79,107 research outputs found
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Mental Imagery and Chunks: Empirical and Computational Findings
To investigate experts’ imagery in chess, players were required to recall briefly-presented positions in which the pieces were placed on the intersections between squares (intersection positions). Position types ranged from game positions to positions where both the piece distribution and location were randomized. Simulations were run with the CHREST model (Gobet & Simon, 2000). The simulations assumed that pieces had to be centered back one by one to the middle of the squares in the mind’s eye before chunks could be recognized. Consistent with CHREST’s predictions, chess players (N = 36), ranging from weak amateurs to grandmasters, exhibited much poorer recall on intersection positions than on standard positions (pieces placed on centers of squares). On the intersection positions, the skill difference in recall was larger on game positions than on the randomized positions. Participants recalled bishops better than knights, suggesting that Stroop-like interference impairs recall of the latter. The data supported both the time parameter in CHREST for shifting pieces in the mind’s eye (125 ms per piece) and the seriality assumption. In general, the study reinforces the plausibility of CHREST as a model of cognition
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Computational Modelling of Mental Imagery in Chess: A Sensitivity Analysis
An important aim of cognitive science is to build computational models that account for a large number of phenomena but have few free parameters, and to obtain more veridical values for the models’ parameters by successive approximations. A good example of this approach is the CHREST model (Gobet & Simon, 2000), which has simulated numerous phenomena on chess expertise and in other domains. In this paper, we are interested in the parameter the model uses for shifting chess pieces in its mind’s eye (125 ms per piece), a parameter that had been estimated based on relatively sparse experimental evidence. Recently, Waters and Gobet (2008) tested the validity of this parameter in a memory experiment that required players to recall briefly presented positions in which the pieces were placed on the intersections between squares. Position types ranged from game positions to positions where both the piece distribution and location were randomised. CHREST, which assumed that pieces must be centred back to the middle of the squares in the mind’s eye before chunks can be recognized, simulated the data fairly well using the default parameter for shifting pieces. The sensitivity analysis presented in the current paper shows that the fit was nearly optimal for all groups of players except the grandmaster group for which, counterintuitively, a slower shifting time gave a better fit. The implications for theory development are discussed
Computational polarimetric microwave imaging
We propose a polarimetric microwave imaging technique that exploits recent
advances in computational imaging. We utilize a frequency-diverse cavity-backed
metasurface, allowing us to demonstrate high-resolution polarimetric imaging
using a single transceiver and frequency sweep over the operational microwave
bandwidth. The frequency-diverse metasurface imager greatly simplifies the
system architecture compared with active arrays and other conventional
microwave imaging approaches. We further develop the theoretical framework for
computational polarimetric imaging and validate the approach experimentally
using a multi-modal leaky cavity. The scalar approximation for the interaction
between the radiated waves and the target---often applied in microwave
computational imaging schemes---is thus extended to retrieve the susceptibility
tensors, and hence providing additional information about the targets.
Computational polarimetry has relevance for existing systems in the field that
extract polarimetric imagery, and particular for ground observation. A growing
number of short-range microwave imaging applications can also notably benefit
from computational polarimetry, particularly for imaging objects that are
difficult to reconstruct when assuming scalar estimations.Comment: 17 pages, 15 figure
Modelling spatial recall, mental imagery and neglect
We present a computational model of the neural mechanisms in the pari-etal and temporal lobes that support spatial navigation, recall of scenes and imagery of the products of recall. Long term representations are stored in the hippocampus, and are associated with local spatial and object-related features in the parahippocampal region. Viewer-centered representations are dynamically generated from long term memory in the parietal part of the model. The model thereby simulates recall and im-agery of locations and objects in complex environments. After parietal damage, the model exhibits hemispatial neglect in mental imagery that rotates with the imagined perspective of the observer, as in the famous Milan Square experiment [1]. Our model makes novel predictions for the neural representations in the parahippocampal and parietal regions and for behavior in healthy volunteers and neuropsychological patients.
Computational Ghost Imaging
Ghost-imaging experiments correlate the outputs from two photodetectors: a
high spatial-resolution (scanning pinhole or CCD camera) detector that measures
a field which has not interacted with the object to be imaged, and a bucket
(single-pixel) detector that collects a field that has interacted with the
object. We describe a computational ghost-imaging arrangement that uses only a
single-pixel detector. This configuration affords background-free imagery in
the narrowband limit and a 3D sectioning capability. It clearly indicates the
classical nature of ghost-image formation.Comment: 4 pages, 3 figure
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