Publications

@article{cannonReducedPrecisionMotor2024,

title = {Reduced Precision of Motor and Perceptual Rhythmic Timing in Autistic Adults},

author = {Jonathan Cannon and Annie Cardinaux and Lindsay Bungert and Cindy Li and Pawan Sinha},

doi = {10.1016/j.heliyon.2024.e34261},

issn = {2405-8440},

year  = {2024},

date = {2024-07-01},

urldate = {2024-07-23},

journal = {Heliyon},

volume = {10},

number = {14},

pages = {e34261},

abstract = {Recent results suggest that autistic individuals exhibit reduced accuracy compared to non-autistic peers in temporally coordinating their actions with predictable external cues, e.g., synchronizing finger taps to an auditory metronome. However, it is not yet clear whether these difficulties are driven primarily by motor differences or extend into perceptual rhythmic timing tasks. We recruited autistic and non-autistic participants for an online study testing both finger tapping synchronization and continuation as well as rhythmic time perception (anisochrony detection). We fractionated each participant's synchronization results into parameters representing error correction, motor noise, and internal time-keeper noise, and also investigated error-correcting responses to small metronome timing perturbations. Contrary to previous work, we did not find strong evidence for reduced synchronization error correction. However, we found compelling evidence for noisier internal rhythmic timekeeping in the synchronization, continuation, and perceptual components of the experiment. These results suggest that noisier internal rhythmic timing processes underlie some sensorimotor coordination challenges in autism.},

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@article{bungertProportionalOverrepresentationGenderDiverse2024,

title = {Proportional Overrepresentation of Gender-Diverse Identities in Two US-Based Autistic Adult Samples from the SPARK Database},

author = {Lindsay Bungert and Cindy E. Li and Annie L. Cardinaux and Amanda M. O'Brien and Jonathan Cannon and Veronica Shkolnik and John D. E. Gabrieli and John F. Strang and Pawan Sinha},

doi = {10.1089/aut.2023.0121},

issn = {2573-9581, 2573-959X},

year  = {2024},

date = {2024-05-01},

urldate = {2024-08-31},

journal = {Autism in Adulthood},

pages = {aut.2023.0121},

abstract = {Background: Previous literature indicates a proportional overrepresentation of both autism and autistic traits within gender-diverse populations (individuals who experience their gender identity as different from their sex assigned at birth). Emerging but limited evidence also suggests a proportional overrepresentation of genderdiverse identities in autism. To our knowledge, this is the first study to report gender diversity prevalence in autistic adults in the United States. Methods: We report the prevalence of gender diversity within two well-characterized samples of autistic adults recruited from SPARK (Simons Foundation Powering Autism Research for Knowledge), the largest online research database of autistic individuals to date. This study includes both an original sample (Dataset 1},

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@article{obrienDevelopmentSelfReportMeasure2024,

title = {Development of a Self-Report Measure of Prediction in Daily Life: The Prediction-Related Experiences Questionnaire},

author = {Amanda M. O'Brien and Toni A. May and Kristin L. K. Koskey and Lindsay Bungert and Annie Cardinaux and Jonathan Cannon and Isaac N. Treves and Anila M. D'Mello and Robert M. Joseph and Cindy Li and Sidney Diamond and John D. E. Gabrieli and Pawan Sinha},

doi = {10.1007/s10803-024-06379-2},

issn = {0162-3257, 1573-3432},

year  = {2024},

date = {2024-05-01},

urldate = {2024-08-31},

journal = {Journal of Autism and Developmental Disorders},

abstract = {Purposeenspace Predictions are complex, multisensory, and dynamic processes involving real-time adjustments based on environmental inputs. Disruptions to prediction abilities have been proposed to underlie characteristics associated with autism. While there is substantial empirical literature related to prediction, the field lacks a self-assessment measure of prediction skills related to daily tasks. Such a measure would be useful to better understand the nature of day-to-day prediction-related activities and characterize these abilities in individuals who struggle with prediction. Methodsenspace An interdisciplinary mixed-methods approach was utilized to develop and validate a self-report questionnaire of prediction skills for adults, the Prediction-Related Experiences Questionnaire (PRE-Q). Two rounds of online field testing were completed in samples of autistic and neurotypical (NT) adults. Qualitative feedback from a subset of these participants regarding question content and quality was integrated and Rasch modeling of the item responses was applied. Resultsenspace The final PRE-Q includes 19 items across 3 domains (Sensory, Motor, Social), with evidence supporting the validity of the measure's 4-point response categories, internal structure, and relationship to other outcome measures associated with prediction. Consistent with models of prediction challenges in autism, autistic participants indicated more prediction-related difficulties than the NT group. Conclusionsenspace This study provides evidence for the validity of a novel self-report questionnaire designed to measure the day-to-day prediction skills of autistic and non-autistic adults. Future research should focus on characterizing the relationship between the PRE-Q and lab-based measures of prediction, and understanding how the PRE-Q may be used to identify potential areas for clinical supports for individuals with prediction-related challenges.},

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}

@article{cannonInferredRepresentationsBehave2024,

title = {Inferred Representations Behave like Oscillators in Dynamic Bayesian Models of Beat Perception},

author = {Jonathan Cannon and Thomas Kaplan},

url = {https://www.sciencedirect.com/science/article/pii/S0022249624000385},

doi = {https://doi.org/10.1016/j.jmp.2024.102869},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Journal of Mathematical Psychology},

number = {122},

pages = {102869},

abstract = {The human's capacity to perceptually entrain to an auditory rhythm has been repeatedly modeled as a dynamical system consisting of one or more forced oscillators. However, a more recent perspective, closely related to the popular theory of Predictive Processing, treats auditory entrainment as an inference process in which the observer infers the phase, tempo, and/or metrical structure of an auditory stimulus based on event timing. Here, we propose a close relationship between these two perspectives. We show for the first time that a system performing variational Bayesian inference about the circular phase underlying a rhythmic stimulus takes the form of a forced, damped oscillator with a specific nonlinear phase response function corresponding to the internal metrical model of the underlying rhythm. This algorithm can be extended to simultaneous inference on both phase and tempo using one of two possible approximations that closely align with the two most prominent models of auditory entrainment: one yields a single oscillator with an adapting period, and the other yields a networked bank of oscillators. We conclude that an inference perspective on rhythm perception can offer similar descriptive power and flexibility to a dynamical systems perspective while also plugging into the fertile unifying framework of Bayesian Predictive Processing.},

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}

@article{cannonRhythmicIntervalBased2023,

title = {Rhythmic and Interval-based Temporal Orienting in Autism},

author = {Jonathan Cannon and Emelie Eldracher and Annie Cardinaux and Fatima Irfan and Lindsay Bungert and Cindy Li and Amanda O'Brien and Isaac Treves and Sidney Diamond and Pawan Sinha},

doi = {10.1002/aur.2892},

issn = {1939-3792, 1939-3806},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-17},

journal = {Autism Research},

volume = {16},

number = {4},

pages = {772–782},

abstract = {Individuals with autism spectrum disorder (ASD) may show secondary sensory and cognitive characteristics, including differences in auditory processing, attention, and, according to a prominent hypothesis, the formulation and utilization of predictions. We explored the overlap of audition, attention, and prediction with an online auditory ``temporal orienting'' task in which participants utilized predictive timing cues (both rhythmic and interval-based) to improve their detection of faint sounds. We compared an autistic (n = 78) with a nonautistic (n = 83) group, controlling for nonverbal IQ, and used signal detection measures and reaction times to evaluate the effect of valid temporally predictive cues. We hypothesized that temporal orienting would be compromised in autism, but this was not supported by the data: the boost in performance induced by predictability was practically identical for the two groups, except for the small subset of the ASD group with co-occurring attention deficit hyperactivity disorder, who received less benefit from interval-based cueing. However, we found that the presence of a rhythm induced a significantly stronger bias toward reporting target detections in the ASD group at large, suggesting weakened response inhibition during rhythmic entrainment.},

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pubstate = {published},

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}

@article{largeDynamicModelsMusical2023,

title = {Dynamic Models for Musical Rhythm Perception and Coordination},

author = {Edward W. Large and Iran Roman and Ji Chul Kim and Jonathan Cannon and Jesse K. Pazdera and Laurel J. Trainor and John Rinzel and Amitabha Bose},

doi = {10.3389/fncom.2023.1151895},

issn = {1662-5188},

year  = {2023},

date = {2023-01-01},

journal = {Frontiers in Computational Neuroscience},

volume = {17},

pages = {1151895},

abstract = {Rhythmicity permeates large parts of human experience. Humans generate various motor and brain rhythms spanning a range of frequencies. We also experience and synchronize to externally imposed rhythmicity, for example from music and song or from the 24-h light-dark cycles of the sun. In the context of music, humans have the ability to perceive, generate, and anticipate rhythmic structures, for example, "the beat." Experimental and behavioral studies offer clues about the biophysical and neural mechanisms that underlie our rhythmic abilities, and about different brain areas that are involved but many open questions remain. In this paper, we review several theoretical and computational approaches, each centered at different levels of description, that address specific aspects of musical rhythmic generation, perception, attention, perception-action coordination, and learning. We survey methods and results from applications of dynamical systems theory, neuro-mechanistic modeling, and Bayesian inference. Some frameworks rely on synchronization of intrinsic brain rhythms that span the relevant frequency range; some formulations involve real-time adaptation schemes for error-correction to align the phase and frequency of a dedicated circuit; others involve learning and dynamically adjusting expectations to make rhythm tracking predictions. Each of the approaches, while initially designed to answer specific questions, offers the possibility of being integrated into a larger framework that provides insights into our ability to perceive and generate rhythmic patterns.},

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pubstate = {published},

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}

@article{trevesAutisticAdultsShow2023,

title = {Autistic Adults Show Intact Learning on a Visuospatial Serial Reaction Time Task},

author = {Isaac N. Treves and Jonathan Cannon and Eren Shin and Cindy E. Li and Lindsay Bungert and Amanda O'Brien and Annie Cardinaux and Pawan Sinha and John D. E. Gabrieli},

doi = {10.1007/s10803-023-05894-y},

issn = {1573-3432},

year  = {2023},

date = {2023-01-01},

urldate = {2023-09-13},

journal = {Journal of Autism and Developmental Disorders},

abstract = {Some theories have proposed that autistic individuals have difficulty learning predictive relationships. We tested this hypothesis using a serial reaction time task in which participants learned to predict the locations of a repeating sequence of target locations. We conducted a large-sample online study with 61 autistic and 71 neurotypical adults. The autistic group had slower overall reaction times, but demonstrated sequence-specific learning equivalent to the neurotypical group, consistent with other findings of typical procedural memory in autism. The neurotypical group, however, made significantly more prediction-related errors early in the experiment when the stimuli changed from repeated sequences to random locations, suggesting certain limited behavioural differences in the learning or utilization of predictive relationships for autistic adults.},

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pubstate = {published},

tppubtype = {article}

}

@article{cinelyteTestingBeatPerception2022,

title = {Testing Beat Perception without Sensory Cues to the Beat: The Beat-Drop Alignment Test (BDAT)},

author = {Urte Cinelyte and Jonathan Cannon and Aniruddh D. Patel and Daniel Müllensiefen},

doi = {10.3758/s13414-022-02592-2},

issn = {1943-3921, 1943-393X},

year  = {2022},

date = {2022-10-01},

urldate = {2022-12-12},

journal = {Attention, Perception, & Psychophysics},

abstract = {Beat perception can serve as a window into internal time-keeping mechanisms, auditory–motor interactions, and aspects of cognition. One aspect of beat perception is the covert continuation of an internal pulse. Of the several popular tests of beat perception, none provide a satisfying test of this faculty of covert continuation. The current study proposes a new beat-perception test focused on covert pulse continuation: The Beat-Drop Alignment Test (BDAT). In this test, participants must identify the beat in musical excerpts and then judge whether a single probe falls on or off the beat. The probe occurs during a short break in the rhythmic components of the music when no rhythmic events are present, forcing participants to judge beat alignment relative to an internal pulse maintained in the absence of local acoustic timing cues. Here, we present two large (N $>$ 100) tests of the BDAT. In the first, we explore the effect of test item parameters (e.g., probe displacement) on performance. In the second, we correlate scores on an adaptive version of the BDAT with the computerized adaptive Beat Alignment Test (CA-BAT) scores and indices of musical experience. Musical experience indices outperform CA-BAT score as a predictor of BDAT score, suggesting that the BDAT measures a distinct aspect of beat perception that is more experience-dependent and may draw on cognitive resources such as working memory and musical imagery differently than the BAT. The BDAT may prove useful in future behavioral and neural research on beat perception, and all stimuli and code are freely available for download.},

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pubstate = {published},

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}

@article{kaplanModelingEnculturatedBias2022,

title = {Modeling Enculturated Bias in Entrainment to Rhythmic Patterns},

author = {Thomas Kaplan and Jonathan Cannon and Lorenzo Jamone and Marcus Pearce},

doi = {10.1371/journal.pcbi.1010579},

issn = {1553-7358},

year  = {2022},

date = {2022-09-01},

urldate = {2022-10-28},

journal = {PLOS Computational Biology},

volume = {18},

number = {9},

pages = {e1010579},

publisher = {Public Library of Science},

abstract = {Long-term and culture-specific experience of music shapes rhythm perception, leading to enculturated expectations that make certain rhythms easier to track and more conducive to synchronized movement. However, the influence of enculturated bias on the moment-to-moment dynamics of rhythm tracking is not well understood. Recent modeling work has formulated entrainment to rhythms as a formal inference problem, where phase is continuously estimated based on precise event times and their correspondence to timing expectations: PIPPET (Phase Inference from Point Process Event Timing). Here we propose that the problem of optimally tracking a rhythm also requires an ongoing process of inferring which pattern of event timing expectations is most suitable to predict a stimulus rhythm. We formalize this insight as an extension of PIPPET called pPIPPET (PIPPET with pattern inference). The variational solution to this problem introduces terms representing the likelihood that a stimulus is based on a particular member of a set of event timing patterns, which we initialize according to culturally-learned prior expectations of a listener. We evaluate pPIPPET in three experiments. First, we demonstrate that pPIPPET can qualitatively reproduce enculturated bias observed in human tapping data for simple two-interval rhythms. Second, we simulate categorization of a continuous three-interval rhythm space by Western-trained musicians through derivation of a comprehensive set of priors for pPIPPET from metrical patterns in a sample of Western rhythms. Third, we simulate iterated reproduction of three-interval rhythms, and show that models configured with notated rhythms from different cultures exhibit both universal and enculturated biases as observed experimentally in listeners from those cultures. These results suggest the influence of enculturated timing expectations on human perceptual and motor entrainment can be understood as approximating optimal inference about the rhythmic stimulus, with respect to prototypical patterns in an empirical sample of rhythms that represent the music-cultural environment of the listener.},

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tppubtype = {article}

}

@article{cannonExpectancybasedRhythmicEntrainment2021,

title = {Expectancy-Based Rhythmic Entrainment as Continuous Bayesian Inference},

author = {Jonathan Cannon},

editor = {Jonathan Rubin},

doi = {10.1371/journal.pcbi.1009025},

issn = {1553-7358},

year  = {2021},

date = {2021-06-01},

urldate = {2022-08-28},

journal = {PLOS Computational Biology},

volume = {17},

number = {6},

pages = {e1009025},

abstract = {When presented with complex rhythmic auditory stimuli, humans are able to track underlying temporal structure (e.g., a ``beat''), both covertly and with their movements. This capacity goes far beyond that of a simple entrained oscillator, drawing on contextual and enculturated timing expectations and adjusting rapidly to perturbations in event timing, phase, and tempo. Previous modeling work has described how entrainment to rhythms may be shaped by event timing expectations, but sheds little light on any underlying computational principles that could unify the phenomenon of expectation-based entrainment with other brain processes. Inspired by the predictive processing framework, we propose that the problem of rhythm tracking is naturally characterized as a problem of continuously estimating an underlying phase and tempo based on precise event times and their correspondence to timing expectations. We present two inference problems formalizing this insight: PIPPET (Phase Inference from Point Process Event Timing) and PATIPPET (Phase and Tempo Inference). Variational solutions to these inference problems resemble previous ``Dynamic Attending'' models of perceptual entrainment, but introduce new terms representing the dynamics of uncertainty and the influence of expectations in the absence of sensory events. These terms allow us to model multiple characteristics of covert and motor human rhythm tracking not addressed by other models, including sensitivity of error corrections to inter-event interval and perceived tempo changes induced by event omissions. We show that positing these novel influences in human entrainment yields a range of testable behavioral predictions. Guided by recent neurophysiological observations, we attempt to align the phase inference framework with a specific brain implementation. We also explore the potential of this normative framework to guide the interpretation of experimental data and serve as building blocks for even richer predictive processing and active inference models of timing.},

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@article{cannonPredictionAutismSpectrum2021a,

title = {Prediction in Autism Spectrum Disorder: A Systematic Review of Empirical Evidence},

author = {Jonathan Cannon and Amanda M. O'Brien and Lindsay Bungert and Pawan Sinha},

doi = {10.1002/aur.2482},

issn = {1939-3792, 1939-3806},

year  = {2021},

date = {2021-04-01},

urldate = {2022-10-17},

journal = {Autism Research},

volume = {14},

number = {4},

pages = {604–630},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cannonHowBeatPerception2021a,

title = {How Beat Perception Co-opts Motor Neurophysiology},

author = {Jonathan J. Cannon and Aniruddh D. Patel},

doi = {10.1016/j.tics.2020.11.002},

issn = {13646613},

year  = {2021},

date = {2021-02-01},

urldate = {2022-08-28},

journal = {Trends in Cognitive Sciences},

volume = {25},

number = {2},

pages = {137–150},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cannon2017a,

title = {Stable Control of Firing Rate Mean and Variance by Dual Homeostatic Mechanisms},

author = {Jonathan Cannon and Paul Miller},

doi = {10.1186/s13408-017-0043-7},

issn = {21908567},

year  = {2017},

date = {2017-01-01},

journal = {Journal of Mathematical Neuroscience},

volume = {7},

number = {1},

publisher = {The Author(s)},

abstract = {Homeostatic processes that provide negative feedback to regulate neuronal firing rates are essential for normal brain function. Indeed, multiple parameters of in-dividual neurons, including the scale of afferent synapse strengths and the densities of specific ion channels, have been observed to change on homeostatic time scales to oppose the effects of chronic changes in synaptic input. This raises the question of whether these processes are controlled by a single slow feedback variable or mul-tiple slow variables. A single homeostatic process providing negative feedback to a neuron's firing rate naturally maintains a stable homeostatic equilibrium with a char-acteristic mean firing rate; but the conditions under which multiple slow feedbacks produce a stable homeostatic equilibrium have not yet been explored. Here we study a highly general model of homeostatic firing rate control in which two slow variables provide negative feedback to drive a firing rate toward two different target rates. Us-ing dynamical systems techniques, we show that such a control system can be used to stably maintain a neuron's characteristic firing rate mean and variance in the face of perturbations, and we derive conditions under which this happens. We also derive expressions that clarify the relationship between the homeostatic firing rate targets and the resulting stable firing rate mean and variance. We provide specific examples of neuronal systems that can be effectively regulated by dual homeostasis. One of these examples is a recurrent excitatory network, which a dual feedback system can robustly tune to serve as an integrator.},

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tppubtype = {article}

}

@article{Cannon2016,

title = {Synaptic and Intrinsic Homeostasis Cooperate to Optimize Single Neuron Response Properties and Tune Integrator Circuits.},

author = {Jonathan Cannon and Paul Miller},

doi = {10.1152/jn.00253.2016},

issn = {1522-1598},

year  = {2016},

date = {2016-01-01},

journal = {Journal of neurophysiology},

pages = {jn.00253.2016},

abstract = {Homeostatic processes that provide negative feedback to regulate neuronal firing rate are essential for normal brain function, and observations suggest that multiple such processes may operate simultaneously in the same network. We pose two questions: why might a diversity of homeostatic pathways be necessary, and how can they operate in concert without opposing and undermining each other? To address these questions, we perform a computational and analytical study of cell-intrinsic homeostasis and synaptic homeostasis in single-neuron and recurrent circuit models. We demonstrate analytically and in simulation that when two such mechanisms are controlled on a long time scale by firing rate via simple and general feedback rules, they can robustly operate in tandem to tune the the mean and variance of single neuron's firing rate to desired goals. This property allows the system to recover desired behavior after chronic changes in input statistics. We illustrate the power of this homeostatic tuning scheme by using it to regain high mutual information between neuronal input and output after major changes in input statistics. We then show that such dual homeostasis can be applied to tune the behavior of a neural integrator, a system that is notoriously sensitive to variation in parameters. These results are robust to variation in goals and model parameters. We argue that a set of homeostatic processes that appear to redundantly regulate mean firing rate may work together to control firing rate mean and variance and thus maintain performance in a parameter-sensitive task such as integration.},

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}

@article{cannonNeuralSequenceGeneration2015a,

title = {Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition},

author = {Jonathan Cannon and Nancy Kopell and Timothy Gardner and Jeffrey Markowitz},

editor = {Olaf Sporns},

doi = {10.1371/journal.pcbi.1004581},

issn = {1553-7358},

year  = {2015},

date = {2015-11-01},

urldate = {2022-08-28},

journal = {PLOS Computational Biology},

volume = {11},

number = {11},

pages = {e1004581},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cannon2015c,

title = {The Leaky Oscillator : Properties of Inhibition-Based Rhythms Revealed through the Singular Phase Response Curve},

author = {Jonathan Cannon and Nancy Kopell},

doi = {https://doi.org/10.1137/14097715},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {SIAM Journal on Applied Dynamical Systems},

volume = {02453},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cannonNeurosystemsBrainRhythms2014a,

title = {Neurosystems: Brain Rhythms and Cognitive Processing},

author = {Jonathan Cannon and Michelle M. McCarthy and Shane Lee and Jung Lee and Christoph Börgers and Miles A. Whittington and Nancy Kopell},

doi = {10.1111/ejn.12453},

issn = {0953-816X, 1460-9568},

year  = {2014},

date = {2014-03-01},

urldate = {2022-08-28},

journal = {European Journal of Neuroscience},

volume = {39},

number = {5},

pages = {705–719},

abstract = {Neuronal rhythms are ubiquitous features of brain dynamics, and are highly correlated with cognitive processing. However, the relationship between the physiological mechanisms producing these rhythms and the functions associated with the rhythms remains mysterious. This article investigates the contributions of rhythms to basic cognitive computations (such as filtering signals by coherence and/or frequency) and to major cognitive functions (such as attention and multi-modal coordination). We offer support to the premise that the physiology underlying brain rhythms plays an essential role in how these rhythms facilitate some cognitive operations.},

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tppubtype = {article}

}