Bayesian multiple comparisons and model selection

Neath, Andrew A.; Flores, Javier E.; Cavanaugh, Joseph E.

doi:10.1002/wics.1420

Cited by 16 publications

(15 citation statements)

References 20 publications

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“…Nevertheless, it would be valuable for methodological advances to consider the possibility of randomly occurring high exceedance probabilities given a large number of independent model comparisons. A multilevel scheme which adjusts priors over models, rather than the current ubiquitous use of flat priors, may be developed as a satisfactory approach [109,110,111]. As the current method is agnostic to the large number of model comparisons we need to stress that we only report preliminary evidence.…”

Section: Discussionmentioning

confidence: 99%

Neural surprise in somatosensory Bayesian learning

et al. 2021

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Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Indications of Bayesian surprise encoding, reflecting model updating, are found in primary somatosensory cortex from around 140ms. This dissociation is compatible with the idea that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its precise mechanisms.

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Section: Discussionmentioning

confidence: 99%

Neural surprise in somatosensory Bayesian learning

et al. 2021

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“…We used Bayesian hierarchical regression to model the data. Because ROI-wise effects were partially pooled across ROIs, this essentially removes the need to correct for multiple comparisons induced by investigating multiple ROIs (59).…”

Section: Primary Analysismentioning

confidence: 99%

Age and sex dependent variability of type 2 dopamine receptors in the human brain: A large-scale PET cohort

Malén

Karjalainen

Isojärvi

et al. 2021

Preprint

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BACKGROUND: The dopamine system contributes to a multitude of functions ranging from reward and motivation to learning and movement control, making it a key component in goal-directed behavior. Altered dopaminergic function is observed in neurological and psychiatric conditions. Numerous factors have been proposed to influence dopamine function, but due to small sample sizes and heterogeneous data analysis methods in previous studies their specific and joint contributions remain unresolved. METHODS: In this cross-sectional register-based study we investigated how age, sex, body mass index (BMI), as well as cerebral hemisphere and regional volume influence striatal type 2 dopamine receptor (D2R) availability in the human brain. We analyzed a large historical dataset (n=156, 120 males and 36 females) of [11C]raclopride PET scans performed between 2004 and 2018. RESULTS: Striatal D2R availability decreased through age for both sexes and was higher in females versus males throughout age. BMI and striatal D2R availability were weakly associated. There was no consistent lateralization of striatal D2R. The observed effects were independent of regional volumes. These results were validated using two different spatial normalization methods, and the age and sex effects also replicated in an independent sample (n=135). CONCLUSIONS: D2R density is dependent on age and sex, which may contribute to the vulnerability of neurological and psychiatric conditions involving altering D2R expression.

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“…For experiments with more than two conditions, we conducted within-group comparisons with corrections for multiple comparisons using JASP. The concern about multiple comparisons centers on the potential inflation of a type I error of a null effect (Neath, Flores, & Cavanaugh, 2018). To deal with this problem, a widely applied correction by Westfall, Johnson, and Utts (1997) calibrated the prior data to regain moderate or high null effects, providing a relatively conservative adjusted posterior probability similar to Bonferroni correction.…”

Section: Bayesian Statistical Analysismentioning

confidence: 99%

Effect of change saliency and neural entrainment on flicker-induced time dilation

Ito

Yotsumoto

2020

Journal of Vision

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When a visual stimulus flickers periodically and rhythmically, the perceived duration tends to exceed its physical duration in the peri-second range. Although flicker-induced time dilation is a robust time illusion, its underlying neural mechanisms remain inconclusive. The neural entrainment account proposes that neural entrainment of the exogenous visual stimulus, marked by steady-state visual evoked potentials (SSVEPs) over the visual cortex, is the cause of time dilation. By contrast, the saliency account argues that the conscious perception of flicker changes is indispensable. In the current study, we examined these two accounts separately. The first two experiments manipulated the level of saliency around the critical fusion threshold (CFF) in a duration discrimination task to probe the effect of change saliency. The amount of dilation correlated with the level of change saliency. The next two experiments investigated whether neural entrainment alone could also induce perceived dilation. To preclude change saliency, we utilized a combination of two high-frequency flickers above the CFF, whereas their beat frequency still theoretically aroused neural entrainment at a low frequency. Results revealed a moderate time dilation induced by combinative high-frequency flickers. Although behavioral results suggested neural entrainment engagement, electroencephalography showed neither larger power nor inter-trial coherence (ITC) at the beat. In summary, change saliency was the most critical factor determining the perception and strength of time dilation, whereas neural entrainment had a moderate influence. These results highlight the influence of higher-level visual processing on time perception.

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Bayesian multiple comparisons and model selection

Cited by 16 publications

References 20 publications

Neural surprise in somatosensory Bayesian learning

Neural surprise in somatosensory Bayesian learning

Age and sex dependent variability of type 2 dopamine receptors in the human brain: A large-scale PET cohort

Effect of change saliency and neural entrainment on flicker-induced time dilation

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