An empirically-driven guide on using Bayes Factors for M/EEG decoding

Teichmann, Lina; Moerel, Denise; Baker, Chris I.; Grootswagers, Tijl

doi:10.1101/2021.06.23.449663

Cited by 19 publications

(22 citation statements)

References 45 publications

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“…For all comparisons, we used the BayesFactor package in R (Morey et al, 2018). Following recommendations in Teichmann et al (2021), we used a JZS prior (Rouder et al, 2009) with a scale factor of 0.707. This is the default prior and scaling in the BayesFactor package because it makes minimal assumptions about the expected effect size, and serves as a "non-informative default" (Rouder et al, 2009, p. 232).…”

Section: Discussionmentioning

confidence: 99%

“…For decoding analyses, we used a series of t-tests using the ttestBF function (Morey et al, 2018) from the BayesFactor package with the parameters described above. The alternate hypothesis is that the decoding is above chance (50%), and the null-interval was effect sizes from negative infinity up to 0.5, as effect sizes during baseline periods prior to stimulus onset from previous work have shown this to be most appropriate (Teichmann et al, 2021). This formed a one-sided hypothesis that the effect size for alternate hypothesis should be positive.…”

Section: Discussionmentioning

confidence: 99%

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Capacity for movement is an organisational principle in object representations

Shatek¹,

Robinson²,

Grootswagers³

et al. 2021

Preprint

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The ability to perceive moving objects is crucial for survival and threat identification. The association between the ability to move and being alive is learned early in childhood, yet not all moving objects are alive. Natural, non-agentive movement (e.g., clouds, fire) causes confusion in children and adults under time pressure. Recent neuroimaging evidence has shown that the visual system processes objects on a spectrum according to their ability to engage in self-propelled, goal-directed movement. Most prior work has used only moving stimuli that are also animate, so it is difficult to disentangle the effect of movement from aliveness or animacy in representational categorisation. In the current study, we investigated the relationship between movement and aliveness using both behavioural and neural measures. We examined electroencephalographic (EEG) data recorded while participants viewed static images of moving or non-moving objects that were either natural or artificial. Participants classified the images according to aliveness, or according to capacity for movement. Behavioural classification showed two key categorisation biases: moving natural things were often mistaken to be alive, and often classified as not moving. Movement explained significant variance in the neural data, during both a classification task and passive viewing. These results show that capacity for movement is an important dimension in the structure of human visual object representations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Capacity for movement is an organisational principle in object representations

Shatek¹,

Robinson²,

Grootswagers³

et al. 2021

Preprint

Self Cite

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show abstract

“…We used a point-nil for the null-hypothesis and specified a half-Cauchy prior for the alternative hypothesis with medium width (r = 0.707) that covered an interval with effect sizes ranging from 0.5 to infinity. We chose this prior range because previous studies that looked at visual decoding found effect sizes as large as 0.5 during a baseline period when there cannot be any meaningful information in the signal 49 . We also performed a robustness check with a wide (r = 1) and ultrawide ( r = 1.414) prior which did not have a pronounced effect on the results.…”

Section: Methodsmentioning

confidence: 99%

“…We used a half-Cauchy prior with medium width (r = 0.707) and adjusted the prior range of the alternative hypothesis from 0.5 to infinity to allow for small effects under the alternative hypothesis (Rouder et al, 2009). We chose this prior range because previous studies using visual decoding found effect sizes as large as 0.5 during a baseline period when there cannot be any meaningful information in the signal (Teichmann et al, 2021). We also performed a robustness check with a wide (r = 1) and ultrawide (𝑟 = 1.414) prior which did not have a pronounced effect on the results.…”

Section: Statistical Inferencementioning

confidence: 99%

Vicarious touch: overlapping neural patterns between seeing and feeling touch

Smit

Moerel

Zopf

et al. 2022

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Empathy allows us to understand and sometimes vicariously experience what someone else is feeling1,2. For some people this extends to tactile sensations, with reports of feeling touch when seeing others being touched3,4. Major theories claim that tactile empathy relies on overlapping neural representations between feeling and seeing touch5–8. Here we test this using time–resolved decoding of electroencephalography (EEG) data collected while participants felt touch to the fingers or watched videos of touch to another person's fingers. A classifier trained and tested on brain activation patterns during tactile trials could distinguish touch to the thumb versus little finger, demonstrating remarkable sensitivity to small neural differences. In people with high tactile empathy, who report feeling touch when watching videos of touch, these tactile classifiers cross-generalised to visual trials. These data show that representations of feeling and seeing touch overlap, including fine-grained information about touch location on the hand. The timing suggests that early visual predictions of touch location involve later (high-level) tactile representations. Our results demonstrate that EEG decoding can extract precise neural signatures for location of touch and suggest that shared neural representations underpin tactile empathy.

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“…We used Bayesian statistics to determine the evidence for the alternative hypothesis of non-zero correlations (across the 16 participants) and the null hypothesis of no correlation for each point in time (Dienes, 2011;Kass and Raftery, 1995;Rouder et al, 2009a;Teichmann et al, 2021;Wagenmakers, 2007), using the Bayes Factor R package (Morey and Rouder, 2018). The prior for the null hypothesis was set at zero.…”

Section: Statistical Inferencementioning

confidence: 99%

Unique Contributions of Perceptual and Conceptual Humanness to Object Representations in the Human Brain

Grootswagers¹,

McKay²,

Varlet³

2022

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The human brain is able to quickly and accurately identify objects in a dynamic visual world. Objects evoke different patterns of neural activity in the visual system, which reflect object category memberships. However, the underlying dimensions of object representations in the brain remain unclear. Recent research suggests that objects similarity to humans is one of the main dimensions used by the brain to organise objects, but the nature of the human-similarity features driving this organisation are still unknown. Here, we investigate the relative contributions of perceptual and conceptual features of humanness to the representational organisation of objects in the human visual system. We collected behavioural judgements of human-similarity of various objects, which were compared with time-resolved neuroimaging responses to the same objects. The behavioural judgement tasks targeted either perceptual or conceptual humanness features to determine their respective contribution to perceived human-similarity. Behavioural and neuroimaging data revealed significant and unique contributions of both perceptual and conceptual features of humanness, each explaining unique variance in neuroimaging data. Furthermore, our results showed distinct spatio-temporal dynamics in the processing of conceptual and perceptual humanness features, with later and more lateralised brain responses to conceptual features. This study highlights the critical importance of social requirements in information processing and organisation in the human brain.

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An empirically-driven guide on using Bayes Factors for M/EEG decoding

Cited by 19 publications

References 45 publications

Capacity for movement is an organisational principle in object representations

Capacity for movement is an organisational principle in object representations

Vicarious touch: overlapping neural patterns between seeing and feeling touch

Unique Contributions of Perceptual and Conceptual Humanness to Object Representations in the Human Brain

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