Predictability-dependent encoding of statistical regularities in the early visual cortex

Previous research recognized that humans could extract statistical regularities of the environment to automatically predict upcoming events. However, it has remained unexplored how the brain encodes the distribution of statistical regularities if it continuously changes. To investigate this question, we devised an fMRI paradigm where participants (N = 32) completed a visual four-choice reaction time (RT) task consisting of statistical regularities. Two types of blocks involving the same perceptual elements alternated with one another throughout the task: While the distribution of statistical regularities was predictable in one block type, it was unpredictable in the other. Participants were unaware of the presence of statistical regularities and of their changing distribution across the subsequent task blocks. Based on the RT results, although statistical regularities were processed similarly in both the predictable and unpredictable blocks, participants acquired less statistical knowledge in the unpredictable as compared with the predictable blocks. Whole-brain random-effects analyses showed increased activity in the early visual cortex and decreased activity in the precuneus for the predictable as compared with the unpredictable blocks. Therefore, the actual predictability of statistical regularities is likely to be represented already at the early stages of visual cortical processing. However, decreased precuneus activity suggests that these representations are imperfectly updated to track the multiple shifts in predictability throughout the task. The results also highlight that the processing of statistical regularities in a changing environment could be habitual.

SPACECRAFT RELIABILITY CALCULATIONS USING STATISTICAL REGULARITIES IN OCCURRENCES OF FAILURES IN DEVICES, UNITS AND ASSEMBLIES DURING THEIR OPERATION

The paper addresses reliability analysis of manned spacecraft with the use of statistical regularities in in-flight failures of their devices, units and assemblies. It formulates validity criteria for using a device failure in reliability analysis, proposes a method for analyzing and classifying failures which enables factoring in different types of failures in reliability analyses. It considers a hypothesis of the absence of statistically significant differences in probabilities of individual valid failures and demonstrates the feasibility of its adoption with the use of dispersion analysis. A method is developed for evaluating product reliability using a functional relationship between reliability and the number of failures occurring in flight which makes it possible to significantly simplify reliability analysis for complex products, to establish the number of in-flight failures that is acceptable from the standpoint of the product reliability requirements, to study various product architectures from the standpoint of reliability criteria. It proposes a method for evaluating the lower boundary for the probability of manned spacecraft completing their missions based on the failure modes, effects and criticality analysis, and demonstrates the feasibility of optimizing the product redundancy scheme based on the fault tolerance requirements. Key words: manned spacecraft, flight, failure, fault tolerance, classification of failures, reliability, probability of failure-free operation, statistical analysis, dispersion analysis.

Improvised music follows human language quantitative properties to optimize music processing

Music is a cognitively demanding task. New tones override the previous tones in quick succession, with only a short window to process them. Language presents similar constraints on the brain. The cognitive constraints associated with language processing have been argued to promote the Chunk-and-Pass processing hypothesis and may influence the statistical regularities associated with word and phenome presentation that have been identified in language and are thought to allow optimal communication. If this hypothesis were true, then similar statistical properties should be identified in music as in language. By searching for real-life musical corpora, rather than relying on the artificial generation of musical stimuli, a novel approach to melodic fragmentation was developed specifically for a corpus comprised of improvisation transcriptions that represent a popular performance practice tradition from the 16th century. These improvisations were created by following a very detailed technique, which was disseminated through music tutorials and treatises across Europe during the 16th century. These music tutorials present a very precise methodology for improvisation, using a pre-defined vocabulary of melodic fragments (similar to modern jazz licks). I have found that these corpora follow two paramount, quantitative linguistics characteristics: (1) Zipf’s rank-frequency law and (2) Zipf’s abbreviation law. According to the working hypothesis, adherence to these laws ensures the optimal coding of the examined music corpora, which facilitates the improved cognitive processing for both the listener and the improviser. Although these statistical characteristics are not consciously implemented by the improviser, they might play a critical role in music processing for both the listener and the improviser.

Improvised music follows human language quantitative properties to optimize music processing

Music is a cognitively demanding task. New tones override the previous tones in quick succession, with only a short window to process them. Language presents similar constraints on the brain. The cognitive constraints associated with language processing have been argued to promote the Chunk-and-Pass processing hypothesis and may influence the statistical regularities associated with word and phenome presentation that have been identified in language and are thought to allow optimal communication. If this hypothesis were true, then similar statistical properties should be identified in music as in language. By searching for real-life musical corpora, rather than relying on the artificial generation of musical stimuli, a novel approach to melodic fragmentation was developed specifically for a corpus comprised of improvisation transcriptions that represent a popular performance practice tradition from the 16th century. These improvisations were created by following a very detailed technique, which was disseminated through music tutorials and treatises across Europe during the 16th century. These music tutorials present a very precise methodology for improvisation, using a pre-defined vocabulary of melodic fragments (similar to modern jazz licks). I have found that these corpora follow two paramount, quantitative linguistics characteristics: (1) Zipf’s rank-frequency law and (2) Zipf’s abbreviation law. According to the working hypothesis, adherence to these laws ensures the optimal coding of the examined music corpora, which facilitates the improved cognitive processing for both the listener and the improviser. Although these statistical characteristics are not consciously implemented by the improviser, they might play a critical role in music processing for both the listener and the improviser.

Spatial and Temporal Context Jointly Modulate the Sensory Response within the Ventral Visual Stream

Both spatial and temporal context play an important role in visual perception and behavior. Humans can extract statistical regularities from both forms of context to help process the present and to construct expectations about the future. Numerous studies have found reduced neural responses to expected stimuli compared with unexpected stimuli, for both spatial and temporal regularities. However, it is largely unclear whether and how these forms of context interact. In the current fMRI study, 33 human volunteers were exposed to pairs of object stimuli that could be expected or surprising in terms of their spatial and temporal context. We found reliable independent contributions of both spatial and temporal context in modulating the neural response. Specifically, neural responses to stimuli in expected compared with unexpected contexts were suppressed throughout the ventral visual stream. These results suggest that both spatial and temporal context may aid sensory processing in a similar fashion, providing evidence on how different types of context jointly modulate perceptual processing.

Individuals With Congenital Amusia Show Degraded Speech Perception but Preserved Statistical Learning for Tone Languages

Purpose: Individuals with congenital amusia exhibit degraded speech perception. This study examined whether adult Chinese Mandarin listeners with amusia were still able to extract the statistical regularities of Mandarin speech sounds, despite their degraded speech perception. Method: Using the gating paradigm with monosyllabic syllable–tone words, we tested 19 Mandarin-speaking amusics and 19 musically intact controls. Listeners heard increasingly longer fragments of the acoustic signal across eight duration-blocked gates. The stimuli varied in syllable token frequency and syllable–tone co-occurrence probability. The correct syllable–tone word, correct syllable-only, correct tone-only, and correct syllable–incorrect tone responses were compared respectively between the two groups using mixed-effects models. Results: Amusics were less accurate than controls in terms of the correct word, correct syllable-only, and correct tone-only responses. Amusics, however, showed consistent patterns of top-down processing, as indicated by more accurate responses to high-frequency syllables, high-probability tones, and tone errors all in manners similar to those of the control listeners. Conclusions: Amusics are able to learn syllable and tone statistical regularities from the language input. This extends previous work by showing that amusics can track phonological segment and pitch cues despite their degraded speech perception. The observed speech deficits in amusics are therefore not due to an abnormal statistical learning mechanism. These results support rehabilitation programs aimed at improving amusics' sensitivity to pitch.

Predictive Processing and Some Disillusions about Illusions

A number of perceptual (exteroceptive and proprioceptive) illusions present problems for predictive processing accounts. In this chapter we’ll review explanations of the Müller-Lyer Illusion (MLI), the Rubber Hand Illusion (RHI) and the Alien Hand Illusion (AHI) based on the idea of Prediction Error Minimization (PEM), and show why they fail. In spite of the relatively open communicative processes which, on many accounts, are posited between hierarchical levels of the cognitive system in order to facilitate the minimization of prediction errors, perceptual illusions seemingly allow prediction errors to rule. Even if, at the top, we have reliable and secure knowledge that the lines in the MLI are equal, or that the rubber hand in the RHI is not our hand, the system seems unable to correct for sensory errors that form the illusion. We argue that the standard PEM explanation based on a short-circuiting principle doesn’t work. This is the idea that where there are general statistical regularities in the environment there is a kind of short circuiting such that relevant priors are relegated to lower-level processing so that information from higher levels is not exchanged (Ogilvie and Carruthers, Review of Philosophy and Psychology 7:721–742, 2016), or is not as precise as it should be (Hohwy, The Predictive Mind, Oxford University Press, Oxford, 2013). Such solutions (without convincing explanation) violate the idea of open communication and/or they over-discount the reliable and secure knowledge that is in the system. We propose an alternative, 4E (embodied, embedded, extended, enactive) solution. We argue that PEM fails to take into account the ‘structural resistance’ introduced by material and cultural factors in the broader cognitive system.

Spatial suppression due to statistical regularities in a visual detection task

Increasing evidence demonstrates that observers can learn the likely location of salient singleton distractors during visual search. To date, the reduced attentional capture at high-probability distractor locations has typically been examined using so called compound search, in which by design a target is always present. Here, we explored whether statistical distractor learning can also be observed in a visual detection task, in which participants respond target present if the singleton target is present and respond target absent when the singleton target is absent. If so, this allows us to examine suppression of the location that is likely to contain a distractor both in the presence, but critically also in the absence, of a priority signal generated by the target singleton. In an online variant of the additional singleton paradigm, observers had to indicate whether a unique shape was present or absent, while ignoring a colored singleton, which appeared with a higher probability in one specific location. We show that attentional capture was reduced, but not absent, at high-probability distractor locations, irrespective of whether the display contained a target or not. By contrast, target processing at the high-probability distractor location was selectively impaired on distractor-present displays. Moreover, all suppressive effects were characterized by a gradient such that suppression scaled with the distance to the high-probability distractor location. We conclude that statistical distractor learning can be examined in visual detection tasks, and discuss the implications for attentional suppression due to statistical learning.

Learning the statistics of pain: computational and neural mechanisms

Pain invariably changes over time, and these temporal fluctuations are riddled with uncertainty about body safety. In theory, statistical regularities of pain through time contain useful information that can be learned, allowing the brain to generate expectations and inform behaviour. To investigate this, we exposed healthy participants to probabilistic sequences of low and high-intensity electrical stimuli to the left hand, containing sudden changes in stimulus frequencies. We demonstrate that humans can learn to extract these regularities, and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian models with dynamic update of beliefs. We studied brain activity using functional MRI whilst subjects performed the task, which allowed us to dissect the underlying neural correlates of these statistical inferences from their uncertainty and update. We found that the inferred frequency (posterior probability) of high intensity pain correlated with activity in bilateral sensorimotor cortex, secondary somatosensory cortex and right caudate. The uncertainty of statistical inferences of pain was encoded in the right superior parietal cortex. An intrinsic part of this hierarchical Bayesian model is the way that unexpected changes in frequency lead to shift beliefs and update the internal model. This is reflected by the KL divergence between consecutive posterior distributions and associated with brain responses in the premotor cortex, dorsolateral prefrontal cortex, and posterior parietal cortex. In conclusion, this study extends what is conventionally considered a sensory pain pathway dedicated to process pain intensity, to include the generation of Bayesian internal models of temporal statistics of pain intensity levels in sensorimotor regions, which are updated dynamically through the engagement of premotor, prefrontal and parietal regions.