Lab 1
Simple Detection, Signal Detection, & Visual Search
Simple Detection, Signal Detection, & Visual Search
Trial Number Reaction Time
1 281.000
2 268.000
3 403.000
4 447.000
5 255.000
6 215.000
7 253.000
8 287.000
9 274.000
10 273.000
11 293.000
12 364.000
13 265.000
14 264.000
15 443.000
16 326.000
17 281.000
18 244.000
19 231.000
20 268.000
● How do your individual results relate to cognitive theories or concepts? Hint: For ways to relate your answer to course concepts, see the lab introduction, the predicted results that come with your output, and the text.
This lab immediately made me recall the number of times I have been at a red light, anticipating green, and went too soon, or took too long to register the light changing. This simple detection exercise, measuring the stimulus-response time, is the type of study that Waston thought to be the only way to measure behavior (Goldstein, 2019). To detect the stimulus (green circle), I had to focus my attention on the correct area. Once my retina received the image, receptors processed this stimulus as being the correct one, then engaged in the decision-making process to hit my target, triggering my action pathway to take the proper action, which was hitting the correct button on my keyboard. By using bottom-up processing in looking at the environment, my cognitive skills enabled me to complete this lab.
● Why can you not react faster than 200ms?
Based on my results and global data, we see that response time is usually between 200-300ms (MindTap , 2014). Even when anticipating the stimuli, it still takes time for your brain to process seeing the circle, recognizing it as the target, deciding to hit the button, and executing that action. Based on these cognitive functions, and the actual mechanics of our brain sending and receiving information, it is not possible to react any faster than 200ms (MindTap, 2014).
Number of Dots d’
C Hits False Alarm Correct Rejections Misses
144 dots 2.563 0.000 0.900 0.100 0.900 0.100
400 dots 5.582 -1.509 1.000 0.100 0.900 0.000
1000 dots 1.366 -0.159 0.800 0.300 0.700 0.200
● How do your individual results relate to cognitive theories or concepts? Hint: For ways to relate your answer to course concepts, see the lab introduction, the predicted results that come with your output, and the text.
This task was much more complicated than the simple detection lab. Although I still had to register a target, I had to distinguish the signal (10 dots) among the noise (all additional dots.) As the rate of noise increased, the longer it took to determine if there was a meaningful pattern among the random stimuli, requiring more attention and additional processing time. I am curious if the oblique effect played any part in my misses. Had been vertical or horizontal, would I have been more likely to recognize it?
Although all the dots were the same, the proximity of the dots helped me recognize this as my target, as Gestalt posits in his principles of perceptual organization (Goldstein, 2019). I also think the top-down knowledge played a part in my missed-present trials. In trials with the highest concentration of dots, when my bottom-up processing did not see the signal, I used logic and reasoning to assume the signal was present, despite not seeing it, and answered incorrectly.
● What does d’ measure and how is it calculated? Why is this a helpful tool when measuring cognitive tasks?
In this lab, d’ is a measurement of sensitivity, how efficiently I am to differentiate between the signal being present from the signal being absent. This is calculated through an algorithm that includes the relevant information of hit rate and false alarms. It is important because it shows how background information affects and can interfere with our ability to perceive the signal (MindTap, 2014).
Number of Distractors Feature Present Feature Absent Conjunction Present Conjunction Absent
4 858.000 840.000 1063.400 1324.800
16 771.400 977.600 1426.000 1991.600
32 759.400 800.000 1784.200 3034.800
64 830.400 853.200 1951.400 4645.600
● How does the pattern of your individual results relate to that predicted for feature vs conjunction searches? Hint: For ways to relate your answer to course concepts, see the lab introduction, the predicted results that come with your output, and the text.
As expected, my trial-by-trial output shows that my fastest trials were feature, when my eyes and mind did not need to individually examine each shape, with conjunctive absent taking the most time. Although these scenes may not have contained high-level information, the additional shapes added another level of complexity, tasking my brain with sorting through the shapes to determine what was meaningful and if the target was present. When the target was absent, my brain took even longer to process, and confirm, that I was not overlooking the signal.
● What is an example of a conjunction search in everyday life? For example, think about decisions that law enforcement officers, educators, medical professionals, or computer scientists make. This example can be personal or hypothetical.
We are constantly sorting information, and processing what is relevant. When evaluating a stimulus, we often have to take many aspects into consideration. I am tasked with repeatedly doing conjunction searches every time I go to the grocery store, which is likely why it is often overwhelming and much easier to do without my two children. When I am looking for my preferred yogurt, I am searching for the correct brand, the preferred flavor, and the one that has lower sugar. I quickly scan each row to find the one I want, but behind the scenes, my brain is working hard to filter out irrelevant information, organize the images I am seeing, and interpret this information.
● Compare and contrast the differences in what your mind had to do to complete the tasks.
When looking for the yogurt, my sense of sight receives input through my retina as I scan the items. I am surrounded by distractors (all the other yogurts, noise, people, products) and must focus my attention on each to eliminate it, engaging my what pathways and using bottom-up processing. My top-down processing aids in the process by using knowledge, such as knowing the color this brand uses and its usual location at this store. Once located, I use my decision-making and action pathways in selecting my item. The inverse projection problem enables me to reach for, and properly pick up the correct item, once it is located (Goldstein, 2014).
References -
Goldstein, B. E. (2019). Cognitive Psychology: Connecting Mind, Research, and Everyday Experience (5th ed.). Cengage.
MindTap - Cengage Learning. (2014). Ng.cengage.com; Cengage Learning. https://ng.cengage.com/static/nb/ui/evo/index.html?deploymentId=5868562500252548489124156979&eISBN=9781337408301&id=2075336089&snapshotId=3952969&