The emergence of kind-based object individuation in infancy

Four experiments investigated whether 12-month-old infants use perceptual property information in a complex object individuation task, using the violation-of-expectancy looking time method (Xu, 2002; Xu & Carey, 1996). Infants were shown two objects with different properties emerge and return behind an occluder, one at a time. The occluder was then removed, revealing either two objects (expected outcome, if property differences support individuation) or one object (unexpected outcome). In Experiments 1-3, infants failed to use color, size, or a combination of color, size, and pattern differences to establish a representation of two distinct objects behind an occluder. In Experiment 4, infants succeeded in using cross-basic-level-kind shape differences to establish a representation of two objects but failed to do so using within-basic-level-kind shape differences. Control conditions found that the methods were sensitive. Infants succeeded when provided unambiguous spatiotemporal information for two objects, and they encoded the property differences during these experiments. These findings suggest that by 12 months, different properties play different roles in a complex object individuation task. Certain salient shape differences enter into the computation of numerical distinctness of objects before other property differences such as color or size. Since shape differences are often correlated with object kind differences, these results converge with others in the literature that suggest that by the end of the first year of life, infants' representational systems begin to distinguish kinds and properties.     

Equivalence classes of functions of finite Markov chains

A matrical representation of a Markov chain consists of the initial vector and transition matrix of the chain, along with matrices that specify which observable response occurs for each state. The likelihood function based on a Markov model can be stated in a general way using the components of the model's matrical representation. It follows directly from that statement that two models are equivalent in likelihood if they are related through matrix operations that constitute a change of basis of the matrical representation. Two necessary properties of a change matrix associating two Markov models that are members of the same equivalence class with respect to likelihood are derived. Examples are provided, involving use of the results in analyzing identifiability of Markov models, including a useful application of diagonalization that provides a connection between the problem of identifiability and the eigenvalue problem.     

When Words Compete: Levels of Processing in Perception of Spoken Words

Current theories of spoken-word recognition posit two levels of representation and process: lexical and sublexical. By manipulating probabilistic phonotactics and similarity-neighborhood density, we attempted to determine if these two levels of representation have dissociable effects on processing. Whereas probabilistic phonotactics have been associated with facilitatory effects on recognition, increases in similarity-neighborhood density typically result in inhibitory effects on recognition arising from lexical competition. Our results demonstrated that when the lexical level is invoked using real words, competitive effects of neighborhood density are observed. However, when strong lexical effects are removed by the use of nonsense word stimuli, facilitatory effects of phonotactics emerge. These results are consistent with a two-level framework of process and representation embodied in certain current models of spoken-word recognition.     

Visual and Affective Multimodal Models of Word Meaning in Language and Mind

One of the main limitations of natural language-based approaches to meaning is that they do not incorporate multimodal representations the way humans do. In this study, we evaluate how well different kinds of models account for people's representations of both concrete and abstract concepts. The models we compare include unimodal distributional linguistic models as well as multimodal models which combine linguistic with perceptual or affective information. There are two types of linguistic models: those based on text corpora and those derived from word association data. We present two new studies and a reanalysis of a series of previous studies. The studies demonstrate that both visual and affective multimodal models better capture behavior that reflects human representations than unimodal linguistic models. The size of the multimodal advantage depends on the nature of semantic representations involved, and it is especially pronounced for basic-level concepts that belong to the same superordinate category. Additional visual and affective features improve the accuracy of linguistic models based on text corpora more than those based on word associations; this suggests systematic qualitative differences between what information is encoded in natural language versus what information is reflected in word associations. Altogether, our work presents new evidence that multimodal information is important for capturing both abstract and concrete words and that fully representing word meaning requires more than purely linguistic information. Implications for both embodied and distributional views of semantic representation are discussed.     

非参数认知诊断方法下诊断结果的概率化表征

非参数认知诊断分类方法非常适合课堂评估,其诊断结果采用0-1形式而缺乏概率化表征,不能精细地区分被试属性掌握程度的差异或变化,还缺乏可用于评价真实测验分类结果的信度和效度指标。要刻画被试属性掌握程度的差异,首要的问题是要为非参数认知诊断方法提供一种可以量化属性掌握概率的方法。针对此问题,基于二项分布和玻尔兹曼分布提出非参数认知诊断方法下诊断结果的概率化表征方法,并用于构建分类准确性和分类一致性指标。模拟研究与实测数据分析结果显示：概率化表征方法与非参数认知诊断方法的分类结果高度一致;概率化表征方法与认知诊断模型所得的属性掌握概率十分接近;概率化表征方法所得的属性（模式）掌握概率可用于计算属性（模式）分类准确性和分类一致性指标,在实际测验情景下可作为信度和效度指标,评价诊断结果的重测一致率和判准率。     

Probabilistic choice (models) as a result of balancing multiple goals

We conceptualize probabilistic choice as the result of the simultaneous pursuit of multiple goals in a vector optimization representation, which is reduced to a scalar optimization that implies goal balancing. The majority of prior theoretical and empirical work on such probabilistic choice is based on random utility models, the most basic of which assume that each choice option has a valuation that has a deterministic (systematic) component plus a random component determined by some specified distribution. An alternate approach to probabilistic choice has considered maximization of one quantity (e.g., utility), subject to constraints on one or more other quantities (e.g., cost). The multiple goal perspective integrates the results regarding the well-studied multinomial logit model of probabilistic choice that has been derived from each of the above approaches; extends the results to other models in the generalized extreme value (GEV) class; and relates them to recent axiomatic work on the utility of gambling.     

A Logic For Inductive Probabilistic Reasoning

Inductive probabilistic reasoning is understood as the application of inference patterns that use statistical background information to assign (subjective) probabilities to single events. The simplest such inference pattern is direct inference: from “70% of As are Bs” and “a is an A” infer that a is a B with probability 0.7. Direct inference is generalized by Jeffrey’s rule and the principle of cross-entropy minimization. To adequately formalize inductive probabilistic reasoning is an interesting topic for artificial intelligence, as an autonomous system acting in a complex environment may have to base its actions on a probabilistic model of its environment, and the probabilities needed to form this model can often be obtained by combining statistical background information with particular observations made, i.e., by inductive probabilistic reasoning. In this paper a formal framework for inductive probabilistic reasoning is developed: syntactically it consists of an extension of the language of first-order predicate logic that allows to express statements about both statistical and subjective probabilities. Semantics for this representation language are developed that give rise to two distinct entailment relations: a relation ⊨ that models strict, probabilistically valid, inferences, and a relation that models inductive probabilistic inferences. The inductive entailment relation is obtained by implementing cross-entropy minimization in a preferred model semantics. A main objective of our approach is to ensure that for both entailment relations complete proof systems exist. This is achieved by allowing probability distributions in our semantic models that use non-standard probability values. A number of results are presented that show that in several important aspects the resulting logic behaves just like a logic based on real-valued probabilities alone.     

Illness representations in patients with multimorbid long-term conditions: Qualitative study

Most research on illness representations explores how patients view single conditions, but many patients report more than one long-term condition (known as multimorbidity). It is not known how multimorbidity impacts on patient illness representations. This exploratory qualitative study examined patients’ representations of multimorbid long-term conditions and sought to assess how models of illness representation might need modification in the presence of multimorbidity. We explored two major issues: (1) the impact of multimorbidity on patient representations of their individual conditions and (2) the representation of multimorbidity itself. Twenty eight adults with at least two long-term conditions (mean of 4) were interviewed. The presence of multimorbidity impacted on patient illness representations in relation to the dimensions of identity, perceived cause, coherence and consequences. Representations of multimorbidity itself concerned representations of the burden of medication and perceived priorities among conditions and synergies and antagonisms between conditions and their management. The results have implications for the measurement of multimorbidity (through scales such as the Illness Perception Questionnaire) and the use of illness representations in the design and delivery of interventions to improve health behaviour and outcomes of patients with multiple long-term conditions.     

Word informativity influences acoustic duration: Effects of contextual predictability on lexical representation

Language-users reduce words in predictable contexts. Previous research indicates that reduction may be stored in lexical representation if a word is often reduced. Because representation influences production regardless of context, production should be biased by how often each word has been reduced in the speaker’s prior experience. This study investigates whether speakers have a context-independent bias to reduce low-informativity words, which are usually predictable and therefore usually reduced. Content word durations were extracted from the Buckeye and Switchboard speech corpora, and analyzed for probabilistic reduction effects using a language model based on spontaneous speech in the Fisher corpus. The analysis supported the hypothesis: low-informativity words have shorter durations, even when the effects of local contextual predictability, frequency, speech rate, and several other variables are controlled for. Additional models that compared word types against only other words of the same segmental length further supported this conclusion. Words that usually appear in predictable contexts are reduced in all contexts, even those in which they are unpredictable. The result supports representational models in which reduction is stored, and where sufficiently frequent reduction biases later production. The finding provides new evidence that probabilistic reduction interacts with lexical representation.     

On the theory of biased bisection operations and their inverses

A theory of fundamental measurement involving multiple operations is presented. The operations are bisection, and two inverses of the bisection operation called right and left displacement, analogous to “displacement” of an interval “up” or “down” a scale. Following Pfanzagl, “biased” operations are permitted, and linear, reflexive representations are presented for the biased operations. Results are obtained on the constructability of unbiased scaling operations, and several methods of constructing standard sequences of equally spaced stimuli using biased operations, are described. A striking property of biased linear representation models is derived, showing that if bias is present, indefinite iterations of one or the other of the two displacement operations will converge to asymptote. Preliminary data support this surprising convergence property.     

The construction of large number representations in adults

What is the nature of our mental representation of quantity? We find that human adults show no performance cost of comparing numerosities across vs. within visual and auditory stimulus sets, or across vs. within simultaneous and sequential sets. In addition, reaction time and performance in such tasks are determined by the ratio of the numerosities to be compared; absolute set size has no effect. These findings suggest that modality-specific stimulus properties undergo a non-iterative transformation into representations of quantity that are independent of the modality or format of the stimulus.     

Goodness of fit tests for probabilistic measurement models

General goodness of fit tests for probabilistic response models are developed. The tests are applicable in psychophysics, in the theory of choice behavior and in mathematical learning theories. The necessary and sufficient constraints that a measurement model puts on the response probabilities are used for testing this model. In addition, representation theorems for some models are proved and the goodness of fit to experimental data is considered.     

Pliers,not fingers: Tool-action effect in a motor intention paradigm

Tool-use representations have been suggested to be supported by the representation of hand actions and/or by the representation of tool actions. A major issue is to know which one of these two representations is preferentially activated when people intend to use a tool. To address this issue, we developed a paradigm in which, in 20% of trials, participants had to press a button and actually use pliers to move an object in response to a predefined target symbol. Importantly, two masks hiding the symbols performed “opening” or “closing” actions before symbols appeared. In Experiment 1, participants used normal pliers: Hand’s opening actions induced pliers’ opening actions and vice versa for hands’ closing actions. Results indicated a compatibility effect between masks’ actions and pliers’ actions. Participants were faster to press the button in response to the target symbol when opening and closing actions of the masks were congruent with the corresponding actions of the hand. In Experiment 2 participants used inverse pliers: Hand’s opening actions involved pliers’ closing actions and vice versa. In this situation, results showed that the congruency of masks’ actions occurred with pliers’ actions and not hand’s actions. Altogether, these findings demonstrate that intention of use is preferentially based on the representation of tool actions, and have important implications for the domain of neuropsychology of tool use and the theories of goal-directed behavior.     

Representation of memory for order of mental operations in cognitive tasks

Recent research shows that people learning a cognitive task acquire a memory for the order of operations applied, independent of the data to which those operations were applied. We designed two experiments to show how this sequence memory is represented. Experiment 1 compared predictions based on 3 possible sequence representation methods: composition, dyad transition, and associative chain. Latency and error results from a simple sequential task supported the associative chain representation. The associative links between operations presumably enhance performance by priming subsequent operations but do not operate in an all-or-none fashion. Experiment 2 explored whether transfer items that matched the first 2 rules and first 3 elements of a training item could bias participants toward executing a composed production learned during training. Latency and undetected error results were consistent with an associative chain representation but not with additional predictions made by the composition representation. These two experiments support the representation of operation sequences in memory as an associative chain.     

Principled and statistical connections in common sense conception

Nominal concepts represent things as tokens of types. We report six experiments that investigate the nature of the relations we represent between the type of thing something is (e.g. DOG) and its other properties. The experiments provide evidence that we represent principled connections between the type of thing something is (e.g. DOG) and some of its properties (k-properties; e.g. having four legs for dogs), but not other properties (t-properties; e.g. being brown for dogs). Principled connections are different from logical, statistical, and causal connections. Principled connections, (i) license the expectation that tokens of the type will generally possess the k-property, (ii) license explanation of the presence of k-properties in tokens of a type by reference to the type of thing it is, and (iii) license normative expectations concerning the presence of the k-property in tokens of the type. The experiments provide evidence for all three of these aspects of principled connections. The experiments also demonstrate that principled connections must be distinguished from merely strong statistical connections. We suggest that principled connections are one of the fundamental types of relations (in addition to logical, statistical, and causal relations) in terms of which our conceptual knowledge is structured. We argue that principled connections reveal a formal mode of understanding and explanation. This mode of understanding complements other modes of understanding that have been studied within the theory-based approach to conceptual representation. Finally, we suggest that kind representations are distinguished from representations of mere types by the representation of principled connections to k-properties.     

Convex models of uncertainty: Applications and implications

Modern engineering has included the basic sciences and their accompanying mathematical theories among its primary tools. The theory of probability is one of the more recent entries into standard engineering practice in various technological disciplines. Probability and statistics serve useful functions in the solution of many engineering problems. However, not all technological manifestations of uncertainty are amenable to probabilistic representation. In this paper we identify the conceptual limitations of probabilistic and related theories as they occur in a wide range of engineering tasks. We discuss the structure and properties of an alternative, non-probabilistic, method — convex modelling — for quantitatively representing uncertain phenomena.     

Building Cognition: The Construction of Computational Representations for Scientific Discovery

Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a theoretical analysis of the cognitive roles such representations play, based on an ethnographic study of the building of computational models in a systems biology laboratory. Specifically, we focus on a case of model‐building by an engineer that led to a remarkable discovery in basic bioscience. Accounting for such discoveries requires a distributed cognition (DC) analysis, as DC focuses on the roles played by external representations in cognitive processes. However, DC analyses by and large have not examined scientific discovery, and they mostly focus on memory offloading, particularly how the use of existing external representations changes the nature of cognitive tasks. In contrast, we study discovery processes and argue that discoveries emerge from the processes of building the computational representation. The building process integrates manipulations in imagination and in the representation, creating a coupled cognitive system of model and modeler, where the model is incorporated into the modeler's imagination. This account extends DC significantly, and we present some of the theoretical and application implications of this extended account.     

Three case studies in the Bayesian analysis of cognitive models

Bayesian statistical inference offers a principled and comprehensive approach for relating psychological models to data. This article presents Bayesian analyses of three influential psychological models: multidimensional scaling models of stimulus representation, the generalized context model of category learning, and a signal detection theory model of decision making. In each case, the model is recast as a probabilistic graphical model and is evaluated in relation to a previously considered data set. In each case, it is shown that Bayesian inference is able to provide answers to important theoretical and empirical questions easily and coherently. The generality of the Bayesian approach and its potential for the understanding of models and data in psychology are discussed.     

Combining exemplar-based category representations and connectionist learning rules.

Adaptive network and exemplar-similarity models were compared on their ability to predict category learning and transfer data. An exemplar-based network (Kruschke, 1990a, 1990b, 1992) that combines key aspects of both modeling approaches was also tested. The exemplar-based network incorporates an exemplar-based category representation in which exemplars become associated to categories through the same error-driven, interactive learning rules that are assumed in standard adaptive networks. Experiment 1, which partially replicated and extended the probabilistic classification learning paradigm of Gluck and Bower (1988a), demonstrated the importance of an error-driven learning rule. Experiment 2, which extended the classification learning paradigm of Medin and Schaffer (1978) that discriminated between exemplar and prototype models, demonstrated the importance of an exemplar-based category representation. Only the exemplar-based network accounted for all the major qualitative phenomena; it also achieved good quantitative predictions of the learning and transfer data in both experiments.     

From ‘Virgin Births’ to ‘Octomom’: Representations of Single Motherhood via Sperm Donation in the UK News

The use of sperm donation by single women has provoked public, professional and political debate. Newspapers serve as a critical means of both broadcasting this debate and effecting a representation of this user group within the public sphere. This study uses the theory of social representations to examine how single motherhood by sperm donation has been represented in the UK news over time. The study sampled news coverage on this topic in eight British newspapers during three 4‐year periods between the years 1988 and 2012. The dataset of news reports (n = 406) was analysed using a qualitative approach. Findings indicated that UK media reports of single women using donor sperm are underpinned by conventional categories of the ‘personal’, the ‘traditional’ and the ‘natural’ that when paired with their corollaries produce a representation of this user group as the social ‘other’. The amount of coverage on this topic over time was found to vary according to the political orientation of different media sources. Using key concepts from social representations theory, this article discusses the relationship between themata and anchoring in the maintenance of representations of the social ‘other’ in mass mediated communication. Findings are explained in relation to theoretical conceptions of the mass media and its position within the public sphere. It is argued that the use of personal narratives in news reports of single mothers by sperm donation may have significant implications for public understandings of this social group. © 2016 The Authors. Journal of Community & Applied Social Psychology published by John Wiley & Sons Ltd.