Effects of strategy training on Progressive Matrices performance

Individual Differences in Reward-Based Learning Predict Fluid Reasoning Abilities

The ability to reason and problem-solve in novel situations, as measured by the Raven's Advanced Progressive Matrices (RAPM), is highly predictive of both cognitive task performance and real-world outcomes. Here we provide evidence that RAPM performance depends on the ability to reallocate attention in response to self-generated feedback about progress. We propose that such an ability is underpinned by the basal ganglia nuclei, which are critically tied to both reward processing and cognitive control. This hypothesis was implemented in a neurocomputational model of the RAPM task, which was used to derive novel predictions at the behavioral and neural levels. These predictions were then verified in one neuroimaging and two behavioral experiments. Furthermore, an effective connectivity analysis of the neuroimaging data confirmed a role for the basal ganglia in modulating attention. Taken together, these results suggest that individual differences in a neural circuit related to reward processing underpin human fluid reasoning abilities.

AI, visual imagery, and a case study on the challenges posed by human intelligence tests

Observations abound about the power of visual imagery in human intelligence, from how Nobel prize-winning physicists make their discoveries to how children understand bedtime stories. These observations raise an important question for cognitive science, which is, what are the computations taking place in someone's mind when they use visual imagery? Answering this question is not easy and will require much continued research across the multiple disciplines of cognitive science. Here, we focus on a related and more circumscribed question from the perspective of artificial intelligence (AI): If you have an intelligent agent that uses visual imagery-based knowledge representations and reasoning operations, then what kinds of problem solving might be possible, and how would such problem solving work? We highlight recent progress in AI toward answering these questions in the domain of visuospatial reasoning, looking at a case study of how imagery-based artificial agents can solve visuospatial intelligence tests. In particular, we first examine several variations of imagery-based knowledge representations and problem-solving strategies that are sufficient for solving problems from the Raven's Progressive Matrices intelligence test. We then look at how artificial agents, instead of being designed manually by AI researchers, might learn portions of their own knowledge and reasoning procedures from experience, including learning visuospatial domain knowledge, learning and generalizing problem-solving strategies, and learning the actual definition of the task in the first place.

Visual mental imagery: A view from artificial intelligence

This article investigates whether, and how, an artificial intelligence (AI) system can be said to use visual, imagery-based representations in a way that is analogous to the use of visual mental imagery by people. In particular, this article aims to answer two fundamental questions about imagery-based AI systems. First, what might visual imagery look like in an AI system, in terms of the internal representations used by the system to store and reason about knowledge? Second, what kinds of intelligent tasks would an imagery-based AI system be able to accomplish? The first question is answered by providing a working definition of what constitutes an imagery-based knowledge representation, and the second question is answered through a literature survey of imagery-based AI systems that have been developed over the past several decades of AI research, spanning task domains of: 1) template-based visual search; 2) spatial and diagrammatic reasoning; 3) geometric analogies and matrix reasoning; 4) naive physics; and 5) commonsense reasoning for question answering. This article concludes by discussing three important open research questions in the study of visual-imagery-based AI systems-on evaluating system performance, learning imagery operators, and representing abstract concepts-and their implications for understanding human visual mental imagery.

Training Cognitive Strategies Underlying Intelligent Problem Solving

Cognitive strategies underlying excellent performance of intelligent people on the Raven's Progressive Matrices Test were used to develop a teaching package. 24 subjects in a Cognitive Strategies group were trained using this teaching package. An Exposure group of 17 subjects were not trained but solved all the examples of puzzles in the package. A Control group, with 13 subjects, received no intervention. Subjects were pre- and posttested on matrix solving ability and were posttested on a Piagetian multiplicative classification task. The Cognitive Strategies group showed the greatest improvement pre- to posttest, followed by the Exposure group and then the Control group. The Cognitive Strategies group was superior to both controls on the Piagetian task, indicating a broad improvement in cognitive functioning.

Das Huckepack-Theorem asymmetrischen Strategietransfers

Zusammenfassung. Im ersten Teil des Beitrags wird ein Theorem vorgestellt, wonach eine speziellere Strategie Transfer auf eine allgemeinere Strategie bewirkt, aber nicht umgekehrt eine allgemeinere Strategie Transfer auf eine speziellere. Im zweiten Teil werden Hypothesen abgeleitet, und es wird gezeigt, daß das Theorem in der Lage ist, unerwartete Befunde aus der Forschung zum Training von Strategien vorherzusagen. Im dritten Teil wird der Ansatz auf die Wirkung komplexer pädagogischer Maßnahmen verallgemeinert und Konsequenzen für die Forschungsplanung werden kurz diskutiert.

A comparative study of dynamic and static testing in abstinent alcoholics

The performance of 44 Finnish alcoholics was measured on a Vygotskian version of the Raven's Progressive Matrices and four traditional static tests (the Digit Symbol and Block Design subtests from the WAIS, Part B of the Trial Making Test, and the Embedded Figures Test). Of the static tests only the Block Design showed consistent congruence with the results of the learning potential test. The other static tests discriminated only partially from each other the groups which were formed according to learning potential. The most fruitful way to test alcoholics is probably to have a flexible combination of static and dynamic tests. We also need to have tests that are applicable to both static and dynamic testing.

Measuring the learning potential of abstinent alcoholics

The performance of 89 Finnish alcoholics was measured on a Vygotskian test of learning potential. There were four questions to analyse: (1) can the performance of alcoholics be improved by means of instruction in non-verbal problem solving tasks; (2) how extensive is the transfer effect; (3) how does the duration of abstinence affect test performance; and (4) how does the actual performance level affect post-test results? Results indicated that instruction had a clear positive effect on the performance of alcoholics in simple tasks, but no transfer effect was evident in the more complex tasks. The duration of abstinence had no general effect. Although analysis of the mean scores of time-level groups showed some improvement in performance with increasing duration of abstinence, intra-group deviation was so high that the trends were not statistically significant. The subjects' actual performance level, on the other hand, was a very relevant factor. This was particularly evident in complex transfer tasks.