scholarly journals A Discrete Formulation of Two Alternative Forced Choice Decision Dynamics Derived from a Double-Well Quantum Landscape

2021 ◽  
Author(s):  
Morgan L Rosendahl ◽  
Jonathan Cohen
Keyword(s):  
Decision Making ◽  
Markov Chain ◽  
Response Time ◽  
Context Effects ◽  
Response Times ◽  
Forced Choice ◽  
External Information ◽  
External Parameter ◽  
Qualitative Properties ◽  
Response Time Distributions

Tools from quantum theory have been effectively leveraged in modeling otherwise poorly understood effects in decision-making such as apparent fallacies in probability judgments and context effects. This approach has described the dynamics of two alternative forced choice (2AFC) decisions in terms of the path of a single quantum particle evolving in a single potential well. Here, we present a variant on that approach, which we name the Multi-Particle and Multi-Well (MPMW) quantum cognitive framework, in which decisions among N alternatives are treated by the sum of positional measurements of many independent quantum particles representing stimulus information, acted on by an N-well landscape that defines the decision alternatives. In this article, we apply the MPMW model to the simplest and most common case of N-alternative decision making, 2AFC dynamics. This application calls for a multi-particle double-well implementation, which allows us to construct a simple, analytically tractable discrete drift diffusion model (DDM), in the form of a Markov chain, wherein the parameters of the attractor wells reflect bottom-up (automatic) and top-down (control-dependent) influences on the integration of external information. We first analyze this Markov chain in its simplest form, as a single integrator with a generative process arising from a static quantum landscape and fixed thresholds, and then consider the case of multi-integrator processing under the same conditions. Within this system, stochasticity arises directly from the double-well quantum attractor landscape as a function of the dimensions of its wells, rather than as an external parameter requiring independent fitting. The simplicity of the Markov chain component of this model allows for easy analytical computation of closed forms for response time distributions and response probabilities that match qualitative properties of the accuracies and reaction times of humans performing 2AFC tasks. The MPMW framework produces response time distributions following inverse gaussian curves familiar from previous DDM models and empirical data, including the common observation that mean response times are faster for incorrect than for correct responses. The work presented in this paper serves as a proof of concept, based on which the MPMW framework can be extended to address more complex decision-making processes, (e.g., N-alternative forced choice, dynamic control allocation, and nesting quantum landscapes to allow for modeling at both the task and stimulus levels of processing) that we discuss as future directions.

scholarly journals Understanding the Structure of Cognitive Noise

2021 ◽  
Author(s):  
Jian-Qiao Zhu ◽  
Pablo Leon-Villagra ◽  
Nick Chater ◽  
Adam N Sanborn
Keyword(s):  
Response Time ◽  
Response Times ◽  
Functional Adaptation ◽  
Human Cognition ◽  
Statistical Sampling ◽  
Patchy Environments ◽  
Heavy Tailed Distributions ◽  
Separate Line ◽  
Heavy Tailed ◽  
Response Time Distributions

Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure. A first line of research has shown that inter-response-time distributions are heavy-tailed. That is, response times between subsequent trials usually change only a small amount, but with occasional large changes. A second, separate, line of research has found that participants’ estimates and response times both exhibit long-range autocorrelations (i.e., 1/f noise). Thus, each judgment and response time not only depends on its immediate predecessor but also on many previous responses. These two lines of research use different tasks and have distinct theoretical explanations: models that account for heavy-tailed response times do not predict 1/f autocorrelations and vice versa. Here, we find that 1/f noise and heavy-tailed response distributions co-occur in both types of tasks. We also show that a statistical sampling algorithm, developed to deal with patchy environments, generates both heavy-tailed distributions and 1/f noise, suggesting that cognitive noise may be a functional adaptation to dealing with a complex world.

Investigating the Adequacy of Response Time Outlier Definitions in Computer-Based Web Surveys Using Paradata SurveyFocus

2017 ◽  
Vol 36 (3) ◽  
pp. 369-378 ◽  
Author(s):  
Jan Karem Höhne ◽  
Stephan Schlosser
Keyword(s):  
Response Time ◽  
Response Times ◽  
Social Research ◽  
Web Surveys ◽  
Step Procedure ◽  
Computer Based ◽  
Response Time Distributions ◽  
Proper Values ◽  
The Web

Web surveys are commonly used in social research because they are usually cheaper, faster, and simpler to conduct than other modes. They also enable researchers to capture paradata such as response times. Particularly, the determination of proper values to define outliers in response time analyses has proven to be an intricate challenge. In fact, to a certain degree, researchers determine them arbitrarily. In this study, we use “SurveyFocus (SF)”—a paradata tool that records the activity of the web-survey pages—to assess outlier definitions based on response time distributions. Our analyses reveal that these common procedures provide relatively sufficient results. However, they are unable to detect all respondents who temporarily leave the survey, causing bias in the response times. Therefore, we recommend a two-step procedure consisting of the utilization of SF and a common outlier definition to attain a more appropriate analysis and interpretation of response times.

scholarly journals Evidence Accumulation Models: Current Limitations and Future Directions

2019 ◽  
Author(s):  
Nathan J. Evans ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Cognitive Processes ◽  
Measurement Tool ◽  
Decision Making Process ◽  
Choice Response ◽  
Experimental Conditions ◽  
Evidence Accumulation ◽  
Choice Response Time ◽  
Response Time Distributions

Evidence accumulation models (EAMs) have been the dominant models of speeded decision-making for several decades. These models propose that evidence accumulates for decision alternatives at some rate, until the evidence for one alternative reaches some threshold that triggers a decision. As a theory, EAMs have provided an accurate account of the choice response time distributions in a range of decision-making tasks, and as a measurement tool, EAMs have provided direct insight into how cognitive processes differ between groups and experimental conditions, resulting in EAMs becoming the standard paradigm of speeded decision-making. However, we argue that there are several limitations to how EAMs are currently tested and applied, which have begun to limit their value as a standard paradigm. Specifically, we believe that a theoretical plateau has been reached for the level of explanation that EAMs can provide about the decision-making process, and that applications of EAMs have started to become restrictive and of limited value. We provide several recommendations for how researchers can help to overcome these limitations. As a theory, we believe that EAMs can provide further value through being constrained by sources of data beyond the standard choice response time distributions, being extended to the entire decision-making process from encoding to responding, and having the random sources of variability replaced by systematic sources of variability. As a measurement tool, we believe that EAMs can provide further value through being a default method of inference for cognitive psychology in place of mean response time and choice, and being applied to a broader range of empirical questions that better capture individual differences in cognitive processes.

scholarly journals Bilingual speakers' enhanced monitoring can slow them down

2021 ◽  
Author(s):  
Roksana Markiewicz ◽  
Ali Mazaheri ◽  
Andrea Krott
Keyword(s):  
Control System ◽  
Cognitive Control ◽  
Response Time ◽  
Evoked Potentials ◽  
Response Times ◽  
Flanker Task ◽  
Response Distribution ◽  
Conflict Task ◽  
Bilingual Speakers ◽  
Response Time Distributions

Performance differences between bilingual and monolingual participants on conflict tasks can be affected by the balance of various sub-processes such as monitoring and stimulus categorisation. Here we investigated the effect of bilingualism on these sub-processes during a conflict task with medium monitoring demand. We examined the behavioural and evoked potentials from a group of bilingual and monolingual speakers during a flanker task with 25% incongruent trials. We analysed behavioural differences by means of averaged response times and ex-Gaussian analyses of response time distributions. For the evoked potentials we focused on the N2 (implicated to be involved in monitoring) and P300 (implicated to be involved in categorisation) responses. We found that bilinguals had significantly longer response distribution tails compared to monolinguals. Additionally, bilinguals exhibited a more pronounced N2 and smaller P3 components compared to their monolingual counterparts, independent of experimental condition, suggesting enhanced monitoring processes and reduced categorisation effort. Importantly, N2 amplitudes were positively and P3 amplitudes were negatively related to the length of response distribution tails. We postulate that these results reflect an overactive monitoring system in bilinguals in a task of medium monitoring demand. This enhanced monitoring leads to less effortful categorisation, but also occasionally to slow responses. These results suggest that changes of the cognitive control system due to bilingual experience changes the balance of processes during conflict tasks, potentially leading to a small behavioural disadvantage.

An Integrated Model of Discrete Choice and Response Time

2008 ◽  
Vol 45 (5) ◽  
pp. 593-607 ◽  
Author(s):  
Thomas Otter ◽  
Greg M. Allenby ◽  
Trish Van Zandt
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Discrete Choice ◽  
Response Times ◽  
Marketing Research ◽  
Race Model ◽  
Exogenous Variables ◽  
Web Based ◽  
Dependent Variables ◽  
Do So

Computer and Web-based interviewing tools have made response times ubiquitous in marketing research. Practitioners use these data as an indicator of data quality, and academics use them as an indicator of latent processes related to memory, attributes, and decision making. The authors investigate a Poisson race model with choice and response times as dependent variables. The model facilitates inference about respondents' preferences for choice alternatives, their diligence in providing responses, and the accessibility of attitudes and the speed of thinking. Thus, the model distinguishes between respondents who are quick to think and those who are quick to react but do so without much thought. Empirically, the authors find support for the endogenous nature of response times and demonstrate that models that treat response times as exogenous variables may result in misleading inferences.

A Multi-Objective Approach to Big Data View Materialization

2021 ◽  
Vol 12 (2) ◽  
pp. 17-37
Author(s):  
Akshay Kumar ◽  
T. V. Vijay Kumar
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Response Time ◽  
Response Times ◽  
Heterogeneous Data ◽  
Complex Problem ◽  
View Selection ◽  
Processing Cost ◽  
View Materialization ◽  
Data View

Big data comprises voluminous and heterogeneous data that has a limited level of trustworthiness. This data is used to generate valuable information that can be used for decision making. However, decision making queries on Big data consume a lot of time for processing resulting in higher response times. For effective and efficient decision making, this response time needs to be reduced. View materialization has been used successfully to reduce the query response time in the context of a data warehouse. Selection of such views is a complex problem vis-à-vis Big data and is the focus of this paper. In this paper, the Big data view selection problem is formulated as a bi-objective optimization problem with the two objectives being the minimization of the query evaluation cost and the minimization of the update processing cost. Accordingly, a Big data view selection algorithm that selects Big data views for a given query workload, using the vector evaluated genetic algorithm, is proposed. The proposed algorithm aims to generate views that are able to reduce the response time of decision-making queries.

Impact of antecedent wetness and precipitation intensity on catchment travel and response times

2021 ◽  
Author(s):  
Julia L.A. Knapp ◽  
Wouter R. Berghuijs ◽  
Jana von Freyberg ◽  
James W. Kirchner
Keyword(s):  
Time Series ◽  
Response Time ◽  
Response Times ◽  
Water Storage ◽  
Precipitation Intensity ◽  
Travel Times ◽  
Flow Paths ◽  
Isotopic Tracer ◽  
Water Fractions ◽  
Response Time Distributions

<p>The time a molecule of rain takes to reach the stream is normally substantially longer than the time for discharge to respond to rainfall. This difference arises because hydraulic potentials propagate through landscapes much faster than water itself does; in other words, the celerity of wave propagation is faster than the velocity of water flow. Although these concepts are well established, most catchment studies are restricted to the calculation of the celerity or response time from hydrometric information. However, to understand the storage, release, and transport of water, as well as identify flow paths through the catchment, one needs to estimate both response and travel times, requiring both hydrometric and tracer data.</p><p>We analyzed hydrometric and tracer data from two contrasting sites, the pre-Alpine Erlenbach catchment in Switzerland and the Upper Hafren catchment at Plynlimon in Wales. For both sites, hydrometric data and sub-daily isotopic tracer time series are available, enabling the calculation of response times as well as travel time distributions and new water fractions. To gain a deeper understanding of the functioning of the two catchments, we quantified these metrics and distributions for different ranges of antecedent wetness and precipitation intensity. Generally, wetter catchment conditions and higher precipitation intensities yielded faster runoff responses and shorter travel times.  Contrasts between travel and response time distributions under varying catchment conditions also facilitated more nuanced insights into catchment functioning and the effects of catchment wetness and precipitation intensity on water storage and release.</p>

Generalizations of the Race Model Inequality

2013 ◽  
Vol 26 (1-2) ◽  
pp. 95-122 ◽  
Author(s):  
Matthias Gondan ◽  
Steven P. Blurton
Keyword(s):  
Response Time ◽  
Statistical Power ◽  
Response Times ◽  
Race Model ◽  
Response Time Distribution ◽  
Stopping Rules ◽  
Race Model Inequality ◽  
Cumulative Response ◽  
Processing Modes ◽  
Response Time Distributions

In redundant signals tasks, participants respond in the same way to two different stimuli which are presented either alone or in combination (redundant stimuli). Responses to redundant stimuli are typically faster than responses to single stimuli. Different explanations account for such redundancy gains, including race models and coactivation models. Race models predict that the cumulative response time distribution for the redundant stimuli never exceeds the summed distributions of the single stimuli (race model inequality, RMI, Miller, 1982). Based on work by Townsend and Nozawa (1995) we demonstrate that the RMI is a special case of a more general interaction contrast of response time distributions for stimuli of different intensity, or stimuli presented with onset asynchrony. The generalization of the RMI is, thus, suited for a much wider class of experiments than the standard setup in which response times for single stimuli are compared to those for double stimuli. Moreover, predictions can be derived not only for the race model, but for serial, parallel, and coactive processing modes with different stopping rules. Compared to the standard RMI, statistical power of these interaction contrasts is satisfactory, even for small onset asynchronies.

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