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![a–c The designs of the three types of cognitive task mentioned: the affective bias task, reversal learning task and two-step task. d–f Examples of how data is typically collapsed and analysed for these tasks. g–i Examples of the additional information that can be gained by taking a computational approach. a In the affective bias task, also known as the ‘ambiguous-cue interpretation task’, participants are first trained to press either the left or right button in response to the extreme stimuli (large or small circles in this example) which are 100% associated with either a £1 or £4 reward (associations counterbalanced across participants). In the test phase, during different trials, participants are shown either one of the original extreme stimuli or a novel, intermediate stimulus, to which they must respond by pressing the button associated with the stimulus they think it is closer to. On intermediate trials, there is a 50% chance of receiving a £1 or £4 reward. d Affective bias is operationalised here as the proportion of times participants press the button associated with the higher reward stimulus on intermediate stimulus trials. g An example of the drift rate, which can be estimated using a drift diffusion model (DDM), allowing us to account for participant accuracy and reaction times. In our work using this task [10], we found that patients with mood and anxiety disorders demonstrate a lower drift rate towards classifying the mid-tone as high reward. b In reversal learning tasks, participants typically choose between two stimuli on screen by pressing the corresponding button. One stimulus is associated with reward, indicated by a smiley face, and the other with punishment, indicated by a sad face. The contingencies are then reversed, so that the previously rewarded stimulus is now punished and vice versa. e The probability of participants choosing a correct (rewarded) choice. h The estimated learning rate; the shallower learning curve and greater latency before performance returns to high accuracy after a reversal is indicative of a slower learning rate in patients here. c In this example of a two-step task, participants start in one state (shown here in grey), and choose between two stimuli (star or hexagon), each of which result in a probabilistic transition (here, high probabilities are represented with a thicker arrow, and low probabilities—‘rare transitions’—with a thinner arrow) to a second-level state (either pink or orange), at which point they can choose between the two stimuli which are available to them in that state. Here, imagine that a participant chooses the star, and probabilistically moves to the orange state (on the right). They then choose the circle, which results in a reward. To obtain this reward again, the participant could perform in a ‘model-free’ way, without understanding the transitional structure of the stages, and simply choose the star again. However, this ‘model-free’ way of behaving is most likely to take them to the pink state, rather than the orange one. A ‘model-based’ choice would entail choosing the hexagon in state one, which is more likely to result in a transition to the desired orange state. When these choices are repeated over many trials, logistic regression or computational modelling can be used to demonstrate the extent to which participants behave in a ‘model-based’ way to seek out the best second state, rather than simply repeating actions which previously led to reward. f The probability of repeating the last trial, split by the outcome and transition type of the previous trial. i A computational modelling analysis of participant data (solid lines) can be used to estimate a ‘weight’ for each participant, which represents the extent to which they rely on model-based (dotted lines) and model-free (dashed lines) strategies](publication/349296722/figure/fig2/AS:990981092540416@1613279712148/a-c-The-designs-of-the-three-types-of-cognitive-task-mentioned-the-affective-bias-task.png)
a–c The designs of the three types of cognitive task mentioned: the affective bias task, reversal learning task and two-step task. d–f Examples of how data is typically collapsed and analysed for these tasks. g–i Examples of the additional information that can be gained by taking a computational approach. a In the affective bias task, also known as the ‘ambiguous-cue interpretation task’, participants are first trained to press either the left or right button in response to the extreme stimuli (large or small circles in this example) which are 100% associated with either a £1 or £4 reward (associations counterbalanced across participants). In the test phase, during different trials, participants are shown either one of the original extreme stimuli or a novel, intermediate stimulus, to which they must respond by pressing the button associated with the stimulus they think it is closer to. On intermediate trials, there is a 50% chance of receiving a £1 or £4 reward. d Affective bias is operationalised here as the proportion of times participants press the button associated with the higher reward stimulus on intermediate stimulus trials. g An example of the drift rate, which can be estimated using a drift diffusion model (DDM), allowing us to account for participant accuracy and reaction times. In our work using this task [10], we found that patients with mood and anxiety disorders demonstrate a lower drift rate towards classifying the mid-tone as high reward. b In reversal learning tasks, participants typically choose between two stimuli on screen by pressing the corresponding button. One stimulus is associated with reward, indicated by a smiley face, and the other with punishment, indicated by a sad face. The contingencies are then reversed, so that the previously rewarded stimulus is now punished and vice versa. e The probability of participants choosing a correct (rewarded) choice. h The estimated learning rate; the shallower learning curve and greater latency before performance returns to high accuracy after a reversal is indicative of a slower learning rate in patients here. c In this example of a two-step task, participants start in one state (shown here in grey), and choose between two stimuli (star or hexagon), each of which result in a probabilistic transition (here, high probabilities are represented with a thicker arrow, and low probabilities—‘rare transitions’—with a thinner arrow) to a second-level state (either pink or orange), at which point they can choose between the two stimuli which are available to them in that state. Here, imagine that a participant chooses the star, and probabilistically moves to the orange state (on the right). They then choose the circle, which results in a reward. To obtain this reward again, the participant could perform in a ‘model-free’ way, without understanding the transitional structure of the stages, and simply choose the star again. However, this ‘model-free’ way of behaving is most likely to take them to the pink state, rather than the orange one. A ‘model-based’ choice would entail choosing the hexagon in state one, which is more likely to result in a transition to the desired orange state. When these choices are repeated over many trials, logistic regression or computational modelling can be used to demonstrate the extent to which participants behave in a ‘model-based’ way to seek out the best second state, rather than simply repeating actions which previously led to reward. f The probability of repeating the last trial, split by the outcome and transition type of the previous trial. i A computational modelling analysis of participant data (solid lines) can be used to estimate a ‘weight’ for each participant, which represents the extent to which they rely on model-based (dotted lines) and model-free (dashed lines) strategies
Source publication
Purpose of Review
Common currency tasks are tasks that investigate the same phenomenon in different species. In this review, we discuss how to ensure the translational validity of common currency tasks, summarise their benefits, present recent research in this area and offer future directions and recommendations.
Recent Findings
We discuss the str...
Citations
... Translational approaches, specifically when equivalent tasks are used to measure the same cognitive construct in humans and non-human animals, benefit the study of avoidance and its relevance to mental ill-health for two important reasons (Bach, 2022;Pike et al., 2021). First, precise causal manipulations of neural circuitry such as chemo/optogenetics are only feasible in non-human animals, whereas only humans can verbalise their subjective experiences -it is only by using translational measures that we can integrate data and theory across species to achieve a comprehensive mechanistic understanding. ...
Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in the measurement of avoidance between humans and non-human animals hinder our progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study (n = 372), participants who experienced greater task-induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested 1 week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety.
... Translational approaches, specifically when equivalent tasks are used to measure the same cognitive construct in humans and non-human animals, benefit the study of avoidance and its relevance to mental ill-health for two important reasons (Bach 2021, Pike, Lowther et al. 2021. First, precise causal manipulations of neural circuitry such as chemo-/optogenetics are only feasible in non-human animals, whereas only humans can verbalise their subjective experiences -it is only by using translational measures that we can integrate data and theory across species to achieve a comprehensive mechanistic understanding. ...
Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in existing measures of avoidance between humans and non-human animals impede progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study, participants (n = 372) who experienced greater task- induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested one week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety.
... Translational approaches, specifically when equivalent tasks are used to measure the same cognitive construct in humans and non-human animals, benefit the study of avoidance and its relevance to mental ill-health for two important reasons (Bach 2021, Pike, Lowther et al. 2021. First, precise causal manipulations of neural circuitry such as chemo-/optogenetics are only feasible in non-human animals, whereas only humans can verbalise their subjective experiences -it is only by using translational measures that we can integrate data and theory across species to achieve a comprehensive mechanistic understanding. ...
Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in existing measures of avoidance between humans and non-human animals impede progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study, participants (n = 372) who experienced greater task-induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested one week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety.
... In the current study, we devised a behavioral task for nonhuman animals following the task designed by Tanaka et al. (2009). An essential step for promoting this line of research for nonhuman animals is to design a simple task according to the animals' level of abilities and motivation (Pike et al., 2021). For example, an experiment on mice (Akam et al., 2021) adapted an original task designed for humans (i.e., two-stage task: Daw et al., 2011) by modifying task features such as the number of action alternatives and reward probabilities, to encourage mice to engage in the task. ...
... Future comparative psychology research taking this approach may provide valuable insight into the timescale of learning abilities from an evolutionary perspective. Moreover, this task could be a common currency task (Pike et al., 2021) to test various species of animals and better understand serotonergic system deficits (Tanaka et al., 2009) and OCD (Sakai et al., 2022). The current study provided data from chimpanzees, one of our evolutionarily closest relatives, taking the first step toward elucidating those issues. ...
The outcome of an action often occurs after a delay. One solution for learning appropriate actions from delayed outcomes is to rely on a chain of state transitions. Another solution, which does not rest on state transitions, is to use an eligibility trace (ET) that directly bridges a current outcome and multiple past actions via transient memories. Previous studies revealed that humans (Homo sapiens) learned appropriate actions in a behavioral task in which solutions based on the ET were effective but transition-based solutions were ineffective. This suggests that ET may be used in human learning systems. However, no studies have examined nonhuman animals with an equivalent behavioral task. We designed a task for nonhuman animals following a previous human study. In each trial, participants chose one of two stimuli that were randomly selected from three stimulus types: a stimulus associated with a food reward delivered immediately, a stimulus associated with a reward delivered after a few trials, and a stimulus associated with no reward. The presented stimuli did not vary according to the participants’ choices. To maximize the total reward, participants had to learn the value of the stimulus associated with a delayed reward. Five chimpanzees (Pan troglodytes) performed the task using a touchscreen. Two chimpanzees were able to learn successfully, indicating that learning mechanisms that do not depend on state transitions were involved in the learning processes. The current study extends previous ET research by proposing a behavioral task and providing empirical data from chimpanzees.
... Translational approaches, specifically when equivalent tasks are used to measure the same cognitive construct in humans and non-human animals, benefit the study of avoidance and its relevance to mental ill-health for two important reasons (Bach, 2022;Pike et al., 2021). First, precise causal manipulations of neural circuitry such as chemo/optogenetics are only feasible in non-human animals, whereas only humans can verbalise their subjective experiences -it is only by using translational measures that we can integrate data and theory across species to achieve a comprehensive mechanistic understanding. ...
Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in existing measures of avoidance between humans and non-human animals impede progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study, participants (n = 372) who experienced greater task- induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested one week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety.
... Behavioural Inhibition/Activation Scale (Carver & White, 1994 Translational approaches, specifically when equivalent tasks are used to measure the same cognitive 57 construct in humans and non-human animals, benefit the study of avoidance and its relevance to 58 mental ill-health for two important reasons (Bach, 2021;Pike et al., 2021). First, precise causal 59 manipulations of neural circuitry such as chemo-/optogenetics are only feasible in non-human 60 animals, whereas only humans can verbalise their subjective experiences -it is only by using 61 translational measures that we can integrate data and theory across species to achieve a 62 comprehensive mechanistic understanding. ...
Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in existing measures of avoidance between humans and non-human animals impede progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study, participants (n = 372) who experienced greater task-induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested one week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety.
... In other words, efficacy of treatments for catastrophizing could be assessed by demonstrating their ability to increase risk-taking. Behavioural measures of such constructs have a core advantage over assessment based on self-report measure, as they can also be assessed in translational non-human models and as a result can be used to screen pharmaceutical interventions and probe underlying neurobiology (Pike, Lowther, et al., 2021). ...
Background: Catastrophizing, when an individual overestimates the probability of a severe negative outcome, is related to various aspects of mental ill-health. Here, we further characterize catastrophizing by investigating the extent to which self-reported catastrophizing is associated with risk-taking, using an online behavioural task and computational modelling. Methods: We performed two online studies: a pilot study (n=69) and a main study (n=263). In the pilot study, participants performed the Balloon Analogue Risk Task (BART), alongside two other tasks (reported in the Supplement), and completed mental health questionnaires. Based on the findings from the pilot, we explored risk-taking in more detail in the main study using two versions of the Balloon Analogue Risk task (BART), with either a high or low cost for bursting the balloon. Results: In the main study, there was a significant negative relationship between self-report catastrophizing scores and risk-taking in the low (but not high) cost version of the BART. Computational modelling of the BART task revealed no relationship between any parameter and Catastrophizing scores in either version of the task. Conclusions: We show that increased self-reported catastrophizing may be associated with reduced behavioural measures of risk-taking, but were unable to identify a computational correlate of this effect.
... This approach will be especially important for approach-avoidance conflict tasks, which are some of the most commonly employed rodent anxiety models (Campos et al., 2013). Future work should develop fear/anxiety tasks that are explicitly designed to engage similar computational processes in both animals and humans, which have been referred to as 'common currency' tasks (Pike et al., 2021). These tasks can also act as preclinical tests that will help to spur drug discovery for fear/anxiety disorders, which is especially important given that psychiatric drug development has slowed over the last decade (Hyman, 2012;Kesselheim et al., 2015). ...
... Looking forward, computational approaches could be extended to better understand basic mechanisms and treatments for fear-and anxiety-related disorders. Further, better cross-species paradigms of defensive behaviour, especially those amenable to computational analysis (Redish, 2022), will be important in integrating findings across the human and animal literature and potentially spurring the development of psychiatric interventions (Pike et al., 2021) (Box 2). ...
Fear and anxiety are adaptive emotions that serve important defensive functions, yet in excess, they can be debilitating and lead to poor mental health. Computational modelling of behaviour provides a mechanistic framework for understanding the cognitive and neurobiological bases of fear and anxiety, and has seen increasing interest in the field. In this brief review, we discuss recent developments in the computational modelling of human fear and anxiety. Firstly, we describe various reinforcement learning strategies that humans employ when learning to predict or avoid threat, and how these relate to symptoms of fear and anxiety. Secondly, we discuss initial efforts to explore, through a computational lens, approach-avoidance conflict paradigms that are popular in animal research to measure fear- and anxiety-relevant behaviours. Finally, we discuss negative biases in decision-making in the face of uncertainty in anxiety.
... Outlook CFM approaches have gained popularity in a variety of areas, and we have focused on those most thoroughly evaluated. More broadly though, CFM has been used to study information seeking (116), deliberation (117), value-free random exploration (118), credit assignment (119), language use (120), foraging (120,121), mental effort avoidance (30), choice stochasticity (122), error-related negativity (123), and the inter-relation of symptom dimensions (124). The approach has been extended to other areas of psychology also, including the study of chronic pain (125), social interactions, learning and evaluations (126)(127)(128), and political leanings (129). ...
... It is for this reason some studies repeatedly interrogate the same factor structure across studies (e.g. AD, CIT and SW: (30,36,40,68,101,116,120,123)), establishing that the association between dimensions and cognitive measures is replicable (e.g. (30,40)), and that results extend to diagnosed patients (32). ...
Most psychiatric disorders do not occur in isolation and most psychiatric symptom dimensions are not uniquely expressed within a single diagnostic category. Our current treatments fail to work for around 25-40% of individuals, perhaps due, at least in part, to an over-reliance on diagnostic categories in treatment development and allocation. This review will describe ongoing efforts in the field to surmount these challenges and precisely characterise psychiatric symptom dimensions using large-scale studies of unselected samples via remote, online and “citizen science” efforts that take a dimensional, mechanistic approach. We discuss the importance that efforts to identify meaningful psychiatric dimensions be coupled with careful computational modelling to formally specify, test, and potentially falsify, candidate mechanisms that underlie transdiagnostic symptom dimensions. We refer to this approach, i.e. where symptom dimensions are identified and validated against computationally well-defined neurocognitive processes, as Computational Factor Modelling (CFM). We describe in detail some recent applications of this method to understand transdiagnostic cognitive processes including model-based planning, metacognition, appetitive processing, and uncertainty estimation. In this context, we highlight how the method has been used to identify specific associations between cognition and symptom dimensions and reveal previously obscured relationships, how findings generalise to smaller in-person clinical and non-clinical samples, and the method is being adapted and optimised beyond its original instantiation. Crucially, we discuss next steps for this area of research, highlighting the value of more direct investigations of treatment response that bridge the gap between basic research and the clinic.
... Here, we identify a number of challenges relating to how well tasks map across rodents and humans (face validity) and how similar the underlying constructs are in their application across species (construct validity). While the predictive validity, or how well the task is able to make predictions about future outcomes, such as response to treatment (59,60), may prove useful in establishing the success of these models, we focus primarily on the two former criteria. ...
Fear and anxiety are largely seen as separate entities, a distinction that inspires and shapes basic and clinical research. Evidence for this distinction has a rich translational base and comes from physiological, behavioural and neurobiological studies. But there is a high degree of inconsistency and a number of fundamental limitations that lead us to question the validity of the distinction. We consider a range of studies examining specifically whether and how the distinction may manifest at the neural, physiological and behavioural levels and we highlight a number of inconsistencies that call the distinction into question. We go on to critically examine assumptions in approaches to the fear-anxiety distinction and consider the implications that these assumptions may have in weighing evidence for and against the distinction. Acknowledging the contention over whether emotional research in animals is easily translatable to subjective experience in humans, we conclude that, although the distinction between fear and anxiety has proved useful and informative, there are a number of reasons for recognising that it is an over-simplification and that future progress may be guided, but should not be limited, by it.
