scholarly journals Motivation and Cognitive Control in Depression

2018 ◽  
Author(s):  
Ivan Grahek ◽  
Amitai Shenhav ◽  
Sebastian Musslick ◽  
Ruth M. Krebs ◽  
Ernst H.W. Koster
Keyword(s):  
Decision Making ◽  
Cognitive Control ◽  
Cognitive Processes ◽  
Cognitive Neuroscience ◽  
Computational Models ◽  
Cognitive Impairments ◽  
Control Allocation ◽  
Mechanistic Models ◽  
Mechanistic Understanding

AbstractDepression is linked to deficits in cognitive control and a host of other cognitive impairments arise as a consequence of these deficits. Despite of their important role in depression, there are no mechanistic models of cognitive control deficits in depression. In this paper we propose how these deficits can emerge from the interaction between motivational and cognitive processes. We review depression-related impairments in key components of motivation along with new cognitive neuroscience models that focus on the role of motivation in the decision-making about cognitive control allocation. Based on this review we propose a unifying framework which connects motivational and cognitive control deficits in depression. This framework is rooted in computational models of cognitive control and offers a mechanistic understanding of cognitive control deficits in depression.

scholarly journals Preprint: Cognitive Control in Depression: Toward Clinical Models Informed by Cognitive Neuroscience

2017 ◽  
Author(s):  
Ivan Grahek ◽  
Jonas Everaert ◽  
Ruth Krebs ◽  
Ernst H. W. Koster
Keyword(s):  
Cognitive Control ◽  
Clinical Research ◽  
Cognitive Neuroscience ◽  
Tripartite Model ◽  
Cognitive Dysfunctions ◽  
Contemporary Research ◽  
Recent Advances ◽  
Clinical Models ◽  
Mechanistic Understanding

Cognitive control dysfunctions are thought to contribute to the onset and maintenance of depression. However, the causes and nature of these dysfunctions remain unknown. Here, we critically review contemporary research on cognitive control in depression. We identify three main conceptual issues in this field: 1) uncritical use of the tripartite model; 2) reliance on descriptive explanations; and 3) lack of integration with emotional and motivational impairments. Recent advances in cognitive neuroscience offer possibilities to resolve these issues. We review this progress focusing on the ability to detect the need for control, the role of motivation, and the flexibility-stability balance. We propose that depression-related dysfunctions arise from issues in detecting when, how, and for how long to engage in goal-oriented processing. In conclusion, we argue that integrating advances in cognitive neuroscience into clinical research can help to move from a descriptive towards a more mechanistic understanding of cognitive dysfunctions in depression.

scholarly journals Cognitive Control in Depression: Toward Clinical Models Informed by Cognitive Neuroscience

2018 ◽  
Vol 6 (4) ◽  
pp. 464-480 ◽  
Author(s):  
Ivan Grahek ◽  
Jonas Everaert ◽  
Ruth M. Krebs ◽  
Ernst H. W. Koster
Keyword(s):  
Cognitive Control ◽  
Clinical Research ◽  
Cognitive Neuroscience ◽  
Tripartite Model ◽  
Cognitive Dysfunctions ◽  
Contemporary Research ◽  
Recent Advances ◽  
Clinical Models ◽  
Mechanistic Understanding

Cognitive control dysfunctions are thought to contribute to the onset and maintenance of depression. However, the causes and nature of these dysfunctions remain unknown. Here, we critically review contemporary research on cognitive control in depression. We identify three main conceptual issues in this field: (a) uncritical use of the tripartite model, (b) reliance on descriptive explanations, and (c) lack of integration with emotional and motivational impairments. Recent advances in cognitive neuroscience offer possibilities to resolve these issues. We review this progress focusing on the ability to detect the need for control, the role of motivation, and the flexibility-stability balance. We propose that depression-related dysfunctions arise from issues in detecting when, how, and for how long to engage in goal-oriented processing. In conclusion, we argue that integrating advances in cognitive neuroscience into clinical research can help to move from a descriptive toward a more mechanistic understanding of cognitive dysfunctions in depression.

Elaborating the role of reflection and individual differences in the study of folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
Kevin Arceneaux
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Processes ◽  
Evolutionary History ◽  
Behavioral Responses ◽  
History Of ◽  
Economic Beliefs

AbstractIntuitions guide decision-making, and looking to the evolutionary history of humans illuminates why some behavioral responses are more intuitive than others. Yet a place remains for cognitive processes to second-guess intuitive responses – that is, to be reflective – and individual differences abound in automatic, intuitive processing as well.

A Ghost in the Machine

2020 ◽  
pp. 14-22
Author(s):  
Thomas Boraud
Keyword(s):  
Decision Making ◽  
Functional Mri ◽  
Motor Function ◽  
Cognitive Processes ◽  
Visual Discrimination ◽  
Visual Area ◽  
Temporal Area ◽  
Normative Approach ◽  
Electrophysiological Activity

This chapter describes the neurobiological approach of decision-making. Until the late 1980s, ignoring the work of experimental economists and behaviourists, electrophysiologists restricted themselves to the study of sensory and motor function, believing it to be impossible for them to access cognitive processes. In 1989, William Newsome and Anthony Movshon broke the dogma while studying the role of neurons in the medio-temporal area of the cortex (an associative visual area) in the visual discrimination of macaques. They became the first researchers who were able to correlate decision-making with a pattern of electrophysiological activity in neurons. This correlation, which they called psychometric–neurometric pairing, became the backbone of all subsequent studies into the neurobiology of decision-making. The chapter then looks at the development of functional MRI, and presents a normative approach to decision-making and learning.

A conceptual framework for the neurobiological study of resilience

2014 ◽  
Vol 38 ◽  
Author(s):  
Raffael Kalisch ◽  
Marianne B. Müller ◽  
Oliver Tüscher
Keyword(s):  
Cognitive Processes ◽  
Cognitive Neuroscience ◽  
Ecological Validity ◽  
Emotional Responses ◽  
Treatment Strategies ◽  
Psychiatric Research ◽  
Negative Appraisal ◽  
Resilience Factors ◽  
Resilience Mechanisms

AbstractThe well-replicated observation that many people maintain mental health despite exposure to severe psychological or physical adversity has ignited interest in the mechanisms that protect against stress-related mental illness. Focusing on resilience rather than pathophysiology in many ways represents a paradigm shift in clinical-psychological and psychiatric research that has great potential for the development of new prevention and treatment strategies. More recently, research into resilience also arrived in the neurobiological community, posing nontrivial questions about ecological validity and translatability. Drawing on concepts and findings from transdiagnostic psychiatry, emotion research, and behavioral and cognitive neuroscience, we propose a unified theoretical framework for the neuroscientific study of general resilience mechanisms. The framework is applicable to both animal and human research and supports the design and interpretation of translational studies. The theory emphasizes the causal role of stimulus appraisal (evaluation) processes in the generation of emotional responses, including responses to potential stressors. On this basis, it posits that a positive (non-negative) appraisal style is the key mechanism that protects against the detrimental effects of stress and mediates the effects of other known resilience factors. Appraisal style is shaped by three classes of cognitive processes – positive situation classification, reappraisal, and interference inhibition – that can be investigated at the neural level. Prospects for the future development of resilience research are discussed.

scholarly journals Unconscious influences on decision making: A critical review

2014 ◽  
Vol 37 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Ben R. Newell ◽  
David R. Shanks
Keyword(s):  
Decision Making ◽  
Social Behavior ◽  
Behavioral Economics ◽  
Cognitive Neuroscience ◽  
Critical Review ◽  
Explanatory Power ◽  
Future Research ◽  
Wide Range ◽  
Cognitive Unconscious

AbstractTo what extent do we know our own minds when making decisions? Variants of this question have preoccupied researchers in a wide range of domains, from mainstream experimental psychology (cognition, perception, social behavior) to cognitive neuroscience and behavioral economics. A pervasive view places a heavy explanatory burden on an intelligent cognitive unconscious, with many theories assigning causally effective roles to unconscious influences. This article presents a novel framework for evaluating these claims and reviews evidence from three major bodies of research in which unconscious factors have been studied: multiple-cue judgment, deliberation without attention, and decisions under uncertainty. Studies of priming (subliminal and primes-to-behavior) and the role of awareness in movement and perception (e.g., timing of willed actions, blindsight) are also given brief consideration. The review highlights that inadequate procedures for assessing awareness, failures to consider artifactual explanations of “landmark” results, and a tendency to uncritically accept conclusions that fit with our intuitions have all contributed to unconscious influences being ascribed inflated and erroneous explanatory power in theories of decision making. The review concludes by recommending that future research should focus on tasks in which participants' attention is diverted away from the experimenter's hypothesis, rather than the highly reflective tasks that are currently often employed.

scholarly journals Trading off the cost of conflict against expected rewards

2018 ◽  
Author(s):  
Nura Sidarus ◽  
Stefano Palminteri ◽  
Valérian Chambon
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Cognitive Control ◽  
Reversal Learning ◽  
Computational Models ◽  
Learning Task ◽  
Learning Rates ◽  
Different Types ◽  
Irrelevant Distractors ◽  
The Cost

AbstractValue-based decision-making involves trading off the cost associated with an action against its expected reward. Research has shown that both physical and mental effort constitute such subjective costs, biasing choices away from effortful actions, and discounting the value of obtained rewards. Facing conflicts between competing action alternatives is considered aversive, as recruiting cognitive control to overcome conflict is effortful. Yet, it remains unclear whether conflict is also perceived as a cost in value-based decisions. The present study investigated this question by embedding irrelevant distractors (flanker arrows) within a reversal-learning task, with intermixed free and instructed trials. Results showed that participants learned to adapt their choices to maximize rewards, but were nevertheless biased to follow the suggestions of irrelevant distractors. Thus, the perceived cost of being in conflict with an external suggestion could sometimes trump internal value representations. By adapting computational models of reinforcement learning, we assessed the influence of conflict at both the decision and learning stages. Modelling the decision showed that conflict was avoided when evidence for either action alternative was weak, demonstrating that the cost of conflict was traded off against expected rewards. During the learning phase, we found that learning rates were reduced in instructed, relative to free, choices. Learning rates were further reduced by conflict between an instruction and subjective action values, whereas learning was not robustly influenced by conflict between one’s actions and external distractors. Our results show that the subjective cost of conflict factors into value-based decision-making, and highlights that different types of conflict may have different effects on learning about action outcomes.

scholarly journals Beyond the “Conceptual Nervous System”: Can Computational Cognitive Neuroscience Transform Learning Theory?

2019 ◽  
Author(s):  
Fabian Soto
Keyword(s):  
Nervous System ◽  
Learning Theory ◽  
Cognitive Processes ◽  
Cognitive Neuroscience ◽  
Cognitive Modeling ◽  
Computational Models ◽  
Levels Of Analysis ◽  
Neural Network Approach ◽  
Behavioral Predictions ◽  
Behavior And Cognition

In the last century, learning theory has been dominated by an approach assuming that associations between hypothetical representational nodes can support the acquisition of knowledge about the environment. The similarities between this approach and connectionism did not go unnoticed to learning theorists, with many of them explicitly adopting a neural network approach in the modeling of learning phenomena. Skinner famously criticized such use of hypothetical neural structures for the explanation of behavior (the “Conceptual Nervous System”), and one aspect of his criticism has proven to be correct: theory underdetermination is a pervasive problem in cognitive modeling in general, and in associationist and connectionist models in particular. That is, models implementing two very different cognitive processes often make the exact same behavioral predictions, meaning that important theoretical questions posed by contrasting the two models remain unanswered. We show through several examples that theory underdetermination is common in the learning theory literature, affecting the solvability of some of the most important theoretical problems that have been posed in the last decades. Computational cognitive neuroscience (CCN) offers a solution to this problem, by including neurobiological constraints in computational models of behavior and cognition. Rather than simply being inspired by neural computation, CCN models are built to reflect as much as possible about the actual neural structures thought to underlie a particular behavior. They go beyond the “Conceptual Nervous System” and offer a true integration of behavioral and neural levels of analysis.

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