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• W2571814606 abstract "Using a Cognitive Model for an In-Depth Analysis of the Tower of London Rebecca Albrecht, Sven Br ussow, Christoph Kaller, and Marco Ragni albrechr@informatik.uni-freiburg.de; sven@cognition.uni-freiburg.de christoph.kaller@uniklinik-freiburg.de; ragni@cognition.uni-freiburg.de Center for Cognitive Science, University of Freiburg Friedrichstr. 50, D-79098 Freiburg, Germany Abstract (A) (A) The Tower of London (ToL) is a transformation task exten- sively used and well-established as a neuropsychological di- agnostic tool for assessing human planning ability in clinical and research contexts. Behavioral experiments have recently shown that planning in the ToL is substantially influenced by structural task parameters. This work presents an ACT-R model of the ToL that explains structural influences by using different strategies, whereby, strategy selection depends on vi- sually observable characteristics. Model evaluation was based on a problem selection that accounted for systematic variations of task demands. Based on comparisons with empirically ob- served planning latencies from previously published data, we argue that task-specific structural characteristics are necessary to explain human planning strategies. (B) (B) (a) Tower of London (b) Tower of Hanoi Figure 1: The goal in both tasks is to transform the start state (A) into the goal state (B). Introduction The Tower of London (ToL) task (see Fig. 1a) is a planning task originally proposed by Shallice as a neuropsychologi- cal tool to measure planning deficits in patients with frontal lobe damages (Shallice, 1982). Today it is widely used as a general assessment tool to evaluate executive and planning functions. In addition, the ToL has also been used in nu- merous studies within the domain of cognitive psychology (cf. Gilhooly, Phillips, Wynn, Logie, & Sala, 1999; Hodgson, Bajwa, Owen, & Kennard, 2000; Newman & Pittman, 2007; Phillips, Wynn, McPherson, & Gilhooly, 2001; Kaller, Unter- rainer, Rahm, & Halsband, 2004; Kaller, Rahm, Bolkenius, & Unterrainer, 2009; Ward & Allport, 1997). Typically participants receive a ToL problem (see Fig. 1a) as a start state (A) and a goal state (B). The task is to find a shortest sequence of moves transforming the start state into the goal state. A move consists of a colored bead, a start peg and a target peg. The constraints for executing a move are: (1) only one bead may be moved at a time and (2) only the top bead on any peg may be moved. ToL problems differ with respect to the number of moves, the number of beads and structural characteristics of problems to be solved (cf. Kaller, Rahm, Spreer, Weiller, & Unterrainer, 2011). The ToL task is in some respects similar to the Tower of Hanoi (ToH) task (see Fig. 1b) as it shares a similar environmental structure, the same constraints concerning the moves, and the kind of problem to be solved (cf. Kaller, Rahm, K¨ostering, & Unter- rainer, 2011). The beads in the ToH task, however, are distinct by their size. Therefore, the task has the additional constraint that only smaller beads may be placed on larger beads. The present work aims at elucidating the influence of struc- tural task properties on planning behavior. Analyses are fo- cused on the ToL that - compared to the ToH - was applied in the vast majority of related publications in MEDLINE-listed journals (cf. Kaller, Rahm, K¨ostering, & Unterrainer, 2011). We provide an ACT-R model which uses a general heuristic capable of solving the ToL tasks assessed in the experimental study. This model uses different strategies which are selected based on the structural distribution of beads in the environ- ment. State of the Art The Cognitive Architecture ACT-R. ACT-R is a modular cognitive architecture with an underlying production system operating on symbolic representations of declarative memory items – so-called chunks (Anderson & Lebiere, 1998; Ander- son et al., 2004). The system consists of specific modules corresponding to certain aspects of human cognition. Hence, the system provides a module for processing visually pre- sented information (vision module), a module for directing goal driven behavior (goal module), a module for inferring new information (imaginal module), and a module to store long-term memory items (declarative module). Each module has a dedicated interface (buffer) which can store one chunk at a time. The functionality of the system is driven by production rules which represent the procedural memory component. A" @default.
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• W2571814606 date "2011-01-01" @default.
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• W2571814606 title "Using a Cognitive Model for an In-Depth Analysis of the Tower of London" @default.
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