Pathfinding in the cognitive map: Network models of mechanisms for search and planning

Perelman, Brandon S.; Simpkins, Benjamin G.

doi:10.1016/j.bica.2013.05.002

Cited by 7 publications

(6 citation statements)

References 42 publications

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“…Bush et al (2015) reviews four more biologically plausible network models on Marr's implementation level. However, LIDA is concerned with the algorithmic level -and there is published behavioural evidence for such a mechanism (Mueller et al, 2013). We have previously succeeded in replicating two multi-goal route planning datasets using our simple model (in virtual as well as real environments -see (Madl, 2016)), which substantiates its cognitive plausibility.…”

Section: Neural Implausibilitiessupporting

confidence: 56%

A computational cognitive framework of spatial memory in brains and robots

Madl

Franklin

Chen

et al. 2018

Cognitive Systems Research

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Computational cognitive models of spatial memory often neglect difficulties posed by the real world, such as sensory noise, uncertainty, and high spatial complexity. On the other hand, robotics is unconcerned with understanding biological cognition. Here, we describe a computational framework for robotic architectures aiming to function in realistic environments, as well as to be cognitively plausible. We motivate and describe several mechanisms towards achieving this despite the sensory noise and spatial complexity inherent in the physical world. We tackle error accumulation during path integration by means of Bayesian localization, and loop closing with sequential gradient descent. Finally, we outline a method for structuring spatial representations using metric learning and clustering. Crucially, unlike the algorithms of traditional robotics, we show that these mechanisms can be implemented in neuronal or cognitive models. We briefly outline a concrete implementation of the proposed framework as part of the LIDA cognitive architecture, and argue that this kind of probabilistic framework is well-suited for use in cognitive robotic architectures aiming to combine spatial functionality and psychological plausibility.

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Section: Neural Implausibilitiessupporting

confidence: 56%

A computational cognitive framework of spatial memory in brains and robots

Madl

Franklin

Chen

et al. 2018

Cognitive Systems Research

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“…To deal with this, we implement a strategy to avoid revisiting failed clues, using counters shown in Figure 2 as Cycle and Attempt (that maps roughly onto a an activation marking past search; cf. Mueller et al, 2013 ). Although this particular implementation is somewhat ad hoc, the basic process is representative of a class of strategies that attempt to seek out novelty.…”

Section: An Recognition-primed Ai Model Of Crossword Playmentioning

confidence: 99%

“…In contrast, we observed that experts tended to make shorter, more deliberate moves from clue to clue, and appeared to solve clues that (1) were close to the current location in the puzzle, and (2) were already partially solved. We adapted a neurocomputational model of search goal selection (Mueller et al, 2013 ; Perelman and Mueller, 2013 ) to guide this Optimizing movement strategy model. The model first computes weights of each unsolved clue by using Equation (7).…”

Section: Model Simulationmentioning

confidence: 99%

Crossword expertise as recognitional decision making: an artificial intelligence approach

Thanasuan

2014

Front. Psychol.

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The skills required to solve crossword puzzles involve two important aspects of lexical memory: semantic information in the form of clues that indicate the meaning of the answer, and orthographic patterns that constrain the possibilities but may also provide hints to possible answers. Mueller and Thanasuan (2013) proposed a model accounting for the simple memory access processes involved in solving individual crossword clues, but expert solvers also bring additional skills and strategies to bear on solving complete puzzles. In this paper, we developed an computational model of crossword solving that incorporates strategic and other factors, and is capable of solving crossword puzzles in a human-like fashion, in order to understand the complete set of skills needed to solve a crossword puzzle. We compare our models to human expert and novice solvers to investigate how different strategic and structural factors in crossword play impact overall performance. Results reveal that expert crossword solving relies heavily on fluent semantic memory search and retrieval, which appear to allow experts to take better advantage of orthographic-route solutions, and experts employ strategies that enable them to use orthographic information. Furthermore, other processes central to traditional AI models (error correction and backtracking) appear to be of less importance for human players.

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“…In Experiment 1, we report two experiments conducted as part of a single project in which we evaluated the impact of mental stress induced by heat burden on a series of cognitive tasks whose summary values were reported by Mueller et al (2010) and Mueller et al (2013). The goal of these experiments was to establish how different cognitive functions were impacted by the physical and heat strain induced by wearing military protective gear (i.e., gas-impermeable clothing, gloves, goggles, and masks that enable the military to work in a potentially contaminated environment).…”

Section: Experiments 1a and 1bmentioning

confidence: 99%

“…Along with serving as a useful exercise in optimization (e.g., Applegate, Bixby, Chvátal, & Cook, 2006), human-solved versions of the TSP (which permit approximate solutions) have become a method of growing interest for understanding visual problem solving, navigation, and planning (see MacGregor & Ormerod, 1996;MacGregor & Chu, 2011). colleagues (Mueller, Jones, Minnery, &Hiland, 2007, Mueller, 2008;2010) identified the problem as measuring an important aspect of biological intelligence, forming one part of a modern-day embodied Turing test, and have also connected it to naturalistic search and mental models of pathfinding (Mueller, Perelman, & Simpkins, 2013;Perelman & Mueller, 2013a;Perelman & Mueller, 2013b;Perelman, 2015;Perelman & Mueller, 2015).…”

mentioning

confidence: 99%

Development of the PEBL Traveling Salesman Problem Computerized Testbed

Perelman¹,

Tan²,

Thanasuan³

2015

The Journal of Problem Solving

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The traveling salesman problem (TSP) is a combinatorial optimization problem that requires finding the shortest path through a set of points ("cities") that returns to the starting point. Because humans provide heuristic near-optimal solutions to Euclidean versions of the problem, it has sometimes been used to investigate human visual problem solving ability. The TSP is also similar to a number of tasks commonly used for neuropsychological assessment (such as the trail-making test), and so its utility in assessing reliable individual differences in problem solving has sometimes been examined. Nevertheless, the task has seen little widespread use in clinical and assessment domains, in part because no standard software implementation or item set is widely available with known psychometric properties. In this paper, we describe a computerized version of TSP running in the free and open source Psychology Experiment Building Language (PEBL). The PEBL TSP task is designed to be suitable for use within a larger battery of tests, and to examine both standard and custom TSP node configurations (i.e., problems). We report the results of a series of experiments that help establish the test's reliability and validity. The first experiment examines test-retest reliability, establishes that the quality of solutions in the TSP are not impacted by mild physiological strain, and demonstrates how solution quality obtained by individuals in a physical version is highly correlated with solution quality obtained in the PEBL version. The second experiment evaluates a larger set of problems, and uses the data to identify a small subset of tests that have maximal coherence. A third experiment examines test-retest reliability of this smaller set that can be administered in about five minutes, and establishes that these problems produce composite scores with moderately high (R = .75) test-retest reliability, making it suitable for use in many assessment situations, including evaluations of individual differences, personality, and intelligence testing.

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Pathfinding in the cognitive map: Network models of mechanisms for search and planning

Cited by 7 publications

References 42 publications

A computational cognitive framework of spatial memory in brains and robots

A computational cognitive framework of spatial memory in brains and robots

Crossword expertise as recognitional decision making: an artificial intelligence approach

Development of the PEBL Traveling Salesman Problem Computerized Testbed

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