Computational explanation in neuroscience

According to some philosophers, computational explanation is proprietary to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that are being developed by a number of authors.     

Interactivism in artificial intelligence (AI) and intelligent robotics

This paper overviews the interactivist model of representation and its applications in artificial intelligence (AI) and intelligent robotics. Selected examples from approaches in AI and robotics are contrasted with the generic interactivist architecture in order to illustrate specific features of it. Petitagé, an artificial agent that instantiates our interactivist-expectative theory of agency and learning (IETAL), is discussed in detail from the interactivist perspective.     

From human to artificial cognition and back: New perspectives on cognitively inspired AI systems

We overview the main historical and technological elements characterising the rise, the fall and the recent renaissance of the cognitive approaches to Artificial Intelligence and provide some insights and suggestions about the future directions and challenges that, in our opinion, this discipline needs to face in the next years.     

MetrIntMeas a novel metric for measuring the intelligence of a swarm of cooperating agents

We propose a novel metric called MetrIntMeas (Metric for the Intelligence Measuring) for an accurate and robust measurement of the difficult problem-solving intelligence of a swarm system. The metric allows the classification if a swarm system belongs to the same class with the systems which have a specific reference intelligence value. For proving the efficiency of the proposed metric we realized a case study on a swarm system specialized in solving a NP-hard problem. As an application of the proposed metric, we present the measurement of the swarm systems’ evolution in intelligence. We gave a new definition to the intelligent evolving systems. The evolution of intelligent systems can be verified using the proposed MetrIntMeas metric.     

Do Bilinguals Automatically Activate Their Native Language When They Are Not Using It?

Most models of lexical access assume that bilingual speakers activate their two languages even when they are in a context in which only one language is used. A critical piece of evidence used to support this notion is the observation that a given word automatically activates its translation equivalent in the other language. Here, we argue that these findings are compatible with a different account, in which bilinguals “carry over” the structure of their native language to the non‐native language during learning, and where there is no activation of translation equivalents. To demonstrate this, we describe a model in which language learning involves mapping native language phonological relationships to the non‐native language, and we show how it can explain the results attributed to automatic activation of translation equivalents.     

Towards classifying propositional probabilistic logics

This paper examines two aspects of propositional probabilistic logics: the nesting of probabilistic operators, and the expressivity of probabilistic assessments. We show that nesting can be eliminated when the semantics is based on a single probability measure over valuations; we then introduce a classification for probabilistic assessments, and present novel results on their expressivity. Logics in the literature are categorized using our results on nesting and on probabilistic expressivity.     

The past,present, and future of computational models of cognitive development

Does modeling matter? We address this question by providing a broad survey of the computational models of cognitive development that have been proposed and studied over the last three decades. We begin by noting the advantages and limitations of computational models. We then describe four key dimensions across which models of development can be organized and classified. With this taxonomy in hand, we focus on how the modeling enterprise has evolved over time. In particular, we separate the timeline into three overlapping historical waves and highlight how each wave of models has not only been shaped by developmental theory and behavioral research, but in return also provided valuable insights and innovations to the study of cognitive development.     

Working memory and fluid intelligence in young children

The present study investigates how working memory and fluid intelligence are related in young children and how these links develop over time. The major aim is to determine which aspect of the working memory system—short-term storage or cognitive control—drives the relationship with fluid intelligence. A sample of 119 children was followed from kindergarten to second grade and completed multiple assessments of working memory, short-term memory, and fluid intelligence. The data showed that working memory, short-term memory, and fluid intelligence were highly related but separate constructs in young children. The results further showed that when the common variance between working memory and short-term memory was controlled, the residual working memory factor manifested significant links with fluid intelligence whereas the residual short-term memory factor did not. These findings suggest that in young children cognitive control mechanisms rather than the storage component of working memory span tasks are the source of their link with fluid intelligence.     

Rule transition on the balance scale task: a case study in belief change

For various domains in proportional reasoning cognitive development is characterized as a progression through a series of increasingly complex rules. A multiplicative relationship between two task features, such as weight and distance information of blocks placed at both sides of the fulcrum of a balance scale, appears difficult to discover. During development, children change their beliefs about the balance scale several times: from a focus on the weight dimension (Rule I) to occasionally considering the distance dimension (Rule II), guessing (Rule III), and applying multiplication (Rule IV; Siegler, 1981). Because of the detailed empirical findings the balance scale task has become a benchmark task for computational models of proportional reasoning. In this article, we present a large empirical study (N = 420) of which the findings provide a challenge for computational models. The effect of feedback and the effect of individually adapted training items on rule transition were tested for children using Rule I or Rule II. Presenting adapted training items initiates belief revision for Rule I but not for Rule II. The experience of making mistakes (by providing feedback) induces a change for both Rule I and Rule II. However, a delayed posttest shows that these changes are preserved after 2 weeks only for children using Rule I. We conclude that the transition from Rule I to Rule II differs from the transition from Rule II to a more complex rule. Concerning these empirical findings, we will review performance of computational models and the implications for a future belief revision model.

It is one Thing, to show a Man that he is in an Error, and another, to put him in possession of Truth. John Locke

     

适于自然情境脑成像研究的分析方法:被试间相关及其应用

被试间相关分析是一种基于大脑活动的时间模式的数据分析方法。该方法通过计算接收相同刺激时被试间脑区活动的一致性,探讨认知加工与脑区功能的关系。与传统的基于激活水平的数据分析方法相比,该方法不需要设置严格的实验条件,能更好地应用于自然情境下的脑成像研究。文章介绍了被试间相关分析的基本原理和方法,分析了该方法如何识别认知功能脑区及其可靠性,并结合其在自然情境脑成像以及特定研究领域的应用,阐明被试间相关在自然情境脑成像研究中的优势,以及该方法在多个研究领域的广泛应用扩展了认知神经科学研究的深度和广度。     

Impacts of artificial intelligence on organizational decision making

Of all of the new technologies emerging in the late 20th century, the production of artificial intelligence may provide the most profound impacts on organizational decision making. Because the development of artificial intelligence technologies and models has largely been based on psychological models of human cognition, the effects of their implementation in complex social settings have not been thoroughly examined. This paper is an attempt to generate research which will develop a comprehensive understanding of the impacts of artificial intelligence and its role in complex organizations. A set of 11 hypotheses has been developed which examine the relationships between artificial intelligence technologies and the dimensions of organizational decision making. It is argued here that the implementation of expert systems will lead to less complex and political decision processes, while the implementation of natural language systems will lead to more complex and political decision processes.     

Decision theory and artificial intelligence II: The hungry monkey

This paper describes a problem-solving framework In which aspects of mathematical decision theory are incorporated into symbolic problem-solving techniques currently predominant in artificial intelligence. The utility function of decision theory IS used to reveal tradeoffs among competing strategies for achieving various goals, taking into account such factors as reliability, the complexity of steps in the strategy, and the value of the goal. The utility function on strategies can therefore be used as a guide when searching for good strategies. It is also used to formulate solutions to the problems of how to acquire a world model, how much planning effort is worthwhile, and whether verification tests should be performed. These techniques are illustrated by application to the classic monkey and bananas problem.     

Number sense and quantifier interpretation

We consider connections between number sense—the ability to judge number—and the interpretation of natural language quantifiers. In particular, we present empirical evidence concerning the neuroanatomical underpinnings of number sense and quantifier interpretation. We show, further, that impairment of number sense in patients can result in the impairment of the ability to interpret sentences containing quantifiers. This result demonstrates that number sense supports some aspects of the language faculty.

Open and closed cortico-subcortical loops: A neuro-computational account of access to consciousness in the distractor-induced blindness paradigm

How the brain decides which information to process ‘consciously’ has been debated over for decades without a simple explanation at hand. While most experiments manipulate the perceptual energy of presented stimuli, the distractor-induced blindness task is a prototypical paradigm to investigate gating of information into consciousness without or with only minor visual manipulation. In this paradigm, subjects are asked to report intervals of coherent dot motion in a rapid serial visual presentation (RSVP) stream, whenever these are preceded by a particular color stimulus in a different RSVP stream. If distractors (i.e., intervals of coherent dot motion prior to the color stimulus) are shown, subjects’ abilities to perceive and report intervals of target dot motion decrease, particularly with short delays between intervals of target color and target motion.We propose a biologically plausible neuro-computational model of how the brain controls access to consciousness to explain how distractor-induced blindness originates from information processing in the cortex and basal ganglia. The model suggests that conscious perception requires reverberation of activity in cortico-subcortical loops and that basal-ganglia pathways can either allow or inhibit this reverberation. In the distractor-induced blindness paradigm, inadequate distractor-induced response tendencies are suppressed by the inhibitory ‘hyperdirect’ pathway of the basal ganglia. If a target follows such a distractor closely, temporal aftereffects of distractor suppression prevent target identification. The model reproduces experimental data on how delays between target color and target motion affect the probability of target detection.