The universality, invariance, and elegance of principles governing the universe may be reflected in principles of the minds that have evolved in that universe – provided that the mental principles are formulated with respect to the abstract spaces appropriate for the representation of biologically significant objects and their properties. (1) Positions and motions of objects conserve their shapes in the geometrically fullest and simplest way when represented as points and connecting geodesic paths in the six-dimensional manifold jointly determined by the (...) Euclidean group of three-dimensional space and the symmetry group of each object. (2) Colors of objects attain constancy when represented as points in a three-dimensional vector space in which each variation in natural illumination is canceled by application of its inverse from the three-dimensional linear group of terrestrial transformations of the invariant solar source. (3) Kinds of objects support optimal generalization and categorization when represented, in an evolutionarily-shaped space of possible objects, as connected regions with associated weights determined by Bayesian revision of maximum-entropy priors. Key Words: apparent motion; Bayesian inference; cognition; color constancy; generalization; mental rotation; perception; psychological laws; psychological space; universal laws. (shrink)

The experience of musical surprise is explained by psychologists in terms of the thwarting of prior musical expectations. The assumption that surprise is always caused by expectations is widespread not just in psychology at large, but also in philosophy. I argue here that this assumption is ill-founded. Many musical surprises, as well as many non-musical instances of perceptual surprise, can be explained by the falsification of assessments of the present, rendering the appeal to expectations unnecessary. I elaborate the positive view (...) before considering objections, and offering replies. (shrink)

A basic function of cognition is to detect regularities in sensory input to facilitate the prediction and recognition of future events. It has been proposed that these implicit expectations arise from an internal predictive coding model, based on knowledge acquired through processes such as statistical learning, but it is unclear how different types of statistical information affect listeners’ memory for auditory stimuli. We used a combination of behavioral and computational methods to investigate memory for non-linguistic auditory sequences. Participants repeatedly heard (...) tone sequences varying systematically in their information-theoretic properties. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. Information-theoretic measures of sequential predictability significantly influenced listeners’ expectedness ratings, and variations in these properties had a significant impact on memory performance. Predictable sequences yielded increasingly better memory performance with increasing exposure. Computational simulations using a probabilistic model of auditory expectation suggest that listeners dynamically formed a new, and increasingly accurate, implicit cognitive model of the information-theoretic structure of the sequences throughout the experimental session. (shrink)

Previous studies of auditory expectation have focused on the expectedness perceived by listeners retrospectively in response to events. In contrast, this research examines predictive uncertainty —a property of listeners' prospective state of expectation prior to the onset of an event. We examine the information-theoretic concept of Shannon entropy as a model of predictive uncertainty in music cognition. This is motivated by the Statistical Learning Hypothesis, which proposes that schematic expectations reflect probabilistic relationships between sensory events learned implicitly through exposure. Using (...) probability estimates from an unsupervised, variable-order Markov model, 12 melodic contexts high in entropy and 12 melodic contexts low in entropy were selected from two musical repertoires differing in structural complexity (simple and complex). Musicians and non-musicians listened to the stimuli and provided explicit judgments of perceived uncertainty (explicit uncertainty). We also examined an indirect measure of uncertainty computed as the entropy of expectedness distributions obtained using a classical probe-tone paradigm where listeners rated the perceived expectedness of the final note in a melodic sequence (inferred uncertainty). Finally, we simulate listeners' perception of expectedness and uncertainty using computational models of auditory expectation. A detailed model comparison indicates which model parameters maximize fit to the data and how they compare to existing models in the literature. The results show that listeners experience greater uncertainty in high-entropy musical contexts than low-entropy contexts. This effect is particularly apparent for inferred uncertainty and is stronger in musicians than non-musicians. Consistent with the Statistical Learning Hypothesis, the results suggest that increased domain-relevant training is associated with an increasingly accurate cognitive model of probabilistic structure in music. (shrink)

The present study investigates shared representations of syntactic knowledge in music and action. We examined whether expectancy violations in musical harmonic sequences are also perceived as violations of the movement sequences necessary to produce them. Pianists imitated silent videos showing one hand playing chord sequences on a muted keyboard. Results indicate that, despite the absence of auditory feedback, imitation of a chord is fastest when it is congruent with the preceding harmonic context. This suggests that the harmonic rules implied by (...) observed actions induce expectations that influence action execution. As evidence that these predictions are derived at a high representational level, imitation was more accurate for harmonically incongruent chords than for congruent chords executed with unconventional fingering. The magnitude of the effects of context and goal prioritization increased with musical training. Thus, musical training may lead to a domain-general representation of musical grammar, i.e., to a grammar of action. (shrink)

An overview of the computational prediction of emotional responses to music is presented. Communication of emotions by music has received a great deal of attention during the last years and a large number of empirical studies have described the role of individual features (tempo, mode, articulation, timbre) in predicting the emotions suggested or invoked by the music. However, unlike the present work, relatively few studies have attempted to model continua of expressed emotions using a variety of musical features from audio-based (...) representations in a correlation design. The construction of the computational model is divided into four separate phases, with a different focus for evaluation. These phases include the theoretical selection of relevant features, empirical assessment of feature validity, actual feature selection, and overall evaluation of the model. Existing research on music and emotions and extraction of musical features is reviewed in terms of these criteria. Examples drawn from recent studies of emotions within the context of film soundtracks are used to demonstrate each phase in the construction of the model. These models are able to explain the dominant part of the listeners’ self-reports of the emotions expressed by music and the models show potential to generalize over different genres within Western music. Possible applications of the computational models of emotions are discussed. (shrink)

Since its inception, Shannon's information theory has attracted interest for the study of language and music. Recently, a wide range of converging studies have shown how efficient communication pervades language, from phonetics to syntax. Efficient principles imply that more resources should be assigned to highly informative items. For instance, average information content was shown to be a better predictor of word length than frequency, revisiting the famous Zipf's law. However, in spite of the success of the efficient communication framework in (...) the study of language and speech, very little work has investigated its relevance in the analysis of music. Here, we examine the organization of harmonic information in two large corpora of Western music, one made of MIDI files directly sequenced from scores, and the other made of MIDI recordings of live performances of highly skilled piano players. We show that there is a clear positive relationship between (contextual) information content of harmonic sequences and two essential musical properties, namely duration and loudness: the more unexpected a harmonic event is, the longer and the louder it is. (shrink)

Using connectionist modeling, we address fundamental questions concerning the acquisition process of musical tonal schema of listeners. Compared to models of previous studies, our connectionist model (Learning Network for Tonal Schema, LeNTS) was better equipped to fulfill three basic requirements. Specifically, LeNTS was equipped with a learning mechanism, bound by culture-general properties, and trained by sufficient melody materials. When exposed to Western music, LeNTS acquired musical ‘scale’ sensitivity early and ‘harmony’ sensitivity later. The order of acquisition of scale and harmony (...) sensitivities shown by LeNTS was consistent with the culture-specific acquisition order shown by musically westernized children. The implications of these results for the acquisition process of a tonal schema of listeners are as follows: (a) the acquisition process may entail small and incremental changes, rather than large and stage-like changes, in corresponding neural circuits; (b) the speed of schema acquisition may mainly depend on musical experiences rather than maturation; and (c) the learning principles of schema acquisition may be culturally invariant while the acquired tonal schemas are varied with exposed culture-specific music. (shrink)

Two themes in music cognition research are highlighted—inspired by the contributions in this volume: (a) statistical learning and (b) evolutionary theorizing. Our ability to test alternatives to statistical learning is threatened by the rapidly diminishing opportunities for cross‐cultural studies unconfounded by bimusicalism. Our ability to infer possible evolutionary origins for music is confounded by the “hedonic plenitude” of modern music‐making—where multiple pleasure channels are activated simultaneously. Cognitively inspired music research will benefit by studying a wider range of musical cultures. Evolutionary (...) theorizing will benefit by further work involving comparative animal behavior. (shrink)

The development of the mind follows the path of biological evolution towards the accumulation and transmission of information with increasing efficiency. In addition to the cognitive constants of speech (Solntsev, 1974), which greatly improved the transmission of information, people have created computing devices, from the abacus to the quantum computer. The capabilities of computers classified as artificial intelligence are developing at a rapid pace. However, at the present stage, artificial intelligence (AI) lacks an emotion module, and this makes AI fundamentally (...) different from human intelligence, since the life of the mind in humans cannot be separated from their feelings (Damasio, 2010; Panksepp, 1997). Consciousness itself is formed through the sensory and motor systems, that is, it is embodied (Foglia & Wilson, 2013), which means that our mental life is inseparable from our sensory motor experience (Wellsby & Pexman, 2014). Evolutionarily, our minds rely on ancient survival mechanisms that influence our decisions and choices. Hence, for example, the question whether the choice of Artificial Intelligence will always be favorable for humanity. (shrink)

The ongoing generation of expectations is fundamental to listeners’ experience of music, but research into types of statistical information that listeners extract from musical melodies has tended to emphasize transition probabilities and n‐grams, with limited consideration given to other types of statistical learning that may be relevant. Temporal associations between scale degrees represent a different type of information present in musical melodies that can be learned from musical corpora using expectation networks, a computationally simple method based on activation and decay. (...) Expectation networks infer the expectation of encountering one scale degree followed in the near (but not necessarily immediate) future by another given scale degree, with previous work suggesting that scale degree associations learned by expectation networks better predict listener ratings of pitch similarity than transition probabilities. The current work outlines how these learned scale degree associations can be combined to predict melodic continuations and tests the resulting predictions on a dataset of listener responses to a musical cloze task previously used to compare two other models of melodic expectation, a variable‐order Markov model (IDyOM) and Temperley's music‐theoretically motivated model. Under multinomial logistic regression, all three models explain significant unique variance in human melodic expectations, with coefficient estimates highest for expectation networks. These results suggest that generalized scale degree associations informed by both adjacent and nonadjacent relationships between melodic notes influence listeners’ melodic predictions above and beyond n‐gram context, highlighting the need to consider a broader range of statistical learning processes that may underlie listeners’ expectations for upcoming musical events. (shrink)

Despite differences in their function and domain-specific elements, syntactic processing in music and language is believed to share cognitive resources. This study aims to investigate whether the simultaneous processing of language and music share the use of a common syntactic processor or more general attentional resources. To investigate this matter we tested musicians and non-musicians using visually presented sentences and aurally presented melodies containing syntactic local and long-distance dependencies. Accuracy rates and reaction times of participants’ responses were collected. In both (...) sentences and melodies, unexpected syntactic anomalies were introduced. This is the first study to address the processing of local and long-distance dependencies in language and music combined while reducing the effect of sensory memory. Participants were instructed to focus on language (language session), music (music session), or both (dual session). In the language session, musicians and non-musicians performed comparably in terms of accuracy rates and reaction times. As expected, groups’ differences appeared in the music session, with musicians being more accurate in their responses than non-musicians and only the latter showing an interaction between the accuracy rates for music and language syntax. In the dual session musicians were overall more accurate than non-musicians. However, both groups showed comparable behavior, by displaying an interaction between the accuracy rates for language and music syntax responses. In our study, accuracy rates seem to better capture the interaction between language and music syntax; and this interaction seems to indicate the use of distinct, however, interacting mechanisms as part of decision making strategy. This interaction seems to be subject of an increase of attentional load and domain proficiency. Our study contributes to the long-lasting debate about the commonalities between language and music by providing evidence for their interaction at a more domain-general level. (shrink)

This study presents a probabilistic model of melody perception, which infers the key of a melody and also judges the probability of the melody itself. The model uses Bayesian reasoning: For any “surface” pattern and underlying “structure,” we can infer the structure maximizing P(structure|surface) based on knowledge of P(surface, structure). The probability of the surface can then be calculated as ∑ P(surface, structure), summed over all structures. In this case, the surface is a pattern of notes; the structure is a (...) key. A generative model is proposed, based on three principles: (a) melodies tend to remain within a narrow pitch range; (b) note‐to‐note intervals within a melody tend to be small; and (c) notes tend to conform to a distribution (or key profile) that depends on the key. The model is tested in three ways. First, it is tested on its ability to identify the keys of a set of folksong melodies. Second, it is tested on a melodic expectation task in which it must judge the probability of different notes occurring given a prior context; these judgments are compared with perception data from a melodic expectation experiment. Finally, the model is tested on its ability to detect incorrect notes in melodies by assigning them lower probabilities than the original versions. (shrink)

A critical issue in perception is the manner in which top‐down expectancies guide lower level perceptual processes. In speech, a common paradigm is to construct continua ranging between two phonetic endpoints and to determine how higher level lexical context influences the perceived boundary. We applied this approach to music, presenting participants with major/minor triad continua after brief musical contexts. Two experiments yielded results that differed from classic results in speech perception. In speech, context generally expands the category of the expected (...) stimuli. We found the opposite in music: The major/minor boundary shifted toward the expected category, contracting it. Together, these experiments support the hypothesis that musical expectancy can feed back to affect lower‐level perceptual processes. However, it may do so in a way that differs fundamentally from what has been seen in other domains. (shrink)

Space—an essential dimension of our life—is analyzable from different viewpoints, which often gives rise to contrasting conceptualizations. Perceptual analyses shed light on the intrinsic anisotropy and deformations of perceived space, raising the issue of which geometry may be able to represent perceptual space. Pictorial drawings and painting have been relevant sources of information about the nature of living and perceived space. Although the geometry of perceptual space is still in its infancy, contributions are beginning to appear. This special issue contributes (...) to the development of the geometry of perceptual space. (shrink)