scholarly journals Combinations of low-level and high-level neural processes can account for distinct patterns of context-dependent choice

2018 ◽  
Author(s):  
Mehran Spitmaan ◽  
Oihane Horno ◽  
Emily Chu ◽  
Alireza Soltani
Keyword(s):  
Risk Aversion ◽  
Cognitive Processes ◽  
Context Effects ◽  
Human Subjects ◽  
Risk Preference ◽  
Neural Adaptation ◽  
Low Level ◽  
Neural Representations ◽  
High Level ◽  
Decoy Effects

AbstractContext effects have been explained by either high-level cognitive processes or low-level neural adjustments but not their combination. It is currently unclear how these processes interact to shape individuals’ responses to context. Here, we used a large cohort of human subjects in experiments involving choice between two or three gambles in order to study the dependence of context effects on neural adaptation and individuals’ risk attitudes. We found no evidence that neural adaptation on long timescales (~100 trials) contributes to context effects. However, we identified two groups of subjects with distinct patterns of responses to decoys, both of which depended on individuals’ risk aversion. Subjects in the first group exhibited strong, consistent decoy effects and became more risk averse due to decoy presentation. In contrast, subjects in the second group did not show consistent decoy effects and became more risk seeking. The degree of change in risk aversion due to decoy presentation was positively correlated with the initial degrees of risk aversion. To explain these results and reveal underlying neural mechanisms, we developed a new model that incorporates both low- and high-level processes to fit individuals’ choice behavior. We found that observed decoy effects can be explained by a combination of adjustments in neural representations and competitive weighting of reward attributes, both of which depend on risk aversion but in opposite directions. Altogether, our results demonstrate how a combination of low- and high-level processes shapes multi-attribute choice, modulates overall risk preference, and explains distinct behavioral phenotypes.Significance statementA large body of experimental work has illustrated that the introduction of a new, and often irrelevant, option can influence preference among the existing options, a phenomenon referred to as context or decoy effects. Although context effects have been explained by high-level cognitive processes—such as comparisons and competitions between attributes—or low-level adjustments of neural representations, it is unclear how these processes interact to shape individuals’ responses to context. Here, we show that both high-level cognitive processes and low-level neural adjustments shift risk preference during choice between multiple options but in opposite directions. Moreover, we demonstrate that a combination of these processes can account for distinct patterns of context effects in human subjects.

scholarly journals Visual features influence thought content in the absence of overt semantic information

2019 ◽  
Author(s):  
Kathryn E Schertz ◽  
Omid Kardan ◽  
Marc Berman
Keyword(s):  
Cognitive Processes ◽  
Semantic Content ◽  
Visual Features ◽  
Edge Density ◽  
Low Level ◽  
Life Journey ◽  
Edge Content ◽  
High Level ◽  
Scene Identification ◽  
Selection Of

It has recently been shown that the perception of visual features of the environment can influence thought content. Both low-level (e.g., fractalness) and high-level (e.g., presence of water) visual features of the environment can influence thought content, in real-world and experimental settings where these features can make people more reflective and contemplative in their thoughts. It remains to be seen, however, if these visual features retain their influence on thoughts in the absence of overt semantic content, which could indicate a more fundamental mechanism for this effect. In this study, we removed this limitation, by creating scrambled edge versions of images, which maintain edge content from the original images but remove scene identification. Non-straight edge density is one visual feature which has been shown to influence many judgements about objects and landscapes, and has also been associated with thoughts of spirituality. We extend previous findings by showing that non-straight edges retain their influence on the selection of a “Spiritual & Life Journey” topic after scene identification removal. These results strengthen the implication of a causal role for the perception of low-level visual features on the influence of higher-order cognitive function, by demonstrating that in the absence of overt semantic content, low-level features, such as edges, influence cognitive processes.

scholarly journals The rhythms of predictive coding: pre-stimulus phase modulates the influence of shape perception on luminance judgments

2016 ◽  
Author(s):  
Biao Han ◽  
Rufin VanRullen
Keyword(s):  
Temporal Dynamics ◽  
Human Subjects ◽  
Predictive Coding ◽  
Shape Perception ◽  
Low Level ◽  
3D Shape ◽  
Coding Efficiency ◽  
Spatial Response ◽  
Left And Right ◽  
High Level

AbstractPredictive coding is an influential model emphasizing interactions between feedforward and feedback signals. Here, we investigated its temporal dynamics. Two gray disks with different versions of the same stimulus, one enabling predictive feedback (a 3D-shape) and one impeding it (random-lines), were simultaneously presented on the left and right of fixation. Human subjects judged the luminance of the two disks while EEG was recorded. Independently of the spatial response (left/right), we found that the choice of 3D-shape or random-lines as the brighter disk (our measure of post-stimulus predictive coding efficiency on each trial) fluctuated along with the pre-stimulus phase of two spontaneous oscillations: a ~5Hz oscillation in contralateral frontal electrodes and a ~16Hz oscillation in contralateral occipital electrodes. This pattern of results demonstrates that predictive coding is a rhythmic process, and suggests that it could take advantage of faster oscillations in low-level areas and slower oscillations in high-level areas.

Human brain activity during mental imagery exhibits signatures of inference in a hierarchical generative model

2018 ◽  
Author(s):  
Jesse Breedlove ◽  
Ghislain St-Yves ◽  
Cheryl Olman ◽  
Thomas Naselaris
Keyword(s):  
Mental Imagery ◽  
Human Subjects ◽  
Brain Activity ◽  
Generative Model ◽  
Formal Structure ◽  
Low Level ◽  
Visual Encoding ◽  
Visual Areas ◽  
High Level ◽  
Echo Effect

Humans have long wondered about the function of mental imagery and its relationship to vision. Although visual representations are utilized during imagery, the computations they subserve are unclear. Building on a theory that treats vision as inference about the causes of sensory stimulation in an internal generative model, we propose that mental imagery is inference about the sensory consequences of predicted or remembered causes. The relation between these complementary inferences yields a relation between the brain activity patterns associated with imagery and vision. We show that this relation has the formal structure of an echo that makes encoding of imagined stimuli in low-level visual areas resemble the encoding of seen stimuli in higher areas. To test for evidence of this echo effect we developed imagery encoding models—a new tool for revealing how imagined stimuli are encoded in brain activity. We estimated imagery encoding models from brain activity measured with fMRI while human subjects imagined complex visual stimuli, and then compared these to visual encoding models estimated from a matched viewing experiment. Consistent with an echo effect, imagery encoding models in low-level visual areas exhibited decreased spatial frequency preference and larger, more foveal receptive fields, thus resembling visual encoding models in high-level visual areas where imagery and vision appeared to be almost interchangeable. Our findings support an interpretation of mental imagery as a predictive inference that is conditioned on activity in high-level visual cortex, and is related to vision through shared dependence on an internal model of the visual world.

Inductive Logic Programming (ILP) and Reasoning by Analogy in Context of Embodied Robot Learning

2010 ◽  
Vol 2 (2) ◽  
pp. 64-73 ◽  
Author(s):  
Vesna Poprcova ◽  
Georgi Stojanov ◽  
Andrea Kulakov
Keyword(s):  
Logic Programming ◽  
Cognitive Processes ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Robot Learning ◽  
Low Level ◽  
Learning Speed ◽  
High Level ◽  
Reasoning By Analogy

The ability to reason by analogy is essential for many cognitive processes from low-level and high-level perception to categorization. Intuitively, the idea is to use what is already known to explain new observations that appear similar to old knowledge. In a sense, it is opposite of induction, where to explain the observations one comes up with new hypotheses/theories. Therefore, a system capable of both types of reasoning would be superior. In this paper, the authors present an overview of Inductive Logic Programming (ILP) systems that use reasoning by analogy and discuss the results of combining Analogical Prediction with an ILP system, showing that, for some cases, it is possible to improve significantly the learning speed of the ILP system. This paper will examine the problems that arise in the context of a physically embodied robot that tries to learn regularities in its environment.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

Determinants of Abuse of the Bodies of the Dead

2019 ◽  
pp. 71-77
Author(s):  
Margarita Khomyakova
Keyword(s):  
Low Income ◽  
Criminal Code ◽  
Value Judgments ◽  
Legal Norms ◽  
Low Level ◽  
Medical Institutions ◽  
Specific Object ◽  
The Subject ◽  
The Dead ◽  
High Level

The author analyzes definitions of the concepts of determinants of crime given by various scientists and offers her definition. In this study, determinants of crime are understood as a set of its causes, the circumstances that contribute committing them, as well as the dynamics of crime. It is noted that the Russian legislator in Article 244 of the Criminal Code defines the object of this criminal assault as public morality. Despite the use of evaluative concepts both in the disposition of this norm and in determining the specific object of a given crime, the position of criminologists is unequivocal: crimes of this kind are immoral and are in irreconcilable conflict with generally accepted moral and legal norms. In the paper, some views are considered with regard to making value judgments which could hardly apply to legal norms. According to the author, the reasons for abuse of the bodies of the dead include economic problems of the subject of a crime, a low level of culture and legal awareness; this list is not exhaustive. The main circumstances that contribute committing abuse of the bodies of the dead and their burial places are the following: low income and unemployment, low level of criminological prevention, poor maintenance and protection of medical institutions and cemeteries due to underperformance of state and municipal bodies. The list of circumstances is also open-ended. Due to some factors, including a high level of latency, it is not possible to reflect the dynamics of such crimes objectively. At the same time, identification of the determinants of abuse of the bodies of the dead will reduce the number of such crimes.

scholarly journals EXPRESS: Marketers Project Their Personal Preferences onto Consumers: Overcoming the Threat of Egocentric Decision Making

2021 ◽  
pp. 002224372199837
Author(s):  
Walter Herzog ◽  
Johannes D. Hattula ◽  
Darren W. Dahl
Keyword(s):  
Decision Making ◽  
Consumer Preferences ◽  
Research Question ◽  
Pilot Studies ◽  
Low Level ◽  
Marketing Managers ◽  
False Consensus Effect ◽  
False Consensus ◽  
High Level

This research explores how marketing managers can avoid the so-called false consensus effect—the egocentric tendency to project personal preferences onto consumers. Two pilot studies were conducted to provide evidence for the managerial importance of this research question and to explore how marketing managers attempt to avoid false consensus effects in practice. The results suggest that the debiasing tactic most frequently used by marketers is to suppress their personal preferences when predicting consumer preferences. Four subsequent studies show that, ironically, this debiasing tactic can backfire and increase managers’ susceptibility to the false consensus effect. Specifically, the results suggest that these backfire effects are most likely to occur for managers with a low level of preference certainty. In contrast, the results imply that preference suppression does not backfire but instead decreases false consensus effects for managers with a high level of preference certainty. Finally, the studies explore the mechanism behind these results and show how managers can ultimately avoid false consensus effects—regardless of their level of preference certainty and without risking backfire effects.

Exploring The Effect of Visual Information Degradation on Human Perception and Performance In A Human-Telerobot System

2020 ◽  
Vol 64 (1) ◽  
pp. 1302-1307
Author(s):  
Richard Stone ◽  
Minglu Wang ◽  
Thomas Schnieders ◽  
Esraa Abdelall
Keyword(s):  
Visual Information ◽  
Human Perception ◽  
Low Level ◽  
Hit Rate ◽  
Word Level ◽  
Level Of Automation ◽  
Significant Difference ◽  
Human Operators ◽  
And Performance ◽  
High Level

Human-robotic interaction system are increasingly becoming integrated into industrial, commercial and emergency service agencies. It is critical that human operators understand and trust automation when these systems support and even make important decisions. The following study focused on human-in-loop telerobotic system performing a reconnaissance operation. Twenty-four subjects were divided into groups based on level of automation (Low-Level Automation (LLA), and High-Level Automation (HLA)). Results indicated a significant difference between low and high word level of control in hit rate when permanent error occurred. In the LLA group, the type of error had a significant effect on the hit rate. In general, the high level of automation was better than the low level of automation, especially if it was more reliable, suggesting that subjects in the HLA group could rely on the automatic implementation to perform the task more effectively and more accurately.

scholarly journals The high-level benefits of low-level sandboxing

2020 ◽  
Vol 4 (POPL) ◽  
pp. 1-32 ◽  
Author(s):  
Michael Sammler ◽  
Deepak Garg ◽  
Derek Dreyer ◽  
Tadeusz Litak
Keyword(s):  
Low Level ◽  
High Level
Sign in / Sign up

Export Citation Format

Share Document