scholarly journals Advancing Methods and Mathematical Models of Perceptual Decision Making

2017 ◽  
Author(s):  
Gabriel Tillman
Keyword(s):  
Decision Making ◽  
Cognitive Processes ◽  
Model Comparison ◽  
Predictive Accuracy ◽  
Sequential Sampling ◽  
Estimation Methods ◽  
Choice Response ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Sequential Sampling Models

In this thesis I argue that cognitive psychologists can use the combination of sequential sampling models, Bayesian estimation methods, and model comparison via predictive accuracy to investigate underlying cognitive processes of perceptual decision-making. I show that sequential sampling models of simple and choice response time allow for researchers to analyze behavioral data and translate them into the constitute components of processing, such as speed of processing, response caution, and the time needed for perceptual encoding and overt motor responses. I use these methods and models to investigate underlying mental processes related to cognitive load, speech perception, and lexical decision-making. I also show that using different sequential sampling models to analyze the same data can lead researchers to draw different conclusions about cognitive processes, which serves as a caution for carelessly using these models. I also present a novel method that researchers can use to observe cognitive processes unfold online during perceptual decision-making tasks. I then discuss a promising collaboration emerging between researchers in the field of mathematical modeling and neuroscience.

scholarly journals Unification of Countermanding and Perceptual Decision-making

2017 ◽  
Author(s):  
Paul G. Middlebrooks ◽  
Bram B. Zandbelt ◽  
Gordon D. Logan ◽  
Thomas J. Palmeri ◽  
Jeffrey D. Schall
Keyword(s):  
Decision Making ◽  
Response Inhibition ◽  
Sequential Sampling ◽  
Race Model ◽  
Decision Time ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Evidence Accumulation ◽  
Sequential Sampling Models ◽  
Choice Tasks

Perceptual decision-making, studied using two-alternative forced-choice tasks, is explained by sequential sampling models of evidence accumulation, which correspond to the dynamics of neurons in sensorimotor structures of the brain1 2. Response inhibition, studied using stop-signal (countermanding) tasks, is explained by a race model of the initiation or canceling of a response, which correspond to the dynamics of neurons in sensorimotor structures3 4. Neither standard model accounts for performance of the other task. Sequential sampling models incorporate response initiation as an uninterrupted non-decision time parameter independent of task-related variables. The countermanding race model does not account for the choice process. Here we show with new behavioral, neural and computational results that perceptual decision making of varying difficulty can be countermanded with invariant efficiency, that single prefrontal neurons instantiate both evidence accumulation and response inhibition, and that an interactive race between two GO and one STOP stochastic accumulator fits countermanding choice behavior. Thus, perceptual decision-making and response control, previously regarded as distinct mechanisms, are actually aspects of more flexible behavior supported by a common neural and computational mechanism. The identification of this aspect of decision-making with response production clarifies the component processes of decision-making.

scholarly journals Inter-trial effects in visual pop-out search: Factorial comparison of Bayesian updating models

2018 ◽  
Author(s):  
Fredrik Allenmark ◽  
Hermann J. Müller ◽  
Zhuanghua Shi
Keyword(s):  
Decision Making ◽  
Reaction Time ◽  
Model Comparison ◽  
Accumulation Rate ◽  
Information Processing Speed ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Decision Bias ◽  
Evidence Accumulation ◽  
Starting Point

AbstractMany previous studies on visual search have reported inter-trial effects, that is, observers respond faster when some target property, such as a defining feature or dimension, or the response associated with the target repeats versus changes across consecutive trial episodes. However, what processes drive these inter-trial effects is still controversial. Here, we investigated this question using a combination of Bayesian modeling of belief updating and evidence accumulation modeling in perceptual decision-making. In three visual singleton (‘pop-out’) search experiments, we explored how the probability of the response-critical states of the search display (e.g., target presence/absence) and the repetition/switch of the target-defining dimension (color/ orientation) affect reaction time distributions. The results replicated the mean reaction time (RT) inter-trial and dimension repetition/switch effects that have been reported in previous studies. Going beyond this, to uncover the underlying mechanisms, we used the Drift-Diffusion Model (DDM) and the Linear Approach to Threshold with Ergodic Rate (LATER) model to explain the RT distributions in terms of decision bias (starting point) and information processing speed (evidence accumulation rate). We further investigated how these different aspects of the decision-making process are affected by different properties of stimulus history, giving rise to dissociable inter-trial effects. We approached this question by (i) combining each perceptual decision making model (DDM or LATER) with different updating models, each specifying a plausible rule for updating of either the starting point or the rate, based on stimulus history, and (ii) comparing every possible combination of trial-wise updating mechanism and perceptual decision model in a factorial model comparison. Consistently across experiments, we found that the (recent) history of the response-critical property influences the initial decision bias, while repetition/switch of the target-defining dimension affects the accumulation rate, likely reflecting an implicit ‘top-down’ modulation process. This provides strong evidence of a disassociation between response- and dimension-based inter-trial effects.

Modeling Simple Decisions and Applications Using a Diffusion Model

2015 ◽  
Author(s):  
Roger Ratcliff ◽  
Philip Smith
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Sequential Sampling ◽  
Choice Response ◽  
Neural Basis ◽  
Sequential Sampling Models ◽  
Choice Decision ◽  
Cognitive Foundations ◽  
Response Biases ◽  
Speed Accuracy

The diffusion model is one of the major sequential-sampling models for two-choice decision-making and choice response time in psychology. The model conceives of decision-making as a process in which noisy evidence is accumulated until one of two response criteria is reached and the associated response is made. The criteria represent the amount of evidence needed to make each decision and reflect the decision maker’s response biases and speed-accuracy trade-off settings. In this chapter we examine the application of the diffusion model in a variety of different settings. We discuss the optimality of the model and review its applications to a number of cognitive tasks, including perception, memory, and language tasks. We also consider its applications to normal and special populations, to the cognitive foundations of individual differences, to value-based decisions, and its role in understanding the neural basis of decision-making.

scholarly journals Dissociating Cognitive Processes During Ambiguous Information Processing in Perceptual Decision-Making

2020 ◽  
Vol 14 ◽  
Author(s):  
Vladimir A. Maksimenko ◽  
Alexander Kuc ◽  
Nikita S. Frolov ◽  
Marina V. Khramova ◽  
Alexander N. Pisarchik ◽  
...  
Keyword(s):  
Decision Making ◽  
Information Processing ◽  
Cognitive Processes ◽  
Perceptual Decision Making ◽  
Ambiguous Information ◽  
Perceptual Decision

scholarly journals Embodied Cognition and Decision-Making in the Primate Superior Colliculus

2020 ◽  
Author(s):  
E.J. Jun ◽  
A. Bautista ◽  
M.D. Nunez ◽  
T. Tak ◽  
E. Alvarez ◽  
...  
Keyword(s):  
Decision Making ◽  
Superior Colliculus ◽  
Embodied Cognition ◽  
Neuronal Activity ◽  
Sequential Sampling ◽  
Movement Preparation ◽  
Perceptual Decision Making ◽  
Reversible Inactivation ◽  
Perceptual Decision ◽  
Cognition Model

A popular model of decision-making suggests that in primates, including humans, decisions evolve within forebrain structures responsible for preparing voluntary actions; a concert referred to as embodied cognition. Embodied cognition posits that in decision tasks, neuronal activity generally associated with preparing an action, actually reflects the accumulation of evidence for a particular decision. Testing the embodied cognition model causally is challenging because dissociating the evolution of a decision from preparing a motor act is difficult, if the same neuronal activity instantiates both processes. Ideally, one would show that manipulation of neuronal activity thought to be involved in movement preparation actually alters decisions, and not movement preparation. Here, trained monkeys performed a two-choice perceptual decision-making task in which they judged the orientation of a dynamic Glass pattern before and after unilateral, reversible inactivation of a brainstem area involved in preparing eye movements, the superior colliculus (SC). Surprisingly, we found that unilateral SC inactivation produced significant decision biases and changes in reaction times consistent with a role for the SC in evidence accumulation. Fitting signal detection theory and sequential sampling models (drift-diffusion and urgency-gating) to the data revealed that SC inactivation produced a decrease in the relative evidence for contralateral decisions. Control experiments showed that SC inactivation did not result in eye movement biases ruling out interpretations based on motor preparation or spatial attentional impairment. The results provide causal evidence for an embodied cognition model of perceptual decision-making and provide compelling evidence that the SC of primates plays a causal role in how evidence is accumulated for perceptual decisions, a process that is usually attributed to the cerebral cortex.

scholarly journals The validity and consistency of continuous joystick response in perceptual decision-making

2018 ◽  
Author(s):  
Maciej J. Szul ◽  
Aline Bompas ◽  
Petroc Sumner ◽  
Jiaxiang Zhang
Keyword(s):  
Decision Making ◽  
Cognitive Processes ◽  
Model Parameters ◽  
Perceptual Decision Making ◽  
Effective Response ◽  
Response Device ◽  
Perceptual Decision ◽  
Movement Trajectories ◽  
Response Modality ◽  
Continuous Movements

AbstractA computer joystick is an efficient and cost-effective response device for recording continuous movements in psychological experiments. Movement trajectories and other measures from continuous responses have expanded the insights gained from discrete responses (e.g. button presses) by providing unique information on how cognitive processes unfold over time. However, few studies have evaluated the validity of joystick responses with reference to conventional key presses, and response modality can affect cognitive processes. Here, we systematically compared human participants’ behavioural performance of perceptual decision-making when they responded with either joystick movements or key presses in a four-alternative motion discrimination task. We found evidence that the response modality did not affect raw behavioural measures including decision accuracy and mean reaction time (RT) at the group level. Furthermore, to compare the underlying decision processes between the two response modalities, we fitted a drift-diffusion model of decision-making to individual participant’s behavioural data. Bayesian analyses of the model parameters showed no evidence that switching from key presses to continuous joystick movements modulated the decision-making process. These results supported continuous joystick actions as a valid apparatus for continuous movements, although we highlighted the need for caution when conducting experiments with continuous movement responses.

Discarding optimality: Throwing out the baby with the bathwater?

2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Reward Rate ◽  
Perceptual Decision ◽  
Standard Observer ◽  
Rate Maximization ◽  
Reward Rate Maximization ◽  
Descriptive Standard ◽  
Observer Model

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.

scholarly journals Flexible categorization in perceptual decision making

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Network Models ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Sensory Evidence ◽  
Winner Take All

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

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