ECOPAMPA: A new tool for automatic fish schools detection and assessment from echo data

Accurate identification of aquatic organisms and their numerical abundance calculation using echo detection techniques remains a great challenge for marine researchers. A software architecture for echo data processing is presented in this article. Within it, it is discussed how to obtain energetic, morphometric and bathymetric fish school descriptors to accurately identify different fish-species. To accomplish this task it was necessary to have a development platform that allowed reading echo data from a particular echosounder, to detect fish aggregations and then to calculate fish school descriptors that would be used for fish-species identification, in an automatic way. This article also describes thoroughly the digital processing algorithms for this automatic detection and classification, as well as the automatic process required for surface and bottom line detection, which is necessary to determine the exploration range. These algorithms are implemented within the ECOPAMPA software, which is the first Argentinean system for marine species identification. Finally, a comparative result over experimental data of ECOPAMPA against EchoviewTM Software Pty Ltd (formerly Myriax Software Pty Ltd), is carefully examined

Adolescents show collective intelligence which can be driven by a geometric mean rule of thumb

How effective groups are in making decisions is a long-standing question in studying human and animal behaviour. Despite the limited social and cognitive abilities of younger people, skills which are often required for collective intelligence, studies of group performance have been limited to adults. Using a simple task of estimating the number of sweets in jars, we show in two experiments that adolescents at least as young as 11 years old improve their estimation accuracy after a period of group discussion, demonstrating collective intelligence. Although this effect was robust to the overall distribution of initial estimates, when the task generated positively skewed estimates, the geometric mean of initial estimates gave the best fit to the data compared to other tested aggregation rules. A geometric mean heuristic in consensus decision making is also likely to apply to adults, as it provides a robust and well-performing rule for aggregating different opinions. The geometric mean rule is likely to be based on an intuitive logarithmic-like number representation, and our study suggests that this mental number scaling may be beneficial in collective decisions

Reciprocity of Social Influence

Humans seek advice, via social interaction, to improve their decisions. While social interaction is often reciprocal, the role of reciprocity in social influence is unknown. Here, we tested the hypothesis that our influence on others affects how much we are influenced by them. Participants first made a visual perceptual estimate and then shared their estimate with an alleged partner. Then, in alternating trials, the participant either revised their decisions or observed how the partner revised theirs. We systematically manipulated the partner's susceptibility to influence from the participant. We show that participants reciprocated influence with their partner by gravitating toward the susceptible (but not insusceptible) partner's opinion. In further experiments, we showed that reciprocity is both a dynamic process and is abolished when people believed that they interacted with a computer. Reciprocal social influence is a signaling medium for human-to-human communication that goes beyond aggregation of evidence for decision improvement

Rescuing Collective Wisdom when the Average Group Opinion Is Wrong

The total knowledge contained within a collective supersedes the knowledge of even its most intelligent member. Yet the collective knowledge will remain inaccessible to us unless we are able to find efficient knowledge aggregation methods that produce reliable decisions based on the behavior or opinions of the collective’s members. It is often stated that simple averaging of a pool of opinions is a good and in many cases the optimal way to extract knowledge from a crowd. The method of averaging has been applied to analysis of decision-making in very different fields, such as forecasting, collective animal behavior, individual psychology, and machine learning. Two mathematical theorems, Condorcet’s theorem and Jensen’s inequality, provide a general theoretical justification for the averaging procedure. Yet the necessary conditions which guarantee the applicability of these theorems are often not met in practice. Under such circumstances, averaging can lead to suboptimal and sometimes very poor performance. Practitioners in many different fields have independently developed procedures to counteract the failures of averaging. We review such knowledge aggregation procedures and interpret the methods in the light of a statistical decision theory framework to explain when their application is justified. Our analysis indicates that in the ideal case, there should be a matching between the aggregation procedure and the nature of the knowledge distribution, correlations, and associated error costs. This leads us to explore how machine learning techniques can be used to extract near-optimal decision rules in a data-driven manner. We end with a discussion of open frontiers in the domain of knowledge aggregation and collective intelligence in general

Comparison of statistical predictions against experiments of social influence.

<p>(<b>A</b>) Probability distribution of estimations before (no info, blue) and after (full info, red) receiving the estimations made by other members of the group. Estimations are pooled from 24 different experiments obtained using different groups and questions, and are plotted together using a z-score, <i>z</i> ≡ (log(<i>x</i>)-<i>μ</i>_<i>p</i>)/<i>σ</i>_<i>p</i>, with <i>x</i> the estimation and <i>μ</i>_<i>p</i> and <i>σ</i>_<i>p</i> the mean and standard deviation before social interactions for each experiment. Points are experimental frequencies sampled at intervals of width 0.25 and solid line is a Gaussian fit. Shadowed surface is the area in which 95 per cent of the experiments are expected by the Gaussian fit. The statistical prediction is that after social interactions the distribution of answers is also a Gaussian in the logarithmic domain with the same mean and smaller standard deviation. (<b>B</b>) Same as (A) but before (no info, blue) and after (aggregated info, red) giving subjects the mean of the estimation of all subjects. The statistical prediction is that after social interactions the distribution of answers is also a Gaussian in the logarithmic domain with higher mean and smaller standard deviation. (<b>C</b>) Real vs predicted estimations after social interactions from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004594#pcbi.1004594.e015" target="_blank">Eq 4</a> as <math><mrow> log<msub><mi>x</mi><mn>2</mn></msub><mo>=</mo><msub><mi>w</mi><mi>p</mi></msub>log<msub><mi>x</mi><mn>1</mn></msub><mo>+</mo><msub><mi>w</mi><mi>s</mi></msub>log<msub><mi>x</mi><mi>s</mi></msub></mrow></math> using <math><mrow><msub><mi>w</mi><mi>s</mi></msub><mo>=</mo><mn>0.53</mn></mrow></math>. Different colors correspond to the six estimation tasks. (<b>D</b>) Distribution of experimental social weights with Gaussian kernel smoothing (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004594#sec004" target="_blank">Methods</a>). Data taken from Lorenz <i>et al</i>. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004594#pcbi.1004594.ref009" target="_blank">9</a>]</p

Comparison of true value, ‘wisdom of the crowds’ (WOC) and the prediction from the subgroup of individuals resisting social information.

<p><b>resist 1</b> computed from individuals with low social weights and contributing more the values of <math><mi>ω</mi></math> with higher significance (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004594#pcbi.1004594.e030" target="_blank">Eq 8</a>). <b>resist 2</b> computed as ‘resist 1’ but not weighting the different <math><mi>ω</mi></math> differently depending on significance levels. <b>resist 3</b> corresponds to the value of <math><mi>ω</mi></math> with highest significance. <math><mrow><msub><mi>γ</mi><mrow><msub><mi>w</mi><mi>s</mi></msub></mrow></msub></mrow></math> = 6, 4, 3, 2 give the central values of the peaks at low social weights obtained from a Gaussian mixture at a resolution in the direction of social weight <math><mrow><msub><mi>w</mi><mi>s</mi></msub></mrow></math> obtained introducing the values of <math><mrow><msub><mi>γ</mi><mrow><msub><mi>w</mi><mi>s</mi></msub></mrow></msub></mrow></math> in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004594#pcbi.1004594.e020" target="_blank">Eq 7</a>. <b>Border</b>, ‘What is length of the Swiss/Italian border?’ <b>Rapes</b>, ‘How many rapes were officially registered in Switzerland in 2006?’ <b>Assaults</b>, ‘How many assaults were officially registered in Switzerland in 2006?’ <b>Population</b>, ‘What is the population density of Switzerland in inhabitants per square kilometer?’</p

Improving Collective Estimations Using Resistance to Social Influence

<div><p>Groups can make precise collective estimations in cases like the weight of an object or the number of items in a volume. However, in others tasks, for example those requiring memory or mental calculation, subjects often give estimations with large deviations from factual values. Allowing members of the group to communicate their estimations has the additional perverse effect of shifting individual estimations even closer to the biased collective estimation. Here we show that this negative effect of social interactions can be turned into a method to improve collective estimations. We first obtained a statistical model of how humans change their estimation when receiving the estimates made by other individuals. We confirmed using existing experimental data its prediction that individuals use the weighted geometric mean of private and social estimations. We then used this result and the fact that each individual uses a different value of the social weight to devise a method that extracts the subgroups resisting social influence. We found that these subgroups of individuals resisting social influence can make very large improvements in group estimations. This is in contrast to methods using the confidence that each individual declares, for which we find no improvement in group estimations. Also, our proposed method does not need to use historical data to weight individuals by performance. These results show the benefits of using the individual characteristics of the members in a group to better extract collective wisdom.</p></div

Wisdom of those resisting social influence for three questions.

<p>Analysis as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004594#pcbi.1004594.g002" target="_blank">Fig 2B and 2C</a> but for the questions (<b>A, B</b>) ‘How many rapes were officially registered in Switzerland in 2006?’, (<b>C, D</b>) ‘How many assaults were officially registered in Switzerland in 2006?’, and (<b>E, F</b>) ‘What is the population density of Switzerland in inhabitants per square kilometer?’ See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004594#pcbi.1004594.s003" target="_blank">S3 Fig</a> for densities in (<b>D</b>) and (<b>F</b>) without ellipses. Data taken from Lorenz <i>et al</i>. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004594#pcbi.1004594.ref009" target="_blank">9</a>].</p