Consciousness and Cognition 29 (2014) 189–198
Contents lists available at ScienceDirect
Consciousness and Cognition journal homepage: www.elsevier.com/locate/concog
Emotion as a boost to metacognition: How worry enhances the quality of confidence Sébastien Massoni ⇑ QuBE – School of Economics and Finance, Queensland University of Technology, Australia
a r t i c l e
i n f o
Article history: Received 26 February 2014 Available online 3 October 2014 Keywords: Confidence Metacognition Emotion Worry Motivation Attention Reaction time
a b s t r a c t Emotion and cognition are known to interact during human decision processes. In this study we focus on a specific kind of cognition, namely metacognition. Our experiment induces a negative emotion, worry, during a perceptual task. In a numerosity task subjects have to make a two alternative forced choice and then reveal their confidence in this decision. We measure metacognition in terms of discrimination and calibration abilities. Our results show that metacognition, but not choice, is affected by the level of worry anticipated before the decision. Under worry individuals tend to have better metacognition in terms of the two measures. Furthermore understanding the formation of confidence is better explained with taking into account the level of worry in the model. This study shows the importance of an emotional component in the formation and the quality of the subjective probabilities. Ó 2014 Elsevier Inc. All rights reserved.
1. Introduction How humans combine emotion and cognition during their decision process is a central question in the behavior sciences. The initial philosophical dissociation between rational and emotional decision (Plato, Descartes, and Kant) has been superseded by a view in which emotions are integrated in the decision process. Emotions are an important component of the decision and are useful to make accurate judgments (Damiaso, 1994; LeDoux, 1996). In this study we will focus on a specific aspect of cognition: metacognition, i.e. the knowledge an individual has about his own cognition. How emotion interacts with this specific kind of cognition is for the moment an open question. Recent studies have focused on inter-subject and inter-task variation in metacognition (Fleming, Weil, Nagy, Dolan, & Rees, 2010; Song et al., 2011; McCurdy et al., 2013) but none have examined how metacognition could be affected by internal processes such as emotions. The only exception that we are aware is Garfinkel et al. (2013) who show that the level of metacognition in a memory task is modulated by the timing of the stimulus with respect to the phase of the heartbeat. This result confirms the idea of the present study: the internal processes may have an effect on the metacognition. Here we want to characterizes this effect and shows that metacognition is improved by the emotional valence of the decision. We measure metacognition via two different abilities: calibration (or bias) reveals how close confidence judgments are on average to real success (Harvey, 1997) and metacognitive accuracy refers to the discrimination (or resolution) of how variations of confidence match the variation of performances (Fleming & Dolan, 2012). A potential relationship between
⇑ Address: School of Economics and Finance, Queensland University of Technology, QUT Gardens Point – Z Block – Level 8 – Office Z833, 2 George St., Brisbane City, QLD 4000, Australia. E-mail address: [email protected] http://dx.doi.org/10.1016/j.concog.2014.08.006 1053-8100/Ó 2014 Elsevier Inc. All rights reserved.
190
S. Massoni / Consciousness and Cognition 29 (2014) 189–198
metacognition and emotion is hypothesized at the light of advances on the neural basis of confidence judgments. Recent studies show that metacognition is associated with activity in the anterolateral prefrontal cortex (PFC) (Yokoyama & et al., 2010 using a short-memory task) and the lateral PFC (Fleming, Huijgen, & Dolan, 2012; Fleming et al., 2010; Rounis, Maniscalco, Rothwell, Passingham, & Lau, 2010 using a perceptual task). Emotions have been more extensively studied and robust evidence has been found in favor of a central role of the orbitofrontal cortex (Bechara, Damasio, & Damiaso, 2000) and the amygdala (Seymour & Dolan, 2008) in the emotional part of decision-making. Nevertheless, some studies document a network of brain area activated during the cognitive – emotional integration rather than specific area for the two aspects (Pessoa, 2008) and this integration of emotions in cognition may occur in lateral PFC (Gray, Braver, & Raichle, 2002). This view gives support to the existence of an emotional effect on the metacognitive abilities. A more intuitive way of thinking about the link between emotion and metacognition is provide by the attentional effect of emotion. The affective significance of a stimulus is known to induce changes in sensory processing and attention (Vuilleumier, 2005; Yiend, 2010). Negative emotions, such as anxiety and worry, are generally associated with a decrease in attentional control (Eysenck, Derakshan, Santos, & Calvo, 2007) and dysregulation of attentional focus (Bishop, 2008). Nevertheless a differentiation between the effects of state and trait anxiety (Pacheco-Unguetti, Acosta, Callejas, & Lupianez, 2010) gives support to a potential positive impact of stated worry on metacognition by increased willingness to (over)control information. This tendency to over-react under worry could have a positive impact on the quality of metacognition with the use of more precise rating strategies and thus better discrimination ability. We might also expect better calibration with a diminishing of the overconfidence by a depressive effect. Our experimental design induces emotions by framing effects. We use loss vs. gain and high vs. low stake frames to generate variations on the self-reported level of worry of subjects. Loss aversion is the tendency to weight loses greater than equivalent gains in decision-making (Kahneman & Tversky, 1979). Recently this well documented bias has been studied with the help of neuronal data and there is evidence that loss aversion could be linked to emotional interactions on the cognitive process (see Takahashi, 2013). Indeed De Martino, Kumaran, Seymour, and Dolan (2006), De Martino, Camerer, and Adolphs (2010) found an activation of the amygdala in the case of loss frame. Even if another fMRI study (Tom, Fox, Trepel, & Poldrack, 2007) was not able to replicate this finding (they found activation of the lateral PFC and the striatum), there exists evidence for an emotional aspect for loss choices compared to gains (Sokol-Hessner, Camerer, & Phelps, 2013; Sokol-Hessner et al., 2009, confirm this hypothesis using physiological and fMRI measures). Thus using a loss vs. gain frame in an experiment should induce variations in the worry felt by subjects facing their choices. We also use a high stake vs. low stake frame to increase the variations of the stated level of worry. High stake decisions are known to be more emotionally demanding than low stake cases (see Kunreuther & et al., 2002, for a review of high stakes decision making). Overall we can expect to succeed in inducing some variations of a negative emotion that will be measured in terms of a worry scale. We define worry as ‘‘a cognitive phenomenon [. . .] concerned with future events where there is uncertainty about the outcome, the future being thought about is a negative one, and this is accompanied by feeling of anxiety’’ (MacLeod, Williams, & Bekerian, 1991 – p. 478). This approach focuses on the central role of uncertainty (Dugas, Gosselin, & Ladouceur, 2001) and makes sense in our design where the outcomes are uncertain and may be negative with important losses. The influence of anxiety on decision-making is well-studied (Hartley & Phelphs, 2012) but it remains unclear that worry leads always to worse decisions. As our design is not based on cognitive tasks but perceptual ones with emotionally neutral stimuli (but worrying frames), we expect to find a positive effect of worry on metacognition due to an increase of energy and attentional effort. We assume that metacognition will be improved when subjects reveal their confidence after making a decision under a worried mood. This assumption that worse mood leads to better metacognition is also supported by results from neuropsychiatric disorders studies. Metacognition, defined as insight or awareness of illness, is improved by negative moods (David, Bedford, Wiffen, & Gilleen, 2012). We can hypothesize that this link is also valid for healthy individuals and thus that metacognition is improved by worried mood. 2. Methods 2.1. Participants The experiment was conducted in May and July 2012 at the Laboratory of Experimental Economics in Paris (LEEP) of the University of Paris 1. Subjects were recruited by standard procedure in the LEEP database and gave written informed consent to take part in the experiment. 103 healthy subjects (54 men; age 18–38 years, mean age, 22.9 years, most enrolled as undergraduate students at the University of Paris) participated in this experiment for pay. The sessions lasted around 120 min and subjects were paid on average €27.1. We excluded 6 subjects from analysis due to insufficient variation (s.d. < 0.03) of confidence or worry. The final sample included 97 subjects for analysis. 2.2. Stimuli The experiment was conducted in MATLAB using Psychophysics Toolbox version 3 (Brainard, 1997). We use a 2AFC numerosity task, which is known to be convenient to fit SDT models (Nieder & Dehaene, 2009) and may be positively affected by emotions (see Phelps, Ling, & Carrasco, 2006, for the effect of emotions on perception). The stimuli consisted of two circles with
S. Massoni / Consciousness and Cognition 29 (2014) 189–198
191
Fig. 1. Experimental design. (A) describes the timeline of the experiment. First subjects observe the characteristics of the bet: the amount of the stake is €20 or €200 and the goal is 2, 3 or 4 successes over 5. In the loss frame they play for not losing and they lose if they fail more than 5 minus the goal times over 5; in the gain frame they play for wining and they win if they succeed at least the goal times over 5. Then they give their level of worry against this bet on a scale between 0 and 9 and their level of confidence in succeeding to the bet on a scale between and 100. After that they do 5 trials of perceptual task with the following sequence: after observing a fixation cross, subjects initiate the stimuli which consist of two circles with a certain amount of dots inside (B is an example of our stimuli). While one circle contains always 50 dots the other circle contains 50 + xc dots. The value of xc is determined by a psychophysical staircase in order to obtain a success rate of 71%. After observing the stimulus for 700 ms subjects have to make their decision and indicate whether it was the right or the left circle that contained the most of dots. Then they have to give the level of confidence on the accuracy of this decision on a scale going from 0% to 100% with steps of 5. (C) explains the mechanisms of the confidence’s elicitation by probabilities matching. It consists in asking individuals whether they prefer to be paid according to the correctness of their answer or according to a specified lottery. A number l1 is drawn between 0 and 100. If their confidence is higher than this number they will be paid on the accuracy of their decision: they win one point if the answer is correct and lose 1 point otherwise. If the confidence is lower than l1 they will be paid according to the following lottery: they will have a probability l1 of winning 1 point and a probability 100 - l1 of losing 1 point. A second number l2 is then drawn between 0 and 100; if it is higher than l1 they win otherwise they lose.
a certain number of dots in each circle (see Fig. 1B). All dots were of the same size and we control for the distance between each dot. One of the two circles always contained 50 dots while the other contained 50 + xc dots. Before the experiment we estimated the value of xc needed to obtain a success rate of 71% using a psychophysical staircase (Levitt, 1971; see below). The position of the circle containing the greater number of dots was randomly assigned to be on the left or right on each trial. 2.3. Task and procedure 2.3.1. Practice and thresholding Subjects initially performed practice trials of the dots task without confidence ratings, in which full feedback was given. We used these trials to calibrate difficulty of the dots task. The calibration phase was done by one-up two-down staircase (Levitt, 1971): after two consecutive correct answers one dot was removed, and after one failure one dot was added. We stopped the calibration after 30 reversals in the staircase and the value of xc was calculated as the mean dot number across the two last reversals of the staircase. Subjects then performed 10 trials of the tasks with confidence elicitation and feedback both on their accuracy and on the results of the elicitation mechanism. Finally they performed 30 trials without feedback to check whether the calibration procedure had succeeded. 2.3.2. Experiment phase The experimental design comprised two aspects, a bet phase in which we induced emotions and a perceptual phase in which subjects performed the numerosity task with choice and confidence. The experiment consisted in 64 blocks of 5 trials. Each block is defined by a risky bet that subjects have to face. These bets have three main components: their goal (the objective of the bet is to obtain at least 2, 3 or 4 success over the 5 next trials), the amount of money in play (€20 or €200) and the frame (bet for win or to avoid loss i.e. a gain or loss framing). The sequence of a block was the following. First subjects observed the three characteristics of the bet. Then they made a judgment about their level of worry about this bet on a 10-point scale from 0 (any worry) to 9 (very worried). We also asked them to reveal their premium coverage for the bet1 and their confidence in the bet’s success. Then they performed 5 trials of the perceptual task. No feedback was provided either on the results of the bet or on the success of each trial. Each trial of the task was based on the following sequence (see Fig. 1A). 1 We elicited this certainty equivalent by a BDM mechanism (Becker, DeGroot & Marschak, 1964). We do not provide a full explanation of this aspect of the experiment as we do not use it in further analysis.
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S. Massoni / Consciousness and Cognition 29 (2014) 189–198
First two outline circles were displayed with fixation crosses at their center. The subject initiated the trial by pressing the ‘‘space’’ key on a standard computer keyboard. The dot stimuli then appeared for 700 ms, and subjects were asked to respond left or right by pressing the ‘‘f’’ or ‘‘j’’ keys, respectively. There was no time limit for responding. After responding subjects were asked to indicate their level of confidence in their choice on a gauge from 0% to 100% with steps of 5%, using the up and down keys, again with no time limit on the response. Overall the experimental phase was divided into 64 blocks (and thus 320 trials of the perceptual task) with the following characteristics: 32 with a goal of 2/5, 16 with a goal of 3/5, 16 with a goal of 4/5; 32 with a stake of €200, 32 with a €20; 32 with the loss frame, 32 with the gain frame. Overall, subjects gave 320 ratings of confidence and 64 levels of worry with the three characteristics balanced between trials. 2.3.3. Payment Subjects’ payment comprised €5 for participation and two variable parts: one bet was randomly chosen and subjects either won the stake, received nothing if they were not covered by the BDM insurance mechanism, or received the certainty equivalent in case of coverage by the insurance; they also accumulated points according to the accuracy of their stated confidence. The incentive mechanism used was the probability matching rule (Fig. 1C) by which they won or lost points on each trial. This mechanism can be seen as a generalization of the no-loss gambling studied by Dienes and Seth (2010). It consists in asking individuals whether they prefer to be paid according to the correctness of their answer or according to a specified lottery. To elicit a subject’s subjective probability about an event E, the subject is asked to provide the probability p that makes him indifferent between a lottery L(E) that gives a positive reward x if E happens, and x otherwise and a lottery L(p) that yields x with probability p, and x with probability (1 p). A random number q is then drawn in the interval [0, 1]. If q is smaller than p, the subject is paid according to the lottery L(E). Otherwise, the subject is paid according to a lottery L(q) that yields x with probability q and x with probability (1 q). This a proper scoring rule i.e. it provides incentives for subjects to reveal their subjective probability truthfully (see Gajdos, Massoni, & Vergnaud, 2014). Suppose that the subject has a probability of success is p but reports a probability r – p. If r < p, the lotteries according to which the subject (given the subjective probability p) is paid are represented in the following table, as a function of the random value q.
Reports r < p Reports p
q
Contents lists available at ScienceDirect
Consciousness and Cognition journal homepage: www.elsevier.com/locate/concog
Emotion as a boost to metacognition: How worry enhances the quality of confidence Sébastien Massoni ⇑ QuBE – School of Economics and Finance, Queensland University of Technology, Australia
a r t i c l e
i n f o
Article history: Received 26 February 2014 Available online 3 October 2014 Keywords: Confidence Metacognition Emotion Worry Motivation Attention Reaction time
a b s t r a c t Emotion and cognition are known to interact during human decision processes. In this study we focus on a specific kind of cognition, namely metacognition. Our experiment induces a negative emotion, worry, during a perceptual task. In a numerosity task subjects have to make a two alternative forced choice and then reveal their confidence in this decision. We measure metacognition in terms of discrimination and calibration abilities. Our results show that metacognition, but not choice, is affected by the level of worry anticipated before the decision. Under worry individuals tend to have better metacognition in terms of the two measures. Furthermore understanding the formation of confidence is better explained with taking into account the level of worry in the model. This study shows the importance of an emotional component in the formation and the quality of the subjective probabilities. Ó 2014 Elsevier Inc. All rights reserved.
1. Introduction How humans combine emotion and cognition during their decision process is a central question in the behavior sciences. The initial philosophical dissociation between rational and emotional decision (Plato, Descartes, and Kant) has been superseded by a view in which emotions are integrated in the decision process. Emotions are an important component of the decision and are useful to make accurate judgments (Damiaso, 1994; LeDoux, 1996). In this study we will focus on a specific aspect of cognition: metacognition, i.e. the knowledge an individual has about his own cognition. How emotion interacts with this specific kind of cognition is for the moment an open question. Recent studies have focused on inter-subject and inter-task variation in metacognition (Fleming, Weil, Nagy, Dolan, & Rees, 2010; Song et al., 2011; McCurdy et al., 2013) but none have examined how metacognition could be affected by internal processes such as emotions. The only exception that we are aware is Garfinkel et al. (2013) who show that the level of metacognition in a memory task is modulated by the timing of the stimulus with respect to the phase of the heartbeat. This result confirms the idea of the present study: the internal processes may have an effect on the metacognition. Here we want to characterizes this effect and shows that metacognition is improved by the emotional valence of the decision. We measure metacognition via two different abilities: calibration (or bias) reveals how close confidence judgments are on average to real success (Harvey, 1997) and metacognitive accuracy refers to the discrimination (or resolution) of how variations of confidence match the variation of performances (Fleming & Dolan, 2012). A potential relationship between
⇑ Address: School of Economics and Finance, Queensland University of Technology, QUT Gardens Point – Z Block – Level 8 – Office Z833, 2 George St., Brisbane City, QLD 4000, Australia. E-mail address: [email protected] http://dx.doi.org/10.1016/j.concog.2014.08.006 1053-8100/Ó 2014 Elsevier Inc. All rights reserved.
190
S. Massoni / Consciousness and Cognition 29 (2014) 189–198
metacognition and emotion is hypothesized at the light of advances on the neural basis of confidence judgments. Recent studies show that metacognition is associated with activity in the anterolateral prefrontal cortex (PFC) (Yokoyama & et al., 2010 using a short-memory task) and the lateral PFC (Fleming, Huijgen, & Dolan, 2012; Fleming et al., 2010; Rounis, Maniscalco, Rothwell, Passingham, & Lau, 2010 using a perceptual task). Emotions have been more extensively studied and robust evidence has been found in favor of a central role of the orbitofrontal cortex (Bechara, Damasio, & Damiaso, 2000) and the amygdala (Seymour & Dolan, 2008) in the emotional part of decision-making. Nevertheless, some studies document a network of brain area activated during the cognitive – emotional integration rather than specific area for the two aspects (Pessoa, 2008) and this integration of emotions in cognition may occur in lateral PFC (Gray, Braver, & Raichle, 2002). This view gives support to the existence of an emotional effect on the metacognitive abilities. A more intuitive way of thinking about the link between emotion and metacognition is provide by the attentional effect of emotion. The affective significance of a stimulus is known to induce changes in sensory processing and attention (Vuilleumier, 2005; Yiend, 2010). Negative emotions, such as anxiety and worry, are generally associated with a decrease in attentional control (Eysenck, Derakshan, Santos, & Calvo, 2007) and dysregulation of attentional focus (Bishop, 2008). Nevertheless a differentiation between the effects of state and trait anxiety (Pacheco-Unguetti, Acosta, Callejas, & Lupianez, 2010) gives support to a potential positive impact of stated worry on metacognition by increased willingness to (over)control information. This tendency to over-react under worry could have a positive impact on the quality of metacognition with the use of more precise rating strategies and thus better discrimination ability. We might also expect better calibration with a diminishing of the overconfidence by a depressive effect. Our experimental design induces emotions by framing effects. We use loss vs. gain and high vs. low stake frames to generate variations on the self-reported level of worry of subjects. Loss aversion is the tendency to weight loses greater than equivalent gains in decision-making (Kahneman & Tversky, 1979). Recently this well documented bias has been studied with the help of neuronal data and there is evidence that loss aversion could be linked to emotional interactions on the cognitive process (see Takahashi, 2013). Indeed De Martino, Kumaran, Seymour, and Dolan (2006), De Martino, Camerer, and Adolphs (2010) found an activation of the amygdala in the case of loss frame. Even if another fMRI study (Tom, Fox, Trepel, & Poldrack, 2007) was not able to replicate this finding (they found activation of the lateral PFC and the striatum), there exists evidence for an emotional aspect for loss choices compared to gains (Sokol-Hessner, Camerer, & Phelps, 2013; Sokol-Hessner et al., 2009, confirm this hypothesis using physiological and fMRI measures). Thus using a loss vs. gain frame in an experiment should induce variations in the worry felt by subjects facing their choices. We also use a high stake vs. low stake frame to increase the variations of the stated level of worry. High stake decisions are known to be more emotionally demanding than low stake cases (see Kunreuther & et al., 2002, for a review of high stakes decision making). Overall we can expect to succeed in inducing some variations of a negative emotion that will be measured in terms of a worry scale. We define worry as ‘‘a cognitive phenomenon [. . .] concerned with future events where there is uncertainty about the outcome, the future being thought about is a negative one, and this is accompanied by feeling of anxiety’’ (MacLeod, Williams, & Bekerian, 1991 – p. 478). This approach focuses on the central role of uncertainty (Dugas, Gosselin, & Ladouceur, 2001) and makes sense in our design where the outcomes are uncertain and may be negative with important losses. The influence of anxiety on decision-making is well-studied (Hartley & Phelphs, 2012) but it remains unclear that worry leads always to worse decisions. As our design is not based on cognitive tasks but perceptual ones with emotionally neutral stimuli (but worrying frames), we expect to find a positive effect of worry on metacognition due to an increase of energy and attentional effort. We assume that metacognition will be improved when subjects reveal their confidence after making a decision under a worried mood. This assumption that worse mood leads to better metacognition is also supported by results from neuropsychiatric disorders studies. Metacognition, defined as insight or awareness of illness, is improved by negative moods (David, Bedford, Wiffen, & Gilleen, 2012). We can hypothesize that this link is also valid for healthy individuals and thus that metacognition is improved by worried mood. 2. Methods 2.1. Participants The experiment was conducted in May and July 2012 at the Laboratory of Experimental Economics in Paris (LEEP) of the University of Paris 1. Subjects were recruited by standard procedure in the LEEP database and gave written informed consent to take part in the experiment. 103 healthy subjects (54 men; age 18–38 years, mean age, 22.9 years, most enrolled as undergraduate students at the University of Paris) participated in this experiment for pay. The sessions lasted around 120 min and subjects were paid on average €27.1. We excluded 6 subjects from analysis due to insufficient variation (s.d. < 0.03) of confidence or worry. The final sample included 97 subjects for analysis. 2.2. Stimuli The experiment was conducted in MATLAB using Psychophysics Toolbox version 3 (Brainard, 1997). We use a 2AFC numerosity task, which is known to be convenient to fit SDT models (Nieder & Dehaene, 2009) and may be positively affected by emotions (see Phelps, Ling, & Carrasco, 2006, for the effect of emotions on perception). The stimuli consisted of two circles with
S. Massoni / Consciousness and Cognition 29 (2014) 189–198
191
Fig. 1. Experimental design. (A) describes the timeline of the experiment. First subjects observe the characteristics of the bet: the amount of the stake is €20 or €200 and the goal is 2, 3 or 4 successes over 5. In the loss frame they play for not losing and they lose if they fail more than 5 minus the goal times over 5; in the gain frame they play for wining and they win if they succeed at least the goal times over 5. Then they give their level of worry against this bet on a scale between 0 and 9 and their level of confidence in succeeding to the bet on a scale between and 100. After that they do 5 trials of perceptual task with the following sequence: after observing a fixation cross, subjects initiate the stimuli which consist of two circles with a certain amount of dots inside (B is an example of our stimuli). While one circle contains always 50 dots the other circle contains 50 + xc dots. The value of xc is determined by a psychophysical staircase in order to obtain a success rate of 71%. After observing the stimulus for 700 ms subjects have to make their decision and indicate whether it was the right or the left circle that contained the most of dots. Then they have to give the level of confidence on the accuracy of this decision on a scale going from 0% to 100% with steps of 5. (C) explains the mechanisms of the confidence’s elicitation by probabilities matching. It consists in asking individuals whether they prefer to be paid according to the correctness of their answer or according to a specified lottery. A number l1 is drawn between 0 and 100. If their confidence is higher than this number they will be paid on the accuracy of their decision: they win one point if the answer is correct and lose 1 point otherwise. If the confidence is lower than l1 they will be paid according to the following lottery: they will have a probability l1 of winning 1 point and a probability 100 - l1 of losing 1 point. A second number l2 is then drawn between 0 and 100; if it is higher than l1 they win otherwise they lose.
a certain number of dots in each circle (see Fig. 1B). All dots were of the same size and we control for the distance between each dot. One of the two circles always contained 50 dots while the other contained 50 + xc dots. Before the experiment we estimated the value of xc needed to obtain a success rate of 71% using a psychophysical staircase (Levitt, 1971; see below). The position of the circle containing the greater number of dots was randomly assigned to be on the left or right on each trial. 2.3. Task and procedure 2.3.1. Practice and thresholding Subjects initially performed practice trials of the dots task without confidence ratings, in which full feedback was given. We used these trials to calibrate difficulty of the dots task. The calibration phase was done by one-up two-down staircase (Levitt, 1971): after two consecutive correct answers one dot was removed, and after one failure one dot was added. We stopped the calibration after 30 reversals in the staircase and the value of xc was calculated as the mean dot number across the two last reversals of the staircase. Subjects then performed 10 trials of the tasks with confidence elicitation and feedback both on their accuracy and on the results of the elicitation mechanism. Finally they performed 30 trials without feedback to check whether the calibration procedure had succeeded. 2.3.2. Experiment phase The experimental design comprised two aspects, a bet phase in which we induced emotions and a perceptual phase in which subjects performed the numerosity task with choice and confidence. The experiment consisted in 64 blocks of 5 trials. Each block is defined by a risky bet that subjects have to face. These bets have three main components: their goal (the objective of the bet is to obtain at least 2, 3 or 4 success over the 5 next trials), the amount of money in play (€20 or €200) and the frame (bet for win or to avoid loss i.e. a gain or loss framing). The sequence of a block was the following. First subjects observed the three characteristics of the bet. Then they made a judgment about their level of worry about this bet on a 10-point scale from 0 (any worry) to 9 (very worried). We also asked them to reveal their premium coverage for the bet1 and their confidence in the bet’s success. Then they performed 5 trials of the perceptual task. No feedback was provided either on the results of the bet or on the success of each trial. Each trial of the task was based on the following sequence (see Fig. 1A). 1 We elicited this certainty equivalent by a BDM mechanism (Becker, DeGroot & Marschak, 1964). We do not provide a full explanation of this aspect of the experiment as we do not use it in further analysis.
192
S. Massoni / Consciousness and Cognition 29 (2014) 189–198
First two outline circles were displayed with fixation crosses at their center. The subject initiated the trial by pressing the ‘‘space’’ key on a standard computer keyboard. The dot stimuli then appeared for 700 ms, and subjects were asked to respond left or right by pressing the ‘‘f’’ or ‘‘j’’ keys, respectively. There was no time limit for responding. After responding subjects were asked to indicate their level of confidence in their choice on a gauge from 0% to 100% with steps of 5%, using the up and down keys, again with no time limit on the response. Overall the experimental phase was divided into 64 blocks (and thus 320 trials of the perceptual task) with the following characteristics: 32 with a goal of 2/5, 16 with a goal of 3/5, 16 with a goal of 4/5; 32 with a stake of €200, 32 with a €20; 32 with the loss frame, 32 with the gain frame. Overall, subjects gave 320 ratings of confidence and 64 levels of worry with the three characteristics balanced between trials. 2.3.3. Payment Subjects’ payment comprised €5 for participation and two variable parts: one bet was randomly chosen and subjects either won the stake, received nothing if they were not covered by the BDM insurance mechanism, or received the certainty equivalent in case of coverage by the insurance; they also accumulated points according to the accuracy of their stated confidence. The incentive mechanism used was the probability matching rule (Fig. 1C) by which they won or lost points on each trial. This mechanism can be seen as a generalization of the no-loss gambling studied by Dienes and Seth (2010). It consists in asking individuals whether they prefer to be paid according to the correctness of their answer or according to a specified lottery. To elicit a subject’s subjective probability about an event E, the subject is asked to provide the probability p that makes him indifferent between a lottery L(E) that gives a positive reward x if E happens, and x otherwise and a lottery L(p) that yields x with probability p, and x with probability (1 p). A random number q is then drawn in the interval [0, 1]. If q is smaller than p, the subject is paid according to the lottery L(E). Otherwise, the subject is paid according to a lottery L(q) that yields x with probability q and x with probability (1 q). This a proper scoring rule i.e. it provides incentives for subjects to reveal their subjective probability truthfully (see Gajdos, Massoni, & Vergnaud, 2014). Suppose that the subject has a probability of success is p but reports a probability r – p. If r < p, the lotteries according to which the subject (given the subjective probability p) is paid are represented in the following table, as a function of the random value q.
Reports r < p Reports p
q
