Acquisition of habitual visual attention and transfer to related tasks.

Psychon Bull Rev

Department of Psychology, University of Minnesota, S504 Elliott Hall, Minneapolis, MN, 55455, USA.

Published: June 2018

Extensive research has shown that statistical learning affects perception, attention, and action control; however, few studies have directly linked statistical learning with the formation of habits. Evidence that learning can induce a search habit has come from location probability learning, in which people prioritize locations frequently attended to in the past. Here, using an alternating training-testing procedure, we demonstrated that the initial attentional bias arises from short-term intertrial priming, whereas probability learning takes longer to emerge, first reaching significance in covert orienting (measured by reaction times) after about 48 training trials, and in overt orienting (measured by eye movements) after about 96 training trials. We further showed that location probability learning is persistent after training is discontinued, by transferring from a letter search task to a scene search task-emulating another characteristic feature of habits. By identifying the onset of probability learning and investigating its task specificity, this study provides evidence that probability cuing can induce habitual spatial attention.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5764836PMC
http://dx.doi.org/10.3758/s13423-017-1341-5DOI Listing

Publication Analysis

Top Keywords

probability learning
16
statistical learning
8
location probability
8
orienting measured
8
training trials
8
learning
7
probability
5
acquisition habitual
4
habitual visual
4
visual attention
4

Similar Publications

Deep learning model to diagnose cardiac amyloidosis from haematoxylin/eosin-stained myocardial tissue.

Eur Heart J Imaging Methods Pract

January 2025

Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.

Aims: Amyloid deposition in myocardial tissue is a definitive feature for diagnosing cardiac amyloidosis, though less invasive imaging modalities such as bone tracer cardiac scintigraphy and cardiac magnetic resonance imaging have been established as first steps for its diagnosis. This study aimed to develop a deep learning model to support the diagnosis of cardiac amyloidosis from haematoxylin/eosin (HE)-stained myocardial tissue.

Methods And Results: This single-centre retrospective observational study enrolled 166 patients who underwent myocardial biopsies between 2008 and 2022, including 76 patients diagnosed with cardiac amyloidosis and 90 with other diagnoses.

View Article and Find Full Text PDF

Placebo effect represents a serious confounder for the assessment of treatment effect to the extent that it has become increasingly difficult to develop antidepressant medications appropriate for outperforming placebo. Treatment effect in randomized, placebo-controlled trials, is usually estimated by the mean baseline adjusted difference of treatment response in active and placebo arms and is function of treatment-specific and non-specific effects. The non-specific treatment effect varies subject by subject conditional to the individual propensity to respond to placebo.

View Article and Find Full Text PDF

Optimizing gelation time for cell shape control through active learning.

Soft Matter

January 2025

Department of Mechanical Engineering and Materials Science, Yale University, New Haven, CT 06510, USA.

Hydrogels are popular platforms for cell encapsulation in biomedicine and tissue engineering due to their soft, porous structures, high water content, and excellent tunability. Recent studies highlight that the timing of network formation can be just as important as mechanical properties in influencing cell morphologies. Conventionally, time-dependent properties can be achieved through multi-step processes.

View Article and Find Full Text PDF

Background And Objectives: Accurate classification of lymphadenopathy is essential for determining the pathological nature of lymph nodes (LNs), which plays a crucial role in treatment selection. The biopsy method is invasive and carries the risk of sampling failure, while the utilization of non-invasive approaches such as ultrasound can minimize the probability of iatrogenic injury and infection. With the advancement of artificial intelligence (AI) and machine learning, the diagnostic efficiency of LNs is further enhanced.

View Article and Find Full Text PDF

Best current practice in the analysis of dynamic contrast enhanced (DCE)-MRI is to employ a voxel-by-voxel model selection from a hierarchy of nested models. This nested model selection (NMS) assumes that the observed time-trace of contrast-agent (CA) concentration within a voxel, corresponds to a singular physiologically nested model. However, admixtures of different models may exist within a voxel's CA time-trace.

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!