Toward A Multilevel Analysis of Human Attentional Networks

Toward A Multilevel Analysis of Human Attentional Networks Hongbin Wang (Hongbin.Wang@uth.tmc.edu)* Jin Fan (Jin.Fan@mssm.edu)** Yingrui Yang (yangyri@rpi.edu)*** *School of Health Information Sciences, University of Texas Health Science Center at Houston **Department of Psychiatry, Mount Sinai School of Medicine ***Department of Cognitive Science, Rensselaer Polytechnic Institute and the connectionist modeling framework. Their conclusion was that both frameworks had great strengths as well as serious limitations as candidates of the unified theory of cognition. Abstract Attention is a complex multilevel system subserved by at least three interacting attentional networks in the brain. This paper describes a multilevel computational model of attentional networks, developed in both the symbolic architecture of ACT-R and the connectionist framework of leabra. We evaluated the model using the Attentional Networks Test and the simulation results fitted the empirical data well. We argue that developing multilevel computational models helps to link findings at different levels. A) 2x+3=9 x=? B) To solve x, I move 3 to the right, and subtract it from 9 so I get 6. Then I divide . …, x is 3. ” Introduction E) C) fact arg ans Suppose a student S was asked to solve the equation “2x + 3 = 9” (Figure 1A), and he used 2 seconds to produce the answer “x = 3”. Both cognitive scientists X and Y were interested in understanding how S did it. Scientist X recorded S’s detailed verbal protocol (Figure 1B), based on which, and other relevant behavioral measures, X hypothesized the possible knowledge structures underlying S’s problem solving and developed a symbolic computational model that simulated the process (Figure 1C). On the other hand, scientist Y adopted sophisticated brain imaging techniques such as electroencephalograph (EEG) and functional Magnetic Resonance Imaging (fMRI) and acquired a high-resolution recording of S’s brain dynamics during problem solving (Figure 1D). Based on some well- established neural computing principles, Y then developed a biologically realistic connectionist model to simulate the brain activities underlying S’s performance (Figure 1E). Though both models fitted the data well, the two models are clearly different. While the symbolic model offers a description of the process with psychological plausibility and high behavioral relevance, the connectionist model emphasizes the process’ biological realism and brain foundations. One question is, do we, cognitive scientists who endeavor to discover unified theories of cognition, have justifiable reasons to prefer one to another? This question and similar others have led to a long debate in the rather brief history of cognitive science (e.g., Churchland & Sejnowski, 1992; Newell, 1990; Rumelhart & McClelland, 1986). Recently a BBS (Behavioral and Brain Sciences) target article was dedicated to this issue (Anderson & Lebiere, 2003). The authors adopted a set of 12 criteria, which they called “The Newell Test”, to systematically compared and contrasted ACT-R, a rule- based cognitive architecture (Anderson & Lebiere, 1998), D) isa O O O. O O O O . O O O O . O sub trac- tion arg prefrontal cortex O O O. O O O O . O O O O . O If the goal is to solve ax+b=c Then transform it to ax=c-b temporal cortex O O O. O O O O . O O O O . O O O O. O O O O . O O O O . O parietal cortex visual cortex Figure 1. A hypothetical equation-solving problem is presented in A. Verbal protocol and brain imaging data are presented in B and D. Sketches of a symbolic model and a connectionist model of task are presented in C and E. This is hardly surprising given the inherent complexity of the human mind itself. It has long been recognized that the mind is a multilevel construct and can be analyzed at different levels. Marr, for example, distinguished and separated among computational theory, representation and algorithm, and hardware implementation (Marr, 1982). Similar distinctions were made by Newell among different bands of cognitive functions (Newell, 1990). Newell argued that different bands utilize different basic operators, which have different time scales. More importantly, different bands form a hierarchy. Multiple lower lever basic operators can be combined to form higher level basic operators. In other words, lower level operators can be summarized up at higher level though this summarization may not be linear.