Biology subjects

Heinzle, J.

Publications and source records attributed to Heinzle, J..

4 recordsLinked to original sources

bioRxiv2018

Feature-specific prediction errors for visual mismatch support predictive coding accounts of perception

Predictive coding (PC) theory posits that our brain employs a predictive model of the environment to infer the causes of its sensory inputs. A fundamental but untested prediction of this theory is that the same stimulus should elicit distinct precision weighted prediction errors (pwPEs) when different (feature-specific) predictions are violated, even in the absence of attention. Here, we tested this hypothesis using functional magnetic resonance imaging (fMRI) and a multi-feature roving visual mismatch paradigm where rare changes in either color (red, green), or emotional expression (happy, fearful) of faces elicited pwPE responses in human participants. Using a computational model for learning and inference, we simulated pwPE trajectories of a Bayes-optimal observer and used these to analyze changes in blood oxygen level dependent (BOLD) responses to changes in color and emotional expression of faces while participants engaged in a distractor task. Controlling for visual attention by eye-tracking, we found pwPE responses to unexpected color changes in the fusiform gyrus. Conversely, unexpected changes of facial emotions elicited pwPE responses in thalamo-cortico-cerebellar structures associated with emotion processing. Our results support a general role of PC across perception, from low-level to complex and socially relevant object features, and suggest that monitoring of the social environment occurs continuously and automatically, even in the absence of attention.\n\nHighlightsPredictive coding (PC) implies that brain responses should reflect transient precision weighted prediction errors (pwPEs). By using fMRI we show that rare changes in color or emotional expression of human faces elicit pwPE responses in dedicated neuronal circuits with known specialization in processing color and emotion-related information, respectively. Our results demonstrate that physically identical stimuli elicit pwPEs in distinct neuronal circuits when different (feature-specific) predictions are violated, depending on current sensory expectations based on prior stimulus context. The demonstration of pwPEs in visual areas as well as in emotion processing structures lends experimental support to theoretical accounts of PC in color and social perception, respectively.

neuroscience↗

bioRxiv2018

Switch costs in inhibitory control and goal-directed behavior: A computational study of the antisaccade task

An integral aspect of human cognition is the ability to inhibit habitual responses in order to initiate complex, rule-guided actions. Moreover, humans have also the ability to alternate between different sets of rules or tasks, at the cost of degraded performance when compared to repeating the same task, a phenomenon called the task switch cost. While it is recognized that switching between tasks requires often to inhibit habitual responses, the interaction between these two forms of cognitive control has been much less studied than each of them separately. Here, we use a computational model to draw a bridge between inhibitory control and voluntary action generation and thereby provide a novel account of seemingly paradoxical findings in the task switch literature. We investigated task switching in the mixed antisaccade task, in which participants are cued to saccade either in the same or in the opposite direction to a peripheral stimulus. Our model demonstrates that stopping a habitual action leads to increased inhibitory control that persists on the next trial. However, enhanced inhibition affects only the probability of generating habitual responses, and, contrary to previous accounts, cannot be characterized as proactive task interference. In addition, our model demonstrates that voluntary actions (but not habitual responses) are slower and more prompt to errors on switch trials compared to repeat trials. We conclude that precisely the interaction between these two effects explains a variety of contradictory findings reported in the literature.

animal behavior and cognition↗

bioRxiv2018

Inhibitory and late errors in the antisaccade task: Influence of task design

In the antisaccade task, subjects are instructed to saccade in the opposite direction of a peripheral visual cue (PVC). Importantly, several psychiatric disorders are associated with increased error rates in this paradigm. Despite this observation, there is no consensus about the mechanism behind antisaccade errors: while often explained as inhibition failures, some studies have suggested that errors are caused by deficits in the ability to initiate voluntary saccades. Using a computational model, we recently showed that under some conditions high latency or late errors can be explained by a race process between voluntary pro- and antisaccades. A limitation of our findings is that in our previous experiment the PVC signaled the trial type, whereas in most studies, subjects are informed about the trial type before the PVC is presented. We refer to these task designs as asynchronous (AC) and synchronous cues (SC) conditions. Here, we investigated to which extent differences in design affect the type and frequency of errors in the antisaccade task. Twenty-four subjects participated in mixed blocks of pro- and antisaccade trials in both conditions. Our results demonstrate that error rates were highly correlated across task designs and a non-negligible fraction of them were classified as late errors in both conditions. In summary, our findings indicate that errors in the AC task are the result of both inhibition failures and inaccurate voluntary action initiation.

neuroscience↗

bioRxiv2017

The Stochastic Early Reaction, Inhibition, and Late Action (SERIA) Model for Antisaccades SERIA - A model for errors and reaction times in the antisaccade task

The antisaccade task is a classic paradigm used to study the voluntary control of eye movements. It requires participants to suppress a reactive eye movement to a visual target and to concurrently initiate a saccade in the opposite direction. Although several models have been proposed to explain error rates and reaction times in this task, no formal model comparison has yet been performed. Here, we describe a Bayesian modeling approach to the antisaccade task that allows us to formally compare different models on the basis of their evidence. First, we provide a formal likelihood function of actions (pro- and antisaccades) and reaction times based on previously published models. Second, we introduce the Stochastic Early Reaction, Inhibition, and late Action model (SERIA), a novel model postulating two different mechanisms that interact in the antisaccade task: an early GO/NO-GO race decision process and a late GO/GO decision process. Third, we apply these models to a data set from an experiment with three mixed blocks of pro- and antisaccade trials. Bayesian model comparison demonstrates that the SERIA model explains the data better than competing models that do not incorporate a late decision process. Moreover, we show that the race decision processes postulated by the SERIA model are, to a large extent, insensitive to the cue presented on a single trial. Finally, we use parameter estimates to demonstrate that changes in reaction time and error rate due to the probability of a trial type (prosaccade or antisaccade) are best explained by faster or slower inhibition and the probability of generating late voluntary prosaccades.\n\nAuthor summaryOne widely replicated finding in schizophrenia research is that patients tend to make more errors in the antisaccade task, a psychometric paradigm in which participants are required to look in the opposite direction of a visual cue. This deficit has been suggested to be an endophenotype of schizophrenia, as first order relatives of patients tend to show similar but milder deficits. Currently, most models applied to experimental findings in this task are limited to fit average reaction times and error rates. Here, we propose a novel statistical model that fits experimental data from the antisaccade task, beyond summary statistics. The model is inspired by the hypothesis that antisaccades are the result of several competing decision processes that interact nonlinearly with each other. In applying this model to a relatively large experimental data set, we show that mean reaction times and error rates do not fully reflect the complexity of the processes that are likely to underlie experimental findings. In the future, our model could help to understand the nature of the deficits observed in schizophrenia by providing a statistical tool to study their biological underpinnings.

neuroscience↗