Quantum Random Walks and Decision Making

How realistic is it to adopt a quantum random walk model to account for decisions involving two choices? Here, we discuss the neural plausibility and the effect of initial state and boundary thresholds on such a model and contrast it with various features of the classical random walk model of decision making.     

Open Parallel Cooperative and Competitive Decision Processes: A Potential Provenance for Quantum Probability Decision Models

In recent years quantum probability models have been used to explain many aspects of human decision making, and as such quantum models have been considered a viable alternative to Bayesian models based on classical probability. One criticism that is often leveled at both kinds of models is that they lack a clear interpretation in terms of psychological mechanisms. In this paper we discuss the mechanistic underpinnings of a quantum walk model of human decision making and response time. The quantum walk model is compared to standard sequential sampling models, and the architectural assumptions of both are considered. In particular, we show that the quantum model has a natural interpretation in terms of a cognitive architecture that is both massively parallel and involves both co‐operative (excitatory) and competitive (inhibitory) interactions between units. Additionally, we introduce a family of models that includes aspects of the classical and quantum walk models.     

A Conceptual and Computational Model of Moral Decision Making in Human and Artificial Agents

Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher‐order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks for computational approaches to higher‐order cognition. The need for increasingly autonomous artificial agents to factor moral considerations into their choices and actions has given rise to another new field of inquiry variously known as Machine Morality, Machine Ethics, Roboethics, or Friendly AI. In this study, we discuss how LIDA, an AGI model of human cognition, can be adapted to model both affective and rational features of moral decision making. Using the LIDA model, we will demonstrate how moral decisions can be made in many domains using the same mechanisms that enable general decision making. Comprehensive models of human cognition typically aim for compatibility with recent research in the cognitive and neural sciences. Global workspace theory, proposed by the neuropsychologist Bernard Baars (1988) , is a highly regarded model of human cognition that is currently being computationally instantiated in several software implementations. LIDA ( Franklin, Baars, Ramamurthy, & Ventura, 2005 ) is one such computational implementation. LIDA is both a set of computational tools and an underlying model of human cognition, which provides mechanisms that are capable of explaining how an agent’s selection of its next action arises from bottom‐up collection of sensory data and top‐down processes for making sense of its current situation. We will describe how the LIDA model helps integrate emotions into the human decision‐making process, and we will elucidate a process whereby an agent can work through an ethical problem to reach a solution that takes account of ethically relevant factors.     

Toward an Integrated Neuroscience of Morality: The Contribution of Neuroeconomics to Moral Cognition

Interest in the neural processes underlying decision making has led to a flurry of recent research in the fields of both moral psychology and neuroeconomics. In this paper, we first review some important findings from both disciplines, and then argue that the two fields can mutually benefit each other. A more explicit recognition of the role of values and norms will likely lead to more accurate models of decision making for neuroeconomists, whereas the tasks, insights into neural mechanisms, and mathematical modeling common in neuroeconomic research offer moral psychologists the opportunity to expand their field and move beyond methodological limitations that may have hindered the field’s progress to this point. We conclude by highlighting an exciting group of recent studies that illustrate the potential of research that embraces the integrated moral/neuroeconomic approach that we suggest here.     

Person-by-person prediction of intuitive economic choice

Decision making is an interdisciplinary field, which is explored with methods spanning from economic experiments to brain scanning. Its dominant paradigms such as utility theory, prospect theory, and the modern dual-process theories all resort to formal algebraic models or non-mathematical postulates, and remain purely phenomenological. An approach introduced by Grossberg deployed differential equations describing neural networks and bridged the gap between decision science and the psychology of cognitive–emotional interactions. However, the limits within which neural models can explain data from real people’s actions are virtually untested and remain unknown. Here we show that a model built around a recurrent gated dipole can successfully forecast individual economic choices in a complex laboratory experiment. Unlike classical statistical and econometric techniques or machine learning algorithms, our method calibrates the equations for each individual separately, and carries out prediction person-by-person. It predicted very well the behaviour of 15%–20% of the participants in the experiment–half of them extremely well–and was overall useful for two thirds of all 211 subjects. The model succeeded with people who were guided by gut feelings and failed with those who had sophisticated strategies. One hypothesis is that this neural network is the biological substrate of the cognitive system for primitive–intuitive thinking, and so we believe that we have a model of how people choose economic options by a simple form of intuition. We anticipate our study to be useful for further studies of human intuitive thinking as well as for analyses of economic systems populated by heterogeneous agents.     

Social decision‐making in the brain: Input‐state‐output modelling reveals patterns of effective connectivity underlying reciprocal choices

During social interactions, decision‐making involves mutual reciprocity—each individual's choices are simultaneously a consequence of, and antecedent to those of their interaction partner. Neuroeconomic research has begun to unveil the brain networks underpinning social decision‐making, but we know little about the patterns of neural connectivity within them that give rise to reciprocal choices. To investigate this, the present study measured the behaviour and brain function of pairs of individuals (N = 66) whilst they played multiple rounds of economic exchange comprising an iterated ultimatum game. During these exchanges, both players could attempt to maximise their overall monetary gain by reciprocating their opponent's prior behaviour—they could promote generosity by rewarding it, and/or discourage unfair play through retaliation. By adapting a model of reciprocity from experimental economics, we show that players' choices on each exchange are captured accurately by estimating their expected utility (EU) as a reciprocal reaction to their opponent's prior behaviour. We then demonstrate neural responses that map onto these reciprocal choices in two brain regions implicated in social decision‐making: right anterior insula (AI) and anterior/anterior‐mid cingulate cortex (aMCC). Finally, with behavioural Dynamic Causal Modelling, we identified player‐specific patterns of effective connectivity between these brain regions with which we estimated each player's choices with over 70% accuracy; namely, bidirectional connections between AI and aMCC that are modulated differentially by estimates of EU from our reciprocity model. This input‐state‐output modelling procedure therefore reveals systematic brain–behaviour relationships associated with the reciprocal choices characterising interactive social decision‐making.     

Construction and evaluation of an integrated dynamical model of visual motion perception

Although numerous models describe the individual neural mechanisms that may be involved in the perception of visual motion, few of them have been constructed to take arbitrary stimuli and map them to a motion percept. Here, we propose an integrated dynamical motion model (IDM), which is sufficiently general to handle diverse moving stimuli, yet sufficiently precise to account for a wide-ranging set of empirical observations made on a family of random dot kinematograms. In particular, we constructed models of the cortical areas involved in motion detection, motion integration and perceptual decision. We analyzed their parameters through dynamical simulations and numerical continuation to constrain their proper ranges. Then, empirical data from a family of random dot kinematograms experiments with systematically varying direction distribution, presentation duration and stimulus size, were used to evaluate our model and estimate corresponding model parameters. The resulting model provides an excellent account of a demanding set of parametrically varied behavioral effects on motion perception, providing both quantitative and qualitative elements of evaluation.     

Where psychology meets physiology: chronic stress and premature mortality--the Central-Eastern European health paradox

A substantial and still growing body of research tries to link different psychological models and chronic diseases, with special emphasis on cardiovascular disease. These efforts have established several conceptual bridges that connect psychological alterations and psychosocial factors to the risks, onset and prognosis of cardiovascular disease. However, several different models have been suggested. Depression and learned helplessness are two central psychological models that have been shown to have major explanatory power in the development of chronic diseases. In this respect the so called Central-Eastern European health paradox, that is the morbidity and mortality crisis in these transforming societies can be regarded as a special experimental model. In this review chronic stress is proposed as an integrating theory that can be applied to different psychological models. Chronic stress and allostatic load has been shown to lead to typical pathogenetic results in animal experiments. Chronic stress theory is applicable to the explanation of the suddenly changing patterns of premature mortality rates in transforming societies. Literature and the different models in the field of psychology, behavioural sciences, and epidemiology are reviewed in terms of the chronic stress theory. The applicability of these results are investigated for further research, clinical and policy implications.     

Toward a Physical Theory of Quantum Cognition

Recently, mathematical models based on quantum formalism have been developed in cognitive science. The target articles in this special issue of Topics in Cognitive Science clearly illustrate how quantum theoretical formalism can account for various aspects of human judgment and decision making in a quantitatively and mathematically rigorous manner. In this commentary, we show how future studies in quantum cognition and decision making should be developed to establish theoretical foundations based on physical theory, by introducing Taketani's three‐stage theory of the development of science. Also, implications for neuroeconomics (another rapidly evolving approach to human judgment and decision making) are discussed.     

The involvement of the striatum in decision making

Decision making has been extensively studied in the context of economics and from a group perspective, but still little is known on individual decision making. Here we discuss the different cognitive processes involved in decision making and its associated neural substrates. The putative conductors in decision making appear to be the prefrontal cortex and the striatum. Impaired decision-making skills in various clinical populations have been associated with activity in the prefrontal cortex and in the striatum. We highlight the importance of strengthening the degree of integration of both cognitive and neural substrates in order to further our understanding of decision-making skills. In terms of cognitive paradigms, there is a need to improve the ecological value of experimental tasks that assess decision making in various contexts and with rewards; this would help translate laboratory learnings into real-life benefits. In terms of neural substrates, the use of neuroimaging techniques helps characterize the neural networks associated with decision making; more recently, ways to modulate brain activity, such as in the prefrontal cortex and connected regions (eg, striatum), with noninvasive brain stimulation have also shed light on the neural and cognitive substrates of decision making. Together, these cognitive and neural approaches might be useful for patients with impaired decision-making skills. The drive behind this line of work is that decision-making abilities underlie important aspects of wellness, health, security, and financial and social choices in our daily lives.     

Decision making in cancer primary prevention and chemoprevention

Background:We know very little about how individuals decide to undertake, maintain, or discontinue cancer primary prevention or chemoprevention.Purpose: The aims of this article are to (a) examine whether and, if so, how traditional health behavior change models are relevant for decision making in this area; (b) review the application of decision aids to forming specific, personal choices between options; and (c) identify the challenges of evaluating these decision processes to suggest areas for future research.Methods: Theoretical models and frameworks derived from the health behavior change and decision-making fields were applied to cancer primary prevention choices. Decision aids for the human papillomavirus (HPV) vaccine, Hormone Replacement Therapy (HRT), and tamoxifen were systematically examined.Results: Traditional concepts such as decisional balance and cues to action are relevant to understanding cancer primary prevention choices; Motivational Interviewing, Self-Determination Theory, and the Preventive Health Model may also explain the facilitators of decision making. There are no well-tested HPV vaccine decision aids, although there have been some studies on aids for HPV testing. There are several effective decision aids for HRT and tamoxifen; evidence-based decision aid components have also been identified.Conclusions: Additional theory-based empirical research on decision making in cancer primary prevention and chemoprevention, particularly at the interface of psychology and behavioral economics, is suggested. We thank Dr. Karen Glanz for her contributions to the literature review for this article and her coleadership of one of the break-out sessions.We also thank an anonymousAnnals of Behavioral Medicine reviewer for suggestions to the text that enriched this article. We thank Dr. Karen Glanz for her contributions to the literature review for this article and her coleadership of one of the break-out sessions.We also thank an anonymous Annals of Behavioral Medicine reviewer for suggestions to the text that enriched this article.     

The impact of stress in decision making in the context of uncertainty

For a number of decades, different fields of knowledge, including psychology, economics, and neurosciences, have focused their research efforts on a better understanding of the decision‐making process. Making decisions based on the probability of future events is routine in everyday life; it occurs whenever individuals select an option from several alternatives, each one associated with a specific value. Sometimes subjects decide knowing the precise outcomes of each option, but commonly they have to decide without knowing the consequences (because either ambiguity or risk is involved). Stress has a broad impact on animal behaviors, affects brain regions involved in decision‐making processes, and, when maladaptive, is a trigger for neuropsychiatric disorders. This Mini‐Review provides a comprehensive overview on how stress impacts decision‐making processes, particularly under uncertain conditions. Understanding this can prove to be useful for intervention related to impairments to decision‐making processes that present in several stress‐triggered neuropsychiatric disorders. © 2014 Wiley Periodicals, Inc.     

Neural reactivation links unconscious thought to decision-making performance

Brief periods of unconscious thought (UT) have been shown to improve decision making compared with making an immediate decision (ID). We reveal a neural mechanism for UT in decision making using blood oxygen level-dependent (BOLD) functional magnetic resonance imaging. Participants (N = 33) encoded information on a set of consumer products (e.g. 48 attributes describing four different cars), and we manipulated whether participants (i) consciously thought about this information (conscious thought), (ii) completed a difficult 2-back working memory task (UT) or (iii) made an immediate decision about the consumer products (ID) in a within-subjects blocked design. To differentiate UT neural activity from 2-back working memory neural activity, participants completed an independent 2-back task and this neural activity was subtracted from neural activity occurring during the UT 2-back task. Consistent with a neural reactivation account, we found that the same regions activated during the encoding of complex decision information (right dorsolateral prefrontal cortex and left intermediate visual cortex) continued to be activated during a subsequent 2-min UT period. Moreover, neural reactivation in these regions was predictive of subsequent behavioral decision-making performance after the UT period. These results provide initial evidence for post-encoding unconscious neural reactivation in facilitating decision making.     

Generalisation,decision making,and embodiment effects in mental rotation: A neurorobotic architecture tested with a humanoid robot

Mental rotation, a classic experimental paradigm of cognitive psychology, tests the capacity of humans to mentally rotate a seen object to decide if it matches a target object. In recent years, mental rotation has been investigated with brain imaging techniques to identify the brain areas involved. Mental rotation has also been investigated through the development of neural-network models, used to identify the specific mechanisms that underlie its process, and with neurorobotics models to investigate its embodied nature. Current models, however, have limited capacities to relate to neuro-scientific evidence, to generalise mental rotation to new objects, to suitably represent decision making mechanisms, and to allow the study of the effects of overt gestures on mental rotation. The work presented in this study overcomes these limitations by proposing a novel neurorobotic model that has a macro-architecture constrained by knowledge held on brain, encompasses a rather general mental rotation mechanism, and incorporates a biologically plausible decision making mechanism. The model was tested using the humanoid robot iCub in tasks requiring the robot to mentally rotate 2D geometrical images appearing on a computer screen. The results show that the robot gained an enhanced capacity to generalise mental rotation to new objects and to express the possible effects of overt movements of the wrist on mental rotation. The model also represents a further step in the identification of the embodied neural mechanisms that may underlie mental rotation in humans and might also give hints to enhance robots’ planning capabilities.     

Behavioural-variant frontotemporal dementia: diagnosis, clinical staging, and management

Patients with behavioural-variant frontotemporal dementia (bvFTD) present with insidious changes in personality and interpersonal conduct that indicate progressive disintegration of the neural circuits involved in social cognition, emotion regulation, motivation, and decision making. The underlying pathological changes are heterogeneous and are characterised by various intraneuronal inclusions. Biomarkers to detect these histopathological changes in life are becoming increasingly important with the development of disease-modifying drugs. Gene mutations have been found that collectively account for around 10-20% of cases. Recently, criteria proposed for bvFTD define three levels of diagnostic certainty: possible, probable, and definite. Detailed history taking from family members to elicit behavioural features underpins the diagnostic process, with support from neuropsychological testing designed to detect impairment in decision making, emotion processing, and social cognition. Brain imaging is important for increasing the level of diagnostic certainty. A recently developed staging instrument shows much promise for monitoring patients and evaluating therapies, which at present are aimed at symptom amelioration. Carer education and support remain of paramount importance.     

The behavioural sciences and undergraduate education in psychiatry

The Behavioural Sciences are becoming increasingly important in medical education and in medical treatment. But medical students and the profession generally, appear to have difficulty accepting behavioural sciences as a valid part of the curriculum and in the practice of medicine. One of the reasons for this may be related to a narrow definition of the physician's social role in the years following the adoption of the Flexner Report in 1911 which emphasized the model of the physician/scientist. The perennial model of the physician occupying a social role in which science is used to increase and rationalize responsiveness to suffering has been eroded in favour of the pursuit of the scientific as the essential goal of medicine. The newer, "softer", sciences of anthropology, ecology, economics, psychology and sociology have not fitted into the professional self-perception of the medical role. Furthermore, some departments of behavioural science have adopted an intellectual stance at times antagonistic to the rest of the medical school emphasizing the shortcomings of the profession in its delivery of health care. The aspirant medical student may have been disaffected by teachers who do not embody acceptable role models. Students should receive specific instructions in the social role of the physician, physicians should be actively involved in teaching the behavioral sciences and the curriculum should at all times emphasize the relevance of the behavioural sciences to the daily practice of medicine.     

In search of the neurobiological basis of decision making: explanation, reduction and emergence

In recent years, the study of decision making has provided a paradigmatic case of "crossbreeding" of different disciplines. The integration of economics, psychology and neurosciences within neuroeconomics calls for more accurate and comprehensive models of human rationality, which may be obtained by combining diverse theoretical approaches and experimental techniques. In this respect, neuroeconomics contributes to a naturalistic, brain-based, explanation of human agency. However, although contemporary naturalism insists on the unitary aspect of reality, we stress that supporting unitary study of nature is not the same as supporting a single, fundamental discipline to which all higher-order analyses could (or should) be reduced. We argue for integration, rather than reduction, as the best approach to a naturalistic explanation of human decision making, and we claim that supporting epistemological pluralism does not mean being committed to any specific ontological position. However, we suggest that an "emergentist" ontology is the best candidate to integrate the epistemological analysis here endorsed.