Reaction Time Models

Source memory is memory for the context in which items were previously encountered. Harlow and Donaldson (2013) found evidence of a retrieval threshold underlying source accuracy in a continuous report task. However, this finding did not account for the influence of decision-making in generating responses in memory retrieval. Additional research has also suggested that participants had no source memory for items which were not recognised, which was also not accounted for (Hautus et al., 2008; Malejka & Broder, 2015). In working towards a comprehensive account of decision-making in source memory retrieval, this study used the Smith (2016) circular diffusion model to introduce diffusion analogues of the threshold and continuous models of source memory retrieval in a replication of the Harlow and Donaldson (2013) task. Participant performance was conditioned on item recognition in order to detangle recognition from a potential source retrieval threshold. Model selection done using the BIC found support for a circular diffusion model where memory discretely fails, as both RT and response accuracy data suggested that there were two components in performance.

Many theories of decision making assume accumulation-to-threshold mechanisms. These thresholds almost always represent a criterion quantity on the 'evidence' or 'information' required to support a decision. We present a quantitative model that involves an additional accumulation-to-threshold mechanism where decisions can be triggered when a sufficient amount of time has been committed to the decision process. We show that a decision architecture composed of a competitive race with evidence-based and time-based thresholds provides a cohesive account of decision-making phenomena, including the speed-accuracy tradeoff. We show that it can also explain phenomena outside the domain of conventional evidence accumulation models, including simultaneously accounting for performance in decision and timing tasks. As a byproduct, the evidence-based vs time-based decision architecture eliminates the need for decision-to-decision variability parameters that are key elements of many accumulation-based theories of decision making.

The objective of the present study is to develop a model applicable to multiple-alternative forced-choice (MAFC) personality measurement data that include both item responses and response times. To this end, we started from the linear ballistic accumulator-item response theory (LBA-IRT) model, which is one of the cognitive models of two-alternative forced-choice item responses and response times. However, this model cannot be applied to MAFC data because of its formulation of the drift rate parameter. To address this problem, the present study proposes a novel formulation allowing the model to be applied to MAFC personality measurement. The proposed formulation of the drift rate is based on Thurstone's random utility model and Luce's choice axiom. We applied the proposed model to two real datasets and showed that the proposed model can estimate respondents’ personality traits more reasonably than existing models for MAFC data that include the Thurstonian diffusion IRT model.

In warned reaction time (RT) tasks, a warning stimulus (S1) initiates a process of temporal preparation which promotes a speeded response to the target stimulus (S2). Variations of the S1-S2 interval (foreperiod) have been shown to affect the RT to S2 across a range of time scales: within trials, between consecutive trials, across trials within an experimental block, and across blocks. RT distribution analyses suggest that these effects share a common mechanism, and yet theories on preparation have thus far failed to offer an integrative account of these phenomena. We present a computational framework (fMTP) that formalizes the principles of a previously proposed theory of temporal preparation: Multiple Trace Theory of Temporal Preparation. With fMTP we combine models and theories on time perception, motor planning, and associative learning into a single, computational theory. fMTP assumes that for each trial a unique trace is formed by means of associative Hebbian learning between a layer of time cells and a motor layer with an inhibition and activation node. On each new trial, traces from the past collectively determine the temporal preparatory state. We compared fMTP to existing theories which were not yet formalized until now. This model exploration demonstrated that fMTP best described existing datasets. In addition, in an experiment that was set out to validate fMTP, we show the data to align with our model predictions. In sum, we find that fMTP’s single implicit learning mechanism suffices to explain a range of phenomena that previously have been considered to be the result of distinct processes.

Integration-to-threshold models of two-choice perceptual decision making have guided our understanding of the behaviour and neural processing of humans and animals for decades. Although such models seem to extend naturally to multiple-choice decision making, consensus on a normative framework has yet to emerge, and hence the implications of threshold characteristics for multiple choices have only been partially explored. Here we consider sequential Bayesian inference as the basis for a normative framework together with a conceptualisation of decision making as a particle diffusing in n-dimensions. This framework implies highly choice-interdependent decision thresholds, where boundaries are a function of all choice-beliefs. We show that in general the optimal decision boundaries comprise a degenerate set of complex structures and speed-accuracy tradeoffs, contrary to current 2-choice results. Such boundaries support both stationary and collapsing thresholds as optimal strategies for decision-making, both of which result from stationary complex boundary representations. This casts new light on the interpretation of urgency signals reported in neural recordings of decision making tasks, implying that they may originate from a more complex decision rule, and that the signal as a distinct phenomenon may be misleading as to the true mechanism. Our findings point towards a much-needed normative theory of multiple-choice decision making, provide a characterisation of optimal decision thresholds under this framework, and inform the debate between stationary and dynamic decision boundaries for optimal decision making.

In this paper we address the question of whether is it possible to model as a dynamical problem the behavior of subjects performing a neuropsychological test. We address the task employing the Trail Making Test in its part A. To be able to model the subjects behavior we first implement a digital version of the test which allow us to register the eye movements of the subjects during the performance. Then we analyze the eye tracking data and explore the behavior to be able to propose an equation representing the dynamics. With the equation at hand, an analysis of the parameters is performed in such a way different magnitudes could be related to neuropsychological constructs like processing speed, visual search speed and mental flexibility.