Vincent Valton PhD

Vincent Valton


Research Interests

My research interests are in psychiatric disorders and computational models of psycho-pathologies in general (with a particular interest for Schizophrenia and the mechanisms of Psychoses). 

Publications:
2017
  Comprehensive review: Computational modelling of Schizophrenia
Valton, V, Romaniuk, L, Steele, D, Lawrie, S & Seriès, P 2017, 'Comprehensive review: Computational modelling of Schizophrenia' Neuroscience and Biobehavioral Reviews. DOI: 10.1016/j.neubiorev.2017.08.022
Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behaviour (e.g. Bayesian models– top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist & neural networks – bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioural vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence. Here we present a review that first synthesises the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the Dopamine, Glutamate, GABA, Dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated.
General Information
Organisations: School of Informatics.
Authors: Valton, Vincent, Romaniuk, Liana, Steele, Douglas, Lawrie, Stephen & Seriès, Peggy.
Publication Date: 1 Sep 2017
Publication Information
Category: Article
Journal: Neuroscience and Biobehavioral Reviews
ISSN: 01497634
Original Language: English
DOIs: 10.1016/j.neubiorev.2017.08.022
2013
  Elucidating Poor Decision-Making in a Rat Gambling Task
Rivalan, M, Valton, V, Series, P, Marchand, AR, Dellu-hagedorn, F & Ravel, N (ed.) 2013, 'Elucidating Poor Decision-Making in a Rat Gambling Task' PLoS One, vol 8, no. 12, pp. e82052. DOI: 10.1371/journal.pone.0082052
Although poor decision-making is a hallmark of psychiatric conditions such as attention deficit/hyperactivity disorder, pathological gambling or substance abuse, a fraction of healthy individuals exhibit similar poor decision-making performances in everyday life and specific laboratory tasks such as the Iowa Gambling Task. These particular individuals may provide information on risk factors or common endophenotypes of these mental disorders. In a rodent version of the Iowa gambling task – the Rat Gambling Task (RGT), we identified a population of poor decision makers, and assessed how these rats scored for several behavioral traits relevant to executive disorders: risk taking, reward seeking, behavioral inflexibility, and several aspects of impulsivity. First, we found that poor decision-making could not be well predicted by single behavioral and cognitive characteristics when considered separately. By contrast, a combination of independent traits in the same individual, namely risk taking, reward seeking, behavioral inflexibility, as well as motor impulsivity, was highly predictive of poor decision-making. Second, using a reinforcement-learning model of the RGT, we confirmed that only the combination of extreme scores on these traits could induce maladaptive decision-making. Third, the model suggested that a combination of these behavioral traits results in an inaccurate representation of rewards and penalties and inefficient learning of the environment. Poor decision-making appears as a consequence of the over-valuation of high-reward-high-risk options in the task. Such a specific psychological profile could greatly impair clinically healthy individuals in decision-making tasks and may predispose to mental disorders with similar symptoms.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Rivalan, Marion, Valton, Vincent, Series, Peggy, Marchand, Alain R. & Dellu-hagedorn, Francoise.
Pages: e82052
Publication Date: 5 Dec 2013
Publication Information
Category: Article
Journal: PLoS One
Volume: 8
Issue number: 12
ISSN: 1932-6203
Original Language: English
DOIs: 10.1371/journal.pone.0082052
2012
  Maladaptive decision-making in a rat version of the Iowa Gambling Task
Valton, V, Marchand, A, Dellu-Hagedom, F & Series, P 2012, 'Maladaptive decision-making in a rat version of the Iowa Gambling Task' 9th Annual Annual Computational and Systems Neurscience Meeting (COSYNE 2012), Salt Lake City, United States, 23/02/12 - 26/02/12, .
Deficits in decision-making have been repeatedly observed in various psychiatric disorders (e.g. ADHD, Mania, OCD) and are often assessed using the Iowa Gambling Task (IGT). The IGT represents a realistic decisionmaking task where subjects have to choose between targets associated with rewards and penalties of varying likelihood and amplitude. Previous studies have shown that a third of healthy subjects perform poorly in the IGT, as observed in psychiatric patients [1]. Recently, the IGT was adapted for rodents (the Rat Gambling Task, RGT).
As in human studies, a third of healthy rats were found to exhibit poor decision-making [2]. These rats were then run on a battery of tests to extract measures of impulsivity, reward sensitivity, behavioral inflexibility and riskseeking.
Poor decision-makers were always characterized by high scores for a combination of these behavioral traits. We modified the TD-learning algorithm to model learning and decision-making in the RGT and include reward sensitivity, inflexibility and risk-seeking. This novel model was then used to assess: (1) how the behavioral traits influence learning (2) Whether they can they explain different performances in healthy subjects. The model was able to account for the performances of good and poor decision-makers. The model was fitted to individual rat performances to describe their levels of reward sensitivity, inflexibility and risk-seeking. The parameters correlated significantly with the scores obtained from experiments assessing these behavioral traits. This suggests that the mathematical description of the traits is valid. This work supports the hypothesis that a combination of high scores for reward sensitivity, inflexibility and risk-seeking affects the rats’ learning by altering reward prediction and their ability to reverse their initial estimations. Biased perception and representation of the environment lead to aberrant decisions according to the real outcome of the task but optimal according to the rat’s internal model.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Valton, Vincent, Marchand, Alain, Dellu-Hagedom, Francoise & Series, Peggy.
Number of pages: 1
Publication Date: Feb 2012
Publication Information
Category: Poster
Original Language: English
2011
  Modeling maladaptive decision-making in a rat version of the Iowa Gambling Task
Valton, V, Marchand, A, Dellu-Hagedom, F & Series, P 2011, 'Modeling maladaptive decision-making in a rat version of the Iowa Gambling Task' Edinburgh Neuroscience Day 2011, Edinburgh, United Kingdom, 16/03/11 - 16/03/11, .
Deficits in decision-making have been repeatedly observed in various psychiatric disorders. Such deficits are often assessed using the Iowa Gambling task (IGT) [1]. This task has been adapted to use with rodents and named the Rat Gambling Task (RGT). It is found that a third of healthy rats perform poorly in the RGT. These rats were also tested in other tasks to measure behavioral traits such as sensitivity to reward, cognitive inflexibility and risk seeking. Interestingly, poor decision makers were found to always score highly for a combination of these traits. To explore how the behavioral traits described above influence learning and decision-making, we modeled learning and decision-making in the RGT using the TD-learning algorithm [3]. The behavioral traits were added to the classical TD-learning algorithm, and influence the learning rate or the perception of rewards by the agent. Parameters of the model were extracted for each rat by fitting their performance to the model. We found that the model could account for the performances of good and poor decision-makers. Additionally, the parameters defining the behavioral traits extracted from the model correlated significantly with those measured experimentally. The model was also able to predict the inflexibility of poor decision makers during reversal conditions. Our work supports the hypothesis that a combination of high scores for risk and cognitive inflexibility leads to poor decision-making. According to the model, behavioral traits affect learning by altering the perception of the environment. This results in poor performances that seem optimal to the rats according to their inaccurate world representation.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Valton, Vincent, Marchand, A., Dellu-Hagedom, Francoise & Series, Peggy.
Publication Date: 2011
Publication Information
Category: Poster
Original Language: English
  Modeling maladaptive decision-making in a rat version of the Iowa Gambling Task
Valton, V, Marchand, A, Dellu-hagedorn, F & Seriès, P 2011, 'Modeling maladaptive decision-making in a rat version of the Iowa Gambling Task' BMC Neuroscience, vol 12, no. Suppl 1, P294. DOI: 10.1186/1471-2202-12-S1-P294
Deficits in decision-making have been repeatedly observed in various psychiatric disorders (i.e. ADHD, Pathological Gambling, Mania, OCD and Substance Abuse) as well as in frontal lobe patients. Such decision-making deficits are often assessed using the Iowa Gambling task (IGT) [1]. The IGT represents a realistic decision-making task where subjects are asked to choose between targets associated with rewards and penalties of varying likelihood and amplitude. Previous studies have shown that when healthy participants take the IGT, around a third of these perform poorly, similar to psychiatric patients [1].

Recently, these behavioral findings were successfully translated to animal research in a rodent version of the IGT, the Rat Gambling Task (RGT). In common with human studies, it was found that a third of a healthy population of rats exhibited poor decision-making performances [2]. The rats were tested in other tasks aiming at characterizing behavioral traits such as impulsivity, sensitivity to reward, cognitive inflexibility and risk seeking. Poor decision makers were always characterized by high scores for a combination of these behavioral traits.

Here we use a model of learning and decision-making in the RGT to answer the following questions: (1) how do the behavioral traits described above influence learning; (2) how is this manifested in terms of their decision-making performance?

In order to model the learning and decision process of the RGT, we used a TD-learning algorithm [3]. The model agent experiences the environment by learning the values of rewards and penalties for each state using trial and error sampling. As the agent gets a more accurate representation of the environment, it takes more appropriate decisions, using a ‘softmax’ action selection process. The RGT is modeled as a Markov decision process and we extended the classical TD-learning algorithm by incorporating risk seeking [4], reward sensitivity and cognitive inflexibility. These behavioral traits were implemented independently and influence either the learning rate or the perception of rewards by the agent. The parameters of the model were extracted for each rat by fitting their performance to the model.

We found that the model could account for the performances of good and poor subpopulations of decision makers. Additionally, the parameters defining the behavioral traits extracted from the model correlated significantly with those measured experimentally for the poor and good decision makers’ subgroups. The model was also able to predict the inflexibility of poor decision makers during reversal conditions.

Our work supports the hypothesis that it is a combination of high scores for risk seeking, sensitivity to reward and cognitive inflexibility that lead to poor decision-making performances. According to the model, behavioral traits affect the learning process of the subjects by altering the estimated value of the received rewards and reducing their ability to reverse their initial estimations. This results in an incorrect perception of the environment, leading to an optimal decision-making according to their world representation but aberrant according to the real outcome of the task.

General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Valton, Vincent, Marchand, Alain, Dellu-hagedorn, Francoise & Seriès, Peggy.
Publication Date: 1 Jan 2011
Publication Information
Category: Meeting abstract
Journal: BMC Neuroscience
Volume: 12
Issue number: Suppl 1
ISSN: 1471-2202
Original Language: English
DOIs: 10.1186/1471-2202-12-S1-P294
  Modeling maladaptive decision-making in a rat version of the Iowa Gambling Task
Valton, V, Marchand, A, Dellu-Hagedom, F & Series, P 2011, 'Modeling maladaptive decision-making in a rat version of the Iowa Gambling Task' 20th Annual Computational Neuroscience Meeting: CNS*2011, Stockholm, Sweden, 23/07/11 - 28/07/11, .
Deficits in decision-making have been repeatedly observed in various psychiatric disorders (i.e. ADHD, Pathological Gambling, Mania, OCD and Substance Abuse) as well as in frontal lobe patients. Such decision-making deficits are often assessed using the Iowa Gambling task (IGT) [1]. The IGT represents a realistic decision-making task where subjects are asked to choose between targets associated with rewards and penalties of varying likelihood and amplitude. Previous studies have shown that when healthy participants take the IGT, around a third of these perform poorly, similar to psychiatric patients [1].
Recently, these behavioral findings were successfully translated to animal research in a rodent version of the IGT, the Rat Gambling Task (RGT). In common with human studies, it was found that a third of a healthy population of rats exhibited poor decision-making performances [2]. The rats were tested in other tasks aiming at characterizing behavioral traits such as impulsivity, sensitivity to reward, cognitive inflexibility and risk seeking. Poor decision makers were always characterized by high scores for a combination of these behavioral traits.
Here we use a model of learning and decision-making in the RGT to answer the following questions: (1) how do the behavioral traits described above influence learning; (2) how is this manifested in terms of their decisionmaking performance?
In order to model the learning and decision process of the RGT, we used a TD-learning algorithm [3]. The model agent experiences the environment by learning the values of rewards and penalties for each state using trial and error sampling. As the agent gets a more accurate representation of the environment, it takes more appropriate decisions, using a ‘softmax’ action selection process. The RGT is modeled as a Markov decision process and we extended the classical TD-learning algorithm by incorporating risk seeking [4], reward sensitivity and cognitive inflexibility. These behavioral traits were implemented
independently and influence either the learning rate or the perception of rewards by the agent. The parameters of the model were extracted for each rat by fitting their performance to the model.
We found that the model could account for the performances of good and poor subpopulations of decision makers. Additionally, the parameters defining the behavioral traits extracted from the model correlated significantly with those measured experimentally for the poor
and good decision makers’ subgroups. The model was also
able to predict the inflexibility of poor decision makers
during reversal conditions.
Our work supports the hypothesis that it is a combination of high scores for risk seeking, sensitivity to reward and cognitive inflexibility that lead to poor decision-making performances. According to the model, behavioral traits affect the learning process of the subjects by altering the estimated value of the received rewards and reducing their ability to reverse their initial estimations. This results in an incorrect perception of the environment, leading to an optimal decision-making according to their world representation but aberrant according to the real outcome of the task.

References
1. Dunn BD, Dalgleish T, Lawrence AD: The somatic marker hypothesis: a critical evaluation. Neuroscience Biobehavioral Reviews 2006, 30(2):239-271.
2. Rivalan M, Ahmed SH, Dellu-Hagedorn F: Risk-Prone Individuals Prefer the Wrong Options on a Rat Version of the Iowa Gambling Task. Biological Psychiatry 2009, 66(8):743-749.
3. Schultz W, Dayan P, Montague R: A neural substrate of prediction and
reward. Science 1997, 275(5306):1593-1599.
4. Li J, Chan L: Reward Adjustment Reinforcement Learning for Risk-averse Asset Allocation. International Joint Conference on Neural Networks 2006, 534-541.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Valton, Vincent, Marchand, A., Dellu-Hagedom, Francoise & Series, Peggy.
Publication Date: 18 Jul 2011
Publication Information
Category: Poster
Original Language: English
2010
  Inter-Individual behavioural traits shape performances in decision making
Valton, V, Marchand, A, Dellu-Hagedom, F & Series, P 2010, 'Inter-Individual behavioural traits shape performances in decision making' DTC Welcome Day, Edinburgh, United Kingdom, 16/09/10 - 16/09/10, .
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Valton, Vincent, Marchand, A., Dellu-Hagedom, Francoise & Series, Peggy.
Publication Date: 2010
Publication Information
Category: Poster
Original Language: English

Projects:
Prediction-Error in psychiatric disorders: From Maladaptive decision-making to the neurobiology of psychosis and schizophrenia (PhD)