scholarly journals Inhibition of Return in Fear of Spiders: Discrepant Eye Movement and Reaction Time Data

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Elisa Berdica ◽  
Antje B. M. Gerdes ◽  
Andre Pittig ◽  
Georg W. Alpers
Keyword(s):  
Visual Search ◽  
Eye Movements ◽  
Reaction Time ◽  
Significant Interaction ◽  
Discrimination Task ◽  
Inhibition Of Return ◽  
Reaction Time Data ◽  
Stimulus Category ◽  
Tracking Data ◽  
Time Data

Inhibition of return (IOR) refers to a bias against returning the attention to a previously attended location. As a foraging facilitator it is thought to facilitate systematic visual search. With respect to neutral stimuli, this is generally thought to be adaptive, but when threatening stimuli appear in our environment, such a bias may be maladaptive. This experiment investigated the influence of phobia-related stimuli on the IOR effect using a discrimination task. A sample of 50 students (25 high, 25 low in spider fear) completed an IOR task including schematic representations of spiders or butterflies as targets. Eye movements were recorded and to assess discrimination among targets, participants indicated with button presses if targets were spiders or butterflies. Reaction time data did not reveal a significant IOR effect but a significant interaction of group and target; spider fearful participants were faster to respond to spider targets than to butterflies. Furthermore, eye-tracking data showed a robust IOR effect independent of stimulus category. These results offer a more comprehensive assessment of the motor and oculomotor factors involved in the IOR effect.

Multiple Diffusion Models to Compare Saccadic and Manual Responses for Inhibition of Return

2017 ◽  
Vol 29 (3) ◽  
pp. 804-824 ◽  
Author(s):  
W. Joseph MacInnes
Keyword(s):  
Reaction Time ◽  
Inhibition Of Return ◽  
Reaction Time Data ◽  
Diffusion Models ◽  
Response Threshold ◽  
Model Parameters ◽  
Time Data ◽  
Manual Responses ◽  
Underlying Mechanisms ◽  
Different Response

Cuing a location in space produces a short-lived advantage in reaction time to targets at that location. This early advantage, however, switches to a reaction time cost and has been termed inhibition of return (IOR). IOR behaves differently for different response modalities, suggesting that it may not be a unified effect. This letter presents new data from two experiments testing the gradient of IOR with random, continuous cue-target Euclidean distance and cue-target onset asynchrony. These data were then used to train multiple diffusion models of saccadic and manual reaction time for these cuing experiments. Diffusion models can generate accurate distributions of reaction time data by modeling a response as a buildup of evidence toward a response threshold. If saccadic and attentional IOR are based on similar processes, then differences in distribution will be best explained by adjusting parameter values such as signal and noise within the same model structure. Although experimental data show differences in the timing of IOR across modality, best-fit models are shown to have similar model parameters for the gradient of IOR, suggesting similar underlying mechanisms for saccadic and manual IOR.

Modeling Within-Person Variance in Reaction Time Data of Older Adults

GeroPsych ◽  
2011 ◽  
Vol 24 (4) ◽  
pp. 169-176 ◽  
Author(s):  
Philippe Rast ◽  
Daniel Zimprich
Keyword(s):  
Reaction Time ◽  
Cognitive Aging ◽  
Reaction Times ◽  
Reaction Time Task ◽  
Reaction Time Data ◽  
Scale Model ◽  
Time Data ◽  
Time Task ◽  
The Mean ◽  
Second Wave

In order to model within-person (WP) variance in a reaction time task, we applied a mixed location scale model using 335 participants from the second wave of the Zurich Longitudinal Study on Cognitive Aging. The age of the respondents and the performance in another reaction time task were used to explain individual differences in the WP variance. To account for larger variances due to slower reaction times, we also used the average of the predicted individual reaction time (RT) as a predictor for the WP variability. Here, the WP variability was a function of the mean. At the same time, older participants were more variable and those with better performance in another RT task were more consistent in their responses.

Absolute Pitch as a Learned Phenomenon: Evidence Consistent with the Hick-Hyman Law

1994 ◽  
Vol 12 (2) ◽  
pp. 267-270 ◽  
Author(s):  
Jasba Simpson ◽  
David Huron
Keyword(s):  
Reaction Time ◽  
Absolute Pitch ◽  
Reaction Time Data ◽  
Western Music ◽  
Frequency Of Occurrence ◽  
Time Data ◽  
Expected Frequency ◽  
Computer Based ◽  
Additional Support

An analysis of reaction time data collected by Miyazaki (1989) provides additional support for absolute pitch as a learned phenomenon. Specifically, the data are shown to be consistent with the Hick- Hyman law, which relates the reaction time for a given stimulus to its expected frequency of occurrence. The frequencies of occurrence are estimated by analyzing a computer-based sample of Western music. The results are consistent with the view that absolute pitch is acquired through ordinary exposure to the pitches of Western music.

A moving average model for sequenced reaction-time data

1981 ◽  
Vol 23 (2) ◽  
pp. 115-133 ◽  
Author(s):  
Joseph B Kadane ◽  
Jill H Larkin ◽  
Richard H Mayer
Keyword(s):  
Reaction Time ◽  
Moving Average ◽  
Reaction Time Data ◽  
Time Data ◽  
Average Model ◽  
Moving Average Model

Reaction times and burning rates for wind tunnel headfires

2003 ◽  
Vol 12 (2) ◽  
pp. 195 ◽  
Author(s):  
Ralph M. Nelson, Jr.
Keyword(s):  
Reaction Time ◽  
Wind Tunnel ◽  
Wildland Fire ◽  
Mass Loss Rate ◽  
Fire Behavior ◽  
Reaction Times ◽  
Reaction Time Data ◽  
Combustion Regime ◽  
Fuel Particle ◽  
Time Data

Catchpole et al. (1998) reported rates of spread for 357 heading and no-wind fires burned in the wind tunnel facility of the USDA Forest Service's Fire Sciences Laboratory in Missoula, Montana for the purpose of developing models of wildland fire behavior. The fires were burned in horizontal fuel beds with differing characteristics due to various combinations of fuel type, particle size, packing ratio, bed depth, moisture content, and wind speed. In the present paper, fuel particle and fuel bed data for 260 heading fires from that study (plus as-yet unreported combustion efficiency and reaction time data) are used to develop models for predicting fuel bed reaction time and mass loss rate. Reaction time is computed from the flameout time of a single particle and fuel bed structural properties. It is assumed that the beds burn in a combustion regime controlled by the rate at which air mixes with volatiles produced during pyrolysis, and that not all air entering the fuel bed reaction zone participates in combustion. Comparison of reaction time and burning rate predictions with experimental values is encouraging in view of the simplified modeling approach and uncertainties associated with the experimental measurements.

scholarly journals Fatigue-life distributions for reaction time data

2018 ◽  
Vol 12 (3) ◽  
pp. 351-356 ◽  
Author(s):  
Mauricio Tejo ◽  
Sebastián Niklitschek-Soto ◽  
Fernando Marmolejo-Ramos
Keyword(s):  
Fatigue Life ◽  
Reaction Time ◽  
Reaction Time Data ◽  
Time Data ◽  
Life Distributions

An Accurate Measure of Reaction Time can Provide Objective Metrics of Concussion

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Mark Tommerdahl ◽  
Eric Francisco ◽  
Jameson Holden ◽  
Rachel Lensch ◽  
Anna Tommerdahl ◽  
...  
Keyword(s):  
Reaction Time ◽  
Computer Systems ◽  
Reaction Time Data ◽  
Assessment Tools ◽  
Computerized Testing ◽  
Alternative Methods ◽  
Time Variability ◽  
Time Data ◽  
Reaction Time Variability ◽  
Significant Difference

There have been numerous reports of neurological assessments of post-concussed athletes and many deploy some type of reaction time assessment. However, most of the assessment tools currently deployed rely on consumer-grade computer systems to collect this data. In a previous report, we demonstrated the inaccuracies that typical computer systems introduce to hardware and software to collect these metrics with robotics (Holden et al, 2020). In that same report, we described the accuracy of a tactile based reaction time test (administered with the Brain Gauge) as approximately 0.3 msec and discussed the shortcoming of other methods for collecting reaction time. The latency errors introduced with those alternative methods were reported as high as 400 msec and the system variabilities could be as high as 80 msec, and these values are several orders of magnitude above the control values previously reported for reaction time (200-220msec) and reaction time variability (10-20 msec). In this report, we examined the reaction time and reaction time variability from 396 concussed individuals and found that there were significant differences in the reaction time metrics obtained from concussed and non-concussed individuals for 14-21 days post-concussion. A survey of the literature did not reveal comparable sensitivity in reaction time testing in concussion studies using alternative methods. This finding was consistent with the prediction put forth by Holden and colleagues with robotics testing of the consumer grade computer systems that are commonly utilized by researchers conducting reaction time testing on concussed individuals. The significant difference in fidelity between the methods commonly used by concussion researchers is attributed to the differences in accuracy of the measures deployed and/or the increases in biological fidelity introduced by tactile based reaction times over visually administered reaction time tests. Additionally, while most of the commonly used computerized testing assessment tools require a pre-season baseline test to predict a neurological insult, the tactile based methods reported in this paper did not utilize any baselines for comparisons. The reaction time data reported was one test of a battery of tests administered to the population studied, and this is the first of a series of papers that will examine each of those tests independently.  

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