Manual choice reaction times in the rate-domain

Generalised exponential-Gaussian distribution: a method for neural reaction time analysis

Reaction times (RTs) are an essential metric used for understanding the link between brain and behaviour. As research is reaffirming the tight coupling between neuronal and behavioural RTs, thorough statistical modelling of RT data is thus essential to enrich current theories and motivate novel findings. A statistical distribution is proposed herein that is able to model the complete RT's distribution, including location, scale and shape: the generalised-exponential-Gaussian (GEG) distribution. The GEG distribution enables shifting the attention from traditional means and standard deviations to the entire RT distribution. The mathematical properties of the GEG distribution are presented and investigated via simulations. Additionally, the GEG distribution is featured via four real-life data sets. Finally, we discuss how the proposed distribution can be used for regression analyses via generalised additive models for location, scale and shape (GAMLSS).

Tracking the contribution of inductive bias to individualised internal models

Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead they rely on an approximate model that is continually adapted throughout learning. However, experimenters assume an ideal observer model, which captures stimulus structure but ignores the diverging hypotheses that humans form during learning. We combine non-parametric Bayesian methods and probabilistic programming to infer rich and dynamic individualised internal models from response times. We demonstrate that the approach is capable of characterizing the discrepancy between the internal model maintained by individuals and the ideal observer model and to track the evolution of the contribution of the ideal observer model to the internal model throughout training. In particular, in an implicit visuomotor sequence learning task the identified discrepancy revealed an inductive bias that was consistent across individuals but varied in strength and persistence.

Instead of mapping stimuli directly to response, humans and other complex organisms are thought to maintain internal models of the environment. These internal models represent parts of the environment that are most relevant for deciding how to act in a given situation and therefore are key to explaining human behaviour. In behavioural experiments it is often assumed that the internal model in the subject’s brain matches the true model that governs the experiment. However this assumption can be violated due to a variety of reasons, such as insufficient training. Furthermore, the deviation of the internal model from the true model is not uniform across individuals, and therefore it summarizes the subjective beliefs of humans. In this paper, we provide a method to reverse engineer the internal model for individual subjects by analysing trial by trial behavioural measurements such as reaction times. We then track and analyse these reverse engineered models over the course of the experiment to see how participants trade off between an early inductive bias towards Markovian dynamics and the model that reflects the evidence that humans accumulate during learning about the actual statistics of the stimuli.

Haste or Speed? Alterations in the Impact of Incentive Cues on Task Performance in Remitted and Depressed Patients With Bipolar Disorder

A variety of evidence suggests that bipolar disorder is associated with disruptions of reward related processes, although the properties, and scope of these changes are not well understood. In the present study, we aimed to address this question by examining performance of patients with bipolar disorder (30 depressed bipolar; 35 euthymic bipolar) on a motivated choice reaction time task. We compared performance with a group of healthy control individuals (n = 44) and a group of patients with unipolar depression (n = 41), who were matched on several demographic variables. The task consists of an "odd-one-out" discrimination, in the presence of a cue signaling the probability of reward on a given trial (10, 50, or 90%) given a sufficiently fast response. All groups showed similar reaction time (RT) performance, and similar shortening of RT following the presentation of a reward predictive cue. However, compared to healthy individuals, the euthymic bipolar group showed a relative increase in commission errors during the high reward compared to low condition. Further correlational analysis revealed that in the healthy control and unipolar depression groups, participants tended either to shorten RTs for the high rather than low reward cue a relatively large amount with an increase in error rate, or to shorten RTs to a lesser extent but without increasing errors to the same degree. By contrast, reward-related speeding and reward-related increase in errors were less well coupled in the bipolar groups, significantly so in the BPD group. These findings suggest that although RT performance on the present task is relatively well matched, there may be a specific failure of individuals with bipolar disorder to calibrate RT speed and accuracy in a strategic way in the presence of reward-related stimuli.

A Reaction Time Experiment on Adult Attachment: The Development of a Measure for Neurophysiological Settings

In the last few decades, there has been an increase of experimental research on automatic unconscious processes concerning the evaluation of the self and others. Previous research investigated implicit aspects of romantic attachment using self-report measures as explicit instruments for assessing attachment style. There is a lack of experimental procedures feasible for neurobiological settings. We developed a reaction time (RT) experiment using a narrative attachment measure with an implicit nature and were interested to capture automatic processes, when the individuals' attachment system is activated. We aimed to combine attachment methodology with knowledge from implicit measures by using a decision RT paradigm. This should serve as a means to capture implicit aspects of attachment. This experiment evaluated participants' response to prototypic attachment sentences in association with their own attachment classification, measured with the Adult Attachment Projective Picture System (AAP). First the AAP was administered as the standardized interview procedure to 30 healthy participants, which were classified into a secure or insecure group. In the following experimental session, both experimenter and participants were blind with respect to classifications. One hundred twenty eight prototypically secure or insecure sentences related to the eight pictures of the AAP were presented to the participants. Their response and RTs were recorded. Based on the response (accept, reject) a continuous security scale was defined. Both the AAP classification and security scale were related to the RTs. Differentiated study hypotheses were confirmed for insecure sentences, which were accepted faster by participants from the insecure attachment group (or with lower security scale), and rejected faster by participants from secure attachment group (or with higher security scale). The elaborating unconscious processes were more activated by insecure sentences with potential attachment conflicts. The introduced paradigm is able to contribute to an experimental approach in attachment research. The RT analysis with the narrative procedure might be of interest for a broader variety of questions in experimental and neurophysiological settings to capture unconscious processes in association with internal working models of attachment. An electrophysiological model based on preliminary research is proposed for assessing the preconscious neuronal network related to secure or insecure attachment representations.

Investigating decision rules with a new experimental design: the EXACT paradigm

In the decision-making field, it is important to distinguish between the perceptual process (how information is collected) and the decision rule (the strategy governing decision-making). We propose a new paradigm, called EXogenous ACcumulation Task (EXACT) to disentangle these two components. The paradigm consists of showing a horizontal gauge that represents the probability of receiving a reward at time t and increases with time. The participant is asked to press a button when they want to request a reward. Thus, the perceptual mechanism is hard-coded and does not need to be inferred from the data. Based on this paradigm, we compared four decision rules (Bayes Risk, Reward Rate, Reward/Accuracy, and Modified Reward Rate) and found that participants appeared to behave according to the Modified Reward Rate. We propose a new way of analysing the data by using the accuracy of responses, which can only be inferred in classic RT tasks. Our analysis suggests that several experimental findings such as RT distribution and its relationship with experimental conditions, usually deemed to be the result of a rise-to-threshold process, may be simply explained by the effect of the decision rule employed.