Anne Hsu | Queen Mary, University of London (original) (raw)

Papers by Anne Hsu

We present a generalized reverse correlation technique that can be used to estimate the spatio-te... more We present a generalized reverse correlation technique that can be used to estimate the spatio-temporal receptive fields (STRFs) of sensory neurons from their responses to arbitrary stimuli such as auditory vocalizations or natural visual scenes. The general solution for STRF estimation requires normalization of the stimulus-response cross-correlation by the stimulus autocorrelation matrix. When the second-order stimulus statistics are stationary, normalization involves only the diagonal elements of the Fourier-transformed auto-correlation matrix (the power spectrum). In the non-stationary case normalization requires the entire auto-correlation matrix. We present modelling studies that demonstrate the feasibility and accuracy of this method as well as neurophysiological data comparing STRFs estimated using natural versus synthetic stimulus ensembles. For both auditory and visual neurons, STRFs obtained with these different stimuli are similar, but exhibit systematic differences that may be functionally significant. This method should be useful for determining what aspects of natural signals are represented by sensory neurons and may reveal novel response properties of these neurons.

Accounts of subjective randomness suggest that people consider a stimulus random when they cannot... more Accounts of subjective randomness suggest that people consider a stimulus random when they cannot detect any regularities characterizing the structure of that stimulus. We explored the possibility that the regularities people detect are shaped by the statistics of their natural environment. We did this by testing the hypothesis that people's perception of randomness in two-dimensional binary arrays (images with two levels of intensity) is inversely related to the probability with which the array's pattern would be encountered in nature. We estimated natural scene probabilities for small binary arrays by tabulating the frequencies with which each pattern of cell values appears. We then conducted an experiment in which we collected human randomness judgments. The results show an inverse relationship between people's perceived randomness of an array pattern and the probability of the pattern appearing in nature.

A rapid eye movement (with speeds of up to 800° per second) that brings the point of maximal visu... more A rapid eye movement (with speeds of up to 800° per second) that brings the point of maximal visual acuity -the fovea -to the image of interest.

A rate code assumes that a neuron's response is completely characterized by its time-varying mean... more A rate code assumes that a neuron's response is completely characterized by its time-varying mean firing rate. This assumption has successfully described neural responses in many systems. The noise in rate coding neurons can be quantified by the coherence function or the correlation coefficient between the neuron's deterministic time-varying mean rate and noise corrupted single spike trains. Because of the finite data size, the mean rate cannot be known exactly and must be approximated. We introduce novel unbiased estimators for the measures of coherence and correlation which are based on the extrapolation of the signal to noise ratio in the neural response to infinite data size. We then describe the application of these estimates to the validation of the class of stimulus-response models that assume that the mean firing rate captures all the information embedded in the neural response. We explain how these quantifiers can be used to separate response prediction errors that are due to inaccurate model assumptions from errors due to noise inherent in neuronal spike trains.

The present study examined individual differences in artistic preferences in a sample of 91,692 p... more The present study examined individual differences in artistic preferences in a sample of 91,692 participants (60% women and 40% men), aged 13-90 years. Participants completed a big five personality inventory and provided preference ratings for 24 Q7 different paintings corresponding to cubism, renaissance, impressionism, and Japanese art, which loaded on to a latent factor of overall art preferences. As expected, the personality trait openness to experience was the strongest and only consistent personality correlate of artistic preferences, affecting both overall and specific preferences, as well as visits to galleries, and artistic (rather than scientific) self-perception. Overall preferences were also positively influenced by age and visits to art galleries, and to a lesser degree, by artistic self-perception and conscientiousness (negatively). As for specific styles, after overall preferences were accounted for, more agreeable, more conscientious and less open individuals reported higher preference levels for impressionist, younger and more extraverted participants showed higher levels of preference for cubism (as did males), and younger participants, as well as males, reported higher levels of preferences for renaissance. Limitations and recommendations for future research are discussed.

Vocal communicators discriminate conspecific vocalizations from other sounds and recognize the vo... more Vocal communicators discriminate conspecific vocalizations from other sounds and recognize the vocalizations of individuals. To identify neural mechanisms for the discrimination of such natural sounds, we compared the linear spectro-temporal tuning properties of auditory midbrain and forebrain neurons in zebra finches with the statistics of natural sounds, including song. Here, we demonstrate that ensembles of auditory neurons are tuned to auditory features that enhance the acoustic differences between classes of natural sounds, and among the songs of individual birds. Tuning specifically avoids the spectro-temporal modulations that are redundant across natural sounds and therefore provide little information; rather, it overlaps with the temporal modulations that differ most across sounds. By comparing the real tuning and a less selective model of spectro-temporal tuning, we found that the real modulation tuning increases the neural discrimination of different sounds. Additionally, auditory neurons discriminate among zebra finch song segments better than among synthetic sound segments.

Simplicity-based approach to language learning 2 Abstract Children appear to be able to learn the... more Simplicity-based approach to language learning 2 Abstract Children appear to be able to learn their native language by exposure to their linguistic and communicative environment, but without requiring that their mistakes are corrected. Such learning from "positive evidence" has been viewed as raising "logical" problems for language acquisition: in particular, without correction, how is the child to recover from conjecturing an over-general grammar, which will be consistent with any sentence that the child hears? There have been many proposals concerning how this "logical problem" can be dissolved. Here we review recent formal results showing that the learner can learn successfully from positive evidenced by favouring the grammar that provides the simplest encoding of the linguistic input.

We examined the neural encoding of synthetic and natural sounds by single neurons in the auditory... more We examined the neural encoding of synthetic and natural sounds by single neurons in the auditory system of male zebra finches by estimating the mutual information in the time-varying mean firing rate of the neuronal response. Using a novel parametric method for estimating mutual information with limited data, we tested the hypothesis that song and song-like synthetic sounds would be preferentially encoded relative to other complex, but non-song-like synthetic sounds. To test this hypothesis, we designed two synthetic stimuli: synthetic songs that matched the power of spectral-temporal modulations but lacked the modulation phase structure of zebra finch song and noise with uniform band-limited spectral-temporal modulations. By defining neural selectivity as relative mutual information, we found that the auditory system of songbirds showed selectivity for song-like sounds. This selectivity increased in a hierarchical manner along ascending processing stages in the auditory system. Midbrain neurons responded with highest information rates and efficiency to synthetic songs and thus were selective for the spectral-temporal modulations of song. Primary forebrain neurons showed increased information to zebra finch song and synthetic song equally over noise stimuli. Secondary forebrain neurons responded with the highest information to zebra finch song relative to other stimuli and thus were selective for its specific modulation phase relationships. We also assessed the relative contribution of three response properties to this selectivity: (1) spiking reliability, (2) rate distribution entropy, and (3) bandwidth. We found that rate distribution and bandwidth but not reliability were responsible for the higher average information rates found for song-like sounds.

Bayesian probability has recently been proposed as a normative theory of argumentation. In this a... more Bayesian probability has recently been proposed as a normative theory of argumentation. In this article, we provide a Bayesian formalisation of the ad Hitlerum argument, as a special case of the ad hominem argument. Across 3 experiments, we demonstrate that people's evaluation of the argument is sensitive to probabilistic factors deemed relevant on a Bayesian formalisation. Moreover, we provide the first quantitative evidence in favour of the Bayesian approach to argumentation.

There is much debate over the degree to which language learning is governed by innate language-sp... more There is much debate over the degree to which language learning is governed by innate language-specific biases, or acquired through cognition-general principles. Here we examine the probabilistic language acquisition hypothesis on three levels: We outline a novel theoretical result showing that it is possible to learn the exact generative model underlying a wide class of languages, purely from observing samples of the language. We then describe a recently proposed practical framework, which quantifies natural language learnability, allowing specific learnability predictions to be made for the first time. In previous work, this framework was used to make learnability predictions for a wide variety of linguistic constructions, for which learnability has been much debated. Here, we present a new experiment which tests these learnability predictions. We find that our experimental results support the possibility that these linguistic constructions are acquired probabilistically from cognition-general principles.

Natural language is full of patterns that appear to fit with general linguistic rules but are ung... more Natural language is full of patterns that appear to fit with general linguistic rules but are ungrammatical. There has been much debate over how children acquire these ''linguistic restrictions,'' and whether innate language knowledge is needed. Recently, it has been shown that restrictions in language can be learned asymptotically via probabilistic inference using the minimum description length (MDL) principle. Here, we extend the MDL approach to give a simple and practical methodology for estimating how much linguistic data are required to learn a particular linguistic restriction. Our method provides a new research tool, allowing arguments about natural language learnability to be made explicit and quantified for the first time. We apply this method to a range of classic puzzles in language acquisition. We find some linguistic rules appear easily statistically learnable from language experience only, whereas others appear to require additional learning mechanisms (e.g., additional cues or innate constraints).