Journal articles on the topic 'Cognitive processing'

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1

Mercier, Pierre. "Automatic Cognitive Processing." Contemporary Psychology: A Journal of Reviews 37, no. 1 (January 1992): 42–43. http://dx.doi.org/10.1037/031788.

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2

Tran, Chi. "Cognitive information processing." Vietnam Journal of Computer Science 1, no. 4 (April 30, 2014): 207–18. http://dx.doi.org/10.1007/s40595-014-0019-4.

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Jung, Seojung, and Karen Siedlecki. "Temporal Relationship Between Activity Engagement and Cognition." Innovation in Aging 4, Supplement_1 (December 1, 2020): 592. http://dx.doi.org/10.1093/geroni/igaa057.1987.

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Abstract Previous studies have shown that activity engagement is related to cognitive function. However, few studies have examined the temporal order between activity engagement and various domains of cognition. Using data from the Virginia Cognitive Aging Project (baseline N =5430, Mage =51.28, SD =18.12), we examined the temporal relationships between engagement in physical and cognitive activity and different cognitive domains (reasoning, spatial visualization, episodic memory, processing speed, vocabulary) after controlling for age, education, self-rated health and depression. Cross-lagged panel analyses indicate that very few of the temporal relationships between activity level and cognition were significant except higher levels of cognitive activity significantly predicted better future processing speed, but not the reverse. Findings suggest the importance of engaging in cognitively stimulating activities, which help adults preserve processing speed over time. This study also highlights the importance of longitudinal design on various domains of cognition to help develop domain-specific interventions.
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Cavanagh, Patrick. "The cognitive impenetrability of cognition." Behavioral and Brain Sciences 22, no. 3 (June 1999): 370–71. http://dx.doi.org/10.1017/s0140525x99272020.

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Cognitive impenetrability is really two assertions: (1) perception and cognition have access to different knowledge bases; and (2) perception does not use cognitive-style processes. The first leads to the unusual corollary that cognition is itself cognitively impenetrable. The second fails when it is seen to be the claim that reasoning is available only in conscious processing.
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Bergman, Jukka-Pekka, Vladimir Platonov, Igor Dukeov, Pekka Röyttä, and Pasi Luukka. "Information Processing Approach in Organisational Cognitive Structures." International Journal of Information Systems and Social Change 7, no. 4 (October 2016): 1–19. http://dx.doi.org/10.4018/ijissc.2016100101.

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Despite of increasing interest in social cognitive research in strategic management during recent years, few studies have examined the relationship between cognitive structures of top management and middle management. This study represents the information processing approach in managerial cognition research assuming that top management communicate their shared cognitions into the organization reducing ambiguity of operative environment faced by the other levels of organization shaping the operations of the organizations. The authors' empirical study examines managerial cognitive maps collected with a cognitive mapping method in a transportation company. In the study, top managers and middle managers separately evaluated sustainability management issues and their relevance for the company providing 75 individual cognitive maps. Based on this, the authors' study aims to fill the gap in research of relationship between top management and middle management cognitive structures and increase understanding on role of managerial cognition in strategic management research.
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Greenwood, John D. "Cognition, consciousness, and the cognitive revolution." Behavioral and Brain Sciences 32, no. 2 (April 2009): 209–10. http://dx.doi.org/10.1017/s0140525x09000971.

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AbstractIt is argued that the cognitive revolution provided general support for the view that associative learning requires cognitive processing, but only limited support for the view that it requires conscious processing. The point is illustrated by two studies of associative learning that played an important role in the development of the cognitive revolution, but which are surprisingly neglected by Mitchell et al. in the target article.
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Kampis, Dora, and Victoria Southgate. "Altercentric Cognition: How Others Influence Our Cognitive Processing." Trends in Cognitive Sciences 24, no. 11 (November 2020): 945–59. http://dx.doi.org/10.1016/j.tics.2020.09.003.

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Dutke, Stephan, Thomas Jaitner, Timo Berse, and Jonathan Barenberg. "Acute Physical Exercise Affected Processing Efficiency in an Auditory Attention Task More Than Processing Effectiveness." Journal of Sport and Exercise Psychology 36, no. 1 (February 2014): 69–79. http://dx.doi.org/10.1123/jsep.2013-0044.

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Research on effects of acute physical exercise on performance in a concurrent cognitive task has generated equivocal evidence. Processing efficiency theory predicts that concurrent physical exercise can increase resource requirements for sustaining cognitive performance even when the level of performance is unaffected. This hypothesis was tested in a dual-task experiment. Sixty young adults worked on a primary auditory attention task and a secondary interval production task while cycling on a bicycle ergometer. Physical load (cycling) and cognitive load of the primary task were manipulated. Neither physical nor cognitive load affected primary task performance, but both factors interacted on secondary task performance. Sustaining primary task performance under increased physical and/or cognitive load increased resource consumption as indicated by decreased secondary task performance. Results demonstrated that physical exercise effects on cognition might be underestimated when only single task performance is the focus.
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Oyebode, F. "Cognitive processing in schizophrenia." British Journal of Psychiatry 186, no. 3 (March 2005): 262. http://dx.doi.org/10.1192/bjp.186.3.262.

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Drummond, PD. "Cognitive Processing in Migraine." Cephalalgia 18, no. 3 (April 1998): 123. http://dx.doi.org/10.1046/j.1468-2982.1998.1803123.x.

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Watts, Fraser N. "Cognitive Processing in Phobias." Behavioural and Cognitive Psychotherapy 14, no. 4 (October 1986): 295–301. http://dx.doi.org/10.1017/s0141347300014919.

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A programme of research is described which investigates the cognitive processing of phobic stimuli. Phobics show good perceptual “pick-up” of phobic words on a version of the Stroop test. However, their encoding appears to be poor, as indexed by recognition memory for phobic stimuli. Consistent with this, cognitive representations of phobic stimuli are poorly elaborated and differentiated. Brief desensitization was found to have stronger effects on perceptual and encoding phenomena than on cognitive representations. Finally, spider phobics were found to have poor recall of phobic words. This contrasts with the opposite phenomenon that has been reported for agrophobics and depressives.
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Gini, Fulvio, and Sergios Theodoridis. "Cognitive Information Processing Workshop." IEEE Aerospace and Electronic Systems Magazine 25, no. 11 (November 2010): 40–41. http://dx.doi.org/10.1109/maes.2010.5638807.

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13

Pereira, Bliss. "Cognitive Information Processing Speed." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1969–78. http://dx.doi.org/10.22214/ijraset.2023.48563.

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Abstract: Using visual learning and meditation based techniques to develop brain training apps that can significantly improve cognitive performance. This paper seeks to understand what causes slow cognitive processing speed and designs an application to diagnose it in early stages and provide support to people having slow processing speed. Moreover, significant primary data is collected to validate the hypothesis that visual learning techniques presented through mobile applications can improve cognitive performance.
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Vagias, Hariklia, Michelle L. Byrne, Lyn Millist, Owen White, Meaghan Clough, and Joanne Fielding. "Visuo-Cognitive Phenotypes in Early Multiple Sclerosis: A Multisystem Model of Visual Processing." Journal of Clinical Medicine 13, no. 3 (January 23, 2024): 649. http://dx.doi.org/10.3390/jcm13030649.

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Background: Cognitive impairment can emerge in the earliest stages of multiple sclerosis (MS), with heterogeneity in cognitive deficits often hindering symptom identification and management. Sensory–motor dysfunction, such as visual processing impairment, is also common in early disease and can impact neuropsychological task performance in MS. However, cognitive phenotype research in MS does not currently consider the relationship between early cognitive changes and visual processing impairment. Objectives: This study explored the relationship between cognition and visual processing in early MS by adopting a three-system model of afferent sensory, central cognitive and efferent ocular motor visual processing to identify distinct visuo-cognitive phenotypes. Methods: Patients with clinically isolated syndrome and relapsing–remitting MS underwent neuro-ophthalmic, ocular motor and neuropsychological evaluation to assess each visual processing system. The factor structure of ocular motor variables was examined using exploratory factor analysis, and phenotypes were identified using latent profile analysis. Results: Analyses revealed three ocular-motor constructs (cognitive control, cognitive processing speed and basic visual processing) and four visuo-cognitive phenotypes (early visual changes, efferent-cognitive, cognitive control and afferent-processing speed). While the efferent-cognitive phenotype was present in significantly older patients than was the early visual changes phenotype, there were no other demographic differences between phenotypes. The efferent-cognitive and cognitive control phenotypes had poorer performance on the Symbol Digit Modalities Test compared to that of other phenotypes; however, no other differences in performance were detected. Conclusion: Our findings suggest that distinct visual processing deficits in early MS may differentially impact cognition, which is not captured using standard neuropsychological evaluation. Further research may facilitate improved symptom identification and intervention in early disease.
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Sugai, Kelli, Nicole Whittle, Christian Herrera Ortiz, Marjorie R. Leek, Grace Lee, and Jonathan H. Venezia. "Relation between temporal fine structure processing and global processing speed." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A231. http://dx.doi.org/10.1121/10.0016111.

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Strelcyk et al. (2019) recently found that interaural phase discrimination in older hearing-impaired listeners was correlated with both visuospatial processing speed and interaural level discrimination. This suggests that temporal fine structure (TFS) processing relies on global processing speed and/or spatial cognition, though it is possible that, generally, complex auditory discrimination engages multiple cognitive domains. Here, 50 Veterans (mean age = 48.1, range = 30–60) with normal or near-normal hearing completed batteries of temporal processing and cognitive tests. Composite cognitive test scores reflecting processing speed/executive function (PS-EX) and working memory (WM) were obtained. Temporal processing tasks included measures of envelope (ENV; gap duration discrimination, forward masking) and TFS (frequency modulation detection, interaural phase modulation detection) processing. Bayesian hierarchical regression was used to fit psychometric functions simultaneously to all ENV and TFS tasks in all subjects. Fixed effects of PS-EX and WM on thresholds and slopes were estimated for the psychometric functions in each temporal task. In general, (i) PS-EX and WM influenced both TFS and ENV thresholds, but not slopes; and (ii) TFS thresholds were best explained by PS-EX scores while ENV thresholds were best explained by WM scores. These findings suggest a specific relation between global processing speed and TFS.
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EBERT, EDWARD S. "The Cognitive Spiral: Creative Thinking and Cognitive Processing." Journal of Creative Behavior 28, no. 4 (December 1994): 275–90. http://dx.doi.org/10.1002/j.2162-6057.1994.tb00734.x.

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17

Clough, M., J. Dobbing, J. Stankovich, A. Ternes, S. Kolbe, O. B. White, and J. Fielding. "Cognitive processing speed deficits in multiple sclerosis: Dissociating sensorial and motor processing changes from cognitive processing speed." Multiple Sclerosis and Related Disorders 38 (February 2020): 101522. http://dx.doi.org/10.1016/j.msard.2019.101522.

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18

Nagai, Masayoshi, Keiga Abe, Kei Fuji, and Tomoko Oe. "Embodied cognition: Interaction between bodily state and cognitive processing." Proceedings of the Annual Convention of the Japanese Psychological Association 78 (September 10, 2014): SS—039—SS—039. http://dx.doi.org/10.4992/pacjpa.78.0_ss-039.

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19

Panchal, Priyanka, Alexander Kaltenboeck, and Catherine J. Harmer. "Cognitive emotional processing across mood disorders." CNS Spectrums 24, no. 1 (January 15, 2019): 54–63. http://dx.doi.org/10.1017/s109285291800130x.

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While impairments in cognitive emotional processing are key to the experience of mood disorders, little is understood of their shared and distinct features across major depressive disorder (MDD) and bipolar disorder (BD). In this review, we discuss the similarities and differences in abnormal emotional processing associated with mood disorders across the cognitive domains of perception, attention, memory, and reward processing, with a particular focus on how these impairments relate to the clinical profile of the disorders. We consider behavioral and neuroimaging evidence, especially that of the growing consensus surrounding mood-congruent biases in cognition, in combination with state- and trait-related characteristics in an attempt to provide a more comprehensive and translational overview of mood disorders. Special consideration is given to the shared phenomenon of mood instability and its role as a potential transdiagnostic marker across the prodrome and maintenance of mood disorders.
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20

Smith, Aaron, Jeff Burns, Jim Backes, Cheryl Gibson, and Matthew Taylor. "Peripheral Insulin Resistance Is Unrelated to Cognition in Highly Educated, Cognitively Normal Adults." Current Developments in Nutrition 5, Supplement_2 (June 2021): 927. http://dx.doi.org/10.1093/cdn/nzab049_040.

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Abstract Objectives Peripheral insulin resistance (IR) and impaired glucose metabolism increase the risk for cognitive decline. However, data looking at peripheral IR's relationship with cognition in cognitively normal adults is limited. This study aimed to assess the relationship between peripheral IR and tests of executive function, attention, and processing speed in cognitively normal older adults using a novel IR measure. Methods Baseline data from 81 cognitively normal older adults participating in a nutrition intervention study (Nutrition Interventions for Cognitive Enhancement study; NICE study) were analyzed. Fasting blood draws were attained, and peripheral IR was measured using Quest Diagnostics’ Cardio IQ Insulin Resistance Panel (Test Code: 36,509). The NIH Toolbox Cognition Battery was conducted by a trained psychometrician. Executive function was measured by the Flanker Inhibitory Control and Dimensional Change Card Sort tests. The Flanker test also measures attention. Processing Speed was measured by the Pattern Comparison Processing Speed test. We constructed ordinary least squares regression models to assess IR's relationships with the individual cognitive tests, including age, education, and gender as covariates. Statistical analyses were performed using R (v. 3.6.2; R Foundation, Vienna, Austria). Statistical significance was set at p < 0.05. Results Participants were 83% female and had a mean age of 72 ± 4.9 years. The mean IR score was 29.9 ± 29.83. There was no statistically significant relationships with IR and any of the cognitive tests: Flanker Inhibitory Control (β = -0.03, P = 0.12), Dimensional Change Card Sort (β = -0.03, P = 0.16), Pattern Comparison Processing Speed (β = -0.10, P = 0.15). Conclusions There were no statistically significant relationships between IR scores and performance on the different cognitive tests. Although the tests were not significantly correlated with IR scores, directionality of the relationships indicated trend for higher IR being related to poorer scores. Consequently, maintaining insulin sensitivity with healthy lifestyle choices may be important for healthy brain aging. Future analyses with a larger sample size will be more informative for understanding the relationship between IR and various cognitive tests at baseline and over time. Funding Sources National Institutes on Aging.
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Cárdenas, José, María J. Blanca, Fernando Carvajal, Sandra Rubio, and Carmen Pedraza. "Emotional Processing in Healthy Ageing, Mild Cognitive Impairment, and Alzheimer’s Disease." International Journal of Environmental Research and Public Health 18, no. 5 (March 9, 2021): 2770. http://dx.doi.org/10.3390/ijerph18052770.

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Emotional processing, particularly facial expression recognition, is essential for social cognition, and dysfunction may be associated with poor cognitive health. In pathological ageing conditions, such as mild cognitive impairment (MCI) and Alzheimer’s disease (AD), in which cognitive impairments are present, disturbed emotional processing and difficulty with social interactions have been documented. However, it is unclear how pathological ageing affects emotional processing and human social behaviour. The aim of this study is to provide insight into how emotional processing is affected in MCI and AD and whether this capacity can constitute a differentiating factor allowing the preclinical diagnosis of both diseases. For this purpose, an ecological emotional battery adapted from five subsets of the Florida Affect Battery was used. Given that emotion may not be separated from cognition, the affect battery was divided into subtests according to cognitive demand, resulting in three blocks. Our results showed that individuals with MCI or AD had poorer performance on the emotional processing tasks, although with different patterns, than that of controls. Cognitive demand may be responsible for the execution patterns of different emotional processing tests. Tasks with moderate cognitive demand are the most sensitive for discriminating between two cognitive impairment entities. In summary, emotional processing tasks may aid in characterising the neurocognitive deficits in MCI or AD. Additionally, identifying these deficits may be useful for developing interventions that specifically target these emotional processing problems.
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Grässler, Bernhard, Anita Hökelmann, and Richard Halti Cabral. "Resting heart rate variability as a possible marker of cognitive decline." Kinesiology 52, no. 1 (2020): 72–84. http://dx.doi.org/10.26582/k.52.1.9.

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Cognition is a major subject to be addressed nowadays due to the increasing number of cognitively affected people in most societies. Because of a lack of pharmaceutical therapies treating cognitive decline, its indicators should be diagnosed before it becomes prevalent. Scientific evidence indicates a relationship between cognition and the nervous system, especially its autonomic part. Heart rate variability (HRV) as an indicator of the autonomic nervous system functioning has been studied as a biological marker for the evaluation of cognitive performance. Therefore, HRV is a possible indicator of cognitive impairment. The aim was to provide a systematic literature review about the association between resting HRV and the cognitive performance. Five cognitive functions were analysed separately: executive functions, memory and learning, language abilities, visuospatial functioning, and processing speed. Furthermore, the global cognitive function evaluated with cognitive test batteries was considered too. An electronic database search was conducted with five databases. Three search fields comprised HRV, cognitive performance, and adult subjects. The final dataset consisted of 27 articles. Significant correlations in each cognitive function were found, except for processing speed, suggesting a positive association between resting HRV and cognitive performance. Mechanisms underlying this association between cardiovascular health and cognition are discussed. For the future, HRV could be used in diagnostics as an indicator of cognitive impairment before symptoms of dementia get apparent. With a timely diagnosis, preventative tools could be initiated at an early stage of dementia.
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Pan, Yuqiao. "Research on the Influence of Advertising Content on Consumer Purchasing Behavior Based on Elaboration Likelihood Model (ELM)." Journal of Education, Humanities and Social Sciences 27 (March 5, 2024): 439–44. http://dx.doi.org/10.54097/mk0f1454.

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Advertising content is the foundation of advertising. Advertising content contains a lot of information and experience to convey to the consumer. How consumers deal with advertising content cognitively will affect the effectiveness of marketing personnel in delivering advertisements to consumers and lead to changes in purchasing behavior. This literature review studies the cognitive processing of consumers' four types of advertising content (informational advertising, comparative advertising, emotional advertising, social responsibility advertising) based on Elaboration Likelihood Model (ELM), and studies their impact on consumers' purchasing behavior. ELM points out two cognitive processing routes, namely the central route and the peripheral route. According to research, informational advertising and comparative advertising use the cognitive processing approach of the center route. Emotional advertising and social responsibility advertising are processed by consumers using peripheral routes. The research finds that informational advertising and comparative advertising participate in the cognitive processing of consumers through the central route. The characteristics and information about the product in the advertising content will affect the cognition, and ultimately affect the consumer's purchase intention. Emotional advertising and social responsibility advertising participate in the process through the peripheral route. These ads can influence consumers' impressions of brands and, ultimately, purchase intentions.
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Wolfe, Christopher R. "Cognitive technologies for gist processing." Behavior Research Methods 38, no. 2 (May 2006): 183–89. http://dx.doi.org/10.3758/bf03192767.

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Koehn, J. D., J. Dickinson, and D. Goodman. "Cognitive Demands of Error Processing." Psychological Reports 102, no. 2 (April 2008): 532–38. http://dx.doi.org/10.2466/pr0.102.2.532-538.

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This study used a dual-task methodology to assess attention demands associated with error processing during an anticipation-timing task. A difference was predicted in attention demands during feedback on trials with correct responses and errors. This was addressed by requiring participants to respond to a probe reaction-time stimulus after augmented feedback presentation. 16 participants (8 men, 8 women) completed two phases, the reaction time task only and the anticipation-timing task with the probe RT task. False feedback indicating error and a financial reward manipulation were used to increase relevance of errors. Data supported the hypothesis that error processing is associated with higher cognitive demands than processing feedback denoting a correct response. Individuals responded with quicker probe reaction times during presentation of feedback on correct trials than on error trials. These results are discussed with respect to the cognitive processes which might occur during error processing and their role in motor learning.
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Rakover and Cahlon. "Cognitive Processing of Scrambled Faces." American Journal of Psychology 126, no. 2 (2013): 235. http://dx.doi.org/10.5406/amerjpsyc.126.2.0235.

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Strupp, Barbara J., Herbert Weingartner, Walter Kaye, and Harry Gwirtsman. "Cognitive Processing in Anorexia nervosa." Neuropsychobiology 15, no. 2 (1986): 89–94. http://dx.doi.org/10.1159/000118248.

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Reynolds, Sharon B., and Joan Hart. "Cognitive Mapping and Word Processing." Journal of Experimental Education 58, no. 4 (July 1990): 273–79. http://dx.doi.org/10.1080/00220973.1990.10806541.

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Brewin, Chris R. "Cognitive processing of adverse experiences." International Review of Psychiatry 8, no. 4 (January 1996): 333–39. http://dx.doi.org/10.3109/09540269609051548.

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Matthews, Douglas B., Phillip J. Best, Aaron M. White, Jim L. Vandergriff, and Peter E. Simson. "Ethanol Impairs Spatial Cognitive Processing." Current Directions in Psychological Science 5, no. 4 (August 1996): 111–15. http://dx.doi.org/10.1111/1467-8721.ep11452772.

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Jun Ma, G. Y. Li, and Biing Hwang Juang. "Signal Processing in Cognitive Radio." Proceedings of the IEEE 97, no. 5 (May 2009): 805–23. http://dx.doi.org/10.1109/jproc.2009.2015707.

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Principe, Jose C., and Rakesh Chalasani. "Cognitive Architectures for Sensory Processing." Proceedings of the IEEE 102, no. 4 (April 2014): 514–25. http://dx.doi.org/10.1109/jproc.2014.2307023.

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de Araujo, Ivan E., Edmund T. Rolls, Maria Inés Velazco, Christian Margot, and Isabelle Cayeux. "Cognitive Modulation of Olfactory Processing." Neuron 46, no. 4 (May 2005): 671–79. http://dx.doi.org/10.1016/j.neuron.2005.04.021.

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Schweizer, Karl. "Preattentive processing and cognitive ability." Intelligence 29, no. 2 (March 2001): 169–86. http://dx.doi.org/10.1016/s0160-2896(00)00049-0.

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Kardash, Carol Anne M., Jeanne T. Amlund, Raymond W. Kulhavy, and Geraldine C. Ellison. "Bilingual referents in cognitive processing." Contemporary Educational Psychology 13, no. 1 (January 1988): 45–57. http://dx.doi.org/10.1016/0361-476x(88)90005-7.

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Willis, W. Grant. "Neurological models of cognitive processing." Learning and Individual Differences 1, no. 4 (January 1989): 371–83. http://dx.doi.org/10.1016/1041-6080(89)90019-8.

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Evers, Stefan. "Cognitive processing in cluster headache." Current Pain and Headache Reports 9, no. 2 (March 2005): 109–12. http://dx.doi.org/10.1007/s11916-005-0047-y.

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Tenorth, Moritz, Dominik Jain, and Michael Beetz. "Knowledge Processing for Cognitive Robots." KI - Künstliche Intelligenz 24, no. 3 (June 18, 2010): 233–40. http://dx.doi.org/10.1007/s13218-010-0044-0.

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Iglesias-Sarmiento, Valentín, and Manuel Deaño. "Cognitive Processing and Mathematical Achievement." Journal of Learning Disabilities 44, no. 6 (March 28, 2011): 570–83. http://dx.doi.org/10.1177/0022219411400749.

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This investigation analyzed the relation between cognitive functioning and mathematical achievement in 114 students in fourth, fifth, and sixth grades. Differences in cognitive performance were studied concurrently in three selected achievement groups: mathematical learning disability group (MLD), low achieving group (LA), and typically achieving group (TA). For this purpose, performance in verbal memory and in the PASS cognitive processes of planning, attention, and simultaneous and successive processing was assessed at the end of the academic course. Correlational analyses showed that phonological loop and successive and simultaneous processing were related to mathematical achievement at all three grades. Regression analysis revealed simultaneous processing as a cognitive predictor of mathematical performance, although phonological loop was also associated with higher achievement. Simultaneous and successive processing were the elements that differentiated the MLD group from the LA group. These results show that, of all the variables analyzed in this study, simultaneous processing was the best predictor of mathematical performance.
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Kutas, M. "Language processing and cognitive potentials." Electroencephalography and Clinical Neurophysiology 61, no. 3 (September 1985): S11. http://dx.doi.org/10.1016/0013-4694(85)90082-3.

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Bose, Pradip, and Alper Buyuktosunoglu. "Architectural Support for Cognitive Processing." IEEE Micro 37, no. 1 (January 2017): 6–7. http://dx.doi.org/10.1109/mm.2017.20.

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Higgs, Suzanne. "Cognitive processing of food rewards." Appetite 104 (September 2016): 10–17. http://dx.doi.org/10.1016/j.appet.2015.10.003.

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Guérin, Fanny, Bernadette Ska, and Sylvie Belleville. "Cognitive Processing of Drawing Abilities." Brain and Cognition 40, no. 3 (August 1999): 464–78. http://dx.doi.org/10.1006/brcg.1999.1079.

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Pachur, Thorsten, and Arndt Bröder. "Judgment: a cognitive processing perspective." Wiley Interdisciplinary Reviews: Cognitive Science 4, no. 6 (September 30, 2013): 665–81. http://dx.doi.org/10.1002/wcs.1259.

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Ciria, Alejandra, Guido Schillaci, Giovanni Pezzulo, Verena V. Hafner, and Bruno Lara. "Predictive Processing in Cognitive Robotics: A Review." Neural Computation 33, no. 5 (April 13, 2021): 1402–32. http://dx.doi.org/10.1162/neco_a_01383.

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Abstract Predictive processing has become an influential framework in cognitive sciences. This framework turns the traditional view of perception upside down, claiming that the main flow of information processing is realized in a top-down, hierarchical manner. Furthermore, it aims at unifying perception, cognition, and action as a single inferential process. However, in the related literature, the predictive processing framework and its associated schemes, such as predictive coding, active inference, perceptual inference, and free-energy principle, tend to be used interchangeably. In the field of cognitive robotics, there is no clear-cut distinction on which schemes have been implemented and under which assumptions. In this letter, working definitions are set with the main aim of analyzing the state of the art in cognitive robotics research working under the predictive processing framework as well as some related nonrobotic models. The analysis suggests that, first, research in both cognitive robotics implementations and nonrobotic models needs to be extended to the study of how multiple exteroceptive modalities can be integrated into prediction error minimization schemes. Second, a relevant distinction found here is that cognitive robotics implementations tend to emphasize the learning of a generative model, while in nonrobotics models, it is almost absent. Third, despite the relevance for active inference, few cognitive robotics implementations examine the issues around control and whether it should result from the substitution of inverse models with proprioceptive predictions. Finally, limited attention has been placed on precision weighting and the tracking of prediction error dynamics. These mechanisms should help to explore more complex behaviors and tasks in cognitive robotics research under the predictive processing framework.
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Davou, Bettina. "Interaction of Emotion and Cognition in the Processing of Textual Material." Meta 52, no. 1 (March 12, 2007): 37–47. http://dx.doi.org/10.7202/014718ar.

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Abstract Cognitive psychology and cognitive science have only recently come to acknowledge that human beings are not “pure” cognitive systems, and that emotions may be more than simply another form of cognition. This paper presents recent theoretical issues on the interaction of cognition with emotion, drawing on findings from evolutionary, neurobiological and cognitive research. These findings indicate that emotions have a fundamental and, often, universal importance for human cognitive functioning. Advanced cognitive processing, such as the processing required for text comprehension and translation, most of the time follows after a first, primary appraisal of the emotional impact of the information on the reader. This type of appraisal is momentary, non-conscious and non-cognitive, and is carried out by some system in the organism that functions with its own distinctive rules, different from those of the cognitive system. Emotional appraisal of the information sets the mode in which the organism (including its cognitive processes) will operate. Evidence suggests that negative emotions can instantly and non-consciously increase processing effort and time and decrease cognitive capacity, while on the other hand, positive emotions generally increase cognitive resources and expand attention and creativity. This implies that both cognitive processing of textual information, as well as its outcome, are influenced not only by the interpreters cognitive skill or by the emotional features of the text per se (the emotional impact that the writer has attempted to generate), but also (and perhaps most importantly) by the subjective emotional significance that the information has for each individual interpreter.
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47

Dinulescu, Stejara, Talha Alvi, David Rosenfield, Cecile S. Sunahara, Junghee Lee, and Benjamin A. Tabak. "Self-Referential Processing Predicts Social Cognitive Ability." Social Psychological and Personality Science 12, no. 1 (April 2, 2020): 99–107. http://dx.doi.org/10.1177/1948550620902281.

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Self-referential processing is critical for making sense of others. However, there remains surprisingly little research examining associations between behavioral assessments of self-referential processing and social cognition (i.e., tasks assessing one’s understanding of others’ thoughts and emotions). This study ( n = 396) examined this link by associating accuracy in a self-referential processing task with two assessments of social cognition (i.e., theory of mind and empathic accuracy). Exploratory analyses included an examination of the relationship between self-referential processing and autism-related traits, as well as depression symptoms, both of which have been previously associated with decreased social cognitive ability. Self-referential processing was positively related to performance in both social cognition tasks, and these relationships were not valence-specific or moderated by gender. Moreover, no associations were found between self-referential processing and autism-related traits or depressive symptoms. Our findings provide behavioral evidence for a relation between self-referential processing and social cognition.
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48

Rodrigues, Humberto, Antonio Jesús Molina-Fernandez2, Richard Lamb, Ikeseon Choi, and Tosha Owens. "Unravelling Student Learning: Exploring Nonlinear Dynamics in Science Education." International Journal of Psychology and Neuroscience 9, no. 3 (December 31, 2023): 118–37. http://dx.doi.org/10.56769/ijpn09311.

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Abstract Background: Traditional education research often relies on static linear approaches to measure dynamic systems involved in student information processing, overlooking the complexity of learning. Emerging research in related fields acknowledges the highly dynamic and nonlinear nature of cognitive states and information processing. Current educational research methods, predominantly based on quantitative and qualitative "snapshot" examinations, inadequately capture the dynamic and nonlinear aspects of cognitive processing during learning. Objective: This study aims to explore nonlinear dynamics as a means to describe and understand student learning processes. Methods: This study analyzed actions of 158,000 high school students in science-based immersive video games, specifically focusing on task completion within a virtual setting. Students aged 14-18, enrolled in Earth Science, Biology, Chemistry, and Physics programs, participated. Tasks, resembling Piagetian tasks, centered on volume conservation within a chemistry classroom context, employing the Student Task and Cognition Model (STAC-M) to emphasize computational cognition modeling. Results: The study tracked alterations in cognitive activations during information processing using derivatives, modeled through parameters from the authors' cognitive dataset. Employing the STAC-M model, achaotic attractors depicted convergence and sensitivity to initial conditions, reflecting cognitive associations and stability. Random data lacked the observed dynamic properties found in cognitive data, while bifurcation plots illustrated transitions from stability to chaos in cognitive processing pathways, highlighting the system's intricate nature. Conclusion: Modern science education explores beyond conventional assessments, acknowledging teaching methods' impact on students' cognitive processing. Achaotic attractors depict shifts from stable to unstable mental activities, highlighting the potential for diverse teaching approaches to minimize misconceptions and enable quicker transitions to responsive, stable learning states, aligning with educational objectives. Keyword: Cognition, Nonlinear Dynamics, Student Learning, Science Education.
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Aleman, André. "Neurocognitive Basis of Schizophrenia: Information Processing Abnormalities and Clues for Treatment." Advances in Neuroscience 2014 (February 9, 2014): 1–15. http://dx.doi.org/10.1155/2014/104920.

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Schizophrenia is a chronic and severe psychiatric disorder that affects all aspects of patients’ lives. Over the past decades, research applying methods from psychology and neuroscience has increasingly been zooming in on specific information processing abnormalities in schizophrenia. Impaired activation of and connectivity between frontotemporal, frontoparietal, and frontostriatal brain networks subserving cognitive functioning and integration of cognition and emotion has been consistently reported. Major issues in schizophrenia research concern the cognitive and neural basis of hallucinations, abnormalities in cognitive-emotional processing, social cognition (including theory of mind), poor awareness of illness, and apathy. Recent findings from cognitive neuroscience studies in these areas are discussed. The findings may have implications for treatment, for example, noninvasive neurostimulation of specific brain areas. Ultimately, a better understanding of the cognitive neuroscience of schizophrenia will pave the way for the development of effective treatment strategies.
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Singh, Balbir, Jordan Axt, Sean M. Hudson, Christopher Lee Mellinger, Bernd Wittenbrink, and Joshua Correll. "When Practice Fails to Reduce Racial Bias in the Decision to Shoot: The Case of Cognitive Load." Social Cognition 38, no. 6 (December 2020): 555–70. http://dx.doi.org/10.1521/soco.2020.38.6.555.

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Practice improves performance on a first-person shooter task (FPST), increasing accuracy and decreasing racial bias. But rather than simply promoting cognitively efficient processing, we argue that the benefits of practice on a difficult, cognitively demanding task like the FPST rely, at least in part, on resource-intensive, cognitively effortful processing. If practice-based improvements require cognitive resources, then cognitive load should compromise the value of practice by depriving trained participants of the cognitive resources on which they depend. This experiment shows that inducing cognitive load eliminates the benefits of training, leading to an increase in racial bias, as predicted.
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