Quantum probability theory (QP) is the best formal representation available of the most common form of judgment involving attribute comparison (inside judgment). People are capable, however, of judgments that involve proportions over sets of instances (outside judgment). Here, the theory does not do so well. I discuss the theory both in terms of descriptive adequacy and normative appropriateness.

This paper presents a new response to the colour similarity argument, an argument that many people take to pose the greatest threat to colour physicalism. The colour similarity argument assumes that if colour physicalism is true, then colour similarities should be scrutable under standard physical descriptions of surface reflectance properties such as their spectral reflectance curves. Given this assumption, our evident failure to find such similarities at the reducing level seemingly proves fatal to colour physicalism. I argue that we should (...) dispense with this assumption, and thus endorse the inscrutability of colour similarity. This strategy is inspired by parallels between the colour similarity argument and the explanatory gap between mind and body made vivid by Jackson’s (1986) knowledge argument, and in particular by type-B physicalist responses to that argument. This inscrutability response is further motivated by cases in chemistry and biochemistry in which analogous scrutability theses fail to hold. Along the way, I present a challenge to standard formulations of the colour similarity argument based on the extreme context sensitivity of the similarity relation. Most presentations of the argument fail to control for such contextual variation, which raises the distinct possibility that the argument equivocates on the similarity relation across its premises. Although ultimately inconclusive, this context challenge forces a significant reformulation of the colour similarity argument, and highlights the need for much greater care in handling claims about colour similarity. (shrink)

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, (...) adapt their judgment strategies from heuristic‐based to instance‐based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT's learning mechanisms have been implemented in an ACT‐R cognitive model. Through a series of experiments, this paper shows how the IBLT's learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments. (shrink)

According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation (...) and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction. Key Words: concept learning; conceptual development; features; perceptual learning; stimulus encoding. (shrink)

The distinction between rules and similarity is central to our understanding of much of cognitive psychology. Two aspects of existing research have motivated the present work. First, in different cognitive psychology areas we typically see different conceptions of rules and similarity; for example, rules in language appear to be of a different kind compared to rules in categorization. Second, rules processes are typically modeled as separate from similarity ones; for example, in a learning experiment, rules and similarity influences would be (...) described on the basis of separate models. In the present article, I assume that the rules versus similarity distinction can be understood in the same way in learning, reasoning, categorization, and language, and that a unified model for rules and similarity is appropriate. A rules process is considered to be a similarity one where only a single or a small subset of an object's properties are involved. Hence, rules and overall similarity operations are extremes in a single continuum of similarity operations. It is argued that this viewpoint allows adequate coverage of theory and empirical findings in learning, reasoning, categorization, and language, and also a reassessment of the objectives in research on rules versus similarity. Key Words: categorization; cognitive explanation; language; learning; reasoning; rules; similarity. (shrink)

Applying machine learning algorithms to automatically infer relationships between concepts from large-scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments (“How similar are cats and bears?”), and how these judgments depend on the features that describe concepts (e.g., size, furriness). However, efforts to date have exhibited a substantial discrepancy between algorithm predictions and human empirical judgments. Here, we introduce a novel approach to generating (...) embeddings for this purpose motivated by the idea that semantic context plays a critical role in human judgment. We leverage this idea by constraining the topic or domain from which documents used for generating embeddings are drawn (e.g., referring to the natural world vs. transportation apparatus). Specifically, we trained state-of-the-art machine learning algorithms using contextually-constrained text corpora (domain-specific subsets of Wikipedia articles, 50+ million words each) and showed that this procedure greatly improved predictions of empirical similarity judgments and feature ratings of contextually relevant concepts. Furthermore, we describe a novel, computationally tractable method for improving predictions of contextually-unconstrained embedding models based on dimensionality reduction of their internal representation to a small number of contextually relevant semantic features. By improving the correspondence between predictions derived automatically by machine learning methods using vast amounts of data and more limited, but direct empirical measurements of human judgments, our approach may help leverage the availability of online corpora to better understand the structure of human semantic representations and how people make judgments based on those. (shrink)

We argue that analogical reasoning, particularly Gentner's structure-mapping theory, provides an integrative theoretical framework through which we can better understand the development of symbol use. Analogical reasoning can contribute both to the understanding of others’ intentions and the establishment of correspondences between symbols and their referents, two crucial components of symbolic understanding. We review relevant research on the development of symbolic representations, intentionality, comparison, and similarity, and demonstrate how structure-mapping theory can shed light on several ostensibly disparate findings in the (...) literature. Focusing on visual symbols, we argue that analogy underlies and supports the understanding of both intention and correspondence, which may enter into a reciprocal bootstrapping process that leads children to gain the prodigious human capacity of symbol use. (shrink)

Two experiments investigated whether infants represent goal‐directed actions of others in a way that allows them to draw inferences to unobserved states of affairs (such as unseen goal states or occluded obstacles). We measured looking times to assess violation of infants' expectations upon perceiving either a change in the actions of computer‐animated figures or in the context of such actions. The first experiment tested whether infants would attribute a goal to an action that they had not seen completed. The second (...) experiment tested whether infants would infer from an observed action the presence of an occluded object that functions as an obstacle. The looking time patterns of 12‐month‐olds indicated that they were able to make both types of inferences, while 9‐month‐olds failed in both tasks. These results demonstrate that, by the end of the first year of life, infants use the principle of rational action not only for the interpretation and prediction of goal‐directed actions, but also for making productive inferences about unseen aspects of their context. We discuss the underlying mechanisms that may be involved in the developmental change from 9 to 12 months of age in the ability to infer hypothetical (unseen) states of affairs in teleological action representations. (shrink)

This article examines the role of similarity in the hybridization of concepts, focusing on hybrid products as an applied test case. Hybrid concepts found in natural language, such as singer songwriter, typically combine similar concepts, whereas dissimilar concepts rarely form hybrids. The hybridization of dissimilar concepts in products such as jogging shoe mp3 player and refrigerator TV thus poses a challenge for understanding the process of conceptual combination. It is proposed that models of conceptual combination can throw light on the (...) judged future success and desirability of hybrid products in general. The composite prototype model proposes two stages of conceptual combination. In the first stage, the concepts are aggregated into an additive hybrid, simply by forming the union of the two sets of attributes. In the second stage, any conflicting attributes are identified and resolved, often with the introduction of emergent attributes, resulting in an integrative hybrid. Across four studies that varied the similarity and type of hybrid products, similar and integrative hybrids were valued more than dissimilar and additive hybrids. Critically, though, dissimilar hybrids were also highly valued if they were integrative. Results supported the two stages proposed by the composite prototype model, and implications for other models of hybrid formation are discussed. (shrink)

Studies exploring how students learn and understand science processes such as diffusion and natural selection typically find that students provide misconceived explanations of how the patterns of such processes arise (such as why giraffes’ necks get longer over generations, or how ink dropped into water appears to “flow”). Instead of explaining the patterns of these processes as emerging from the collective interactions of all the agents (e.g., both the water and the ink molecules), students often explain the pattern as being (...) caused by controlling agents with intentional goals, as well as express a variety of many other misconceived notions. In this article, we provide a hypothesis for what constitutes a misconceived explanation; why misconceived explanations are so prevalent, robust, and resistant to instruction; and offer one approach of how they may be overcome. In particular, we hypothesize that students misunderstand many science processes because they rely on a generalized version of narrative schemas and scripts (referred to here as a Direct-causal Schema) to interpret them. For science processes that are sequential and stage-like, such as cycles of moon, circulation of blood, stages of mitosis, and photosynthesis, a Direct-causal Schema is adequate for correct understanding. However, for science processes that are non-sequential (or emergent), such as diffusion, natural selection, osmosis, and heat flow, using a Direct Schema to understand these processes will lead to robust misconceptions. Instead, a different type of general schema may be required to interpret non-sequential processes, which we refer to as an Emergent-causal Schema. We propose that students lack this Emergent Schema and teaching it to them may help them learn and understand emergent kinds of science processes such as diffusion. Our study found that directly teaching students this Emergent Schema led to increased learning of the process of diffusion. This article presents a fine-grained characterization of each type of Schema, our instructional intervention, the successes we have achieved, and the lessons we have learned. (shrink)

In the present dissertation, I propose a general, robust, and objective characteristic of the information environment, according to which negative information is more diverse than positive information. I present an explanatory framework for this phenomenon based on the non-extremity of positive qualities. Specifically, most attribute dimensions host one “positive” range which is surrounded by two distinct “negative” ranges, resulting in a greater diversity of negative compared to positive attributes, stimuli, and information in general. Chapter 1 of my dissertation reviews evidence (...) for the proposed greater diversity of negative information, showing that positive stimuli are consistently perceived as more similar than negative stimuli. Chapter 2 presents a line of research that demonstrates the same phenomenon in the domain of person perception. People perceive other people they like as more similar to one another than other people they dislike. I further suggest that the greater diversity of negative information as an “extrapsychic” phenomenon can account for a number of valence asymmetries in information processing that have previously been explained based on “intrapsychic” affective reactions. In line with this idea, Chapter 3 presents research showing that negative words enjoy a recognition advantage over positive words because they are more diverse. Chapter 4 discusses limitations, open questions and further implications of the current empirical findings and of the explanatory framework provided. (shrink)

We advance a theory of inductive reasoning based on similarity, and test it on arguments involving mammal categories. To measure similarity, we quantified the overlap of neural activation in left Brodmann area 19 and the left ventral temporal cortex in response to pictures of different categories; the choice of of these regions is motivated by previous literature. The theory was tested against probability judgments for 40 arguments generated from 9 mammal categories and a common predicate. The results are interpreted in (...) the context of Hume’s thesis relating similarity to inductive inference. (shrink)

“In reality, all arguments from experience are founded on the similarity which we discover among natural objects, and by which we are induced to expect effects similar to those which we have found to follow from such objects. ... From causes which appear similar we expect similar effects.”.

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)

A growing consensus in the philosophy and psychology of concepts is that while theories such as the prototype, exemplar, and theory theories successfully account for some instances of concept formation and application, none of them successfully accounts for all such instances. I argue against this ‘new consensus’ and show that the problem is, in fact, more severe: the explanatory force of each of these theories is limited even with respect to the phenomena often cited to support it, as each fails (...) to satisfy an important explanatory desideratum with respect to these phenomena. I argue that these explanatory shortcomings arise from a shared assumption on the part of these theories, namely, they take similarity judgements and application of causal knowledge to be discrete elements in a theory of concepts. I further propose that the same assumption carries over into alternative theories offered by proponents of the new consensus: pluralism, eliminativism, and hybrid theories. I put forth a sketch of an integrated model of concept formation and application, which rejects this shared assumption and satisfies the explanatory desiderata I discuss. I suggest that this model undermines the motivation for hybrid, pluralist, and eliminativist accounts of concepts. _1_ Introduction _2_ The Similarity-Based Approach and the Importance of Theory _2.1_ The similarity-based approach _2.2_ The selection desideratum _2.3_ Causal knowledge as satisfying the selection desideratum _3_ The Theory-Based Approach and the Importance of Similarity _3.1_ The theory-based approach _3.2_ The range desideratum _3.3_ Similarity as satisfying the range desideratum _4_ An Integrated Approach to Concepts _4.1_ An integrated model _4.2_ The integrated theory versus hybrid theories of concepts _5_ Conclusion. (shrink)

Measuring perceived similarity is an important issue in visual perception of faces, since a measure of the perceived similarity between faces may be used to investigate fundamental tasks like face categorization and recognition. Despite its fundamental role, measuring perceived similarity between faces is not trivial from both a theoretical and methodological point of view. In this paper we present theoretical arguments that undermine the method currently most used to measure perceived similarity between faces in visual perception, and we propose an (...) alternative method. We finally compare the two methods and find some empirical evidence that the proposed method can provide a more reliable evaluation of the perceived similarity between faces. (shrink)

Similarity is central for the definition of concepts in several theories in cognitive psychology. However, similarity encounters several problems which were emphasized by Goodman in 1972. At the end of his article, Goodman banishes similarity from any serious philosophical or scientific investigations. If Goodman is right, theories of concepts based on similarity encounter a huge problem and should be revised entirely. In this paper, we would like to analyze the notion of similarity with some insight from psychological works on analogical (...) cognition. Analogical cognition compares two situations or objects in order to find similarities between them. In doing so, the analogical process sorts the different features of the two situations or objects and determines the most important ones. The analogical process is also highly sensitive to context. Context-sensitivity is desirable at some level, but it is also problematic as it leads to a computational explosion. To answer this problem, we would like to consider salience as a possible heuristic in the analogical process. We will distinguish three forms of salience: Sensory, categorical, and operational. By taking salience into account, we can introduce a shortcut into the computation of similarity and circumvent computational explosion. (shrink)

This paper argues for the theoretical and practical validity of similarity as a useful epistemological tool in scientific knowledge generation, specifically in chemistry. Classical analyses of similarity in philosophy of science do not account for the concept’s practical significance in scientific activities. We recur to examples from chemistry to counter the claim of authors like Quine or Goodman to the effect that similarity must be excluded from scientific practices . In conclusion we argue that more recent conceptualizations of the notion (...) of similarity, particularly Giere’s one, are appropriate for a philosophical analysis that considers scientific practices on equal terms with scientific theory. (shrink)

In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the “here-and-now.” Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as ahierarchy, ranging from the concrete to the abstract; however, we argue that (...) there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged,diversity.Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into:modality-specificrepresentations, instantiated on perceptual similarity;multimodalrepresentations, instantiated primarily on the discovery of spatiotemporal contiguity; andcategoricalrepresentations, instantiated primarily on social interaction. These elements serve as the building blocks ofcomplex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e.,predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required to account for humans’ predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology. (shrink)

Political discourse is saturated with metaphor, but evidence for the persuasive power of this language has been hard to come by. We addressed this issue by investigating whether voting intentions were affected by implicit mappings suggested by a metaphorically framed message, drawing on a real-world example of political rhetoric about the federal budget. In the first experiment, the federal budget was framed as similar to or different from a household budget, though the information participants received was identical in both conditions. (...) When the federal budget was described as similar to a household's, people considered the personal finances of a presidential candidate more relevant—a finding we replicated in a larger, pre-registered study. In a follow-up experiment, we presented participants with a more explicit rhetorical argument and found a similar effect, moderated by political affiliation. These studies illuminate how metaphorical comparison affects cognition for important real-world issues, sometimes in unintended ways. (shrink)

The standard approach to the so-called paradoxes of identity has been to argue that these paradoxes do not essentially concern the notion of identity but rather betray misconceptions on our part regarding other metaphysical notions, like that of an object or a property. This paper proposes a different approach by pointing to an ambiguity in the identity predicate and arguing that the concept of identity that figures in many ordinary identity claims, including those that appear in the paradoxes, is not (...) the traditional philosophical concept but one that can be defined in terms of relevant similarity. This approach to the paradoxes will be argued to be superior to the standard one. (shrink)

Similarity is one of the most important relations humans perceive, arguably subserving category learning and categorization, generalization and discrimination, judgment and decision making, and other cognitive functions. Researchers have proposed a wide range of representations and metrics that could be at play in similarity judgment, yet have not comprehensively compared the power of these representations and metrics for predicting similarity within and across different semantic categories. We performed such a comparison by pairing nine prominent vector semantic representations with seven established (...) similarity metrics that could operate on these representations, as well as supervised methods for dimensional weighting in the similarity function. This approach yields a factorial model structure with 126 distinct representation‐metric pairs, which we tested on a novel dataset of similarity judgments between pairs of cohyponymic words in eight categories. We found that cosine similarity and Pearson correlation were the overall best performing unweighted similarity functions, and that word vectors derived from free association norms often outperformed word vectors derived from text (including those specialized for similarity). Importantly, models that used human similarity judgments to learn category‐specific weights on dimensions yielded substantially better predictions than all unweighted approaches across all types of similarity functions and representations, although dimension weights did not generalize well across semantic categories, suggesting strong category context effects in similarity judgment. We discuss implications of these results for cognitive modeling and natural language processing, as well as for theories of the representations and metrics involved in similarity. (shrink)

Similarity‐based theories of concepts have a broad intuitive appeal and have been successful in accounting for various phenomena related to the formation and application of concepts. Their adequacy as theories of concepts has been questioned, however, as similarity is often taken as too flexible, too unconstrained, to be explanatory of categorization. In this article, I propose an account of similarity that takes the “foil” against which the target items are measured as integral to the process of comparison, making the similarity (...) relation a fundamentally triadic one. I argue that this account delivers more internal constraints on the process of comparison than the traditional, dyadic account of similarity does. Finally, I propose that this account is advantageous for a similarity‐based theory of concepts, as it easily integrates with the notion of conceptual taxonomy and uses it to deliver additional constraints on categorization. On such a theory of concepts, the foil, whose variability is in large part responsible for the flexible nature of similarity judgements, is turned within the framework of conceptual taxonomy to the very element that constrains similarity judgements in categorization. Therefore, this notion of similarity mitigates the worry that similarity is too flexible to be explanatory of categorization. (shrink)