Biology subjects

D. Sam Schwarzkopf

Publications and source records attributed to D. Sam Schwarzkopf.

4 recordsLinked to original sources

bioRxiv2016

Induction of Kanizsa contours requires awareness of the inducing context

It remains unknown to what extent the human visual system interprets information about complex scenes without conscious analysis. Here we used visual masking techniques to assess whether illusory contours (Kanizsa shapes) are perceived when the inducing context creating this illusion does not reach awareness. In the first experiment we tested perception directly by having participants discriminate the orientation of an illusory contour. In the second experiment, we exploited the fact that the presence of an illusory contour enhances performance on a spatial localization task. Moreover, in the latter experiment we also used a different masking method to rule out the effect of stimulus duration. Our results suggest that participants do not perceive illusory contours when they are unaware of the inducing context. This is consistent with theories of a multistage, recurrent process of perceptual integration. Our findings thus challenge some reports, including those from neurophysiological experiments in anaesthetized animals. Furthermore, we discuss the importance to test the presence of the phenomenal percept directly with appropriate methods.

Neuroscience↗

bioRxiv2016

Metacognitive ability predicts hippocampal and prefrontal microstructure

The ability to introspectively evaluate our experiences to form accurate metacognitive beliefs, or insight, is an essential component of decision-making. Previous research suggests individuals vary substantially in their level of insight, and that this variation predicts brain volume and function, particularly in the anterior prefrontal cortex (aPFC). However, the neurobiological mechanisms underlying these effects are unclear, as qualitative, macroscopic measures such as brain volume can be related to a variety of microstructural features. Here we used a newly developed, high-resolution (800{micro}m isotropic) multi-parameter mapping technique in 48 healthy individuals to delineate quantitative markers of in vivo histological features underlying metacognitive ability. Specifically, we examined how neuroimaging markers of local grey matter myelination, macromolecular and iron content relate to insight. Extending previous volumetric findings, we found that metacognitive ability, as determined by a signal-detection theoretic model, was positively related to the myelo-architectural integrity of aPFC grey matter. Interestingly, perceptual metacognition predicted decreased macromolecule content coupled with increased iron in the hippocampus and precuneus, areas previously implicated in meta-memory rather than meta-perception. Further, the relationship of hippocampal-precuneus and prefrontal microstructure to an auditory memory measure was respectively mediated or suppressed by metacognitive ability, suggesting a dynamic trade-off between participants memory and metacognition. These results point towards a novel understanding of the relationship between memory, brain microstructure, and metacognition.\n\nSignificance StatementBy combining a signal-theoretic model of individual metacognitive ability with state of the art quantitative neuroimaging, our results shed new light on the neurobiological mechanisms underlying introspective insight. Myelination and iron are core determinants of both healthy brain maturation and neurodegeneration; particularly in the hippocampus where iron accumulation is linked to oxidative stress and inflammation. Our results may thus indicate that metacognition depends upon the development and integrity of a memory-related brain network, potentially revealing novel biomarkers of neurodegeneration. These results highlight the power of quantitative mapping to reveal neurobiological correlates of behaviour.

Neuroscience↗

bioRxiv2015

Cortical idiosyncrasies predict the perception of object size

Perception is subjective. Even basic judgments, like those of visual object size, vary substantially between observers and also across the visual field within the same observer. The way in which the visual system determines the size of objects remains unclear, however. We hypothesize that object size is inferred from neuronal population activity in V1 and predict that idiosyncrasies in cortical functional architecture should therefore explain individual differences in size judgments. Indeed, using novel behavioral methods and functional magnetic resonance imaging (fMRI) we demonstrate that biases in size perception are correlated with the spatial tuning of neuronal populations in healthy volunteers. To explain this relationship, we formulate a population read-out model that directly links the spatial distribution of V1 representations to our perceptual experience of visual size. Altogether, we suggest that the individual perception of simple stimuli is warped by idiosyncrasies in visual cortical organization.

Neuroscience↗

bioRxiv2015

Non-dichotomous inference using bootstrapped evidence

The problems with classical frequentist statistics have recently received much attention, yet the enthusiasm of researchers to adopt alternatives like Bayesian inference remains modest. Here I present the bootstrapped evidence test, an objective resampling procedure that takes the precision with which both the experimental and null hypothesis can be estimated into account. Simulations and reanalysis of actual experimental data demonstrate that this test minimizes false positives while maintaining sensitivity. It is equally applicable to a wide range of situations and thus minimizes problems arising from analytical flexibility. Critically, it does not dichotomize the results based on an arbitrary significance level but instead quantifies how well the data support either the alternative or the null hypothesis. It is thus particularly useful in situations with considerable uncertainty about the expected effect size. Because it is non-parametric, it is also robust to severe violations of assumptions made by classical statistics.

Neuroscience↗