Why Do We SLIP to the Basic Level? A Formal Model

This dissertation introduces a new measure of basic-level performance (Strategy Length & Internal Practicability, SLIP). SLIP implements two computational constraints on the organisation of categories in a taxonomy: the minimum number of feature tests required to place the input in a category (strategy length) and the ease with which these tests are performed (internal practicability). The predictive power of SLIP is compared with that of four other basic-level measures: context model (Medin & Schaffer, 1978; modified by Estes, 1994), category feature-possession (Jones, 1983), category utility (Corter & Gluck, 1992), and compression measure (Pothos & Chater, 1998a), drawing data from the empirical work of Rosch et al. (1976), Murphy and Smith (1982), Mervis and Crisafi (1982), Hoffmann and Ziessler (1983), Corter, Gluck and Bower (1988), Murphy (1991), Lassaline (1990), Tanaka and Taylor (1991), and Johnson and Mervis (1997). Nine experiments further test the validity of the computational constraints of SLIP using computer-synthesised 3-D artificial objects, artificial scenes, and letter strings. The first five experiments examine the two constraints of SLIP in verification. Experiment 1 isolates the effect of strategy length on basic-levelness, and Experiments 2a and 2b that of internal practicability. Experiment 3 examines the interactions between the two factors. Experiment 4 tests, whether, as predicted by SLIP, there is a linear relationship between strategy length and response times. The last four experiments study the two computational constraints in naming. Experiment 5a isolates the effect of strategy length, and Experiment 5b that of internal practicability. Experiment 6 examines the time-course of the effect of strategy length. Finally, Experiment 7 looks at the effect of robustness (i.e., the idea an approximate categorisation is better than none) on the order of feature tests in length 2 strategies

Neural computations in prosopagnosia

We report an investigation of the neural processes involved in the processing of faces and objects of brain-lesioned patient PS, a well-documented case of pure acquired prosopagnosia. We gathered a substantial dataset of high-density electrophysiological recordings from both PS and neurotypicals. Using representational similarity analysis, we produced time-resolved brain representations in a format that facilitates direct comparisons across time points, different individuals, and computational models. To understand how the lesions in PS’s ventral stream affect the temporal evolution of her brain representations, we computed the temporal generalization of her brain representations. We uncovered that PS’s early brain representations exhibit an unusual similarity to later representations, implying an excessive generalization of early visual patterns. To reveal the underlying computational deficits, we correlated PS’ brain representations with those of deep neural networks (DNN). We found that the computations underlying PS’ brain activity bore a closer resemblance to early layers of a visual DNN than those of controls. However, the brain representations in neurotypicals became more akin to those of the later layers of the model compared to PS. We confirmed PS’s deficits in high-level brain representations by demonstrating that her brain representations exhibited less similarity with those of a DNN of semantics

Neural computations in prosopagnosia

We report an investigation of the neural processes involved in the processing of faces and objects of brain-lesioned patient PS, a well-documented case of pure acquired prosopagnosia. We gathered a substantial dataset of high-density electrophysiological recordings from both PS and neurotypicals. Using representational similarity analysis, we produced time-resolved brain representations in a format that facilitates direct comparisons across time points, different individuals, and computational models. To understand how the lesions in PS’s ventral stream affect the temporal evolution of her brain representations, we computed the temporal generalization of her brain representations. We uncovered that PS’s early brain representations exhibit an unusual similarity to later representations, implying an excessive generalization of early visual patterns. To reveal the underlying computational deficits, we correlated PS’ brain representations with those of deep neural networks (DNN). We found that the computations underlying PS’ brain activity bore a closer resemblance to early layers of a visual DNN than those of controls. However, the brain representations in neurotypicals became more akin to those of the later layers of the model compared to PS. We confirmed PS’s deficits in high-level brain representations by demonstrating that her brain representations exhibited less similarity with those of a DNN of semantics

Decoding face recognition abilities in the human brain

Why are some individuals better at recognising faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multi-modal data-driven approach combining neuroimaging, computational modelling, and behavioural tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities—super-recognisers—and typical recognisers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 second of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared representations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognisers, we found stronger associations between early brain representations of super-recognisers and mid-level representations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognisers and representations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multi-modal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain

Statistical ecology comes of age

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.Peer reviewe