A rational model of the effects of distributional information on feature learning

Most psychological theories treat the features of objects as being fixed and immediately available to observers. However, novel objects have an infinite array of properties that could potentially be encoded as features, raising the question of how people learn which features to use in representing those objects. We focus on the effects of distributional information on feature learning, considering how a rational agent should use statistical information about the properties of objects in identifying features. Inspired by previous behavioral results on human feature learning, we present an ideal observer model based on nonparametric Bayesian statistics. This model balances the idea that objects have potentially infinitely many features with the goal of using a relatively small number of features to represent any finite set of objects. We then explore the predictions of this ideal observer model. In particular, we investigate whether people are sensitive to how parts co-vary over objects they observe. In a series of four behavioral experiments (three using visual stimuli, one using conceptual stimuli), we demonstrate that people infer different features to represent the same four objects depending on the distribution of parts over the objects they observe. Additionally in all four experiments, the features people infer have consequences for how they generalize properties to novel objects. We also show that simple models that use the raw sensory data as inputs and standard dimensionality reduction techniques (principal component analysis and independent component analysis) are insufficient to explain our results.     

Serial order processing in human infants and the role of multisensory redundancy

Perception, production, and understanding of sequences is fundamental to human behavior and depends, in large part, on the ability to detect serial order. Despite the importance of this issue across many domains of human functioning, the development of serial order skills has been neglected in developmental studies. The current article reviews evidence that the basic temporal and spatiotemporal skills that are necessary for the development of serial order skills emerge early in human development. The article then presents recent evidence from the authors laboratory showing that serial order perceptual skills emerge at the same time and improve rapidly. Consistent with a multisensory redundancy view of perception, when serial order perceptual abilities first emerge in infancy, they depend critically on the redundant specification of sequences in both the auditory and visual modalities. The findings suggest that infants ability to perceive the surface serial order characteristics of sequentially organized events provides the necessary antecedents to the development of more complex serial order skills that ultimately enable us to extract meanings from sequentially organized events and perform complex sequential actions.Edited by: Marie-Hélène Giard and Mark Wallace     

Temporal representation impairment in developmental dyslexia for unisensory and multisensory stimuli

Dyslexia has been associated with a problem in visual–audio integration mechanisms. Here, we investigate for the first time the contribution of unisensory cues on multisensory audio and visual integration in 32 dyslexic children by modelling results using the Bayesian approach. Non‐linguistic stimuli were used. Children performed a temporal task: they had to report whether the middle of three stimuli was closer in time to the first one or to the last one presented. Children with dyslexia, compared with typical children, exhibited poorer unimodal thresholds, requiring greater temporal distance between items for correct judgements, while multisensory thresholds were well predicted by the Bayesian model. This result suggests that the multisensory deficit in dyslexia is due to impaired audio and visual inputs rather than impaired multisensory processing per se. We also observed that poorer temporal skills correlated with lower reading skills in dyslexic children, suggesting that this temporal capability can be linked to reading abilities.     

A rational account of pedagogical reasoning: Teaching by,and learning from,examples

Much of learning and reasoning occurs in pedagogical situations—situations in which a person who knows a concept chooses examples for the purpose of helping a learner acquire the concept. We introduce a model of teaching and learning in pedagogical settings that predicts which examples teachers should choose and what learners should infer given a teacher’s examples. We present three experiments testing the model predictions for rule-based, prototype, and causally structured concepts. The model shows good quantitative and qualitative fits to the data across all three experiments, predicting novel qualitative phenomena in each case. We conclude by discussing implications for understanding concept learning and implications for theoretical claims about the role of pedagogy in human learning.     

A computational model of early argument structure acquisition

How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms that can grasp generalizations from examples of specific usages, and that exhibit patterns of behavior over the course of learning similar to those in children. Early learning of verb argument structure is an area of language acquisition that provides an interesting testbed for such approaches due to the complexity of verb usages. A range of linguistic factors interact in determining the felicitous use of a verb in various constructions —associations between syntactic forms and properties of meaning that form the basis for a number of linguistic and psycholinguistic theories of language. This article presents a computational model for the representation, acquisition, and use of verbs and constructions. The Bayesian framework is founded on a novel view of constructions as a probabilistic association between syntactic and semantic features. The computational experiments reported here demonstrate the feasibility of learning general constructions, and their exceptions, from individual usages of verbs. The behavior of the model over the timecourse of acquisition mimics, in relevant aspects, the stages of learning exhibited by children. Therefore, this proposal sheds light on the possible mechanisms at work in forming linguistic generalizations and maintaining knowledge of exceptions.     

The co-occurrence of multisensory competition and facilitation

Previous studies of multisensory integration have often stressed the beneficial effects that may arise when information concerning an event arrives via different sensory modalities at the same time, as, for example, exemplified by research on the redundant target effect (RTE). By contrast, studies of the Colavita visual dominance effect (e.g., [Colavita, F. B. (1974). Human sensory dominance. Perception & Psychophysics, 16, 409–412]) highlight the inhibitory consequences of the competition between signals presented simultaneously in different sensory modalities instead. Although both the RTE and the Colavita effect are thought to occur at early sensory levels and the stimulus conditions under which they are typically observed are very similar, the interplay between these two opposing behavioural phenomena (facilitation vs. competition) has yet to be addressed empirically. We hypothesized that the dissociation may reflect two of the fundamentally different ways in which humans can perceive concurrent auditory and visual stimuli. In Experiment 1, we demonstrated both multisensory facilitation (RTE) and the Colavita visual dominance effect using exactly the same audiovisual displays, by simply changing the task from a speeded detection task to a speeded modality discrimination task. Meanwhile, in Experiment 2, the participants exhibited multisensory facilitation when responding to visual targets and multisensory inhibition when responding to auditory targets while keeping the task constant. These results therefore indicate that both multisensory facilitation and inhibition can be demonstrated in reaction to the same bimodal event.     

A rational analysis of rule-based concept learning

This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space—a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well-known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7-feature concepts—a more natural setting in several ways—and again finds that the model explains human performance.     

Attention and the multiple stages of multisensory integration: A review of audiovisual studies

Multisensory integration and crossmodal attention have a large impact on how we perceive the world. Therefore, it is important to know under what circumstances these processes take place and how they affect our performance. So far, no consensus has been reached on whether multisensory integration and crossmodal attention operate independently and whether they represent truly automatic processes. This review describes the constraints under which multisensory integration and crossmodal attention occur and in what brain areas these processes take place. Some studies suggest that multisensory integration and crossmodal attention take place in higher heteromodal brain areas, while others show the involvement of early sensory specific areas. Additionally, the current literature suggests that multisensory integration and attention interact depending on what processing level integration takes place. To shed light on this issue, different frameworks regarding the level at which multisensory interactions takes place are discussed. Finally, this review focuses on the question whether audiovisual interactions and crossmodal attention in particular are automatic processes. Recent studies suggest that this is not always the case. Overall, this review provides evidence for a parallel processing framework suggesting that both multisensory integration and attentional processes take place and can interact at multiple stages in the brain.     

Neuroimaging of multisensory processing in vision,audition, touch,and olfaction

The development of neuroimaging methods has had a significant impact on the study of the human brain. Functional MRI, with its high spatial resolution, provides investigators with a method to localize the neuronal correlates of many sensory and cognitive processes. Magneto- and electroencephalography, in turn, offer excellent temporal resolution allowing the exact time course of neuronal processes to be investigated. Applying these methods to multisensory processing, many research laboratories have been successful in describing cross-sensory interactions and their spatio-temporal dynamics in the human brain. Here, we review data from selected neuroimaging investigations showing how vision can influence and interact with other senses, namely audition, touch, and olfaction. We highlight some of the similarities and differences in the cross-processing of the different sensory modalities and discuss how different neuroimaging methods can be applied to answer specific questions about multisensory processing.Edited by: Marie-Hélène Giard and Mark Wallace     

The influence of embodiment on multisensory integration using the mirror box illusion

We examined the relationship between subcomponents of embodiment and multisensory integration using a mirror box illusion. The participants’ left hand was positioned against the mirror, while their right hidden hand was positioned 12″, 6″, or 0″ from the mirror – creating a conflict between visual and proprioceptive estimates of limb position in some conditions. After synchronous tapping, asynchronous tapping, or no movement of both hands, participants gave position estimates for the hidden limb and filled out a brief embodiment questionnaire. We found a relationship between different subcomponents of embodiment and illusory displacement towards the visual estimate. Illusory visual displacement was positively correlated with feelings of deafference in the asynchronous and no movement conditions, whereas it was positive correlated with ratings of visual capture and limb ownership in the synchronous and no movement conditions. These results provide evidence for dissociable contributions of different aspects of embodiment to multisensory integration.     

Structure and strength in causal induction

We present a framework for the rational analysis of elemental causal induction-learning about the existence of a relationship between a single cause and effect-based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relationship might be. We show that two leading rational models of elemental causal induction, DeltaP and causal power, both estimate causal strength, and we introduce a new rational model, causal support, that assesses causal structure. Causal support predicts several key phenomena of causal induction that cannot be accounted for by other rational models, which we explore through a series of experiments. These phenomena include the complex interaction between DeltaP and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates. Causal support also provides a better account of a number of existing datasets than either DeltaP or causal power.     

Schizotypal traits are not related to multisensory integration or audiovisual speech perception

Multisensory integration, the binding of sensory information from different sensory modalities, may contribute to perceptual symptomatology in schizophrenia, including hallucinations and aberrant speech perception. Differences in multisensory integration and temporal processing, an important component of multisensory integration, are consistently found in schizophrenia. Evidence is emerging that these differences extend across the schizophrenia spectrum, including individuals in the general population with higher schizotypal traits. In the current study, we investigated the relationship between schizotypal traits and perceptual functioning, using audiovisual speech-in-noise, McGurk, and ternary synchrony judgment tasks. We measured schizotypal traits using the Schizotypal Personality Questionnaire (SPQ), hypothesizing that higher scores on Unusual Perceptual Experiences and Odd Speech subscales would be associated with decreased multisensory integration, increased susceptibility to distracting auditory speech, and less precise temporal processing. Surprisingly, these measures were not associated with the predicted subscales, suggesting that these perceptual differences may not be present across the schizophrenia spectrum.     

Concurrent intramodal learning enhances multisensory responses of symmetric crossmodal learning in robotic audio-visual tracking

Tracking an audio-visual target involves integrating spatial cues about target position from both modalities. Such sensory cue integration is a developmental process in the brain involving learning, with neuroplasticity as its underlying mechanism. We present a Hebbian learning-based adaptive neural circuit for multi-modal cue integration. The circuit temporally correlates stimulus cues within each modality via intramodal learning as well as symmetrically across modalities via crossmodal learning to independently update modality-specific neural weights on a sample-by-sample basis. It is realised as a robotic agent that must orient towards a moving audio-visual target. It continuously learns the best possible weights required for a weighted combination of auditory and visual spatial target directional cues that is directly mapped to robot wheel velocities to elicit an orientation response. Visual directional cues are noise-free and continuous but arising from a relatively narrow receptive field while auditory directional cues are noisy and intermittent but arising from a relatively wider receptive field. Comparative trials in simulation demonstrate that concurrent intramodal learning improves both the overall accuracy and precision of the orientation responses of symmetric crossmodal learning. We also demonstrate that symmetric crossmodal learning improves multisensory responses as compared to asymmetric crossmodal learning. The neural circuit also exhibits multisensory effects such as sub-additivity, additivity and super-additivity.     

The multisensory basis of the self: From body to identity to others

By grounding the self in the body, experimental psychology has taken the body as the starting point for a science of the self. One fundamental dimension of the bodily self is the sense of body ownership that refers to the special perceptual status of one’s own body, the feeling that “my body” belongs to me. The primary aim of this review article is to highlight recent advances in the study of body ownership and our understanding of the underlying neurocognitive processes in three ways. I first consider how the sense of body ownership has been investigated and elucidated in the context of multisensory integration. Beyond exteroception, recent studies have considered how this exteroceptively driven sense of body ownership can be linked to the other side of embodiment, that of the unobservable, yet felt, interoceptive body, suggesting that these two sides of embodiment interact to provide a unifying bodily self. Lastly, the multisensorial understanding of the self has been shown to have implications for our understanding of social relationships, especially in the context of self–other boundaries. Taken together, these three research strands motivate a unified model of the self inspired by current predictive coding models.     

Constraining bridges between levels of analysis: A computational justification for locally Bayesian learning

Different levels of analysis provide different insights into behavior: computational-level analyses determine the problem an organism must solve and algorithmic-level analyses determine the mechanisms that drive behavior. However, many attempts to model behavior are pitched at a single level of analysis. Research into human and animal learning provides a prime example, with some researchers using computational-level models to understand the sensitivity organisms display to environmental statistics but other researchers using algorithmic-level models to understand organisms’ trial order effects, including effects of primacy and recency. Recently, attempts have been made to bridge these two levels of analysis. Locally Bayesian Learning (LBL) creates a bridge by taking a view inspired by evolutionary psychology: Our minds are composed of modules that are each individually Bayesian but communicate with restricted messages. A different inspiration comes from computer science and statistics: Our brains are implementing the algorithms developed for approximating complex probability distributions. We show that these different inspirations for how to bridge levels of analysis are not necessarily in conflict by developing a computational justification for LBL. We demonstrate that a scheme that maximizes computational fidelity while using a restricted factorized representation produces the trial order effects that motivated the development of LBL. This scheme uses the same modular motivation as LBL, passing messages about the attended cues between modules, but does not use the rapid shifts of attention considered key for the LBL approximation. This work illustrates a new way of tying together psychological and computational constraints.     

The effects of videotape modeling on staff acquisition of functional analysis methodology

Lectures and two types of video modeling were compared to determine their relative effectiveness in training 3 staff members to conduct functional analysis sessions. Video modeling that contained a larger number of therapist exemplars resulted in mastery-level performance eight of the nine times it was introduced, whereas neither lectures nor partial video modeling produced significant improvements in performance. Results demonstrated that video modeling provided an effective training strategy but only when a wide range of exemplars of potential therapist behaviors were depicted in the videotape.     

Highlights as an Early Predictor of Student Comprehension and Interests

When engaging with a textbook, students are inclined to highlight key content. Although students believe that highlighting and subsequent review of the highlights will further their educational goals, the psychological literature provides little evidence of benefits. Nonetheless, a student’s choice of text for highlighting may serve as a window into her mental state—her level of comprehension, grasp of the key ideas, reading goals, and so on. We explore this hypothesis via an experiment in which 400 participants read three sections from a college-level biology text, briefly reviewed the text, and then took a quiz on the material. During initial reading, participants were able to highlight words, phrases, and sentences, and these highlights were displayed along with the complete text during the subsequent review. Consistent with past research, the amount of highlighted material is unrelated to quiz performance. Nonetheless, highlighting patterns may allow us to infer reader comprehension and interests. Using multiple representations of the highlighting patterns, we built probabilistic models to predict quiz performance and matrix factorization models to predict what content would be highlighted in one passage from highlights in other passages. We find that quiz score prediction accuracy reliably improves with the inclusion of highlighting data (by about 1%–2%), both for held-out students and for held-out student questions (i.e., questions selected randomly for each student), but not for held-out questions. Furthermore, an individual’s highlighting pattern is informative of what she highlights elsewhere. Our long-term goal is to design digital textbooks that serve not only as conduits of information into the reader’s mind but also allow us to draw inferences about the reader at a point where interventions may increase the effectiveness of the material.     

Investigation concerning the possibility of rational ethics

Ethical behavior such as self‐sacrifice and striving for juistice—the natural striving for a general prosperity for humans—is not mainly learned but is carried by a natural altruism from one generation to the next. It is based on group (rather than individual) altruism which has concentrated on protection of the aged (as preservers of knowledge) and the young (as preservers of future generations).

It is proposed that the strongest possible nature form of community is the “amorisation community"—in effect, one in which love produces more love. A case is presented for the creation of a world scientific and cultural transformation based on the ethics of the “amorisation community.”     

A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word‐Order Universal

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language‐learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners’ input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment ( Culbertson, Smolensky, & Legendre, 2012 ) targeting the learning of word‐order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross‐linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners’ inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization—Greenberg's Universal 18—which bans a particular word‐order pattern relating nouns, adjectives, and numerals.     

Object Concepts in the Chemical Senses

This paper examines the applicability of the object concept to the chemical senses, by evaluating them against a set of criteria for object‐hood. Taste and chemesthesis do not generate objects. Their parts, perceptible from birth, never combine. Orthonasal olfaction (sniffing) presents a strong case for generating objects. Odorants have many parts yet they are perceived as wholes, this process is based on learning, and there is figure‐ground segregation. While flavors are multimodal representations bound together by learning, there is no functional need for flavor objects in the mouth. Rather, food identification occurs prior to ingestion using the eye and nose, with the latter retrieving multimodal flavor objects via sniffing (e.g., sweet smelling caramel). While there are differences in object perception between vision, audition, and orthonasal olfaction, the commonalities suggest that the brain has adopted the same basic solution when faced with extracting meaning from complex stimulus arrays.