Relationships between working memory and intelligence from a developmental perspective: Convergent evidence from a neo-Piagetian and a psychometric approach

The objective of this paper, in line with the other papers of this special issue, is to show the potentialities of combining intelligence research and cognitive psychology. The development of intelligence is here addressed from two usually separate perspectives, a psychometric one, and a neo-Piagetian one. Two studies are presented. In Experiment 1, children aged 6, 7, 9, and 11 years (N = 100) were administered two working memory tasks and three Piagetian tasks. In Experiment 2, children aged 8–12 years (N = 207), young adults aged 20–35 (N = 160), and older adults aged 60–88 years (N = 135) were administered working memory and processing speed tasks, as well as the Raven Standard Matrices task. Regression and commonality analyses were run to analyse the age-related variance in the Piagetian tasks (Study 1), and in the Raven task (Study 2). In both experiments, working memory accounted for a large part of the age differences observed, but more so in Study 1 (Piagetian tasks) than in Study 2 (Raven task). It is concluded that working memory mediates the effect of age on fluid intelligence during childhood and during adulthood.     

Epistemology and artificial intelligence

In this essay we advance the view that analytical epistemology and artificial intelligence are complementary disciplines. Both fields study epistemic relations, but whereas artificial intelligence approaches this subject from the perspective of understanding formal and computational properties of frameworks purporting to model some epistemic relation or other, traditional epistemology approaches the subject from the perspective of understanding the properties of epistemic relations in terms of their conceptual properties. We argue that these two practices should not be conducted in isolation. We illustrate this point by discussing how to represent a class of inference forms found in standard inferential statistics. This class of inference forms is interesting because its members share two properties that are common to epistemic relations, namely defeasibility and paraconsistency. Our modeling of standard inferential statistical arguments exploits results from both logical artificial intelligence and analytical epistemology. We remark how our approach to this modeling problem may be generalized to an interdisciplinary approach to the study of epistemic relations.     

个体差异对工作记忆训练迁移效果的调节

本研究考察了流体智力基线水平对工作记忆训练迁移效果的影响。采用前后测设计,以视觉和听觉双任务n-back作为工作记忆训练任务,对训练组进行为期一个月的训练;积极对照组采用阅读任务进行训练。结果发现积极控制组的流体智力水平在基线与后测之间无显著变化;而训练组流体智力水平在后测时与基线相比有显著提高,且工作记忆训练提升量越大的个体其流体智力改善越大。说明认知训练有效迁移到了流体智力水平的改善上。我们还发现流体智力基线水平调节了工作记忆训练对流体智力水平的迁移,即工作记忆训练提升量越大,流体智力改善值越大,对于那些流体智力基线水平较高的人来说,工作记忆训练对流体智力改善的效果更大。流体智力基线水平、工作记忆训练提升量及两者的乘积共同影响了流体智力改善值。这一结果表明个体差异如流体智力基线水平可以调节工作记忆训练对流体智力水平的迁移。     

Phenomenology and artificial intelligence: Husserl learns Chinese

For over a decade John Searle's ingenious argument against the possibility of artificial intelligence has held a prominent place in contemporary philosophy. This is not just because of its striking central example and the apparent simplicity of its argument. As its appearance in Scientific American testifies, it is also due to its importance to the wider scientific community. If Searle is right, artificial intelligence in the strict sense, the sense that would claim that mind can be instantiated through a formal program of symbol manipulation, is basically wrong. No set of formal conditions can provide us with the characteristic feature of mind which is the intentionally of its mental contents. Formally regarded, such intentionally is an irreducible primitive. It cannot be analyzed into non-intentional (purely syntactic, symbolic) components. This paper will argue that this objection is based on a misunderstanding. Intentionality is not simply something given which is incapable of further analysis. It only appears so when we mistakenly abstract it from time. When we regard its temporal structure, it shows itself as a rule-governed, synthetic process, one capable of being instantiated both by machines and men.     

Playing and working through: a neglected analogy

The author proposes an analogy between certain features of playing and aspects of working through. Conceptualizing psychoanalysis as the process whereby unconscious fantasy is uncovered and then subjected to rigorous scrutiny, and building on Freud's (1908) insight that play is the same as fantasy--with the essential difference that fantasy links itself to real objects in play, such as toys and playthings--the author proposes that play can be thought of as not merely symbolic, as a fantasy bearer, so to speak, but as a fantasy tester as well. In the process of working through, some analysands attach their unconscious fantasies not only to a transference object, a primary libidinal object, or a significant loved one, but also to actual props within the analytic setting (a Kleenex box, for example), making the analogy with play even more obvious and palpable.     

On intelligence II: A neo-Spearman model to replace Cattell's theory of fluid and crystallized intelligence

While many empirical studies now support second-order simple structure factor analytic distinctions consistent with Cattell's theory of fluid and crystallized intelligence, the hierarchical order analyses carried out in the present study show that these factors are structured so as to suggest a neo-Spearman hierarchical model where fluid intelligence is equivalent to a general intelligence factor and broad group factors of verbal-educational knowledge, speediness, and visualization appear. The two models have different implications regarding the status of the fluid and crystallized "intelligence" factors and the relationship between them.     

Projective identification and working through of the countertransference: A multiphase model

Referring to Melanie Klein's unpublished views on projective identification, Bion's theory of container/contained and Money‐Kyrle's understanding of countertransference as a process of transformation, the author develops a multiphase model of projective identification. He differentiates five subphases of (1) adhesion, (2) penetration, (3) linking of the projection with an internal object of the analyst, (4) transformation and (5) re‐projection. In the author's view the differentiation of overlapping subphases may be helpful to better localize problems of working through the countertransference. Some technical implications are illustrated by brief clinical vignettes. To conclude, the paper discusses typical impasses and options for interpretation.     

Clearing the mind: a working memory model of distraction from negative mood

The present research examined whether and how loading working memory can attenuate negative mood. In three experiments, participants were exposed to neutral, weakly negative, or strongly negative pictures followed by a task and a mood scale. Working memory demands were varied by manipulating task presence (Study 1), complexity (Study 2), and predictability (Study 3). Participants in all three experiments reported less negative moods in negative trials with high compared to low working memory demand. Working memory demands did not affect mood in the neutral trials. When working memory demands were high, participants no longer reported more negative moods in response to strongly negative pictures than to weakly negative pictures. These findings suggest that loading working memory prevents mood-congruent processing, and thereby promotes distraction from negative moods.     

人工智能决策的公平感知

不平等问题是全球社会和经济发展需要应对的首要挑战,也是实现全球可持续发展目标的核心障碍。人工智能(artificial intelligence, AI)为缓解不平等、促进社会公平提供了新的途径。然而,新近研究发现,即使客观上AI决策具有公平性和准确性,个体仍可能对AI决策的公平感知较低。因此,近年来越来越多的研究开始关注AI决策公平感知的影响因素。然而,目前研究较为分散,呈现出研究范式不统一、理论不清晰和机制未厘清等特征。这既不利于跨学科的研究对话,也不利于研究者和实践者对AI决策公平感知形成系统性理解。基于此,通过系统的梳理,现有研究可以划分为两类:(1) AI单一决策的公平感知研究,主要聚焦于AI特征和个体特征如何影响个体对AI决策的公平感知;(2) AI-人类二元决策的公平感知研究,主要聚焦于对比个体对AI决策与人类决策公平感知的差异。在上述梳理基础上,未来研究可以进一步探索AI决策公平感知的情绪影响机制等方向。     

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.     

Children's working memory: Its structure and relationship to fluid intelligence

Working memory (WM) has been predominantly studied in adults. The insights provided by these studies have led to the development of competing theories on the structure of WM and conflicting conclusions on how strongly WM components are related to higher order thinking skills such as fluid intelligence. However, it remains unclear whether and to what extent the theories and findings derived from adult data generalize to children. The purpose of the present study is therefore to investigate children's WM (N = 161), who attended classes at the end of kindergarten in Luxembourg. Specifically, we examine different structural models of WM and how its components, as defined in these models, are related to fluid intelligence. Our results indicate that short-term storage capacity primarily explains the relationship between WM and fluid intelligence. Based on these observations we discuss the theoretical and methodological issues that arise when children's WM is investigated.     

Towards truly human-level intelligence in artificial applications

Despite the fact that there are now a large number of successful bio-inspired applications in use in science and technology, we are still quite far removed from creating applications that display human-like intelligence. Putting together successful bio-inspired applications remains something of a black art; this is due to a lack of fundamental understanding of brain function. The causes for these problems were analysed in a ’Roadmap for Neuro-IT’ and were deemed to be sufficiently pressing to motivate one of five ’Grand Challenges’ in Neuro-IT: the ’Constructed Brain’. The challenge argued that one of the main bottlenecks to progress is that data taking and modelling in the neurosciences are being fractured across many research groups and communities; it makes proposals for addressing the issue. Similar observations, raised in two OECD workgroup papers have led to the formation of the International Neuroinformatics Coordinating Facility. As a consequence we can conclude that there is now a much higher awareness of the problems and that in the neurosciences the situation has improved dramatically. I will review recent initiatives to facilitate data management, modelling and simulation in the neurosciences. One problem remains unaddressed, however. The project-based funding of the brain sciences sets an upper limit to the complexity of brain models. Since the brain is truly complex, any individual project will fall short of capturing the brain’s complexity. The creation of a central infrastructure for the brain sciences is inescapable, but is unlikely to be realised soon. I will outline suggestions to handle the current situation.     

Predication,fiction, and artificial intelligence

This paper describes the SNePS knowledge-representation and reasoning system. SNePS is an intensional, propositional, semantic-network processing system used for research in AI. We look at how predication is represented in such a system when it is used for cognitive modeling and natural-language understanding and generation. In particular, we discuss issues in the representation of fictional entities and the representation of propositions from fiction, using SNePS. We briefly survey four philosophical ontological theories of fiction and sketch an epistemological theory of fiction (implemented in SNePS) using a story operator and rules for allowing propositions to migrate into and out of story spaces.     

A competence framework for artificial intelligence research

ABSTRACT

While over the last few decades AI research has largely focused on building tools and applications, recent technological developments have prompted a resurgence of interest in building a genuinely intelligent artificial agent – one that has a mind in the same sense that humans and animals do. In this paper, I offer a theoretical and methodological framework for this project of investigating “artificial minded intelligence” (AMI) that can help to unify existing approaches and provide new avenues for research. I first outline three desiderata that a framework for AMI research should satisfy. In Section 1, I further motivate the desiderata as well as the need for a new framework. Section 2 develops a general methodological approach, the “generative methodology,” and Section 3 develops a version of this methodology, the “Competence Framework for AI research” (CFAI).