Biology subjects

Yarrow, K.

Publications and source records attributed to Yarrow, K..

4 recordsLinked to original sources

bioRxiv2023

A robust measure of metacognitive sensitivity and confidence criteria

Humans experience feelings of confidence in their decisions. In perception, these feelings are typically accurate - we tend to feel more confident about correct decisions. The degree of insight people have into the accuracy of their decisions is known as metacognitive sensitivity. Currently popular methods of estimating metacognitive sensitivity are subject to interpretive ambiguities because they assume that humans experience normally-shaped distributions of different experiences when they are repeatedly exposed to a single input. If, however, people have skewed distributions of experiences, or distributions with excess kurtosis (i.e. a distribution containing greater numbers of extreme experiences than is predicted by a normal distribution), calculations can erroneously underestimate metacognitive sensitivity. Here, we describe a means of estimating metacognitive sensitivity that is more robust against violations of the normality assumption. This improved method relies on estimating the precision with which people transition between making categorical decisions with relatively low to high confidence, and on comparing this with the precision with which they transition between making different types of perceptual category decision. The new method can easily be added to standard behavioral experiments. We provide free Matlab code to help researchers implement these analyses and procedures in their own experiments. Public Significance StatementSignal-detection theory is one of the most popular frameworks for analysing data from experiments of human behaviour - including investigations of confidence. The authors demonstrate that if a key assumption of this framework is inadvertently violated, analyses of confidence can lead to unwarranted conclusions. They develop a new and more robust measure of confidence.

neuroscience↗

bioRxiv2022

On why we lack confidence in signal-detection-based analyses of confidence

Signal-detection theory (SDT) is one of the most popular frameworks for analyzing data from studies of human behavior - including investigations of confidence. SDT-based analyses of confidence deliver both standard estimates of sensitivity (d), and a second estimate based only on high-confidence decisions - meta d. The extent to which meta d estimates fall short of d estimates is regarded as a measure of metacognitive inefficiency, quantifying the contamination of confidence by additional noise. These analyses rely on a key but questionable assumption - that repeated exposures to an input will evoke a normally-shaped distribution of perceptual experiences (the normality assumption). Here we show, via analyses inspired by an experiment and modelling, that when distributions of experiences do not conform with the normality assumption, meta d can be systematically underestimated relative to d. Our data therefore highlight that SDT-based analyses of confidence do not provide a ground truth measure of human metacognitive inefficiency. Public Significance StatementSignal-detection theory is one of the most popular frameworks for analysing data from experiments of human behaviour - including investigations of confidence. The authors show that the results of these analyses cannot be regarded as ground truth. If a key assumption of the framework is inadvertently violated, analyses can encourage conceptually flawed conclusions.

neuroscience↗

bioRxiv2020

Are alpha-band oscillations a default dynamic of the human visual brain, or a product of ongoing visual processes?

One of the seminal findings of cognitive neuroscience is that the power of alpha-band ([~]10 Hz) brain waves, in occipital regions, increases when people close their eyes. This has encouraged the view that alpha oscillations are a default dynamic, to which the visual brain returns in the absence of input. Accordingly, we might be unable to increase the power of alpha oscillations when the eyes are closed, above the level that would usually ensue. Here we report counter evidence. We used electroencephalography (EEG) to record brain activity when people had their eyes open and closed, before and after they had adapted to radial motion. The increase in the power of alpha oscillations when people closed their eyes was enhanced by adaptation to a broad range of radial motion speeds. This effect was greatest for 10Hz motion, but robust for other frequencies, and specifically for 7.5Hz. This last observation is important, as it rules against an ongoing entrainment of activity, at the adaptation frequency, as an explanation for our results. Instead, our data show that visual processes remain active when people close their eyes, and these can be modulated by adaptation to increase the power of alpha oscillations in occipital brain regions.

neuroscience↗

bioRxiv2020

Visual predictions, neural oscillations and Newtonian physics.

Prediction is considered a core function of the human visual brain, but relating this suggestion to real life is problematic, as findings regarding the neural correlates of prediction rely on abstracted experiments, not reminiscent of a typical visual diet. We addressed this by having people view videos of basketball, and asking them to predict jump shot outcomes while we recorded eye movements and brain activity. We used the brains understanding of physics to manipulate predictive success, by inverting footage. People had enhanced alpha-band activity in occipital brain regions when watching upright videos, and this predicted both an increase in predictive success and enhanced ball tracking. Alpha-band activity in visual brain regions has been linked to inhibition, so we regard our results as evidence that inhibition of task irrelevant information is a core function of predictive processes in the visual brain, enacted as people complete visual tasks typical of daily life.

neuroscience↗