The Reasoned Action Model

2012 ◽  
Vol 640 (1) ◽  
pp. 58-80 ◽  
Author(s):  
James Jaccard
Keyword(s):  
Short Term Memory ◽  
Reasoned Action ◽  
Full Range ◽  
Explanatory Power ◽  
Future Research ◽  
Short Term ◽  
Individual Level ◽  
Reasoned Action Model ◽  
The Social ◽  
Action Model

The reasoned action model (RAM) of Fishbein and Ajzen has been highly influential in the social and health sciences. This article describes three areas for future research that should expand its explanatory power. One area of research focuses on an idiographic RAM that encourages researchers to pursue the estimation of RAM parameters on a per-individual level rather than through traditional nomothetic modeling. The second area encourages scientists to develop a split-second RAM, that is, a RAM that can provide perspectives on the split-second decisions people make in everyday life. Integration of the RAM with models of working (short-term) memory is stressed. The third area for research encourages scientists to develop a multioption RAM that incorporates and is responsive to the choices that people make when confronted with multiple alternatives. This perspective stresses the need to apply the RAM to the full range of behavioral options that are available to people as they contemplate performing one behavior versus another. Perspectives for theoretical advancement in each area are developed.

scholarly journals Multiple Pedestrians and Vehicles Tracking in Aerial Imagery Using a Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1953
Author(s):  
Seyed Majid Azimi ◽  
Maximilian Kraus ◽  
Reza Bahmanyar ◽  
Peter Reinartz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Object Tracking ◽  
Short Term Memory ◽  
Aerial Imagery ◽  
Future Research ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-resolution aerial imagery by intensive evaluation of a number of traditional and Deep Learning based Single- and Multi-Object Tracking methods. We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for more accurate and stable tracking. Moreover, we investigate the influence of the Squeeze-and-Excitation layers and Online Hard Example Mining on the performance of AerialMPTNet. To the best of our knowledge, we are the first to use these two for regression-based Multi-Object Tracking. Additionally, we studied and compared the L1 and Huber loss functions. In our experiments, we extensively evaluate AerialMPTNet on three aerial Multi-Object Tracking datasets, namely AerialMPT and KIT AIS pedestrian and vehicle datasets. Qualitative and quantitative results show that AerialMPTNet outperforms all previous methods for the pedestrian datasets and achieves competitive results for the vehicle dataset. In addition, Long Short-Term Memory and Graph Convolutional Neural Network modules enhance the tracking performance. Moreover, using Squeeze-and-Excitation and Online Hard Example Mining significantly helps for some cases while degrades the results for other cases. In addition, according to the results, L1 yields better results with respect to Huber loss for most of the scenarios. The presented results provide a deep insight into challenges and opportunities of the aerial Multi-Object Tracking domain, paving the way for future research.

scholarly journals Dissociating sensory and cognitive theories of harmony perception through computational modeling

2018 ◽  
Author(s):  
Peter Harrison ◽  
Marcus Thomas Pearce
Keyword(s):  
Statistical Learning ◽  
Short Term Memory ◽  
Syntactic Structure ◽  
Explanatory Power ◽  
Memory Models ◽  
Acoustic Properties ◽  
Probabilistic Prediction ◽  
Short Term ◽  
Cognitive Approach ◽  
Term Memory

Two approaches exist for explaining harmonic expectation. The sensory approach claims that harmonic expectation is a low-level process driven by sensory responses to acoustic properties of musical sounds. Conversely, the cognitive approach describes harmonic expectation as a high-level cognitive process driven by the recognition of syntactic structure learned through experience. Many previous studies have sought to distinguish these two hypotheses, largely yielding support for the cognitive hypothesis. However, subsequent re-analysis has shown that most of these results can parsimoniously be explained by a computational model from the sensory tradition, namely Leman’s (2000) model of auditory short- term memory (Bigand, Delbé, Poulin-Charronnat, Leman, & Tillmann, 2014). In this research we re-examine the explanatory power of auditory short-term memory models, and compare them to a new model in the Information Dynamics Of Music (IDyOM) tradition, which simulates a cognitive theory of harmony perception based on statistical learning and probabilistic prediction. We test the ability of these models to predict the surprisingness of chords within chord sequences (N = 300), as reported by a sample group of university undergraduates (N = 50). In contrast to previous studies, which typically use artificial stimuli composed in a classical idiom, we use naturalistic chord sequences sampled from a large dataset of popular music. Our results show that the auditory short-term memory models have remarkably low explanatory power in this context. In contrast, the new statistical learning model predicts surprisingness ratings relatively effectively. We conclude that auditory short-term memory is insufficient to explain harmonic expectation, and that cognitive processes of statistical learning and probabilistic prediction provide a viable alternative.

scholarly journals Memory in Linguistic Narrative vs. Postmodern Multitasked Multimedia Art Memory

2018 ◽  
Vol 1 (2) ◽  
pp. 101
Author(s):  
Vesna Srnic ◽  
Emina Berbic Kolar ◽  
Igor Ilic
Keyword(s):  
Short Term Memory ◽  
Social Role ◽  
Procedural Memory ◽  
Short Term ◽  
The Social ◽  
Multimedia Art ◽  
The Media

<p><em>In addition to the well-known classification of long-term and short-term memory, we are also interested in distinguishing episodic, semantic and procedural memory in the areas of linguistic narrative and multimedial semantic deconstruction in postmodernism. We compare the liveliness of memorization in literary tradition and literature art with postmodernist divisions and reverberations of traditional memorizations through human multitasking and performative multimedia art, as well as formulate the existence of creative, intuitive and superhuman paradigms.</em></p><em>Since the memory can be physical, psychological or spiritual, according to neurobiologist Dr. J. Bauer (Das Gedächtnis des Körpers, 2004), the greatest importance for memorizing has the social role of collaboration, and consequently the personal transformation and remodelling of genomic architecture, yet the media theorist Mark Hansen thinks technology brings different solutions of framing function (Hansen, 2000). We believe that postmodern deconstruction does not necessarily damage memory, especially in the field of human multitasking that utilizes multimedia performative art by means of anthropologization of technology, thereby enhancing artistic and affective pre&amp;post-linguistic experience while unifying technology and humans through intuitive empathy in society.</em>

scholarly journals The importance of financial recession for mental health among students: short- and long-term analyses from an ecosocial perspective

2019 ◽  
Vol 8 (2) ◽  
Author(s):  
Anne Hammarström ◽  
Pekka Virtanen
Keyword(s):  
Mental Health ◽  
Labour Market ◽  
Somatic Symptoms ◽  
High Response Rate ◽  
Future Research ◽  
Short Term ◽  
Individual Level ◽  
Cohort Differences ◽  
Short And Long Term

Background and aim: Referring to the ecosocial theory and utilising the ‘natural experiment’ setting provided by the global recession at the beginning of 1990s, the aim of our study was to analyse the short- and long-term associations between trade and mental health in young students followed until mid-adulthood. Method: The study was based on two prospective cohort studies, the older and the younger Northern Swedish Cohort which both consisted of all pupils in a middle-sized industrial town in Northern Sweden. At age 21, the younger cohort entered the labour market during the deep recession of the early 1990s, while the older cohort entered the labour market during the boom of the 1980s. Both cohorts were followed up with a high response rate in mid adulthood. For this study, all students were selected at age 21. Results: At age 21, those who studied during recession had more depressive and functional somatic symptoms than those who studied during boom. The cohort differences did not remain over age: by the follow-up in early middle age the differences between the cohorts were non-significant, most notably due to decreased depressive symptoms in the younger cohort and increase of functional somatic symptoms in the older cohort. Conclusions: The short-term mental health consequences of the business cycle seem to be more extensive than limited only to those who are unemployed, even though the possible long-term consequences seem to be more complex. Thus, the macrolevel had a great short-term impact on the individual level in relation to the microlevel setting of university/school. The chronosystem was also of major importance. Future research would benefit from taking the context into account.

Introduction to the special issue on syntax and verbal short term/working memory in developmental disorders

2020 ◽  
Vol 40 (4) ◽  
pp. 335-340
Author(s):  
Stavroula Stavrakaki
Keyword(s):  
Working Memory ◽  
Verbal Memory ◽  
Developmental Disorders ◽  
Short Term Memory ◽  
Verbal Working Memory ◽  
Future Research ◽  
Special Issue ◽  
Short Term ◽  
Main Research ◽  
The Status

In the field of developmental disorders, two main research approaches, the linguistic approach and the cognitive psychology of memory approach, have been used to a great extent independently. Recently, researchers have investigated simultaneously the language and verbal memory abilities – especially verbal short term memory (VSTM) and verbal working memory (VWM) – of individuals with developmental disorders. The present Special Issue contributes to the discussion of the relation between VSTM/VWM and syntax in developmental disorders. It reports empirical data from six studies on the relation between verbal memory and syntax in different disorders and languages, and it raises theoretical issues concerning these cognitive mechanisms. It concludes with three commentary articles where the authors raise crucial theoretical and methodological issues: they pose questions concerning the status of VSTM/VWM and syntax, and spell out directions for future research in this field.

Technology Adoption, Expectancy Value and Prediction Models

2008 ◽  
pp. 1294-1302 ◽  
Author(s):  
Maria Teresa Martín ◽  
Maria Victoria Román ◽  
Manuel Recio
Keyword(s):  
Social Psychology ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Reasoned Action ◽  
Value Theory ◽  
Expectancy Value Theory ◽  
Expectancy Value ◽  
Reasoned Action Model ◽  
Action Model ◽  
Acceptance Model

During the last few decades, various theoretical developments have been carried out with a view to describing the characteristic and distinct behavioral process that lies under any adoption of technological services and products. These developments are based mainly on the Social Psychology approach. There are three extensive theories within the field of Social Psychology whose ultimate purpose has been to define the internal psychological factors that explain human behavior: the expectancy-value theory, the cognitive dissonance theory, and the self-perception theory. While the expectancy-value theory has been widely used in the research of adoption and usage of information systems, the other two theories have been less recognized. Of all expectancy-value theory models, we should draw our attention to the reasoned action model (Azjen & Fishbein, 1980), because it underlies many of the studies on usage of technology. The planned behavior model (Azjen, 1985, 1991) represents a reformulation of the reasoned action model, justified by the existence of conducts that, albeit in part, a person cannot voluntarily keep under control. A rough description of both models is presented in this chapter, inasmuch as they served as a basis for the construction of the technology acceptance model (Davis, 1989; Davis, Bagozzi & Warshaw, 1989), known as one of the main models for the technology readiness concept. The technology acceptance model seems to possess a similar or even better explicating power than its predecessors (Davis et al., 1989; Mathieson, 1991; Taylor & Todd, 1995a; Chau & Hu, 2002).

Where Are They Going? Predicting Human Behaviors in Crowded Scenes

2021 ◽  
Vol 17 (4) ◽  
pp. 1-19
Author(s):  
Bo Zhang ◽  
Rui Zhang ◽  
Niccolo Bisagno ◽  
Nicola Conci ◽  
Francesco G. B. De Natale ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Behavior Prediction ◽  
Short Term ◽  
Memory Module ◽  
Term Memory ◽  
The Social ◽  
Spatial Configurations ◽  
Long Short Term Memory ◽  
Crowd Behaviors ◽  
Crowded Scenes

In this article, we propose a framework for crowd behavior prediction in complicated scenarios. The fundamental framework is designed using the standard encoder-decoder scheme, which is built upon the long short-term memory module to capture the temporal evolution of crowd behaviors. To model interactions among humans and environments, we embed both the social and the physical attention mechanisms into the long short-term memory. The social attention component can model the interactions among different pedestrians, whereas the physical attention component helps to understand the spatial configurations of the scene. Since pedestrians’ behaviors demonstrate multi-modal properties, we use the generative model to produce multiple acceptable future paths. The proposed framework not only predicts an individual’s trajectory accurately but also forecasts the ongoing group behaviors by leveraging on the coherent filtering approach. Experiments are carried out on the standard crowd benchmarks (namely, the ETH, the UCY, the CUHK crowd, and the CrowdFlow datasets), which demonstrate that the proposed framework is effective in forecasting crowd behaviors in complex scenarios.

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