Cognitive and Biological Psychology Assignment Sample

• Type Assignment
• Downloads534
• Pages12
• Words2989

Introduction

Situational awareness (SA) is one of the cognitive abilities that has to do with perception, understanding and anticipation of events in various complex milieux. Sustaining SA when deciding on complex tasks involving high-risk levels, such as driving a car is crucial (Reale et al., 2023). Since cognitive overloading is fatal, SA has been explored in various fields, such as aviation and health care. In driving simulations, workload manipulation has been a helpful technique for Hu to investigate mental load's effects on SA and performance. All these are supported by the cognitive load theory that assumes that humans have limited working memory capacity when subjected to high workloads. For students seeking help in assignment writing, understanding these concepts is crucial. Such a decrease in cognitive affordances may present a decline in the ability to interpret situational signals accurately, especially where several streams of information must be combined.

Aim

This study examines workload levels' impact on SA and error detection in incongruent text-video tasks within a simulated driving environment.

• Hypothesis 1 (H1): Workload level will affect situational awareness, with a higher workload leading to reduced SA scores.
• Hypothesis 2 (H2): Higher workload levels will result in more incorrect responses to incongruent text-video tasks, indicating decreased performance accuracy.

Significance of the Study

This research is relevant to industries dealing with equipment and systems with life-critical consequences, such as transportation and emergency services. Therefore, understanding how workload levels influence SA and task performance in this study allows other organizations to develop appropriate training modules and implement effective operational procedures in high-stress settings.

Method

Design

The design used in this study was a between-participants design with workload as the independent variable (IV) and this was partitioned into three groups: Low, Medium and High (Sepich et al., 2022). The dependent measure variables (DVs) were situational awareness and the number of accurate answers on the incongruent text-video items. Thus, two one-way analyses of variance were conducted to use in the analysis of the collected data.

Participants

Data was collected from 30 participants in the age range of 18 to 59, in three conditions; control A, control B and experimental condition, each comprising of 10 participants Participants had a personal driver’s license and had no conditions that affected their cognition. Participants’ permission was sought and, therefore, all the participants provided their informed consent.

Cognitive and Biological Psychology Assignment Sample

Liked This Sample? Hire Me Now

David Moore 9 Years | MSc

Materials

To perform the driving simulation task, it was used [insert software/platform] for the participants to complete self-rated scores on the congruent and incongruent text-video stimuli. Workload levels were manipulated by adjusting secondary task frequency and complexity:

Low workload: Minimal secondary tasks.

Medium workload: Occasional secondary tasks.

High workload: Chronic, high workload secondary tasks.

Procedure

Participants finished the study in a quiet environment. They were then given options for consent having been informed, and then administered the simulation during the familiarization phase. Participants were then divided at random into one of the workload conditions and required to attempt the task. Reaction to stimuli was captured and measured on SA and accuracy. Participants were post-tested after the study was completed.

Procedure

The practical tests were conducted in isolation with very low noise levels for all participants (Kotwal et al., 2020). Subsequently, participants received information and signed informed consent and then underwent a briefing on the task and response buttons as a warm-up. The participants were then divided equally according to workload to form three groups.

In performing the task, participants were required to complete the text-video stimuli and at the same time, drive a simulated vehicle. The clarifications and answers given by the students were taken on tape for further discussion. The final activity conducted as part of the experiment was debriefing.

Data Analysis

In this study all data collected were keyed into SPSS for analysis. Two one-way ANOVAs were conducted to evaluate the effect of workload level on:

Real SLA condition awareness..

The number of accurate answers given to the Text-video items that are conflict in nature.

Results

Descriptive statistics of age group, gender, and the other variables related to task demands give information about the sample and participants’ reactions to the workload. Age group ranges from 1 to signify the youngest as 18-24 years and 5 to represent the oldest as 55-59 years. The mean age group was 2.87, while the standard deviation equalled 1.196, which means that a majority of participants was closer to the middle age ranges of (25-34 or 35-44 years. This distribution indicates a fairly reasonable spread between younger participants and middle-aged participants but with variations slightly.

For gender the results varied from 1 to 4 which includes all the given options like Male, Female, Non-binary/Other and Prefer not to say. The mean gender value of the sample was 2.50, standard derivation=0.820. This is a promising signal in that it shows gender distribution are quite equal with no preference on any of the main categories.

Descriptive statistics for the task related variables supplement the participants’ experiences during the conduct of the study. The following table presents the minimum and the maximum values in regard to mental demand, task success, and effort across workload levels. It will be seen that in most cases the mean values describe the general temper or trend of the participants’ perceptions and that the standard deviations portray the amount of sample fluctuation. Mental demand and task effort reveals higher variability within participants, which can be attributed to the cognitive characteristics and their ability to handle workload conditions.

The ANOVA of the Indices, mental demand, success rate (hit rate to mismatched text-video pairs), and task effort for subjects under varying workload levels were conducted.

For mental demand, the findings show that there is no a significant difference in workload groups with F(4, 25)= 0.994 and p = 0.429. Workload levels did not affect the mentally demanding participants’ perception of the task since the p-value is larger than the accepted level of significance of 0.05. The value obtained for the variance between groups was similar to the variance within groups (Mean Square = 0.596 as against Mean Square = 0.599), therefore an indication that there were hardly any differences.

Thus, for success identified by the MF in mismatched text-video pairs there were also no statistically significant differences between the workload groups, with F(4, 25) = 1.642 and p = 0.195. Once more the p-value was above the significance level, this means that workload level did not significantly affect the participants’ performance in terms of identifying accurately mismatched text video pairs. In addition, the between-groups variance – Mean Square 1.768 was considerably smaller than the within-groups variance – Mean Square 1.077 This also supports this conclusion.

However, the findings for task effort depicted a significant relationship between workload levels with F (4, 25) = 6.203, p = 0.001. Since the obtained p-value is less than the predetermined level of.05, there is evidence that the perceived effort of participants varies across the workload condition. The between-groups variance (Mean Square = 5.131) was significantly greater than the within-groups variance (Mean Square = 0.827) thereby establishing that workload levels made a significant difference to the perceived effort level to accomplish the task from the part of the participants.

Discussion

The purpose of undertaking this research was to determine the impact that high and low workload levels have on SA and the ability to perform tasks in a driving simulation (Li et al., 2022). In particular, the impact of workload on mental demand, accuracy of selecting mismatched text-video pairs and task difficulty was investigated in this study with low, medium, and high levels of the independent variable being employed (Li et al., 2022). To a certain extent, the null hypotheses are supported because there were significant differences in amount of effort needed to complete the task but no difference for mental demand and perceived task completion rate for different workloads.

The findings reveal that workload had a very strong effect on the perceived effort in doing the task. The quantitative results of the study, which involve analysis of variance, show that participants who worked under higher workload conditions exerted significantly higher effort compared to those under low workload conditions F(4,25) = 6.203, p = 0.001. This result is consistent with cognitive load theory holds that as the task complexity increases, the cognitive resources are demands, resulting in high perceived effort (Kim et al., 2022). Therefore, it can be concluded that in high workload conditions the participants received more attention and cognitive load demands in order to cope with the increased task complexity that has been reported by previous studies on the interference of dual task and cognitive load.

Conversely, the results showed that there was no essential variation in mental demand for the different workload levels (F (4, 25) = 0.994 F value, p = 0.429 probability value). This finding could imply that the participants believed that the overall task was quite difficult regardless of the workload conditions, caused perhaps by the cognitive nature of the driving simulation. Another issue may be that participants adjusted to the conditions of the specific task over time, thus, having less stress between the different workload conditions (Luzipho, Joubert and Dhurup, 2023). This adaptation might help to explain why there were no noticeable differences in perceived mental demand even though the task really was more complex.

Likewise, no workload level differences were observed for the results of the task involving identification of mismatched text-video pairs (F(4, 25) = 1.642, p = 0.195). Based on the fact that higher workload conditions limit attentiveness, participants’ accuracy in all conditions was comparable with a two-tailed p > 0.05. This result might be due to the fact that all participants might have been able to perform the same well regardless of the perceived workload or could also be due to the insufficient complexity of the task to distinguish the various conditions. However, it is also possible individual differences in cognition intensity moderated the impact of workload factors with some participants able to cope with the extra burden.

The large variation across these workload conditions has important implications for the design of systems and training in applications such as transportation and healthcare. Such power(productivity) specifications provide insights into the degree to which workload affects productivity in order to refine task layouts in next-generation CPS. For example, in executing tasks it would be possible for the secondary demands to be made less often as well as being generalized to lessen their difficulty during high priority operations so as to enable efficient use of cognitive capital.

However, the study has limitations that can be discussed here. First, the sample size was relatively small (N = 30), thus it is may not be easy to generalize the findings of the study. That way we could gather more data and possibly achieve higher levels of statistical significance and subsequently make more accurate conclusions with regards to the impact of workload. Second, the driving environment of SME-D was artificial and the condition of the experiment was controlled; this might lead to a decrease in the ecological validity of the experiment. Future studies should use different type of simulation or employ real life situation to improve generalizations.

The former is the inability to control variations in participants’ cognitive capabilities which may have affected their response and performance. Subsequent research may also require using control variables that perhaps define cognitive intensity or workload capacity to assess how generic aspects would respond to different forms of workload. Further, more difficult tasks or greater within-task variability could be used to improve differentiation of performance across varying workloads.

This paper offers useful data in understanding the impact of workload on perceived measures such as effort, demand and achievement in a driving-simulation scenario. This proved the workloads’ effects on effort, intensifying across different workload levels, with mental demand and task success exhibiting no significant impact. Following the prior research on workload and situational awareness, these findings provide theoretical implications for higher level theory expansion as well as plausible real-life implications on how such systems and the overall workload should be designed to enhance optimal performance of high-risk tasks. More specifically, future investigations should extend this research by examining the fit of workload and individual differential factors to the observed effects on task performance within actual work environments.

The purpose of the current investigation was to examine the impact of workload condition on SA and performance measures in a driving-task environment. In particular, it focused on whether the workload influenced mental demand, performance when it comes to mismatched text-video pairs, and the amount of effort to complete the task. These results partially supported the hypotheses. There were only moderate differences in effort with workload conditions, however, there were no differences in mental demand rank, or in ability to formerly match text and video correctly.

The feedback results shown below prove the hypothesis that the levels of workload bear a statistically significant , direct relationship to the amount of effort taken by the subjects in order to complete the task There is a highly significant F value, F(4,25)= 6.203 (p<0.05). Subjects from high workload conditions employed significantly higher effort than subjects in low or medium workload conditions. This conforms with the cogni ti ve load theory which suggest that, when the demands of the task challenge the resources of the performers, the performers have to put in extra effort in order to perform the task successfully. This result is also consistent with dual-task interference theory arguing that high workload conditions that place a demand on attentional resource increase perceived effort.

However, the variations between effort levels were not translated into differences in perceived mental demands which showed no significant effect of workload (F(4, 25) = 0.994, p = 0.429). The absence of interaction between the outcome variables and workload condition indicates that the participants probably percieved the overall task as fairly difficult. This could be explained by the fact that there exist cognitive demands upon performing the task, such as processing of text-video stimuli and situational awareness that did not reach noticeable differences between different conditions. Another possibility is that participants’ expectations were about the level of difficulty of the tasks affected mental demand expectation by making participants adapt easily to workloads they anticipated to be heavy (Liu et al., 2022). This might have been due to some adaptive strategies such as prioritizing one task over another which may have lead to the observed absence of significant differences.

Likewise, the number of mismatched text-video pairs in comparison with the correct ones was not dependent on the level of workload (F(4, 25) = 1.642, p = 0.195). This shift did not affect the accuracy of the participants in the mismatch stimuli reaching optimal performance even with the presence of workload conditions (Liu et al., 2022). Such results may be accrued due to such factors as; ceiling effect for participants across all conditions responded well because the stimuli used in the mismatch was easily comprehensible. However, cognitive characteristics that chronic-google users possess including attention control, or multitasking proficiency, might have otherwise modulated the impacts of workload on task outcomes.

Conclusion

The impact of workload levels on SA, performance and perceived effort was investigated in this study in a simulated driving environment. In particular, the study brought out the fact that workload informs the efforts needed to complete a given exercise; the higher the workload, the more effort. But, there were no changes in perceived mental demand and task success for different levels of workload meaning that participants were equally efficient in the tasks regardless of the type of workload.

The results agree with the cognitive load theory whereby the overall load increases under high workload situations especially in terms of effort. However, there was no such improvement on mental demand and task success, which may perhaps suggest that participants got used to with the demands or the task required just more than what was required by the different levels of workload. These results are generalizable toward determining the features of tasks and systems in safety-critical environments and to the requirement for equalizing cognitive loads.

However, the following were major drawbacks: a limited number of subjects, a controlled simulation technique, and lack of a measure of individual cognitive ability. Future research should transcend these limitations by involving more subjects, with the inclusion of persons from a diverse background, in addition to the use of quasi-real life activities or reconstructions and studies on individual variability of workload and cognitive ability. Such research may offer new insights into how workload patterns influence SA and task outcomes, and help design improved, workload mitigation approaches for real-world, operational environments.

References

• Kim, J., Kirsten, Langdon, P., Mark Philip Bradley, Politis, I., Thompson, S. G., Skrypchuk, L., J.P. O'Donoghue, Richardson, J., & Stanton, N. A. (2022). Partially automated driving has higher workload than manual driving: An on‐road comparison of three contemporary vehicles with SAE Level 2 features. Human Factors and Ergonomics in Manufacturing & Service Industries, 33(1), 40–54. https://doi.org/10.1002/hfm.20969
• Kotwal, A. A., Holt‐Lunstad, J., Newmark, R. L., Cenzer, I., Smith, A. K., Covinsky, K. E., Escueta, D. P., Lee, J. M., & Perissinotto, C. M. (2020). Social Isolation and Loneliness Among San Francisco Bay Area Older Adults During the COVID ‐19 Shelter‐in‐Place Orders. Journal of the American Geriatrics Society, 69(1). https://doi.org/10.1111/jgs.16865
• Li, Q., Ng, K. K. H., Yu, S. C. M., Yiu, C. Y., & Lyu, M. (2022). Recognising situation awareness associated with different workloads using EEG and eye-tracking features in air traffic control tasks. Knowledge-Based Systems, 110179. https://doi.org/10.1016/j.knosys.2022.110179
• Liu, Y., Xian, J. S., Wang, R., Ma, K., Li, F., Wang, F. L., Yang, X., Mu, N., Xu, K., Quan, Y. L., Wang, S., Lai, Y., Yang, C. Y., Li, T., Zhang, Y., Tan, B., Feng, H., Chen, T. N., & Wang, L. H. (2022). Factoring and correlation in sleep, fatigue and mental workload of clinical first-line nurses in the post-pandemic era of COVID-19: A multi-center cross-sectional study. Frontiers in Psychiatry, 13. https://doi.org/10.3389/fpsyt.2022.963419
• Luzipho, N., Joubert, P. A., & Dhurup, M. (2023). Job stressors, work tension and job satisfaction of academics at a university in South Africa. SA Journal of Human Resource Management, 21. https://doi.org/10.4102/sajhrm.v21i0.2015
• Reale, C., Salwei, M. E., Militello, L. G., Weinger, M. B., Burden, A., Sushereba, C., Torsher, L. C., Andreae, M. H., Gaba, D. M., McIvor, W. R., Banerjee, A., Slagle, J., & Anders, S. (2023). Decision-Making During High-Risk Events: A Systematic Literature Review. Journal of Cognitive Engineering and Decision Making, 17(2), 155534342211474. https://doi.org/10.1177/15553434221147415
• Sepich, N. C., Jasper, A., Fieffer, S., Gilbert, S. B., Dorneich, M. C., & Kelly, J. W. (2022). The impact of task workload on cybersickness. Frontiers in Virtual Reality, 3. https://doi.org/10.3389/frvir.2022.943409