, a bias toward “black” when processing “raven”. I report a series of experiments in which I find no evidence for a direct inheritance from composition. This supports the view that default inheritance is rather an inference than a bias. (shrink)

We give derivations of two formal models of Gricean Quantity implicature and strong exhaustivity in bidirectional optimality theory and in a signalling games framework. We show that, under a unifying model based on signalling games, these interpretative strategies are game-theoretic equilibria when the speaker is known to be respectively minimally and maximally expert in the matter at hand. That is, in this framework the optimal strategy for communication depends on the degree of knowledge the speaker is known to have concerning (...) the question she is answering. In addition, and most importantly, we give a game-theoretic characterisation of the interpretation rule Grice (formalising Quantity implicature), showing that under natural conditions this interpretation rule occurs in the unique equilibrium play of the signalling game. (shrink)

This paper uses sim-max games to model perceptual categorization with the goal of answering the following question: to what degree should we expect the perceptual categories of biological actors to track properties of the world around them? I will argue that an analysis of these games suggests that the relationship between real-world structure and evolved perceptual categories is mediated by successful action in the sense that organisms evolve to categorize together states of nature for which similar actions lead to similar (...) results. This conclusion indicates that both strongly realist and strongly anti-realist views about perceptual categories are too simple. (shrink)

This article concerns how rule-following behavior might evolve, how an old evolved rule might come to be appropriated to a new context, and how simple concepts might coevolve with rule-following behavior. In particular, we consider how the transitive inferential rule-following behavior exhibited by pinyon and scrub jays might evolve in the context of a variety of the Skyrms-Lewis signaling game, then how such a rule might come to be appropriated to carry out inferences regarding stimuli different from those involved in (...) the original evolution of the rule, and how this appropriation involves a step toward the evolution of basic ordinal concepts. (shrink)

The present paper introduces a simple framework for modeling the relationship between environmental states, perceptual states, and action. The framework represents situations where an agent’s perceptual state forms the basis for choosing an action, and what action the agent performs determines the agent’s payoff, as a function of the environmental conditions in which the action is performed. The framework is used as the basis for a simulation study of the sorts of correspondence between perceptual and environmental states that are important (...) for successful navigation of the world. Some of the results are surprising and conflict with long held views about the kind of perception-to-environment correspondence that is important for knowledge of the world. The results also raise doubts concerning the view that our perceptual states provide a basis for knowledge of the real structure of the external world. (shrink)

We give derivations of two formal models of Gricean Quantity implicature and strong exhaustivity in bidirectional optimality theory and in a signalling games framework. We show that, under a unifying model based on signalling games, these interpretative strategies are game-theoretic equilibria when the speaker is known to be respectively minimally and maximally expert in the matter at hand. That is, in this framework the optimal strategy for communication depends on the degree of knowledge the speaker is known to have concerning (...) the question she is answering. In addition, and most importantly, we give a game-theoretic characterisation of the interpretation rule Grice (formalising Quantity implicature), showing that under natural conditions this interpretation rule occurs in the unique equilibrium play of the signalling game. (shrink)

We argue that concepts from the social sciences can be as natural as those from physics and chemistry, thereby answering in the positive the question of whether social metaphysics is or can be substantive. The argument takes as a starting point Douven & Gärdenfors’ (Mind & Language, 35, 313–334 2020) optimality account of natural concepts, according to which natural concepts are represented by the cells of an optimally partitioned similarity space. While the account applies straightforwardly to perceptual concepts, it does (...) not apply as straightforwardly to more abstract concepts, such as many scientific concepts. We propose an extension of Douven and Gärdenfors’ optimality account that pertains to scientific concepts as well. The proposal reuses ideas from Lewis’ Best Systems Account of laws of nature, but rather than invoking natural concepts (as Lewis does) defines natural scientific concepts as those that figure in the or a best systematization of the totality of facts. On this proposal, there is nothing especially different between concepts from the natural sciences and those from the social sciences. (shrink)

Game theory is a formal approach to behavior that focuses on the strategic aspect of situations. The game theoretic approach originates in economics but has been embraced by scholars across disciplines, including many philosophers and biologists. This approach has an important weakness: the strategic aspect of a situation, which is its defining quality in game theory, is often not its most salient quality in human cognition. Evidence from a wide range of experiments highlights this shortcoming. Previous theoretical and empirical work (...) has sought to address this weakness by considering learning across an ensemble of multiple games simultaneously. Here we extend this framework, incorporating artificial neural networks, to allow for an investigation of the interaction between the perceptual and functional similarity of the games composing the larger ensemble. Using this framework, we conduct a theoretical investigation of a population that encounters both stag hunts and prisoner’s dilemmas, two situations that are strategically different but which may or may not be perceptually similar. (shrink)

Game theory is a formal approach to behavior that focuses on the strategic aspect of situations. The game theoretic approach originates in economics but has been embraced by scholars across disciplines, including many philosophers and biologists. This approach has an important weakness: the strategic aspect of a situation, which is its defining quality in game theory, is often not its most salient quality in human cognition. Evidence from a wide range of experiments highlights this shortcoming. Previous theoretical and empirical work (...) has sought to address this weakness by considering learning across an ensemble of multiple games simultaneously. Here we extend this framework, incorporating artificial neural networks, to allow for an investigation of the interaction between the perceptual and functional similarity of the games composing the larger ensemble. Using this framework, we conduct a theoretical investigation of a population that encounters both stag hunts and prisoner’s dilemmas, two situations that are strategically different but which may or may not be perceptually similar. (shrink)