Working memory involved in predicting future outcomes based on past experiences.

Brain Cogn

Department of Psychology, University of Wisconsin, 1202 West Johnson Street, Madison, WI 53706-1696, USA.

Published: February 2008

Deficits in working memory have been shown to contribute to poor performance on the Iowa Gambling Task [IGT: Bechara, A., & Martin, E.M. (2004). Impaired decision making related to working memory deficits in individuals with substance addictions. Neuropsychology, 18, 152-162]. Similarly, a secondary memory load task has been shown to impair task performance [Hinson, J., Jameson, T. & Whitney, P. (2002). Somatic markers, working memory, and decision making. Cognitive, Affective, & Behavioural Neuroscience, 2, 341-353]. In the present study, we investigate whether the latter findings were due to increased random responding [Franco-Watkins, A. M., Pashler, H., & Rickard, T. C. (2006). Does working memory load lead to greater impulsivity? Commentary on Hinson, Jameson, and Whitney's (2003). Journal of Experimental Psychology: Learning, Memory & Cognition, 32, 443-447]. Participants were tested under Low Working Memory (LWM; n=18) or High Working Memory (HWM; n=17) conditions while performing the Reversed IGT in which punishment was immediate and reward delayed [Bechara, A., Dolan, S., & Hindes, A. (2002). Decision making and addiction (part II): Myopia for the future or hypersensitivity to reward? Neuropsychologia, 40, 1690-1705]. In support of a role for working memory in emotional decision making, compared to the LWM condition, participants in the HWM condition made significantly greater number of disadvantageous selections than that predicted by chance. Performance by the HWM group could not be fully explained by random responding.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.bandc.2007.05.006DOI Listing

Publication Analysis

Top Keywords

working memory
32
decision making
16
memory
9
working
8
memory load
8
random responding
8
memory involved
4
involved predicting
4
predicting future
4
future outcomes
4

Similar Publications

Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed users. This study aims to enhance spectrum sensing in multi-user cooperative cognitive radio systems by leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. A novel multi-user cooperative spectrum sensing model is developed, utilizing CNN's local feature extraction capability and LSTM's advantage in handling sequential data to optimize sensing accuracy and efficiency.

View Article and Find Full Text PDF

Accurate energy demand forecasting is critical for efficient energy management and planning. Recent advancements in computing power and the availability of large datasets have fueled the development of machine learning models. However, selecting the most appropriate features to enhance prediction accuracy and robustness remains a key challenge.

View Article and Find Full Text PDF

Personalized recommendation system to handle skin cancer at early stage based on hybrid model.

Network

January 2025

Computer Science and Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India.

Skin cancer is one of the most prevalent and harmful forms of cancer, with early detection being crucial for successful treatment outcomes. However, current skin cancer detection methods often suffer from limitations such as reliance on manual inspection by clinicians, inconsistency in diagnostic accuracy, and a lack of personalized recommendations based on patient-specific data. In our work, we presented a Personalized Recommendation System to handle Skin Cancer at an early stage based on Hybrid Model (PRSSCHM).

View Article and Find Full Text PDF

Explaining cognitive function in multiple sclerosis through networks of grey and white matter features: a joint independent component analysis.

J Neurol

January 2025

NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.

Cognitive impairment (CI) in multiple sclerosis (MS) is only partially explained by whole-brain volume measures, but independent component analysis (ICA) can extract regional patterns of damage in grey matter (GM) or white matter (WM) that have proven more closely associated with CI. Pathology in GM and WM occurs in parallel, and so patterns can span both. This study assessed whether joint-ICA of GM and WM features better explained cognitive function compared to single-tissue ICA.

View Article and Find Full Text PDF

Alzheimer's disease is a complex neurodegenerative disease characterized by progressive decline in cognitive function and behaviour. Ginger is the rhizome of the plant Zingiber officinale Roscoe, has been an important ingredient of many Ayurveda formulations to treat neurological disorders. The present study aims to estimate the variation of 6-gingerol content in nine different ginger samples collected from Manipur, India, investigate the neuroprotective potential of the most potent ginger sample against scopolamine-induced cognitively impaired mice, and validate the therapeutic claim by molecular docking analysis.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!