We present a model of similarity‐based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (a) structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory; (b) in retrieval, superficial similarity is more important than structural similarity; and yet (c) purely structural (analogical) remindings e sometimes experienced. Our model, MAC/FAC, explains these phenomena in terms of a two‐stage process. The first stage uses a computationally cheap, non‐structural matcher to (...) filter candidate long‐term memory items. It uses content vectors, a redundant encoding of structured representations whose dot product estimates how well the corresponding structural representations will match. The second stage uses SME (structure‐mapping engine) to compute structural matches on the handful of items found by the first stage. We show the utility of the MAC/FAC model through a series of computational experiments: (a) We demonstrate that MAC/FAC can model patterns of access found in psychological data; (b) we argue via sensitivity analyses that these simulation results rely on the theory; and (c) we compare the performance of MAC/FAC with ARCS, an alternate model of similarity‐based retrieval, and demonstrate that MAC/FAC explains the data better than ARCS. Finally, we discuss limitations and possible extensions of the model, relationships with other recent retrieval models, and place MAC/FAC in the context of other recent work on the nature of similarity. (shrink)

If connectionism is to be an adequate theory of mind, we must have a theory of representation for neural networks that allows for individual differences in weighting and architecture while preserving sameness, or at least similarity, of content. In this paper we propose a procedure for measuring sameness of content of neural representations. We argue that the correct way to compare neural representations is through analysis of the distances between neural activations, and we present a method for doing so. (...) We then use the technique to demonstrate empirically that different artificial neural networks trained by backpropagation on the same categorization task, even with different representational encodings of the input patterns and different numbers of hidden units, reach states in which representations at the hidden units are similar. We discuss how this work provides a rebuttal to Fodor and Lepore's critique of Paul Churchland's state space semantics. (shrink)

In their article on the limited duty to reinterpret genetic variants, Watts and Newson argue that clinical labs are not morally obligated to conduct routine reinterpretation despite its potential clinical and personal value.1 We endorse the authors’ argument for a circumscribed duty to reclassify genomic variants in certain cases, including to promote diagnostic equity for racial and ethnic minority populations that have been historically excluded from and exploited by genomic research and medicine. However, given the history and resilience of scientific (...) racism, the use of socially constructed racial and ethnic categories to enhance diagnostic equity raises several ethical and practical challenges. In the short term, using socially constructed racial and ethnic categories to identify patients for variant reclassification is a practical approach for addressing concerns of diagnostic equity. In contrast, diagnostic accuracy depends on patients’ genetic similarity to reference genomes, rather than their socially determined race or ethnicity.2 Thus, in the long term, securing both diagnostic equity and diagnostic accuracy through targeted variant reclassification will require a shift away from relying on race and ethnicity and towards assessing genetic similarity. Critically, the identification of patients for genomic variant reclassification using socially constructed race or ethnicity runs the risk of perpetuating the harmful …. (shrink)

If we speak of degrees of similarity, what kinds of judgment are we assuming to make sense? It will be argued that the necessary and sufficient condition for there to be degrees of similarity is that there should be a four-termed relation of comparative similarity — w resembles x at least as much as y resembles z—obeying certain constraints. Of course, nothing turns on how we use the words 'degree of similarity'. Rather, the point is to (...) distinguish the different levels of ideologicalcommitment which different kinds of judgment of similarity involve. (shrink)

abstract Applied ethics engages with concrete moral issues. This engagement involves the application of philosophical tools. When the philosophical tools used in applied ethics are problematic, conclusions about applied problems can become skewed. In this paper, I focus on problems with the idea that comparison cases must be exactly alike, except for the moral issue at hand. I argue that this idea has skewed the debate regarding the moral distinction between killing and letting die. I begin with problems that can (...) arise from attempts to produce comparison cases that are exactly alike, except for the moral issue at hand. I then argue that attempts to produce such examples are doomed to failure. Finally, I argue that abandoning concerns about similarity advances the debate regarding the moral distinction between killing and letting die. (shrink)

In this paper we ask whether approximation for abstract argumentation is useful in practice, and in particular whether reasoning with grounded semantics – which has polynomial runtime – is already an approximation approach sufficient for several practical purposes. While it is clear from theoretical results that reasoning with grounded semantics is different from, for example, skeptical reasoning with preferred semantics, we investigate how significant this difference is in actual argumentation frameworks. As it turns out, in many graphs models, reasoning with (...) grounded semantics actually approximates reasoning with other semantics almost perfectly. An algorithm for grounded reasoning is thus a conceptually simple approximation algorithm that not only does not need a learning phase – like recent approaches – but also approximates well – in practice – several decision problems associated to other semantics. (shrink)

We propose and investigate an Analogy Principle in the context of Unary Inductive Logic based on a notion of support by structural similarity which is often employed to motivate scientific conjectures.

In section I the notions of logical and inductive probability will be discussed as well as two explicanda, viz. degree of confirmation, the base for inductive probability, and degree of evidential support, Popper's favourite explicandum. In section II it will be argued that Popper's paradox of ideal evidence is no paradox at all; however, it will also be shown that Popper's way out has its own merits.

Previous work has shown that the Complex Conceptual Spaces − Single Observation Mathematical framework is a useful tool for event characterization. This mathematical framework is developed on the basis of Conceptual Spaces and uses integer linear programming to find the needed similarity values. The work of this paper is focused primarily on space event characterization. In particular, the focus is on the ranking of threats for malicious space events such as a kinetic kill. To make the Conceptual Spaces framework (...) work, the similarity values between the contents of observations on the one hand and the properties of the entities observed on the other needs to be found. This paper shows how to exploit Dempster-Shafer theory to implement a statistical approach for finding these similarities values. This approach will allow a user to identify the uncertainty involved in similarity value data, which can later be propagated through the developed mathematical model in order for the user to know the overall uncertainty in the observation-to-concept mappings needed for space event characterization. (shrink)

We agree that treating rules and similarity as dichotomous opposites is unproductive. However, describing all categorization operations as a continuum of varied similarity process obscures a multidimensional contrast. We describe two processes, instantiated rules and abstract analogy, both of which have aspects of rules and similarity, and question whether they can be compared informatively as points on a continuum.

The common law system is a legal system that values precedent, or previous court decisions, in the resolution of current cases. As the availability of legal documents in digital form has increased, it has become more difficult for legal professionals to manually identify relevant past cases due to the vast amount of data. Researchers have developed automated systems for determining the similarity between legal documents to address this issue. Our research explores various representations of a legal document and discusses (...) a novel paragraph filtering process to identify key paragraphs using legal citation information to remove unnecessary text paragraphs without disturbing the concept of the legal document. State-of-the-art techniques like TF-IDF, BERT, Legal Bert, Doc2Vec, and Legal-longformer are used for the performance analysis of the proposed approach with document comparison. It has been shown that a model trained on the proposed filtered paragraphs can achieve better results than a model trained on the complete text and can also shorten the document by over 40%. The proposed filtering strategy could be helpful for models like BERT, where the maximum token length is fixed. (shrink)

A promising approach to more refined models consistent with the Caplan & Waters hypothesis is based on similarity-based interference, a general principle that applies across working memory domains. This may explain both the fine details of syntactic working memory phenomena and the gross fractionation for which Caplan & Waters have found evidence. Detailed models of syntactic processing that embody similarity-based interference fare well cross-linguistically.

The thesis proposes an account of the means of scientific representation focused on similarity, or more specifically, on the notion of “creative similarity”. I first distinguish between two different questions regarding the problem of representation: the question about the constituents and the question about the means of representation (following Suárez 2003; van Fraassen 2008). I argue that, although similarity is not a good candidate for constituent of representation, it can satisfactorily answer the question about the means of (...) representation if adequately characterized. To motivate this position, I dispute the main arguments offered against similarity as means of representation, namely the arguments from variety, vagueness, and misrepresentation, and contend that similarity plays a central epistemic role in practices of representing in science. The study of the role of similarity in scientific practice, I argue, requires the analysis of the uses of judgments of similarity in the construction of scientific models. I examine the cases of the Mississippi Basin Model and the San Francisco Bay Model to illustrate how judgments of similarity are directly involved in the production of epistemically fruitful models. Informed by the practical investigation developed throughout the thesis, I finally outline what I call the creative similarity account of the means of representation. The notion of “creative similarity” helps to capture the way in which similarity, as means of representation, intervenes in actual practices of representing, namely, in the form of a productive interplay of judgments of similarity and distortions (i.e. idealizations, abstractions, simplifications), which is employed by resourceful agents with the aim of understanding aspects of the natural world. (shrink)

Previous studies of the lexical psycholinguistic properties in second language production have assessed the degree of an LPP dimension of an L2 corpus by computing the mean ratings of unique content words in the corpus for that dimension, without considering the possibility that learners at different proficiency levels may perceive the degree of that dimension of the same words differently. This study extended a dynamic semantic similarity algorithm to estimate the degree of five different LPP dimensions of several sub-corpora (...) of the Education First-Cambridge Open Language Database representing L2 English learners at different proficiency levels. Our findings provide initial evidence for the validity of the algorithm for assessing the LPPs in L2 production and contribute useful insights into between-proficiency relationships and cross-proficiency differences in the LPPs in L2 production as well as the relationships among different LPP dimensions. (shrink)

The target article postulates that rule-based and similarity-based categorization are best described by a unitary process. A number of recent empirical dissociations between rule-based and similarity-based categorization severely challenge this view. Collectively, these new results provide strong evidence that these two types of category learning are mediated by separate systems.

We argue that the Rules-Similarity continuum is only a useful formalism for particular, isolated tasks and must rest on the assumption that representations formed during a particular task are independent of other tasks. We show this to be an unrealistic conjecture. We additionally point out that describing categorization as selective weighing and abstracting of features misses the important step of discovering what the possible features are.