A spiking neural model applied to the study of human performance and cognitive decline on Raven's Advanced Progressive Matrices

• Published in: Intelligence (Norwood) Vol. 42; pp. 53 - 82
• Main Authors: Rasmussen, Daniel, Eliasmith, Chris
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.01.2014; Elsevier; Elsevier Science Ltd
• Subjects: Adult. Elderly; Aging; Biological and medical sciences; Cognitive decline; Cognitive impairment; Cognitive psychology; Correlation analysis; Developmental psychology; Fundamental and applied biological sciences. Psychology; Human performance; Human subjects; Humans; Intelligence; Intelligence tests; Matrices; Neurons; Neuropsychology; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Raven's Progressive Matrices; Research subjects; Spiking neural model; Task performance; Vector symbolic architectures; Cognitive decline; Aging; Spiking neural model; Raven's Progressive Matrices; Vector symbolic architectures; Human; Senescence; Cognitive disorder; Central nervous system; Cognitive ability; Psychometrics; Cognition; Progressive Matrices Test Raven; Encephalon; Intellectual deterioration; Models
• ISSN: 0160-2896; 1873-7935
• DOI: 10.1016/j.intell.2013.10.003

We present a spiking neural model capable of solving a popular test of intelligence, Raven's Advanced Progressive Matrices (RPM). The central features of this model are its ability to dynamically generate the rules needed to solve the RPM and its biologically detailed implementation in spiking neurons. We describe the rule generation processes, and demonstrate the model's ability to use the resulting rules to solve the RPM with similar performance and error patterns to human subjects. Investigating the rules in more detail, we show that they successfully capture abstract patterns in the data, enabling them to generalize to novel matrices. We also show that the same model can be used to solve a separate reasoning task, and demonstrates the expected positive correlation in performance across tasks. Finally, we demonstrate the advantages of the biologically detailed implementation by using the model to connect behavioral and neurophysiological data. Specifically, we investigate two neurophysiological explanations of cognitive decline in aging: neuron loss and representational “dedifferentiation”. We show that manipulations to the model that reflect these neurophysiological hypotheses result in performance changes that match observed human behavioral data. •We propose a spiking neural model capable of performing a standard intelligence test.•The model's performance compares well with human scores and error patterns.•The model infers rules that capture abstract, general patterns in its input.•When applied to a new task, the model shows a common ability across tasks.•We use the model to connect neurophysiological and behavioral data on aging.