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Information and perception of meaningful patterns.

Del Viva MM, Punzi G, Benedetti D - PLoS ONE (2013)

Bottom Line: In this work we formulate a model of early vision based on a pattern-filtering architecture, partly inspired by high-speed digital data reduction in experimental high-energy physics (HEP).This allows a much stronger data reduction than models based just on redundancy reduction.These results suggest that the limitedness of computing resources can play an important role in shaping the nature of perception, by determining what is perceived as "meaningful features" in the input data.

View Article: PubMed Central - PubMed

Affiliation: NEUROFARBA Dipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino Sezione di Psicologia, Università di Firenze, Firenze, Italy. Michela@in.cnr.it

ABSTRACT
The visual system needs to extract the most important elements of the external world from a large flux of information in a short time for survival purposes. It is widely believed that in performing this task, it operates a strong data reduction at an early stage, by creating a compact summary of relevant information that can be handled by further levels of processing. In this work we formulate a model of early vision based on a pattern-filtering architecture, partly inspired by high-speed digital data reduction in experimental high-energy physics (HEP). This allows a much stronger data reduction than models based just on redundancy reduction. We show that optimizing this model for best information preservation under tight constraints on computational resources yields surprisingly specific a-priori predictions for the shape of biologically plausible features, and for experimental observations on fast extraction of salient visual features by human observers. Interestingly, applying the same optimized model to HEP data acquisition systems based on pattern-filtering architectures leads to specific a-priori predictions for the relevant data patterns that these devices extract from their inputs. These results suggest that the limitedness of computing resources can play an important role in shaping the nature of perception, by determining what is perceived as "meaningful features" in the input data.

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Percentage of correct discrimination, averaged over all subjects, plotted as a function of the number of matched patterns, for the same data as in fig. 5c and 5d.
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pone-0069154-g006: Percentage of correct discrimination, averaged over all subjects, plotted as a function of the number of matched patterns, for the same data as in fig. 5c and 5d.

Mentions: It must however be noted that the distributions of the number of points found in these two sets, taken over the whole image database (fig. 5f), have different average values: ∼14000 for the alternative set and ∼24000 for the set predicted by the model. Therefore, we performed an additional test to exclude that the observed difference in average performance might be due to the difference in the average number of visible points. For each experimental trial, we reweighted in the final average the data taken with the pattern set predicted by the model by a factor equal to the ratio of the probability distributions of the two sets, i.e. the ratio of the heights of the histogram bars in (fig 5f) corresponding to the number of points of the sketch presented. In this way, the density distribution of the predicted patterns is forced to match that of the rarer patterns, and any possible dependence of the result on the density of the image gets equalized between the two sets. The results (fig. 5e) show that the reweighting procedure has no significant effect (it shifts the results by less than one standard deviation). To further investigate the issue, we replotted our data differently, splitting the trials in different sets, according to classes defined by the number of points in the sketches (fig. 6). The difference in discrimination performance between the two sets is apparent over the whole range: even densely-populated sketches made of rare patterns are less visible than those from the standard set confirming that the number of displayed points plays no measurable role in our measurements.


Information and perception of meaningful patterns.

Del Viva MM, Punzi G, Benedetti D - PLoS ONE (2013)

Percentage of correct discrimination, averaged over all subjects, plotted as a function of the number of matched patterns, for the same data as in fig. 5c and 5d.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3716808&req=5

pone-0069154-g006: Percentage of correct discrimination, averaged over all subjects, plotted as a function of the number of matched patterns, for the same data as in fig. 5c and 5d.
Mentions: It must however be noted that the distributions of the number of points found in these two sets, taken over the whole image database (fig. 5f), have different average values: ∼14000 for the alternative set and ∼24000 for the set predicted by the model. Therefore, we performed an additional test to exclude that the observed difference in average performance might be due to the difference in the average number of visible points. For each experimental trial, we reweighted in the final average the data taken with the pattern set predicted by the model by a factor equal to the ratio of the probability distributions of the two sets, i.e. the ratio of the heights of the histogram bars in (fig 5f) corresponding to the number of points of the sketch presented. In this way, the density distribution of the predicted patterns is forced to match that of the rarer patterns, and any possible dependence of the result on the density of the image gets equalized between the two sets. The results (fig. 5e) show that the reweighting procedure has no significant effect (it shifts the results by less than one standard deviation). To further investigate the issue, we replotted our data differently, splitting the trials in different sets, according to classes defined by the number of points in the sketches (fig. 6). The difference in discrimination performance between the two sets is apparent over the whole range: even densely-populated sketches made of rare patterns are less visible than those from the standard set confirming that the number of displayed points plays no measurable role in our measurements.

Bottom Line: In this work we formulate a model of early vision based on a pattern-filtering architecture, partly inspired by high-speed digital data reduction in experimental high-energy physics (HEP).This allows a much stronger data reduction than models based just on redundancy reduction.These results suggest that the limitedness of computing resources can play an important role in shaping the nature of perception, by determining what is perceived as "meaningful features" in the input data.

View Article: PubMed Central - PubMed

Affiliation: NEUROFARBA Dipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino Sezione di Psicologia, Università di Firenze, Firenze, Italy. Michela@in.cnr.it

ABSTRACT
The visual system needs to extract the most important elements of the external world from a large flux of information in a short time for survival purposes. It is widely believed that in performing this task, it operates a strong data reduction at an early stage, by creating a compact summary of relevant information that can be handled by further levels of processing. In this work we formulate a model of early vision based on a pattern-filtering architecture, partly inspired by high-speed digital data reduction in experimental high-energy physics (HEP). This allows a much stronger data reduction than models based just on redundancy reduction. We show that optimizing this model for best information preservation under tight constraints on computational resources yields surprisingly specific a-priori predictions for the shape of biologically plausible features, and for experimental observations on fast extraction of salient visual features by human observers. Interestingly, applying the same optimized model to HEP data acquisition systems based on pattern-filtering architectures leads to specific a-priori predictions for the relevant data patterns that these devices extract from their inputs. These results suggest that the limitedness of computing resources can play an important role in shaping the nature of perception, by determining what is perceived as "meaningful features" in the input data.

Show MeSH