Mandevillian Intelligence: From Individual Vice to Collective Virtue

Abstract Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive vices (i.e., shortcomings, limitations, constraints and biases) are seen to play a positive functional role in yielding collective forms of cognitive success. In this talk, I will introduce the concept of mandevillian intelligence and review a number of strands of empirical research that help to shed light on the phenomenon. I will also attempt to highlight the value of the concept of mandevillian intelligence from a philosophical, scientific and engineering perspective. Inasmuch as we accept the notion of mandevillian intelligence, then it seems that the cognitive and epistemic value of a specific social or technological intervention will vary according to whether our attention is focused at the individual or collective level of analysis. This has a number of important implications for how we think about the cognitive impacts of a number of Web-based technologies (e.g., personalized search mechanisms). It also forces us to take seriously the idea that the exploitation (or even the accentuation!) of individual cognitive shortcomings could, in some situations, provide a productive route to collective forms of cognitive and epistemic success. Speaker Biography Dr Paul Smart Paul Smart is a senior research fellow in the Web and Internet Science research group at the University of Southampton in the UK. He is a Fellow of the British Computer Society, a professional member of the Association of Computing Machinery, and a member of the Cognitive Science Society. Paul’s research interests span a number of disciplines, including philosophy, cognitive science, social science, and computer science. His primary area of research interest relates to the social and cognitive implications of Web and Internet technologies. Paul received his bachelors degree in Psychology from the University of Nottingham. He also holds a PhD in Experimental Psychology from the University of Sussex.

Using the interaural time difference and cross-correlation to localise short-term complex noises

The mammalian binaural cue of interaural time difference (ITD) and cross-correlation have long been used to determine the point of origin of a sound source. The ITD can be defined as the different points in time at which a sound from a single location arrives at each individual ear [1]. From this time difference, the brain can calculate the angle of the sound source in relation to the head [2]. Cross-correlation compares the similarity of each channel of a binaural waveform producing the time lag or offset required for both channels to be in phase with one another. This offset corresponds to the maximum value produced by the cross-correlation function and can be used to determine the ITD and thus the azimuthal angle θ of the original sound source. However, in indoor environments, cross-correlation has been known to have problems with both sound reflections and reverberations. Additionally, cross-correlation has difficulties with localising short-term complex noises when they occur during a longer duration waveform, i.e. in the presence of background noise. The crosscorrelation algorithm processes the entire waveform and the short-term complex noise can be ignored. This paper presents a technique using thresholding which enables higher-localisation abilities for short-term complex sounds in the midst of background noise. To determine the success of this thresholding technique, twenty-five sounds were recorded in a dynamic and echoic environment. The twenty-five sounds consist of hand-claps, finger-clicks and speech. The proposed technique was compared to the regular cross-correlation function for the same waveforms, and an average of the azimuthal angles determined for each individual sample. The sound localisation ability for all twenty-five sound samples is as follows: average of the sampled angles using cross-correlation: 44%; cross-correlation technique with thresholding: 84%. From these results, it is clear that this proposed technique is very successful for the localisation of short-term complex sounds in the midst of background noise and in a dynamic and echoic indoor environment.

Is Experts' Knowledge Modular?

This paper explores, both with empirical data and with computer simulations, the extent to which modularity characterises experts' knowledge. We discuss a replication of Chase and Simon's (1973) classic method of identifying 'chunks', i.e., perceptual patterns stored in memory and used as units. This method uses data about the placement of pairs of items in a memory task and consists of comparing latencies between these items and the number and type of relations they share. We then compare the human data with simulations carried out with CHREST, a computer model of perception and memory. We show that the model, based upon the acquisition of a large number of chunks, accounts for the human data well. This is taken as evidence that human knowledge is organised in a modular fashion.

SOCIAL AND ECOLOGICAL FACTORS INFLUENCING COLLECTIVE BEHAVIOR IN GIANT DANIO

A fundamental problem in biology is understanding how and why things group together. Collective behavior is observed on all organismic levels - from cells and slime molds, to swarms of insects, flocks of birds, and schooling fish, and in mammals, including humans. The long-term goal of this research is to understand the functions and mechanisms underlying collective behavior in groups. This dissertation focuses on shoaling (aggregating) fish. Shoaling behaviors in fish confer foraging and anti-predator benefits through social cues from other individuals in the group. However, it is not fully understood what information individuals receive from one another or how this information is propagated throughout a group. It is also not fully understood how the environmental conditions and perturbations affect group behaviors. The specific research objective of this dissertation is to gain a better understanding of how certain social and environmental factors affect group behaviors in fish. I focus on two ecologically relevant decision-making behaviors: (i) rheotaxis, or orientation with respect to a flow, and (ii) startle response, a rapid response to a perceived threat. By integrating behavioral and engineering paradigms, I detail specifics of behavior in giant danio Devario aequipinnatus (McClelland 1893), and numerically analyze mathematical models that may be extended to group behavior for fish in general, and potentially other groups of animals as well. These models that predict behavior data, as well as generate additional, testable hypotheses. One of the primary goals of neuroethology is to study an organism's behavior in the context of evolution and ecology. Here, I focus on studying ecologically relevant behaviors in giant danio in order to better understand collective behavior in fish. The experiments in this dissertation provide contributions to fish ecology, collective behavior, and biologically-inspired robotics.

Understanding information use in multiattribute decision making

An inference task in one in which some known set of information is used to produce an estimate about an unknown quantity. Existing theories of how humans make inferences include specialized heuristics that allow people to make these inferences in familiar environments quickly and without unnecessarily complex computation. Specialized heuristic processing may be unnecessary, however; other research suggests that the same patterns in judgment can be explained by existing patterns in encoding and retrieving memories. This dissertation compares and attempts to reconcile three alternate explanations of human inference. After justifying three hierarchical Bayesian version of existing inference models, the three models are com- pared on simulated, observed, and experimental data. The results suggest that the three models capture different patterns in human behavior but, based on posterior prediction using laboratory data, potentially ignore important determinants of the decision process.

The influence of surface and deep cues on primary and secondary school students’ assessment of relevance in Web menus

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Verbal emotional memory in children and adolescents with temporal lobe epilepsy: A first study

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SMS length and function: A comparative study of 13- to 18-year-old girls and boys

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An exploratory study of creativity, personality and schooling achievement

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The “handwriting brain”: a motor/linguistic network with specialized “writing-specific” areas?

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The writing brain. Functional neuroanatomy of written language production

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EFFECTS OF AGING ON MIDBRAIN AND CORTICAL SPEECH-IN-NOISE PROCESSING

Older adults frequently report that they can hear what they have been told but cannot understand the meaning. This is particularly true in noisy conditions, where the additional challenge of suppressing irrelevant noise (i.e. a competing talker) adds another layer of difficulty to their speech understanding. Hearing aids improve speech perception in quiet, but their success in noisy environments has been modest, suggesting that peripheral hearing loss may not be the only factor in the older adult’s perceptual difficulties. Recent animal studies have shown that auditory synapses and cells undergo significant age-related changes that could impact the integrity of temporal processing in the central auditory system. Psychoacoustic studies carried out in humans have also shown that hearing loss can explain the decline in older adults’ performance in quiet compared to younger adults, but these psychoacoustic measurements are not accurate in describing auditory deficits in noisy conditions. These results would suggest that temporal auditory processing deficits could play an important role in explaining the reduced ability of older adults to process speech in noisy environments. The goals of this dissertation were to understand how age affects neural auditory mechanisms and at which level in the auditory system these changes are particularly relevant for explaining speech-in-noise problems. Specifically, we used non-invasive neuroimaging techniques to tap into the midbrain and the cortex in order to analyze how auditory stimuli are processed in younger (our standard) and older adults. We will also attempt to investigate a possible interaction between processing carried out in the midbrain and cortex.

Neurophysiologie du langage : apports de la neuro-imagerie et état des connaissances

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Langage et cerveau : vingt ans d’imagerie fonctionnelle

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A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.