A probabilistic model of semantic plausibility in sentence processing

Experimental research shows that human sentence processing uses information from different levels of linguistic analysis, for example, lexical and syntactic preferences as well as semantic plausibility. Existing computational models of human sentence processing, however, have focused primarily on lexico-syntactic factors. Those models that do account for semantic plausibility effects lack a general model of human plausibility intuitions at the sentence level. Within a probabilistic framework, we propose a wide-coverage model that both assigns thematic roles to verb–argument pairs and determines a preferred interpretation by evaluating the plausibility of the resulting ( verb , role , argument ) triples. The model is trained on a corpus of role-annotated language data. We also present a transparent integration of the semantic model with an incremental probabilistic parser. We demonstrate that both the semantic plausibility model and the combined syntax/semantics model predict judgment and reading time data from the experimental literature.     

Criteria for the design and evaluation of cognitive architectures

Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition comprehensible to individual cognitive scientists and organizing groups of like-minded cognitive scientists into genuine communities. (b) Successful architectures provide idioms that structure the design and interpretation of computational models. (c) Successful architectures are strange: They make provocative, often disturbing, and ultimately compelling claims about human information processing that demand evaluation.     

Solving geometric analogy problems through two-stage analogical mapping

Evans' 1968 ANALOGY system was the first computer model of analogy. This paper demonstrates that the structure mapping model of analogy, when combined with high-level visual processing and qualitative representations, can solve the same kinds of geometric analogy problems as were solved by ANALOGY. Importantly, the bulk of the computations are not particular to the model of this task but are general purpose: We use our existing sketch understanding system, CogSketch, to compute visual structure that is used by our existing analogical matcher, Structure Mapping Engine (SME). We show how SME can be used to facilitate high-level visual matching, proposing a role for structural alignment in mental rotation. We show how second-order analogies over differences computed via analogies between pictures provide a more elegant model of the geometric analogy task. We compare our model against human data on a set of problems, showing that the model aligns well with both the answers chosen by people and the reaction times required to choose the answers.     

The plausibility of spiritual intelligence: spiritual experience,problem solving and neural sites

Australian teachers in Church related schools have begun to use the term ‘spiritual intelligence’ in their educational discourse. Is it accurate to describe spirituality as a form of intelligence? This paper explores whether the notion of spiritual intelligence is plausible. It addresses this firstly by discussing the notion of spiritual experience as a mechanism for problem solving—one of the central themes that underlies the concept of intelligence. Secondly, it examines some of the neural sites of the human brain that have been found to be active in those who apperceive spiritual experience. In light of this discussion, this paper argues that although some concerns prevail in considering spirituality as a form of intelligence, the concept of spiritual intelligence may nonetheless be rendered as plausible.     

Symbolically speaking: a connectionist model of sentence production

The ability to combine words into novel sentences has been used to argue that humans have symbolic language production abilities. Critiques of connectionist models of language often center on the inability of these models to generalize symbolically (Fodor & Pylyshyn, 1988; Marcus, 1998). To address these issues, a connectionist model of sentence production was developed. The model had variables (role‐concept bindings) that were inspired by spatial representations (Landau & Jackendoff, 1993). In order to take advantage of these variables, a novel dual‐pathway architecture with event semantics is proposed and shown to be better at symbolic generalization than several variants. This architecture has one pathway for mapping message content to words and a separate pathway that enforces sequencing constraints. Analysis of the model's hidden units demonstrated that the model learned different types of information in each pathway, and that the model's compositional behavior arose from the combination of these two pathways. The model's ability to balance symbolic and statistical behavior in syntax acquisition and to model aphasic double dissociations provided independent support for the dual‐pathway architecture.     

动态系统研究方法在社会心理学中的应用

文章综述了动态系统研究方法在社会心理学中的应用。社会心理学现象具有复杂性、时间性和动态性特点,动态分析方法如动态系统行为模式确定、动态系统稳定性分析、人工神经网络和元细胞自动机等计算机模型的应用,可以有效的捕捉到人类思维和行为的这些特点。目前动态分析方法已经在社会心理学的动态属性研究中取得了一定的进展。     

The effects of presentation and content on syllogistic reasoning by children with and without specific language impairment

This study examines performance at a syllogistic reasoning task for a group of children (age 10 years) with specific language impairment (SLI) along with age- and language-matched controls. The syllogisms were presented either verbally or verbally/pictorially, and contained two types of item: imaginary versus real, both intended not to evoke strong beliefs. Children with SLI performed worse than age-matched controls, and equivalently to language-matched controls. Patterns of performance indicate this may be due to cognitive ability deficits rather than specific language deficits. For all groups, pictorial presentation interfered with reasoning processes. It is suggested that, for syllogisms, this pictorial information contextualises the interpretation of the task, and that in turn either raises working memory load or evokes belief bias. Additionally, these results suggest that caution should be exhibited before using visual aids to help children with SLI in the classroom.     

The role of plausibility in influencing environmental attributions

The aim of the study was to examine how plausibility influences attributions about the cause of perceived arousal. Subjects watched one of three arousing (humorous, sexual, aggressive) or a nonarousing televised program under conditions of appropriate or inappropriate interpersonal spacing. The subjects then completed a questionnaire indicating how crowded they felt and rating the movie on a number of dimensions. The results indicated that under close interpersonal distances, subjects felt less crowded while watching the arousing movies than while viewing the nonarousing movie. There were no differences in the experience of crowding between the movie conditions under the far interpersonal distance conditions. In addition, subjects rated the arousing movies as more arousing under close as opposed to far interpersonal distance conditions. Further, the humorous movie was viewed as more funny, and the violent movie was rated more violent under the close as opposed to the far conditions. Finally, there was a tendency to enjoy the arousing movies more under the close conditions, while the reverse effect was found for the nonarousing movie. It was argued that the arousing movies reduced the experience of crowding under close interpersonal conditions because subjects attributed their arousal to the movies rather than to the spatial restrictions. It was further suggested that the results showed that individuals will attribute their arousal to salient stimuli in their environment that could plausibly be sources of arousal. The results not only offer support for an attribution model of crowding, but they also show that depending on the individual's attribution, spatial conditions that can give rise to crowding may increase enjoyment of events in the environment.     

Face recognition algorithms and the other‐race effect: computational mechanisms for a developmental contact hypothesis

People recognize faces of their own race more accurately than faces of other races. The “contact” hypothesis suggests that this “other‐race effect” occurs as a result of the greater experience we have with own‐ versus other‐race faces. The computational mechanisms that may underlie different versions of the contact hypothesis were explored in this study. We replicated the other‐race effect with human participants and evaluated four classes of computational face recognition algorithms for the presence of an other‐race effect. Consistent with the predictions of a developmental contact hypothesis, “experience‐based models” demonstrated an other‐race effect only when the representational system was developed through experience that warped the perceptual space in a way that was sensitive to the overall structure of the model's experience with faces of different races. When the model's representation relied on a feature set optimized to encode the information in the learned faces, experience‐based algorithms recognized minority‐race faces more accurately than majority‐race faces. The results suggest a developmental learning process that warps the perceptual space to enhance the encoding of distinctions relevant for own‐race faces. This feature space limits the quality of face representations for other‐race faces.     

Modeling recognition memory using the similarity structure of natural input

The natural input memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During recognition, the model compares incoming preprocessed natural input to stored representations. By complementing the recognition memory process with a perceptual front end, the NIM model is able to make predictions about memorability based directly on individual natural stimuli. We demonstrate that the NIM model is able to simulate experimentally obtained similarity ratings and recognition memory for individual stimuli (i.e., face images).     

The availability explanation of excessive plausibility assessments

The assessment of hypotheses in hypothesis generation involves a comparison between those hypotheses that have been generated (specified) and those that are not generated (unspecified). Experiment 1 was an investigation of an “availability explanation” for subjects' overconfidence in the probability of specified hypotheses. The conjecture is that subjects have difficulty retrieving unspecified hypotheses from memory. Therefore, the underpopulated set of unspecified hypotheses is assessed as less probable then it actually is and the specified set is assessed as more probable. Two manipulations to increase the availability of unspecified hypotheses were investigated. One involved explicitly requesting subjects to populate the unspecified set; the other was a computer presentation of candidate unspecified hypotheses. Results of experiment 1 were that assessment overconfidence for both experimental groups was reduced. The results support the conjecture that the availability heuristic is at least partially responsible for subjects' overconfidence. The main result of experiment 2 was that the overconfidence bias persisted with different assessment methods for both subject-generated and experimenter-supplied hypotheses.     

Modeling how, when, and what is learned in a simple fault-finding task

We have developed a process model that learns in multiple ways while finding faults in a simple control panel device. The model predicts human participants' learning through its own learning. The model's performance was systematically compared to human learning data, including the time course and specific sequence of learned behaviors. These comparisons show that the model accounts very well for measures such as problem-solving strategy, the relative difficulty of faults, and average fault-finding time. More important, because the model learns and transfers its learning across problems, it also accounts for the faster problem-solving times due to learning when examined across participants, across faults, and across the series of 20 trials on an individual participant basis. The model shows how learning while problem solving can lead to more recognition-based performance, and helps explain how the shape of the learning curve can arise through learning and be modified by differential transfer. Overall, the quality of the correspondence appears to have arisen from procedural, declarative, and episodic learning all taking place within individual problem-solving episodes.     

A cognitive model of insomnia

Insomnia is one of the most prevalent psychological disorders, causing sufferers severe distress as well as social, interpersonal, and occupational impairment. Drawing on well-validated cognitive models of the anxiety disorders as well as on theoretical and empirical work highlighting the contribution of cognitive processes to insomnia, this paper presents a new cognitive model of the maintenance of insomnia. It is suggested that individuals who suffer from insomnia tend to be overly worried about their sleep and about the daytime consequences of not getting enough sleep. This excessive negatively toned cognitive activity triggers both autonomic arousal and emotional distress. It is proposed that this anxious state triggers selective attention towards and monitoring of internal and external sleep-related threat cues. Together, the anxious state and the attentional processes triggered by it tricks the individual into overestimating the extent of the perceived deficit in sleep and daytime performance. It is suggested that the excessive negatively toned cognitive activity will be fuelled if a sleep-related threat is detected or a deficit perceived. Counterproductive safety behaviours (including thought control, imagery control, emotional inhibition, and difficulty problem solving) and erroneous beliefs about sleep and the benefits of worry are highlighted as exacerbating factors. The unfortunate consequence of this sequence of events is that the excessive and escalating anxiety may culminate in a real deficit in sleep and daytime functioning. The literature providing preliminary support for the model is reviewed and the clinical implications and limitations discussed.     

An Evolutionary Analysis of Learned Attention

Humans and many other species selectively attend to stimuli or stimulus dimensions—but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it within an organism's processing capacity limitations. Instead, being able to shift attention provides adaptive benefit by allowing faster learning with fewer errors in a range of ecologically plausible environments.     

A cognitive model of suicidal behavior: Theory and treatment

With some prominent exceptions, much of the research designed to elucidate the nature, prevalence, and correlates of suicidal behavior has been conducted from an atheoretical perspective. Conversely, psychological theories to explain suicidal behavior are largely untested by rigorous experimental designs. We propose a cognitive model of suicidal behavior that is grounded in the empirical literature on cognitive and behavioral correlates of and risk factors for suicidal behavior. In addition, we demonstrate the manner in which the theoretical components are targeted in cognitive therapy for suicidal patients. We highlight aspects of the model with less empirical support, and we propose ways those constructs can be tested in future research.     

A simplicity principle in unsupervised human categorization

We address the problem of predicting how people will spontaneously divide into groups a set of novel items. This is a process akin to perceptual organization. We therefore employ the simplicity principle from perceptual organization to propose a simplicity model of unconstrained spontaneous grouping. The simplicity model predicts that people would prefer the categories for a set of novel items that provide the simplest encoding of these items. Classification predictions are derived from the model without information either about the number of categories sought or information about the distributional properties of the objects to be classified. These features of the simplicity model distinguish it from other models in unsupervised categorization (where, for example, the number of categories sought is determined via a free parameter), and we discuss how these computational differences are related to differences in modeling objectives. The predictions of the simplicity model are validated in four experiments. We also discuss the significance of simplicity in cognitive modeling more generally.     

Modeling parallelization and flexibility improvements in skill acquisition: from dual tasks to complex dynamic skills

Emerging parallel processing and increased flexibility during the acquisition of cognitive skills form a combination that is hard to reconcile with rule-based models that often produce brittle behavior. Rule-based models can exhibit these properties by adhering to 2 principles: that the model gradually learns task-specific rules from instructions and experience, and that bottom-up processing is used whenever possible. In a model of learning perfect time-sharing in dual tasks (Schumacher et al., 2001), speedup learning and bottom-up activation of instructions can explain parallel behavior. In a model of a complex dynamic task (Carnegie Mellon University Aegis Simulation Program [CMU-ASP], Anderson et al., 2004), parallel behavior is explained by the transition from serially organized instructions to rules that are activated by both top-down (goal-driven) and bottom-up (perceptually driven) factors. Parallelism lets the model opportunistically reorder instructions, leading to the gradual emergence of new task strategies.     

Temporomandibular Disorder in Brazilian Patients: A Preliminary Study

The objective of the current study was to evaluate disease-related beliefs, adherence to treatment, quality of life, coping strategies and cognitive status in a group of Brazilian patients with Temporomandibular Disorder (TMD). Thirty patients were evaluated with a semi-directed interview, the Coping Strategies Inventory, and a Mini-Mental State Examination. Although half (50%) of the patients had known their diagnosis long term, 40% of the sample were not correctly following proposed treatment. All patients had a similar pattern of pain behavior related to TMD, while disease-related beliefs, quality of life and coping strategies were variable. Expectations about treatment also had significant association with treatment adherence. The findings of this study suggest that a more thorough understanding of individual differences in TMD is warranted.