The unfolding action model of initiation times, movement times, and movement paths

Converging evidence has led to a consensus in favor of computational models of behavior implementing continuous information flow and parallel processing between cognitive processing stages. Yet, such models still typically implement a discrete step between the last cognitive stage and motor implementation. This discrete step is implemented as a fixed decision bound that activation in the last cognitive stage needs to cross before action can be initiated. Such an implementation is questionable as it cannot account for two important features of behavior. First, it does not allow to select an action while withholding it until the moment is appropriate for executing it. Second, it cannot account for recent evidence that cognition is not confined prior to movement initiation, but consistently leaks into movement. To address these two features, we propose a novel neurocomputational model of cognitionaction interactions, namely the unfolding action model (UAM). Crucially, the model implements adaptive information flow between the last cognitive processing stage and motor implementation. We show that the UAM addresses the two abovementioned features. Empirically, the UAM accounts for traditional response time data, including positively skewed initiation time distribution, functionally fixed decision bounds and speed-accuracy trade-offs in button-press experimental designs. Moreover, it accounts for movement times, movement paths, and how they are influenced by cognitive-experimental manipulations. This move should close the current gap between abstract decision-making models and behavior observed in natural habitats.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Evidence or Confidence: What Is Really Monitored during a Decision?

Assessing our confidence in the choices we make is important to making adaptive decisions, and it is thus no surprise that we excel in this ability. However, standard models of decision-making, such as the drift-diffusion model (DDM), treat confidence assessment as a post hoc or parallel process that does not directly influence the choice, which depends only on accumulated evidence. Here, we pursue the alternative hypothesis that what is monitored during a decision is an evolving sense of confidence (that the to-be-selected option is the best) rather than raw evidence. Monitoring confidence has the appealing consequence that the decision threshold corresponds to a desired level of confidence for the choice, and that confidence improvements can be traded off against the resources required to secure them. We show that most previous findings on perceptual and value-based decisions traditionally interpreted from an evidence-accumulation perspective can be explained more parsimoniously from our novel confidence-driven perspective. Furthermore, we show that our novel confidence-driven DDM (cDDM) naturally generalizes to decisions involving any number of alternative options – which is notoriously not the case with traditional DDM or related models. Finally, we discuss future empirical evidence that could be useful in adjudicating between these alternatives

Evidence integration and decision confidence are modulated by stimulus consistency

Evidence integration is a normative algorithm for choosing between alternatives with noisy evidence, which has been successful in accounting for vast amounts of behavioural and neural data. However, this mechanism has been challenged by non-integration heuristics, and tracking decision boundaries has proven elusive. Here we first show that the decision boundaries can be extracted using a model-free behavioural method termed decision classification boundary, which optimizes choice classification based on the accumulated evidence. Using this method, we provide direct support for evidence integration over non-integration heuristics, show that the decision boundaries collapse across time and identify an integration bias whereby incoming evidence is modulated based on its consistency with preceding information. This consistency bias, which is a form of pre-decision confirmation bias, was supported in four cross-domain experiments, showing that choice accuracy and decision confidence are modulated by stimulus consistency. Strikingly, despite its seeming sub-optimality, the consistency bias fosters performance by enhancing robustness to integration noise

Non-parametric mixture modeling of cognitive psychological data: A new method to disentangle hidden strategies

In a wide variety of cognitive domains, participants have access to several alternative strategies to perform a particular task and, on each trial, one specific strategy is selected and executed. Determining how many strategies are used by a participant as well as their identification at a trial level is a challenging problem for researchers. In the current paper, we propose a new method - the non-parametric mixture model - to efficiently disentangle hidden strategies in cognitive psychological data, based on observed response times. The developed method derived from standard hidden Markov modeling. Importantly, we used a model-free approach where a particular shape of a response time distribution does not need to be assumed. This has the considerable advantage of avoiding potentially unreliable results when an inappropriate response time distribution is assumed. Through three simulation studies and two applications to real data, we repeatedly demonstrated that the non-parametric mixture model is able to reliably recover hidden strategies present in the data as well as to accurately estimate the number of concurrent strategies. The results also showed that this new method is more efficient than a standard parametric approach. The non-parametric mixture model is therefore a useful statistical tool for strategy identification that can be applied in many areas of cognitive psychology. To this end, practical guidelines are provided for researchers wishing to apply the non-parametric mixture models on their own data set

Striatal activation reflects urgency in perceptual decision making

Deciding between multiple courses of action often entails an increasing need to do something as time passes - a sense of urgency. This notion of urgency is not incorporated in standard theories of speeded decision making that assume information is accumulated until a critical fixed threshold is reached. Yet, it is hypothesized in novel theoretical models of decision making. In two experiments, we investigated the behavioral and neural evidence for an “urgency signal” in human perceptual decision making. Experiment 1 found that as the duration of the decision making process increased, participants made a choice based on less evidence for the selected option. Experiment 2 replicated this finding, and additionally found that variability in this effect across participants covaried with activation in the striatum. We conclude that individual differences in susceptibility to urgency are reflected by striatal activation. By dynamically updating a response threshold, the striatum is involved in signaling urgency in humans

A biologically motivated synthesis of accumulator and reinforcement-learning models for describing adaptive decision-making

Cognitive process models, such as reinforcement learning (RL) and accumulator models of decision-making, have proven to be highly insightful tools for studying adaptive behaviors as well as their underlying neural substrates. Currently, however, two major barriers exist preventing these models from being applied in more complex settings: 1) the assumptions of most accumulator models break down for decisions involving more than two alternatives; 2) RL and accumulator models currently exist as separate frameworks, with no clear mapping between trial-to-trial learning and the dynamics of the decision process. Recently I showed how a modified accumulator model, premised off of the architecture of cortico-basal ganglia pathways, both predicts human decisions in uncertain situations and evoked activity in cortical and subcortical control circuits. Here I present a synthesis of RL and accumulator models that is motivated by recent evidence that the basal ganglia acts as a site for integrating trial-wise feedback from midbrain dopaminergic neurons with accumulating evidence from sensory and associative cortices. I show how this hybrid model can explain both adaptive go/no-go decisions and multi-alternative decisions in a computationally efficient manner. More importantly, by parameterizing the model to conform to various underlying assumptions about the architecture and physiology of basal ganglia pathways, model predictions can be rigorously tested against observed patterns in behavior as well as neural recordings. The result is a biologically-constrained and behaviorally tractable description of trial-to-trial learning effects on decision-making among multiple alternatives

The effects of stimulus information and stimulus reliability on the time course of bias effects in decision-making.

Bias is a pervasive element in many decisions we make throughout our lives. Researchers have studied the mechanisms underlying biases using two-alternative forced choice tasks, examining choice proportions, response times, and fitting evidence accumulation models. In doing so, researchers have distinguished between two types of bias: prior bias and dynamic bias. Prior bias is described as a contextually driven predisposition towards a certain response. Dynamic bias refers to bias that feeds into the decision process across the course of a decision and is generally thought to be driven by the evaluation of stimulus information. Current literature shows mixed evidence for the dissociation between prior and dynamic bias, where some researchers proposed that certain manipulations selectively influence each type of bias, while others showed a covariation of both types of biases as a result of the same manipulations. Hanks et al. (2011) proposed individuals might integrate task-relevant information that induces prior bias (such as relative stimulus frequency) into the decision process as dynamic bias when there is an inversely proportional relationship between accuracy and response time. The current thesis aims to investigate Hanks et al.’s claim, and subsequently, the relationship between prior and dynamic bias. In this thesis, we identify the effect of relative-stimulus-frequency manipulations and different stimulus characteristics on prior and dynamic bias. We propose that a certain characteristic, stimulus reliability, is an important factor for the induction of dynamic bias and demonstrate that dynamic bias effects can vary across stimuli as a function of stimulus reliability. We also emphasise the importance of feedback for the induction of prior and dynamic bias. In our final chapter, we discuss a variety of theoretical accounts of the dynamic bias effects we observed throughout our experiments. We evaluate different time-dependent evidence accumulation models using simulated data, as well as discuss how stimulus reliability may be integrated with stimulus information when an individual evaluates a stimulus. We conclude by emphasising that researchers need to consider the adaptive flexibility of individuals when identifying the important features of a decision-making environment during the development of useful models of decision-making