Traffic Signal Color Recognition Is a Problem for Both Protan and Deutan Color-Vision Deficients

2003 ◽  
Vol 45 (3) ◽  
pp. 495-503 ◽  
Author(s):  
David A. Atchison ◽  
Carol A. Pedersen ◽  
Stephen J. Dain ◽  
Joanne M. Wood
Keyword(s):  
Color Vision ◽  
Response Times ◽  
Reaction Times ◽  
Error Rates ◽  
Traffic Signal ◽  
White Background ◽  
Signal Design ◽  
Traffic Light ◽  
Light Signals ◽  
Signal Color

We investigated the effect of color-vision deficiency on reaction times and accuracy of identification of traffic light signals. Participants were 20 color-normal and 49 color-deficient males, the latter divided into subgroups of different severity and type. Participants performed a tracking task. At random intervals, stimuli simulating standard traffic light signals were presented against a white background at 5° to right or left. Participants identified stimulus color (red/yellow/green) by pressing an appropriate response button. Mean response times for color normals were 525, 410, and 450 ms for red, yellow, and green lights, respectively. For color deficients, response times to red lights increased with increase in severity of color deficiency, with deutans performing worse than protans of similar severity: response times of deuteranopes and protanopes were 53% and 35% longer than those of color normals. A similar pattern occurred for yellow lights, with deuteranopes and protanopes having increased response times of 85% and 53%, respectively. For green lights, response times of all groups were similar. Error rates showed patterns similar to those of response times. Contrary to previous studies, deutans performed much worse than protans of similar severity. Actual or potential applications of this research include traffic signal design and driver licensing.

scholarly journals Multifaceted aspects of chunking enable robust algorithms

2014 ◽  
Vol 112 (8) ◽  
pp. 1849-1856 ◽  
Author(s):  
Daniel E. Acuna ◽  
Nicholas F. Wymbs ◽  
Chelsea A. Reynolds ◽  
Nathalie Picard ◽  
Robert S. Turner ◽  
...  
Keyword(s):  
Motor Behavior ◽  
Response Times ◽  
Reaction Times ◽  
Error Rates ◽  
Physiological Data ◽  
Bayesian Algorithm ◽  
Robust Algorithms ◽  
Sequence Production ◽  
Standard Tool

Sequence production tasks are a standard tool to analyze motor learning, consolidation, and habituation. As sequences are learned, movements are typically grouped into subsets or chunks. For example, most Americans memorize telephone numbers in two chunks of three digits, and one chunk of four. Studies generally use response times or error rates to estimate how subjects chunk, and these estimates are often related to physiological data. Here we show that chunking is simultaneously reflected in reaction times, errors, and their correlations. This multimodal structure enables us to propose a Bayesian algorithm that better estimates chunks while avoiding overfitting. Our algorithm reveals previously unknown behavioral structure, such as an increased error correlations with training, and promises a useful tool for the characterization of many forms of sequential motor behavior.

The Influence of Traffic Signal Solutions on Self-Reported Road-Crossing Behavior

2014 ◽  
Vol 17 ◽  
Author(s):  
Leandro L. Di Stasi ◽  
Alberto Megías ◽  
Antonio Cándido ◽  
Antonio Maldonado ◽  
Andrés Catena
Keyword(s):  
Modern Society ◽  
Traffic Signals ◽  
Traffic Signal ◽  
Motor Vehicles ◽  
Signal Design ◽  
Traffic Light ◽  
Modern Cities ◽  
The Impact ◽  
Road Crossing

AbstractInjury to pedestrians is a major safety hazard in many countries. Since the beginning of the last century, modern cities have been designed around the use of motor vehicles despite the unfavourable interactions between the vehicles and pedestrians. This push towards urbanization resulted in a substantial number of crashes and fatalities involving pedestrians every day, all over the world. Thus, improving the design of urban cities and townships is a pressing issue for modern society. The study presented here provides a characterization of pedestrian safety problems, with the emphasis on signalized crosswalks (i.e. traffic signal) design solutions. We tested the impact of seven different traffic light configurations (steady [green, yellow, and red], flashing [green, yellow, and red], and light off) on pedestrian self-reported road-crossing behavior, using a 11-point scale -ranging from 0 (“I never cross in this situation”) to 10 (“I always cross in this situation”). Results showed that mandatory solutions (steady green vs. steady red) are the best solutions to avoid unsafe pedestrian behaviors while crossing controlled intersections (frequency of crossing: Mgreen = 9.4 ± 1 vs. Mred = 2.6 ± 2). These findings offer important guidelines for the design of future traffic signals for encouraging a pedestrian/transit-friendly environment.

scholarly journals The Effect of Prior Knowledge of Color on Behavioral Responses and Event-Related Potentials During Go/No-go Task

2021 ◽  
Vol 15 ◽  
Author(s):  
Nami Kubo ◽  
Tatsunori Watanabe ◽  
Xiaoxiao Chen ◽  
Takuya Matsumoto ◽  
Keisuke Yunoki ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Red Light ◽  
Reaction Times ◽  
Behavioral Responses ◽  
Event Related Potentials ◽  
Light Signal ◽  
Traffic Light ◽  
Related Potentials ◽  
Light Signals ◽  
Set Up

In daily life, the meaning of color plays an important role in execution and inhibition of a motor response. For example, the symbolism of traffic light can help pedestrians and drivers to control their behavior, with the color green/blue meaning go and red meaning stop. However, we don’t always stop with a red light and sometimes start a movement with it in such a situation as drivers start pressing the brake pedal when a traffic light turns red. In this regard, we investigated how the prior knowledge of traffic light signals impacts reaction times (RTs) and event-related potentials (ERPs) in a Go/No-go task. We set up Blue Go/Red No-go and Red Go/Blue No-go tasks with three different go signal (Go) probabilities (30, 50, and 70%), resulting in six different conditions. The participants were told which color to respond (Blue or Red) just before each condition session but didn’t know the Go probability. Neural responses to Go and No-go signals were recorded at Fz, Cz, and Oz (international 10–20 system). We computed RTs for Go signal and N2 and P3 amplitudes from the ERP data. We found that RT was faster when responding to blue than red light signal and also was slower with lower Go probability. Overall, N2 amplitude was larger in Red Go than Blue Go trial and in Red No-go than Blue No-go trial. Furthermore, P3 amplitude was larger in Red No-go than Blue No-go trial. Our findings of RT and N2 amplitude for Go ERPs could indicate the presence of Stroop-like interference, that is a conflict between prior knowledge about traffic light signals and the meaning of presented signal. Meanwhile, the larger N2 and P3 amplitudes in Red No-go trial as compared to Blue No-go trial may be due to years of experience in stopping an action in response to a red signal and/or attention. This study provides the better understanding of the effect of prior knowledge of color on behavioral responses and its underlying neural mechanisms.

The Latin Square Task as a Measure of Relational Reasoning

2020 ◽  
Vol 36 (2) ◽  
pp. 296-302 ◽  
Author(s):  
Luke J. Hearne ◽  
Damian P. Birney ◽  
Luca Cocchi ◽  
Jason B. Mattingley
Keyword(s):  
Brain Imaging ◽  
Response Times ◽  
Latin Square ◽  
Error Rates ◽  
Functional Brain Imaging ◽  
Inspection Time ◽  
Relational Reasoning ◽  
Validity And Reliability ◽  
The Stability ◽  
Test Retest Reliability

Abstract. The Latin Square Task (LST) is a relational reasoning paradigm developed by Birney, Halford, and Andrews (2006) . Previous work has shown that the LST elicits typical reasoning complexity effects, such that increases in complexity are associated with decrements in task accuracy and increases in response times. Here we modified the LST for use in functional brain imaging experiments, in which presentation durations must be strictly controlled, and assessed its validity and reliability. Modifications included presenting the components within each trial serially, such that the reasoning and response periods were separated. In addition, the inspection time for each LST problem was constrained to five seconds. We replicated previous findings of higher error rates and slower response times with increasing relational complexity and observed relatively large effect sizes (η2p > 0.70, r > .50). Moreover, measures of internal consistency and test-retest reliability confirmed the stability of the LST within and across separate testing sessions. Interestingly, we found that limiting the inspection time for individual problems in the LST had little effect on accuracy relative to the unconstrained times used in previous work, a finding that is important for future brain imaging experiments aimed at investigating the neural correlates of relational reasoning.

Time after time: support for memory accounts of temporal preparation.

2019 ◽  
Author(s):  
Joe Butler ◽  
Samuel Ngabo ◽  
Marcus Missal
Keyword(s):  
Response Times ◽  
Reaction Times ◽  
Evidence Base ◽  
Higher Order ◽  
Order Effects ◽  
Previous Trial ◽  
Adaptive Responses ◽  
Temporal Preparation ◽  
Subsequent Trial ◽  
Higher Order Effects

Complex biological systems build up temporal expectations to facilitate adaptive responses to environmental events, in order to minimise costs associated with incorrect responses, and maximise the benefits of correct responses. In the lab, this is clearly demonstrated in tasks which show faster response times when the period between warning (S1) and target stimulus (S2) on the previous trial was short and slower when the previous trial foreperiod was long. The mechanisms driving such higher order effects in temporal preparation paradigms are still under debate, with key theories proposing that either i) the foreperiod leads to automatic modulation of the arousal system which influences responses on the subsequent trial, or ii) that exposure to a foreperiod results in the creation of a memory trace which is used to guide responses on the subsequent trial. Here we provide data which extends the evidence base for the memory accounts, by showing that previous foreperiod exposures are cumulative with reaction times shortening after repeated exposures; whilst also demonstrate that the higher order effects associated with a foreperiod remain active for several trials.

Current Task Activation Predicts General Effects of Advance Preparation in Task Switching

2006 ◽  
Vol 53 (4) ◽  
pp. 260-267 ◽  
Author(s):  
Edita Poljac ◽  
Ab de Haan ◽  
Gerard P. van Galen
Keyword(s):  
Task Performance ◽  
Task Switching ◽  
Shape Matching ◽  
Response Times ◽  
Error Rates ◽  
Matching Task ◽  
Task Switch ◽  
Current Task ◽  
Within Subjects ◽  
And Task

Two experiments investigated the way that beforehand preparation influences general task execution in reaction-time matching tasks. Response times (RTs) and error rates were measured for switching and nonswitching conditions in a color- and shape-matching task. The task blocks could repeat (task repetition) or alternate (task switch), and the preparation interval (PI) was manipulated within-subjects (Experiment 1) and between-subjects (Experiment 2). The study illustrated a comparable general task performance after a long PI for both experiments, within and between PI manipulations. After a short PI, however, the general task performance increased significantly for the between-subjects manipulation of the PI. Furthermore, both experiments demonstrated an analogous preparation effect for both task switching and task repetitions. Next, a consistent switch cost throughout the whole run of trials and a within-run slowing effect were observed in both experiments. Altogether, the present study implies that the effects of the advance preparation go beyond the first trials and confirms different points of the activation approach ( Altmann, 2002) to task switching.

Smart Traffic Light System

2021 ◽  
Vol 9 (9) ◽  
pp. 679-686
Author(s):  
Rashi Maheshwari
Keyword(s):  
Traffic Congestion ◽  
Traffic Signals ◽  
Traffic Monitoring ◽  
Traffic Signal ◽  
Traffic Signal Control ◽  
Traffic Light ◽  
Mathematical Functions ◽  
Light System ◽  
Smart Traffic ◽  
And Control

Abstract: Traffic signal control frameworks are generally used to monitor and control the progression of cars through the intersection of roads. Moreover, a portable controller device is designed to solve the issue of emergency vehicles stuck in overcrowded roads. The main objective of this paper is to design and implement a suitable algorithm and its simulation for an intelligent traffic signal simulator. The framework created can detect the presence or nonappearance of vehicles within a specific reach by setting appropriate duration for traffic signals to react accordingly. By employing mathematical functions and algorithms to ascertain the suitable timing for the green signal to illuminate, the framework can assist with tackling the issue of traffic congestion. The explanation relies on recent fixed programming time. Keywords: Smart Traffic Light System, Smart City, Traffic Monitoring.

Hands-Free versus Hand-Held Cell Phone Conversation on a Braking Response by Young Drivers

2007 ◽  
Vol 105 (2) ◽  
pp. 514-522 ◽  
Author(s):  
Joy L. Hendrick ◽  
Jamie R. Switzer
Keyword(s):  
Cell Phone ◽  
Response Times ◽  
Cell Phones ◽  
Reaction Times ◽  
Free Cell ◽  
Visual Signal ◽  
Young Drivers ◽  
Speed Of Information Processing ◽  
Additional Support ◽  
The Right

As some states allow motorists to use hands-free cell phones only while driving, this study was done to examine some braking responses to see if conversing on these two types of cell phones affects quick responding. College-age drivers ( n = 25) completed reaction time trials in go/no-go situations under three conditions: control (no cell phone or conversation), and conversing on hands-free and hand-held cell phones. Their task involved moving the right foot from one pedal to another as quickly as possible in response to a visual signal in a lab setting. Significantly slower reaction times, movement times, and total response times were found for both cell phone conditions than for the control but no differences between hands-free and hand-held phone conditions. These findings provide additional support that talking on cell phones, regardless if it is hands-free or hand-held, reduces speed of information processing.

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