The Neural Basis of Implicit Attitudes

ABSTRACT— Evidence that human preferences, beliefs, and behavior are influenced by sources that are outside the reach of conscious awareness, control, intention, and self-reflection is incontrovertible. Recent advances in neuroscience have enabled researchers to investigate the neural basis of these implicit attitudes, particularly attitudes involving social groups. From this research, a model with three identified neural components related to the automatic activation and regulation of implicit attitudes is beginning to emerge. The amygdala is implicated in the automatic evaluation of socially relevant stimuli, while the anterior cingulate and dorsolateral prefrontal cortices are involved in the detection and regulation, respectively, of implicit attitudes. Further support for this model comes from the inclusion of these regions in current models concerning the cognitive regulation of emotion and the detection of conflict. The identification of a putative neural substrate for implicit attitudes has had a direct impact on psychological research into their nature and operational characteristics. We discuss how this emerging neural model has influenced current research on implicit attitudes and describe the importance of such models for directing future research.     

Strategy generalization across orientation tasks: testing a computational cognitive model

Humans use their spatial information processing abilities flexibly to facilitate problem solving and decision making in a variety of tasks. This article explores the question of whether a general strategy can be adapted for performing two different spatial orientation tasks by testing the predictions of a computational cognitive model. Human performance was measured on an orientation task requiring participants to identify the location of a target either on a map (find-on-map) or within an egocentric view of a space (find-in-scene). A general strategy instantiated in a computational cognitive model of the find-on-map task, based on the results from Gunzelmann and Anderson (2006) , was adapted to perform both tasks and used to generate performance predictions for a new study. The qualitative fit of the model to the human data supports the view that participants were able to tailor a general strategy to the requirements of particular spatial tasks. The quantitative differences between the predictions of the model and the performance of human participants in the new experiment expose individual differences in sample populations. The model provides a means of accounting for those differences and a framework for understanding how human spatial abilities are applied to naturalistic spatial tasks that involve reasoning with maps.     

A computational model of early argument structure acquisition

How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms that can grasp generalizations from examples of specific usages, and that exhibit patterns of behavior over the course of learning similar to those in children. Early learning of verb argument structure is an area of language acquisition that provides an interesting testbed for such approaches due to the complexity of verb usages. A range of linguistic factors interact in determining the felicitous use of a verb in various constructions —associations between syntactic forms and properties of meaning that form the basis for a number of linguistic and psycholinguistic theories of language. This article presents a computational model for the representation, acquisition, and use of verbs and constructions. The Bayesian framework is founded on a novel view of constructions as a probabilistic association between syntactic and semantic features. The computational experiments reported here demonstrate the feasibility of learning general constructions, and their exceptions, from individual usages of verbs. The behavior of the model over the timecourse of acquisition mimics, in relevant aspects, the stages of learning exhibited by children. Therefore, this proposal sheds light on the possible mechanisms at work in forming linguistic generalizations and maintaining knowledge of exceptions.     

An Invitation to Behavior Analysts: Review of in Search of Memory: The Emergence of A New Science of Mind by Eric R. Kandel

This fascinating autobiography and multifaceted case history in neuroscience research is accessible to laymen and potentially instructive to working scientists. Kandel takes the reader through his thought processes as he describes experiments that led to some of the past decades'' major neuroscience discoveries (some highlights of which are summarized in the review''s Appendix), and eventually to his Nobel Prize. The review analyzes some of the terminological and conceptual issues that have often inhibited communication between behavior analysts and neuroscientists, with special attention to some of Bennett and Hacker''s admonitions viewed from the perspective of language evolution and linguistics. The review then discusses opportunities for behavior analysts to collaborate with neuroscientists by applying behavioral contingency analysis to help specify the independent variables of neuroscience experiments described by Kandel. Finally, it examines Kandel''s provocative heuristics for locating important research problems, and the lessons that can be gleaned from the book regarding the attributes of potentially great achievers.     

Autonomous construction of ecologically and socially relevant semantics

This article presents a synthetic modeling approach to the problem of grounded construction of concepts. In many computational models of grounded language acquisition and evolution, meanings are created in the process of discrimination between a chosen object and other objects present on the scene of communication. We argue that categories constructed for the purpose of identification rather than discrimination are more suitable for the detached language use (talking about things not present here and now). We describe a semantics based on so-called identification criteria constructed by extracting cross-situational similarities among instances of a category, and present several computational models. In the model of individual category construction, the instances are grouped to categories by common motor programs (affordances), while in the model of social learning, focused on the influence of naming on category formation, entities are considered members of the same category, if they are labeled with the same word by an external teacher. By these two mechanisms, the learner can construct interactionally grounded representation of objects, properties, relations, changes, complex situations and events. We also report and analyze simulation results of an experiment focused on the dynamics of meanings in iterated intergenerational transmission.     

Automatic evaluation of syntactic learners in typologically-different languages

Human syntax acquisition involves a system that can learn constraints on possible word sequences in typologically-different human languages. Evaluation of computational syntax acquisition systems typically involves theory-specific or language-specific assumptions that make it difficult to compare results in multiple languages. To address this problem, a bag-of-words incremental generation (BIG) task with an automatic sentence prediction accuracy (SPA) evaluation measure was developed. The BIG–SPA task was used to test several learners that incorporated n-gram statistics which are commonly found in statistical approaches to syntax acquisition. In addition, a novel Adjacency–Prominence learner, that was based on psycholinguistic work in sentence production and syntax acquisition, was also tested and it was found that this learner yielded the best results in this task on these languages. In general, the BIG–SPA task is argued to be a useful platform for comparing explicit theories of syntax acquisition in multiple languages.     

晚发性抑郁的生物学机制

晚发性抑郁是指发病年龄大于60岁的一组抑郁综合征,发病机制至今尚不明确。研究表明,晚发性抑郁的遗传因素不明显,而血管损害则与之密切相关;多数患者伴有明显的执行功能损害,痴呆的发病风险增高。围绕晚发性抑郁的两个基本特征:血管损害与认知障碍做一回顾,初步探讨了晚发性抑郁生物学机制对临床决策的影响,并由此展望了未来的研究方向。     

心算加工的认知神经科学研究

近几年(1999-2005)有关心算加工的脑活动机制研究发现,类似九九乘法表这样的算术知识提取(如3×5)主要与左脑顶内沟有关,但当心算变得更复杂时(如26×38),左脑额叶下部出现明显激活,这表明心算与语言和工作记忆关系密切。另一方面,也存在不依赖于语言的即表现为视觉表象活动的心算,右脑的一些脑区在其中起了作用。简言之,所有与心算有关的脑区涉及到大脑前额皮层和颞顶枕联合皮层的综合作用,并总体表现为左脑优势,但具有特殊心算能力的人其心算还与右脑前额叶和颞叶内侧脑区的活动有关。     

Modeling the Developmental Patterning of Finiteness Marking in English, Dutch, German, and Spanish Using MOSAIC

In this study, we apply MOSAIC (model of syntax acquisition in children) to the simulation of the developmental patterning of children's optional infinitive (OI) errors in 4 languages: English, Dutch, German, and Spanish. MOSAIC, which has already simulated this phenomenon in Dutch and English, now implements a learning mechanism that better reflects the theoretical assumptions underlying it, as well as a chunking mechanism that results in frequent phrases being treated as 1 unit. Using 1, identical model that learns from child-directed speech, we obtain a close quantitative fit to the data from all 4 languages despite there being considerable cross-linguistic and developmental variation in the OI phenomenon. MOSAIC successfully simulates the difference between Spanish (a pro-drop language in which OI errors are virtually absent) and obligatory subject languages that do display the OI phenomenon. It also highlights differences in the OI phenomenon across German and Dutch, 2 closely related languages whose grammar is virtually identical with respect to the relation between finiteness and verb placement. Taken together, these results suggest that (a) cross-linguistic differences in the rates at which children produce OIs are graded, quantitative differences that closely reflect the statistical properties of the input they are exposed to and (b) theories of syntax acquisition need to consider more closely the role of input characteristics as determinants of quantitative differences in the cross-linguistic patterning of phenomena in language acquisition.     

Attention, Joint Attention, and Social Cognition

ABSTRACT— Before social cognition there is joint processing of information about the attention of self and others. This joint attention requires the integrated activation of a distributed cortical network involving the anterior and posterior attention systems. In infancy, practice with the integrated activation of this distributed attention network is a major contributor to the development of social cognition. Thus, the functional neuroanatomies of social cognition and the anterior–posterior attention systems have much in common. These propositions have implications for understanding joint attention, social cognition, and autism.