Know thy agency in predictive coding: Meta-monitoring ... - Google Sites

Consciousness and Cognition 51 (2017) 82–99

Contents lists available at ScienceDirect

Consciousness and Cognition journal homepage: www.elsevier.com/locate/concog

Know thy agency in predictive coding: Meta-monitoring over forward modeling Tomohisa Asai ⇑ NTT Communication Science Laboratories, Human Information Science Laboratory, Kanagawa, Japan

a r t i c l e

i n f o

Article history: Received 11 July 2016 Revised 9 January 2017 Accepted 2 March 2017

Keywords: Agency Motor prediction Prediction error Schizotypy Accuracy-confidence correlation Metacognition Predictive coding

a b s t r a c t Though the computation of agency is thought to be based on prediction error, it is important for us to grasp our own reliability of that detected error. Here, the current study shows that we have a meta-monitoring ability over our own forward model, where the accuracy of motor prediction and therefore of the felt agency are implicitly evaluated. Healthy participants (N = 105) conducted a simple motor control task and SELF or OTHER visual feedback was given. The relationship between the accuracy and confidence in a mismatch detection task and in a self-other attribution task was examined. The results suggest an accuracy-confidence correlation in both tasks, indicating our meta-monitoring ability over such decisions. Furthermore, a statistically identified group with low accuracy and low confidence was characterized as higher schizotypal people. Finally, what we can know about our own forward model and how we can know it is discussed. Ó 2017 Elsevier Inc. All rights reserved.

1. Introduction 1.1. Brain as the detection machine of prediction error The predictive coding theory suggests that our brain always sees the future (Pickering & Clark, 2014; Rao & Ballard, 1999). The brain has developed to predict what happens next on the basis of the current situation (Friston, Stephan, Montague, & Dolan, 2014; Seth, 2013). Depending on the domain, this process has been given different names, such as causal inference (sensory event, Lochmann & Deneve, 2011), forward modeling (motor control, Wolpert & Miall, 1996), contextual reasoning (decision making, Sharps & Martin, 2002) and mind/intention reading (in interpersonal situation, Uithol & Paulus, 2014). The shared mechanism among them is that when a mismatch between the predicted outcome and actual one (i.e., prediction error) is detected, the brain can work to rapidly correct that error either by changing the prediction (i.e., through perceptual updating) or changing the sensory samples (‘‘active inference”, i.e., through action), so that the prediction error is attenuated to permit the following compensative process (Brown, Adams, Parees, Edwards, & Friston, 2013). It seems that we use surprising (unpredictable) outcomes to learn about the world and update our beliefs about how our sensations are caused—both by ourselves and others (Brooks, Carriot, & Cullen, 2015; Friston, 2010; Kouider et al., 2015; Stahl & Feigenson, 2015). In predictive coding, it is not only necessary to predict sensory input. One also has to predict the reliability or precision of sensory information, in relation to top-down prior predictions. This is usually referred to as the precision or confidence assigned to various prediction errors that ascend cortical hierarchies (Adams, Stephan, Brown, Frith, & Friston, 2013;

⇑ Address: 3-1, Morinosato-Wakamiya, Atsugi-shi, Kanagawa-ken 243-0198, Japan. E-mail address: [email protected] http://dx.doi.org/10.1016/j.concog.2017.03.001 1053-8100/Ó 2017 Elsevier Inc. All rights reserved.

T. Asai / Consciousness and Cognition 51 (2017) 82–99

83

Picard & Friston, 2014). Crucially, this means that a key metacognitive capacity is the ability to predict the precision of sensory cues; in other words, to infer the relative confidence placed in sensory evidence and prior beliefs (Adams et al., 2013; Wolpe et al., 2016). This inference about beliefs is quintessentially metacognitive. Physiologically, precision is usually associated with the excitability or gain of neuronal populations reporting prediction errors. Psychologically, it has been linked to attentional gain. In other words, if we predict that a sensory stream is precise, then this sensory (prediction error) stream is afforded more attention or precision (Picard & Friston, 2014). Similarly, there are situations in which one wants to attenuate precision or ignore sensory information. An important example of this is when moving: the sensory (proprioceptive) evidence against movement (Brown et al., 2013) has to be ignored to allow prior beliefs or intentions about movements to be fulfilled (by motor reflexes). A failure of sensory attenuation—or an inability to contextualize or ignore the consequences of our action—has been proposed as the basis of many neuropsychiatric conditions, ranging from autism to schizophrenia and from obsessional compulsive disorder through to hysterical (functional) medical symptoms (e.g., Adams et al., 2013; Lawson, Rees, & Friston, 2014; Picard & Friston, 2014). 1.2. Feeling of control, illusion of control, and alien control The embodied self-consciousness or sense of self (Blanke, Slater, & Serino, 2015; Sevdalis & Keller, 2014), which has been focused on this past decade, might be grounded within this prediction-outcome loop (Apps & Tsakiris, 2014; Picard & Friston, 2014), where the prediction error is detectable. When we perform some action, we get a pre-reflective sense that we ourselves are causing our own action (Legrand, 2007) unless the prediction error is detected (Asai, Sugimori, & Tanno, 2011b; Asai & Tanno, 2007; Fourneret & Jeannerod, 1998; Knoblich & Kircher, 2004; Nielsen, 1963). The sense that ‘‘I am the one causing the action” is called as the sense of agency or feeling of control (Gallagher, 2000). A good example is visually guided reaching. When participants are required to reach to the goal point with visual feedback (e.g., a cursor), they feel that they are moving the cursor by themselves. This is how we put the ‘‘self-label” on sensory input (i.e., the visual feedback in this case). Since sensorimotor coupling has been learned as an internal model all though the life (Wolpert, 1997; Wolpert, Miall, & Kawato, 1998), it is easy for us to predict the next cursor position from the current one as long as we ourselves generate that cursor movement (Wolpert, Ghahramani, & Jordan, 1995). Therefore, if a small noise (e.g., angular bias) is inserted in the middle, where agency is still kept for that cursor movement, we soon detect a mismatch and then try, even unconsciously, to correct the prediction error to reach the goal by making compensative movements (Knoblich & Kircher, 2004). This means that the sense of agency (feeling of control) can realize feedback control of one’s own movement (Asai, 2015). On the contrary, when the fake movement is deceivably presented as participants’ own movement, where the prediction error (i.e., mismatch) is small, participants might attribute it to themselves (illusion of control) and try to control it (i.e., perform an impossible compensative movement) (Nielsen, 1963). On the other hand, people with schizophrenia or even schizotypal personality traits in the general population might not attribute their own action to themselves (Asai, 2016; Asai, Sugimori, & Tanno, 2008; Asai & Tanno, 2007, 2008, 2013; Waters, Woodward, Allen, Aleman, & Sommer, 2012). Therefore, such patients exhibit a passivity symptom, where they feel as if their movements are caused by others (alien control) (Frith, 2005; Frith, Blakemore, & Wolpert, 2000a, 2000b). This might be because they detect a false prediction error even for their own action because of their less optimized forward modeling (e.g., motor prediction, Asai et al., 2008) in the internal model for motor control (see below). In predictive coding, detecting a false prediction error during action corresponds to a failure to attenuate the precision of that prediction error. This explains the abnormalities of agency seen in schizophrenia and the apparent resistance of people with schizophrenia to things like the self-ticklishing or force matching illusion (Brown et al., 2013; Lemaitre, Luyat, & Lafargue, 2016; Shergill, Samson, Bays, Frith, & Wolpert, 2005; Teufel, Kingdon, Ingram, Wolpert, & Fletcher, 2010). See Brown et al. (2013) for a fuller discussion of how predictions of precision are necessary for movement and how false inference can result from a failure of sensory attenuation. 1.3. Agency on the basis of prediction error As shown above, agency and its illusion or disorder depends on the detection of precise prediction errors. The feeling of control (i.e., sense of agency) is experienced when the prediction error is attenuated to permit action. The illusion of control is felt for fake movement unless the prediction error is detected, where our own action is also conducted. Alien control is experienced when patients detect unattenuated prediction error for their own action. Though detected small error is acceptable for agency since agency is susceptible to other cognitive factors like positive bias (Asai & Tanno, 2008, 2013; Synofzik, Vosgerau, & Newen, 2008), error detection and agency are negatively correlated, so they might sometimes be identified as the same thing (David, Newen, & Vogeley, 2008; Weiss, Tsakiris, Haggard, & Schutz-Bosbach, 2014; Werner, Trapp, Wustenberg, & Voss, 2014). As the inserted bias gets larger, participants detect mismatch more easily and feel less agency for that movement (Asai & Tanno, 2008, 2013). This has been nicely explained by a computational model of motor control (Wolpert, 1997; Wolpert et al., 1995). The forward model (Wolpert & Miall, 1996), a part of the computational model of motor control, predicts the sensory outcome of the motor commands when it is sent to the body by using an efference copy. This predicted outcome is then compared with the actual sensory outcome (i.e., sensory feedback) when the action is indeed executed. Since a self-produced outcome can be correctly predicted by this forward modeling, there should be little mismatch (i.e., prediction error) from that comparison. In contrast, a sensation that is generated by another is not subject to sensory attenuation (Blakemore, Frith, & Wolpert, 2001; Blakemore, Wolpert, & Frith, 1998) and thereby enables oneself to

84


attend to the consequences of another’s action. In this way, the forward model automatically distinguishes between selfproduced sensations and those produced by others (Miall & Wolpert, 1996). As the precision of the prediction error increases so does the likelihood that the sensation was generated externally. That is, the feeling or sense of agency is accompanied by an attenuation of sensory precision, relative to the precision of prior beliefs about the intended act (Brown et al., 2013; Picard & Friston, 2014). 1.4. Schizophrenia as a disorder of motor prediction People with schizophrenia, however, detect that error less (Fourneret et al., 2002; Franck et al., 2001; Knoblich, Stottmeister, & Kircher, 2004) and hence feel illusional agency for an other-attributed outcome (biased feedback or otherproduced outcome, Daprati et al., 1997) and detect false error for their own non-biased feedback as well (Werner et al., 2014). As a result, they feel less agency for self-attributed outcome (Johns et al., 2001). These superficially contradictory attribution patterns (illusory self-attribution as well as illusory other-attribution) are expressed as their positive symptoms (Haggard, Martin, Taylor-Clarke, Jeannerod, & Franck, 2003): the delusion of reference or megalomania, where patients attribute irrelevant eternal events to themselves; and delusions of influence (the alien motor control or auditory verbal hallucination), where patients don’t attribute their own motor action to themselves. The former has been shown empirically (Haggard et al., 2003; Hauser, Knoblich et al., 2011; Hauser, Moore et al., 2011; Hur, Kwon, Lee, & Park, 2014), as has the latter (Waters et al., 2012). A failure to attenuate sensory precision means that the sensory consequences of movements produced by self and others would become indistinguishable. In the context of signal detection theory (SDT), this means low discriminability (d0 ) between self-produced ‘‘signal” and other-produced ‘‘noise”, where the probability distribution for each is largely overlapped because d0 is expressed as the distance between signal and noise distributions (Stanislaw & Todorov, 1999). Some previous studies have indeed indicated that people with schizophrenia and schizotypy have imprecise motor prediction from different aspects, aside from their anomalous motor-related self-recognition. First of all, they can’t detect a visuo-motor mismatch during motor control (e.g., reaching task) as mentioned above (Fourneret et al., 2002; Franck et al., 2001; Knoblich et al., 2004; Synofzik, Thier, Leube, Schlotterbeck, & Lindner, 2010). Therefore, maybe automatic compensative movements (i.e., error correction) were not observed (Frith & Done, 1989, but Knoblich et al., 2004). Other studies have shown that sensory attenuation for self-produced sensory outcome is not observed (Blakemore, Smith, Steel, Johnstone, & Frith, 2000; Ford et al., 2012; Shergill et al., 2005; Teufel et al., 2010). The predicted timing of sensory feedback might also be altered (Bulot, Thomas, & Delevoye-Turrell, 2007). More directly, without visual feedback, people with higher schizotypy in the general population point to a target with larger errors (Asai et al., 2008), suggesting that their motor prediction is unoptimized and therefore becomes more distributed from trial to trial (Synofzik et al., 2010) (see also Fig. 7). 1.5. Meta-monitoring over motor prediction and agency As mentioned above, a failure to attenuate the precision of sensory prediction errors may lead to a compensatory increase in the precision of high-level prior beliefs. In other words, people with schizophrenia or schizotypy will have an unduly high confidence in their thoughts and intentions (Brown et al., 2013; Joyce, Averbeck, Frith, & Shergill, 2013). On the other hand, though it might sound contradictory, some studies have implied that such people ‘‘know” that their motor prediction is imprecise. Schizophrenia patients show increased adaptation (i.e., over-fitting or over-learning) when a visual feedback is rotated in reaching movement, where imprecise motor prediction could prompt patients to rely more on external cues (Synofzik et al., 2010). This might be because the weighting or reliability for their own forward model is reduced. In addition, highly schizotypal people have learned not to utilize their own sensory feedback in feedback control tasks in audio-motor domain because they might be aware that their attribution of whether that feedback is self-generated or not is imprecise (Asai & Tanno, 2013). It seems that we have a monitoring process over the accuracy or reliability of our own motor prediction. This process should be a meta-representative monitor since the forward model itself is thought to serve as a monitor over our own action, which is sometimes referred to as ‘‘self-monitoring” (i.e., self-other discrimination) (e.g., Knoblich et al., 2004). As a result, people who have poorer motor prediction can take an additional aforementioned ‘‘strategy” to compensate for that poorness (Synofzik et al., 2010; Werner et al., 2014), where patients give more weight to external information (Synofzik et al., 2010) or rely less on their own motor prediction (Werner et al., 2014) and sensory information (Asai & Tanno, 2013; Teufel et al., 2015). This meta-monitoring over self-monitoring might work as a real-world functioning and the its dysfunction might be the real cause of schizophrenia (Koren, Seidman, Goldsmith, & Harvey, 2006). The compensative process could be escalated from perception to thought in terms of a hierarchic Bayesian framework (Fletcher & Frith, 2009), thereby producing a high confidence in their weird belief as a by-product, without any error correction process. Here, the current study aims to show that we have a meta-monitoring process over our own forward model (i.e., a monitoring over selfmonitoring), where the relative precision of motor predictions and the precision of their sensory consequences are implicitly evaluated—thereby maintaining a correlation between the sense of felt agency and confidence in our perception. How is that meta-monitoring process expressed? The literature of metacognition in tandem with SDT has revealed the existence of such meta-monitoring ability. Metacognition is generally referred to as the ability to recognize one’s own successful cognitive processing like in perceptual or memory tasks (Fleming & Lau, 2014; Robinson, Johnson, & Herndon, 1997; Shea et al., 2014). This ability is quantitatively measurable as the correspondence between accuracy and one’s own confi-


85

dence, which is known as accuracy-confidence correlation: when we are confident, we are more likely to be accurate (DePaulo, Charlton, Cooper, Lindsay, & Muhlenbruck, 1997; Fetsch, Kiani, & Shadlen, 2014; Fleming & Lau, 2014; Smith, Kassin, & Ellsworth, 1989). This indicates a superior evaluation process over decisions that produce confidence. Since we generally have this ability, though ‘‘how” we do it is still unclear (see also Discussion), the degree of association between accuracy and confidence can be taken as a quantitative measure of meta-monitoring process (Fleming & Lau, 2014). Therefore, if people can figure out in some way their degree of accuracy in their own motor prediction and agency, it would emerge as an accuracy-confidence correlation. In the current study, healthy participants conducted a simple motor control task where the relationship between the accuracy and confidence in a mismatch detection task (motor prediction) and selfother attribution task (agency) was examined. 1.6. The current study Participants were required to trace a sine wave line with visual feedback (i.e., visually-guided reaching) in the current study. They received two types of visual feedback: their own SELF movement or OTHER movement that was independent from their own executed movement. When they became aware of which was presented while tracing, they quit tracing and made reports. The correct response ratio and the time to decide (response latency) was calculated. In order to examine the accuracy-confidence correlation both in motor prediction and agency, d0 was further calculated for accuracy. Latency was used as the confidence measure because the time to decide is known to be an implicit measure of confidence: longer latency means lower confidence (e.g., Kiani, Corthell, & Shadlen, 2014; Koriat & Ackerman, 2010; Koriat & Sorka, 2015; Lak et al., 2014) across different domains in many cognitive and perceptual tasks (Fetsch et al., 2014; Koriat, 2011). Though one might intuitively expect a positive correlation between the two (longer latency produces higher accuracy, i.e., speed-accuracy tradeoff), a negative correlation was hypothesized [when one’s decision is faster (= more confident), one is more likely to be accurate] on the basis of previous studies that indicated an accuracy-confidence correlation (DePaulo et al., 1997; Fetsch et al., 2014; Fleming & Lau, 2014; Lederman et al., 2007; Smith et al., 1989) if participants can assess their own accuracy for decisions in mismatch detection or agency judgment. This was the first purpose of the current study. The second purpose was to examine the schizotypal individual difference in those accuracy-confidence correlations. It has been reported that both motor prediction and agency is disrupted in people with schizophrenia or schizotypy. If so, higher schizotypal traits would be related to less accuracy in both tasks as well as to longer latency (= less confidence) since accuracy and confidence have an inseparable relationship. In other words, if people with high levels of schizotypy fail to attenuate sensory precision, they will be less able to discriminate accurately between movements generated by self and others. At the same time, the relative precision of their prior predictions should be reduced, leading to lower confidence and increased decision latencies. Finally, the relationship between mismatch (i.e., prediction error) detection and agency judgment was examined in terms of individual differences. As mentioned above, though the two are generally consistent (e.g., correlated), agency judgment is susceptible to more cognitive factors. Therefore, ‘‘agency sensitivity” should be generally lower than ‘‘mismatch sensitivity”—the former includes some response biases. The relative comparison between agency and mismatch would reveal each participant’s relative sensitivity for agency or mismatch detection, where their reliance on perceptual information (mismatch detection) for agency judgment could be examined: more sensitivity for agency might mean less reliance on perception and therefore more reliance on other cognitive compensation or strategy (e.g., context) for agency judgment. This argument is presented schematically in Fig. 7. In particular, what we can ‘‘know” about our motor prediction and how we can assess our own accuracy for detection of prediction error is discussed. That would explain schizotypal anomalous feeling of agency. Some schizophrenic symptoms might be excessive compensations, since patients might be aware that their motor prediction or forward model is unoptimized. 2. Method 2.1. Participants A hundred-and-five healthy participants participated in the current experiment (males = 27, average age = 30.6, SD = 6.7). They were recruited from local community and were paid for their participation. All participants were right-handed, and reported normal or corrected-to-normal vision and hearing. All provided written informed consent before the experiments were conducted. The experiment was conducted in accordance with the Declaration of Helsinki. The protocol of the present study was approved by the local ethics committee. Participants were later divided into some groups according to the statistical criterion (see meta-monitoring ability on the schizotypal continuum in the Results section for details). 2.2. Apparatus A LED monitor (PTFBLF-22W, Princeton) and a digitizing tablet (Intuos4 PTK-1240/K0, Wacom) were used for the visually-guided reaching task. The physical size of the input area of the digitizer and that of the plotting area of the monitor were almost the same (473.8 296.1 mm for the monitor and 487.7 304.8 mm for the digitizer. The latter was about 3% larger than the former). The monitor was set 20 cm vertically above the digitizer. Participants manipulated the pen device on

86


the digitizer with its visual feedback (the cursor) on the monitor (Fig. 1). The visual or auditory stimuli were controlled by Hot Soup Processor 3.3 (Onion Software) installed on a Windows computer. 2.3. Procedure The basic procedure in the current experiment followed the previous study (Asai, 2015) where participants were required to trace the target line (sine wave: sinusoidal movement) as accurately as possible in accordance with 1-Hz metronomic clicks. The cursor movements left no visible trajectory. Specifically, they first set their pen position at the starting point (lower left) within five clicks and then started to move it from the count of 0. After that, they smoothly moved the pen (i.e., the cursor) to the goal point (lower right) so that the timing of its reaching each peak of the sine wave matched the timing of each metronomic click. Since the onset and offset of the cursor movement must be sensitive to mismatch detection and then agency judgment (for example, the cursor could move even before their pen movement in the OTHER condition, see below), the cursor was masked during the first 1.5 and last 0.5 s. The longer mask was necessary for the onset because it takes time for participants to manage smooth movements from the static state at the starting position. Therefore, the cursor disappeared right after participants set their pen position to the starting point, and they had to start moving without the cursor. After 1.5 s, the cursor reappeared and they made some judgments regarding the cursor movement. Similarly, after 9.5 s from the start, the cursor disappeared again, and they had to try to reach the goal without it. White noise was played through headphones (RP-WF7-K, Panasonic) along with the metronomic clicks in order to mask potential pen scratching sounds. The refresh rate of the monitor was 60 Hz. There were two conditions regarding the visual feedback of their movement (the circle cursor: 9-mm diameter): SELF or OTHER movement. In the SELF condition, participants received the visual feedback of their own pen movement as the cursor movement. In the OTHER condition, they received cursor movement that was independent of their own pen movement. This was, however, actually their own pre-recorded movement that was secretly recorded in a practice session. In this practice, participants just traced the target line as accurately as possible. This might be most preferable when we treat ‘‘others’” movements (e.g., Asai, 2015; David, Newen, et al., 2008; Kaneko & Tomonaga, 2011) among other options like feedback alteration or actual others’ movements (see the discussions by Asai, 2015; Asai & Tanno, 2013). This is because the motor property should be almost the same between their own pen movement and the OTHER movement even though, unlike in the feedback alteration paradigm, we can’t define and control ‘‘others’” movements as stimuli in terms of specific motor properties (e.g., Asai & Tanno, 2007; Franck et al., 2001; Johns et al., 2001). There might be a concern, however, about the possibility that participants may have tried to adjust their own actual movement automatically in order to match the presented movement for minimizing mismatch in this situation, but that was not the case (Asai, 2015). Even when participants were presented their own pre-recorded movements, the distance between their actual and the presented movements was not reduced gradually during the time course of a trial. This ensured that the participants’ reports (mismatch detection or agency judgment, see below) were based only on the spatiotemporal correspondence between there intended and sensed movements (Asai, 2015, 2016). The task was to make agency judgments of the cursor, or to detect spatio-temporal mismatch (i.e., prediction error) between their own pen movement and the cursor movement. In the AGENCY JUDGEMENT block, they were informed that there were two conditions: one was their own movement and the other was PC-controlled others’ movement (SELF or OTHER condition, see above). Participants were required to lift the pen up into the air from the digitizer immediately when they became aware of which movement was presented (Knoblich & Kircher, 2004). The response latency (time to the point of lifting up the pen) was recorded (0–10 s, in theory). Then, the trial was finished and they reported subjective self/other attribution of the cursor: their own movement or others’ movement (Two Alternative Forced Choice Task: 2AFC). Participants didn’t have any time pressure so that they could trace the target until they became confident enough to make judgments. If necessary, they could reach the goal point. In the MISMATCH DETECTION block, the procedure was the same as that in the agency block except for the instruction. Participants were required to lift the pen up immediately when they had become aware if there was a mismatch between their own and the cursor movement: matching or mismatching. Participants were especially instructed that they didn’t need to make an agency judgment on the basis of mismatch detection, so that they could judge intuitively. This was also true for the mismatch detection task where they could report (mis)matchings regardless of agency (Asai & Tanno, 2007, 2008, 2013). Before the main experiment, participants were briefly trained to get accustomed to the device and procedures. They did practice trials where they just traced the target with the cursor, and those movements were secretly recorded and used later in the main experiment as OTHER movements (see above). There were 40 practice trials, but the first 10 were not used as the OTHER movement because participants had not become accustomed to the procedure: the OTHER movements were randomly chosen from the other 30 recorded movements. After that, they conducted the main experiment, which consisted of two blocks of 40 trials for each (20 repetitions for each of the two conditions) in a random order (since there were 20 repetitions for the OTHER condition, 20 out of the 30 recorded movements were used). The order of the two blocks was counterbalanced among participants. It was confirmed that there were no individual differences in movement error (i.e., motor performance) in this practice session (no correlation with other variables). These null results are provided as Supplementary Information (Table S1).


488 mm

87

Display 200 mm 305 mm

Pen Digitizing tablet

450 mm

700 mm

Fig. 1. The current experimental setup.

2.4. Questionnaire To assess the individual differences in schizotypal personality traits (i.e., schizotypy), the Japanese versions of self-report questionnaires below were administered after the experiment. The validity and reliability of all questionnaires has been confirmed. Schizotypal personality traits: The Schizotypal Personality Questionnaire Brief (SPQB: Raine & Benishay, 1995) is a shortened version of the Schizotypal Personality Questionnaire (SPQ: Raine, 1991). The SPQB is a 22-item true/false self-report questionnaire that measures schizotypal personality traits. It consists of three subscales: Cognitive (positive schizotypy), Interpersonal (negative schizotypy), and Disorganization (disorganized schizotypy); the possible scores on this scale range from 0 to 22 (0–8, 0–8, and 0–6 for the three subscales, respectively). Positive schizotypy is thought to include hallucination experiences as shown below. Hallucination proneness: The Launay/Slade Hallucination Scale (LSHS; Launay & Slade, 1981) measures hallucination-like experiences, including auditory hallucinations. The LSHS is a self-report 12-item questionnaire with responses based on a 5point Likert scale (1–5) measuring the frequency of hallucination-like experiences. The possible score on this scale ranges from 12 to 60. Auditory hallucination proneness: The Auditory Hallucination Experiences Scale 17 (AHES-17, Asai, Sugimori, & Tanno, 2011a) is a shortened version of the AHES (Sugimori, Asai, & Tanno, 2009); both have been used in our studies (Asai et al., 2008, 2011b; Asai, Sugimori, & Tanno, 2009; Asai & Tanno, 2013; Kanemoto, Asai, Sugimori, & Tanno, 2013; Sugimori, Asai, & Tanno, 2011a, 2011b) to supplement previous scales measuring hallucination experiences (e.g., LSHS). This is a 17-item self-report questionnaire, scored on a 5-point Likert scale. The possible score for this scale ranges from 17 to 85. 2.5. Analysis The data obtained in the current study were analyzed by using SPSS 17.0j (IBM) as follows. First of all, the averaged raw data (response ratios and response latency) for all participants as a demographic statistics were presented. In the AGENCY task, Hit means participants’ ‘self’ responses for actual self-movements. Correct Rejection (CR) means their ‘other’ responses for actual other movements (their own pre-recorded movements, though). On the other hand, Miss and False Alarm (FA) are the error responses: Miss means participants’ ‘other’ responses for self-movements and FA means participants’ ‘self’ responses for other movements. In the MISMATCH task, Hit means participants ‘matching’ responses for self movements, while Correct Rejection means their ‘‘mismatching” responses for other movements. On the other hand, Miss means participants’ ‘mismatching’ responses for self-movements and FA means participants’ ‘matching’ responses for other movements.

88


After that, for the main analysis of the current study, the above-mentioned variables were summarized into two main measures: discrimination accuracy and confidence (for AGENCY or MISMATCH). This was because the relationship between them on a raw data basis is difficult to interpret. In such a case, we would get a 4 4 2 6 matrix [response ratio (for Hit, Miss, CR and FA, each) response latency (for Hit, Miss, CR and FA, each) two tasks (agency judgement and mismatch detection) schizotypal personality traits (6 scores)]. Regarding accuracy, d0 (along with b for response bias) was calculated from the ratios of Hit and Correct Rejection responses according to previous studies where agency tasks were analyzed by SDT (signal detection theory, see below) (David, Gawronski, et al., 2008; Repp & Knoblich, 2007). Regarding confidence, the duration of tracing (to the point of lifting up the pen for decision) in each trial was calculated as response latency (Knoblich & Kircher, 2004). It could vary from 0 to 10 s in theory. However, since there was no cursor appearing during the first 1.5 s, as mentioned in procedure section, this value should range from 1.5 to 10 s. Longer response latency means less confidence in their decision (Koriat & Ackerman, 2010; Koriat & Sorka, 2015). Finally, we can see the simpler results for the accuracyconfidence correlation [d0 condition-collapsed latency two tasks ( six schizotypy scores)]. The d0 of accuracy is defined as the combination of the z-scored percentage of hits with the z-scored percentage of false alarms (e.g., Green, 1966; Macmillan, 2004; Stanislaw & Todorov, 1999): d0 = z(P(H)) z(P(FA)). Therefore, for agency judgment, when a participant reported ‘‘self” responses for all self-movement trials (maximum Hit responses or no Miss responses) and simultaneously reported ‘‘other” responses for all other-movement trials (maximum Correct Rejection responses or no False Alarm responses), d0 scored the highest value. For mismatch detection, when a participant reported ‘‘matching” responses for all self-movement trials and simultaneously reported ‘‘mismatching” responses for all othermovement trials, d0 scored the highest value. If the Hit or CR ratio was 1.00, the value was corrected to 0.95 in order to avoid null computation (Macmillan, 2004), so that the maximum d0 value was 3.29. If d0 was minus, this means the participant tended to report in the opposite way (e.g., ‘‘self” for other-movement). A higher d0 score means better discrimination between self- and other-movement in terms of agency judgment or of mismatch detection. The b of response bias is defined as the distance between the ideal criterion and the actual criterion: b = 1/2 ⁄ (z(P(H)) + z(P(FA))). Though this value ranges from 0 to infinite depending on the value of d0 , b under 1 means the more of a preference for ‘‘self” or ‘‘matching”; b over 1 means the more of a preference for ‘‘other” or ‘‘mismatching”. b of 1 means no preference (no response bias). Regarding the relationship between confidence and response latency, recent studies have suggested that confidence is informed by the time taken to form a decision (e.g., Kiani et al., 2014). Therefore, the latency serves as an implicit measure of confidence across different domains in many cognitive and perceptual tasks (Fetsch et al., 2014; Koriat, 2011), even for rats (Lak et al., 2014) or for children (Koriat & Ackerman, 2010). Furthermore, the accuracy-confidence relationship is also observed across domains (DePaulo et al., 1997; Fetsch et al., 2014; Fleming & Lau, 2014; Koriat, 2011; Lederman et al., 2007; Smith et al., 1989). Therefore, d0 -latency correlation would emerge even for agency where the current experiment applied the previous procedure to measure d0 for agency (Desantis, Roussel, & Waszak, 2014; Repp & Knoblich, 2007) and measure latency for agency (Knoblich & Kircher, 2004; Werner et al., 2014). For the accuracy-confidence correlation, the Pearson product-moment correlation coefficients between d0 and latency were examined in agency judgment and mismatch detection, respectively. After that, the potential individual difference of schizotypy in each accuracy-confidence correlation was examined, where the statistically defined clusters [hierarchical cluster analysis with the Ward method based on Pearson correlation (i.e., Mahalanobis’ distance)] were identified in accuracy-confidence correlations. A cluster analysis first needs to define the clustering rule and the distance (or similarity) (Jain et al., 1999). Ward’s minimum variance method (Ward, 1963) is one of the most often used options for clustering rule, where the criterion, based on the optimal value of an objective function, minimizes the total within-cluster variance. Mahalanobis’ distance (Mahalanobis, 1936), instead of Euclidian distance, is preferable when the variables are associated with each other (Toyoda & Ikehara, 2011). The Mahalanobis distance is a generalization of the quadratic form of the Euclidean distance which is free from the scale of the measures used (Carvalho, Albuquerque, de Almeida Junior, & Guimaraes, 2009). These options are available in hierarchical cluster analysis by SPSS. The schizotypy scores were compared between such clusters where the cluster (d0 -latency combination) is the independent variable and schizotypy scores are dependent variables. This means that schizotypal experiences, including hallucination-like experience, are regarded as the results of poor accuracy and confidence in agency/mismatch tasks. Though many individual differences studies, including our own (e.g., Asai & Tanno, 2007, 2008), have assumed personality traits as the independent variable (i.e., the cause) for the experimental results, this time the anomalous agency (experimental results in the current study) should be the cause of schizophrenic symptoms or schizotypal experiences (questionnaire scores in the current study) in theory (see also the discussion in Asai & Tanno, 2013). Finally, the ‘‘sensitivity” for agency judgment and mismatch detection within each participant were compared, where the sensitivity index was calculated on the basis of both of z-scored d0 and z-scored latency values as follows: sensitivity index = (z-scored d0 ) + (z-scored latency) (Asai, Mao, Sugimori, & Tanno, 2011). The comparison of this index between agency and mismatch tasks would reveal each participant’s relative sensitivity for agency or mismatch, where their reliance on perceptual information (mismatch detection) for agency judgment could be examined: more sensitivity for agency might mean less reliance on perception. The statistically identified clusters were identified again in agency-mismatch sensitivity correlation and, lastly, the individual difference in schizotypy scores was examined among such clusters.


89

3. Results 3.1. Descriptive statistics for all participants Fig. 2 shows the general results for experimental measures of the response ratio and latency in each response type (Hit, Miss, CR and FA) in terms of the difference between agency judgment and mismatch detection. Regarding the response ratio (Fig. 2A), a two-way ANOVA (analysis of variance) with response type (Hit or CR) as the within-subject independent variable, task (agency judgment or mismatch detection) as the within-subject independent variable, and response ratio as the dependent variable confirmed that the main effect of task [F(1, 104) = 5.10, p = 0.026] and the main effect of response type [F (1, 104) = 44.0, p < 0.001] were statistically significant, while the interaction was not [F(1, 104) = 1.86, p = 0.176]. This suggests that the Hit response was higher than the CR response regardless of the task and the correct response ratio (i.e., Hit and CR) was higher in the agency task than that in the mismatch task. The former result might depend on the current experimental setup, where the other movement was pre-recorded participants’ own movement (that should be close to the actual movements in distance) and this should cause misattribution of that cursor movement to themselves (Asai, 2015). Though the latter result looks interesting, indicating that agency judgment might be more accurate than mismatch detection, this comparison is not reasonable since response bias is not considered between tasks where the theoretically-same stimuli were used but different instructions were given between tasks. For this concern, an SDT-based analysis would be useful (see also analysis section). When d0 as the accuracy index and b as the response bias index were calculated for agency judgment and mismatch detection (see also following analysis), t-tests revealed a statistical difference in b [t(104) = 5.02, p = 0.027] between agency (averaged b = 0.75, SD = 0.45) and mismatch (averaged b = 0.89, SD = 0.59) tasks but no difference in d0 [t(104) = 2.72, p = 0.102] between agency (averaged d0 = 2.67, SD = 0.62) and mismatch (averaged d0 = 2.54, SD = 0.95) tasks. This suggests that participants’ response was biased toward a ‘self’ response in agency judgement (b under 1 means more of a preference for ‘‘self”), while there was no difference in accuracy between both tasks. This is sometimes referred to as self bias or positive bias in agency studies (Asai & Tanno, 2008, 2013; Synofzik et al., 2008; Weiss et al., 2014) and is observed even for children (Miyazaki & Hiraki, 2006). Regarding response latency (Fig. 2B), a three-way ANOVA was conducted with task (agency judgment or mismatch detection) as the within-subject independent variable, condition (self or other stimuli) as the within-subject independent variable, response type [self (no-mismatching) or other (mismatching) response] as the within-subject independent variable, and with response latency as the dependent variable. Since participants who made no errors (i.e., Miss or FA) had no latency information for that response type, the averaged available value, with such participants excluded, was substituted for each (Miss and FA) for the ANOVA (mean substitution for missing data). As a result, the second-order interaction (task condition response) was not significant [F(1, 104) = 0.24, p = 0.622] but the first order interactions between task and condition [F (1, 104) = 23.16, p < 0.001] and between condition and response [F(1, 104) = 88.37, p < 0.001] were significant. The main effect of response was significant [F(1, 104) = 54.75, p < 0.001] but that of task [F(1, 104) = 0.40, p = 0.530] or condition [F (1, 104) = 1.27, p = 0.262] was not. This means that when participants judged ‘‘self (or no-mismatching)” the latency was longer than when they judged ‘‘other (or mismatching)”. This sounds reasonable, suggesting that participants needed to accumulate successively consistent evidence in order to judge ‘‘self”, while they could judge ‘‘other” immediately if they detected a single (or a fewer) contradictory cue, though the effect of response interacted with that of condition as shown above. Indeed, the simple main effect of the interaction between condition and response revealed significant differences for all pairs except for the difference between Hit and Miss, regardless of tasks (ps < 0.05). This means that the latency in CR (‘‘other” for other stimuli) was shorter than in Hit (‘‘self” for self stimuli) as correct responses and that in Miss (‘‘other” for self stimuli) was shorter than in FA (‘‘self” for other stimuli) as incorrect responses. 3.2. Accuracy-confidence correlation among participants For each participant, d0 (accuracy index), b (response bias index), and response latency (confidence index) were calculated for agency judgment and mismatch detection. For accuracy-confidence correlation, Fig. 3 shows the relationship between d0 (accuracy) and latency (confidence) in the agency or mismatch detection task. To begin with, d0 for agency and d0 for mismatch exhibited modest significant correlation (r = 0.52, p < 0.001). This not-so-high value indicates that agency judgment is based on mismatch detection but not equivalent to it (Asai & Tanno, 2007, 2008, 2013) (see also Relative comparison between agency and mismatch sensitivity below). Agency is thought to be susceptible to more factors, including context or decision-making bias (Synofzik et al., 2008). On the other hand, latency for agency is highly correlated to latency for mismatch (r = 0.80, p < 0.001). Even if agency judgment is susceptible to cognitive factors than mismatch detection, the time to judge agency might not be affected, as also suggested from Fig. 2B, where there was no difference in latency between tasks (the main effect of task was not significant). More importantly, accuracy-confidence correlation was observed both for agency judgment and mismatch detection, respectively. Negative coefficients means that participants, whose d0 (accuracy) was worse, took longer to make their decision (e.g., was less confident). The d0 for agency was more correlated to latency for agency (r = 0.40, p < 0.001) than to latency for mismatch (r = 0.26, p = 0.007), and the d0 for mismatch was more correlated to latency for mismatch (r = 0.43, p < 0.001) than to latency for agency (r = 0.32, p = 0.001), indicating the convergent and divergent validity of

90


(A)

(B) (sec)

Hit (Miss) Self condition

Agency judgment Mismatch detection

Response latency

Response ratio

Agency judgment Mismatch detection

Hit

CR (FA) Other condition

Miss

CR

Self condition

FA

Other condition

Fig. 2. General results for response ratio and latency. Note: Hit and Correct Rejection (CR) are the correct responses while Miss and False Alarm (FA) are the error responses. Miss ratio = (1.00 Hit ratio). FA ratio = (1.00 CR ratio).

Latency (agency)

d’ (mismatch)

d’ (agency) -2

4 0

0

5

Latency (mismatch) 10 0

5

10

3.5

d’ (agency)

2

r = .52**

0 4

r = -.40**

r = -.26**

r = -.32**

r = -.43**

d’ (mismatch) 0 -2 10

Latency (agency)

5

r = .80**

0

Latency (mismatch) Fig. 3. Inter-correlation between d0 and response latency. Note:

means accuracy (d0 )-confidence (response latency) correlation.

**

p < 0.01, N = 105.

0

accuracy-confidence correlation. That is, d -latency correlation here is a task-specific or task-dependent relationship, not a general relationship between task accuracy and response latency. Participants had a meta-monitoring ability to assess the certainty of each decision in mismatch detection or in agency judgment, like in other cognitive functions (Fleming & Lau, 2014). This accuracy-confidence correlation can be interpreted to mean that participants are aware of their own accuracy for detection of prediction error (see also Fig. 7 and Discussion). This result (accuracy-confidence correlation both in agency and mismatch task) holds statistically when the relationship is examined on a raw-data basis (i.e., for Hit, Miss, CR and FA each, Fig. 4). The correct response ratio (Hit and CR, respectively) is negatively correlated with each response latency both in agency (Fig. 4A) and mismatch (Fig. 4B) tasks [the correlations for Miss and FA are skewed and should be ignored here since these correlations are only among participants who made errors (i.e., Miss and FA)]. 3.3. Meta-monitoring ability on the schizotypal continuum In order to examine individual differences in schizotypal personality traits in this accuracy-confidence correlation, the cluster analysis with the Ward method based on Mahalanobis’ distance (this is applicable for associated variables; see analysis section) was conducted to divide participants into some groups. For simplicity, two groups (i.e., clusters) were statistically identified for each agency or mismatch task. As a result, a lower d0 and longer latency group (cluster 1a or cluster 2a), and higher d0 and shorter latency group (cluster 1b or cluster 2b) were identified for each task, in accordance with the regression line among all participants (Fig. 5A and B): there were significant differences in d0 and latency between two clusters in agency and mismatch detection tasks (ps < 0.001, Fig. S1A and S1B). Then, schizotypy scores were compared between these groups (Fig. 5C and D). A multivariate analysis of variance (MANOVA) with group (two clusters) as the between-subject independent variable and each questionnaire score as the dependent variable revealed that the whole model was significant (Wilks Lambda Approximate F = 2.73, df = 5, 99, p = 0.023), indicating the effect of group on schizotypy scores as a whole.

91


(A) Agency judgment

(B) Mismatch detection

Latency Miss (2)

Latency

CR (3)

FA (4)

Hit (1)

(1)

Miss (2)

CR (3)

FA (4)

(1)

Response

Response

Hit (1)

(2) (3) (4)

(2) (3)

Response ratio

Response ratio

(4)

CR Hit

CR Hit (sec)

(sec)

Response latency

Response latency

Fig. 4. Relationship between response ratio and latency in each response type. Note: N = 105, ** p < 0.01. The correlations for Miss and FA are skewed and should be ignored here since these correlations are only among participants who made errors (i.e., Miss and FA).

(B) Mismatch detection

(A) Agency judgment

d’

d’

Cluster 1a (N=46, lower d’ and longer latency)

Cluster 2a (N=45)

Cluster 1b (N=59, higher d’ and shorter latency)

Cluster 2b (N=60)

(sec)

(sec) Response latency

Response latency

(C)

+

*

*

Cluster 1a Cluster 1b

(D)

*

*

*

*

*


z-score

*

*

Schizotypy scores Fig. 5. Clusters in accuracy-confidence correlation and schizotypy. Note: d0 as a function of accuracy and latency as a function of confidence, + p < 0.10, * p < 0.05. Error bars = ±1 S.E. Cluster analyses identified a lower d0 and longer latency group (cluster 1a or cluster 2a), and higher d0 and shorter latency group (cluster 1b or cluster 2b) for each task.

A post hoc t-tests revealed that some schizotypy scores were significantly elevated for cluster 1a compared to 1b [t(104) = 4.44, p = 0.037 for AHES, t(104) = 2.64, p = 0.100 for LSHS, t(104) = 9.90, p = 0.002 for Positive, t(104) = 6.88, p = 0.010 for Negative, but t(104) = 0.32, p = 0.568 for Disorganized].

92


Table 1 Inter-correlation between experimental index and schizotypy scale. Agency judgment

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Mismatch detection

Schizotypal personality scale

d0 (1)

b (2)

Latency (3)

d0 (4)

b (5)

Latency (6)

AHES (7)

LSHS (8)

SPQB (9)

Pos. (10)

Neg. (11)

Dis. (12)

–

0.46** –

0.40** 0.28** –

0.52** 0.22* 0.32** –

0.15 0.27** 0.08 0.04 –

0.26** 0.15 0.80** 0.43** 0.06 –

0.19 + 0.13 0.28** 0.24* 0.02 0.24* –

0.16 0.09 0.19 + 0.23* 0.05 0.17 + 0.78** –

0.25* 0.13 0.20* 0.31** 0.06 0.22* 0.55** 0.50** –

0.32** 0.14 0.23* 0.31** 0.15 0.17 + 0.48** 0.46** 0.76** –

0.17 + 0.17 + 0.23* 0.21* 0.08 0.24* 0.44** 0.30** 0.78** 0.37** –

0.08 0.03 0.02 0.20* 0.11 0.09 0.31** 0.38** 0.74** 0.38** 0.35** –

Note: N = 105. + p < 0.10. * p < 0.05. ** p < 0.01.

The same MANOVA and post hoc t-tests revealed that some schizotypy scores were significantly elevated for cluster 2a compared to 2b [Wilks Lambda Approximate F = 2.06, df = 5, 99, p = 0.077 as a whole model, t(104) = 6.50, p = 0.012 for AHES, t(104) = 5.28, p = 0.024 for LSHS, t(104) = 4.69, p = 0.033 for Positive, t(104) = 6.74, p = 0.011 for Negative, but t (104) = 0.91, p = 0.342 for Disorganized]. This is not surprising since most members in the statistically identified clusters across these two independent cluster analyses were overlapped due to the agency-mismatch correlation (see Fig. 3 and Table 1). Only 11 participants were differently assigned across analyses (i.e., 11 members in cluster 1a were assigned as cluster 2b, while the other 36 members in cluster 1a were assigned as cluster 2a). This indicates that people who exhibit lower accuracy and lower confidence at the same time tend to have schizotypal experiences including hallucinations and delusions, as hypothesized. Table 1, the full intercorrelation matrix among all measures including questionnaire scores, also supports this (see also Table 2 on a raw-data basis). The d0 and latency (for agency or mismatch) were significantly correlated with some schizotypal scores (including trends toward significance, p < 0.100). On the other hand, b for response bias had no correlation with such schizotypal scores. This suggests that schizotypal personality traits are free from response bias but related to the lower discriminability and to the less confidence simultaneously in mismatch detection and maybe therefore in agency judgment. Furthermore, the canonical correlation between the experimental index for the accuracy-confidence calibration (d0 and latency both in agency or mismatch task) and schizotypy score (AHES, LSHS, Pos, Neg, and Dis) was 0.420. Such canonical correlation only with the positive schizotypy scores (AHES, LSHS, and Pos) was 0.400, while the same correlation with other schizotypy scores (Neg and Dis) dropped to 0.283. This is consistent with many previous studies on schizophrenia (especially positive symptomathology): patients have difficulty in detecting mismatch or self-agency (Daprati et al., 1997; Fourneret et al., 2002; Franck et al., 2001; Hur et al., 2014; Johns et al., 2001; Knoblich et al., 2004; Waters et al., 2012) and need more time to judge (Werner et al., 2014). 3.4. Relative comparison between agency and mismatch sensitivity Finally, the relationship between mismatch detection and agency judgment was examined. For that purpose, a sensitivity index for agency judgment or mismatch detection was calculated as follows: sensitivity index = (z-scored d0 ) + (z-scored latency). Since d0 and latency were correlated both in agency and mismatch, and d0 (or latency) for agency and d0 (or latency) for mismatch was correlated (see Fig. 3), mismatch sensitivity and agency sensitivity should be highly correlated. Indeed, that was the case (Fig. 6A, r = 0.64, p < 0.001). Based on this mismatch-agency correlation, the cluster analysis with the Ward method based on Mahalanobis’ distance was conducted to divide participants into two groups, again. As a result, two clusters were statistically identified in mismatch-agency coordination, where the regression line was approximately distinguishing two clusters, unlike the accuracy-confidence correlation (see Figs. 5A, B and 6A). Cluster 3a was characterized as higher sensitivity for agency than for mismatch and cluster 3b was the opposite (Fig. S1C). A two-way ANOVA with group (two clusters) as the between-subject independent variable, task (agency judgment or mismatch detection) as the within-subject independent variable, and sensitivity as the dependent variable confirmed that statistically (Fig. 6B): the interaction [F(1, 103) = 154.5, p < 0.001], the simple main effect of task in cluster 3a [F(1, 103) = 81.6, p < 0.001] and in cluster 3b [F(1, 103) = 73.0, p < 0.001], and the simple main effect of group under the agency [F(1, 206) = 20.4, p < 0.001] and mismatch tasks [F(1, 206) = 6.73, p = 0.010] were all significant. Then, schizotypy scores were compared between these groups (Fig. 6C). A MANOVA with group (two clusters) as the between-subject independent variable and each questionnaire score as the dependent variable revealed no significant group effect on schizotypy scores as a whole (Wilks Lambda Approximate F = 1.327, df = 6, 98, p = 0.253) as expected from Fig. 6C.

Agency judgment

Mismatch detection

Response ratio

Schizotypy score

(1) (2) (3) (4) (5) (6)

Response latency

Response ratio

Response latency

Hit

Miss

CR

FA

Hit

Miss

CR

FA

Hit

Miss

CR

FA

Hit

Miss

CR

FA

0.22* 0.19* 0.03 0.00 0.00 0.07

(0.22*) (0.19*) (0.03) (0.00) (0.00) (0.07)

0.16 0.14 0.30** 0.40** 0.21* 0.01

(0.16) (0.14) (0.30**) (0.40**) (0.21*) (0.01)

0.33** 0.24* 0.21* 0.24* 0.24* 0.00

(0.10) (0.12) (0.26) (0.12) (0.02) (0.40*)

0.13 0.08 0.13 0.15 0.15 0.01

(0.16) (0.05) (0.23*) (0.17) (0.24*) (0.13)

0.23* 0.14 0.20* 0.20* 0.08 0.19*

(0.23*) (0.14) (0.20*) (0.20*) (0.08) (0.19*)

0.15 0.19* 0.27** 0.28** 0.21* 0.12

(0.15) (0.19*) (0.27**) (0.28**) (0.21*) (0.12)

0.25** 0.17+ 0.22* 0.17+ 0.20* 0.11

(0.14) (0.16) (0.23) (0.14) (0.11) (0.23)

0.17+ 0.14 0.19* 0.15 0.22* 0.05

(0.07) (0.08) (0.04) (0.08) (0.11) (0.14)

Note: N = 105. (1) AHES, (2) LSHS, (3) SPQB, (4) Positive, (5) Negative, and (6) Disorganized. The correlation with the Miss (or FA) ratio is the reversed value of that with the Hit (or CR) ratio, in definition. The calculation of correlations with Miss and FA latency did not include participants who made no errors (i.e., Miss and FA). + p < 0.10. * p < 0.05. ** p < 0.01.


Table 2 Relationship between schizotypal scores and response ratio/latency.

93

94


Cluster 3a (N=51, higher agency sensitivity than mismatch)

Agency

Mismatch

Cluster 3b (N=54, higher mismatch sensitivity than agency)

(A)

(B)

**

Sensitivity

Agency sensitivity

**

r = .64** Cluster 3a

Cluster 3b

Mismatch sensitivity

**


z-score

*

Schizotypy scores Fig. 6. Clusters in agency-mismatch sensitivity correlation and schizotypy.

Fig. 7. Schematic interpretation of the current results.

(C)


95

However, post hoc t-tests revealed that the scores of AHES and LSHS were significantly elevated for cluster 3a than 3b [t (104) = 4.52, p = 0.036 for AHES, and t(104) = 7.61, p = 0.007 for LSHS, but t(104) = 1.05, p = 0.307 for Positive, t(104) = 0.69, p = 0.794 for Negative, and t(104) = 0.27, p = 0.607 for Disorganized]. This indicates that people with higher hallucination proneness are more sensitive to agency than to mismatch, while people with lower proneness are more sensitive to mismatch. Passivity symptoms, including hallucinations, could be the result of less reliance on perception and therefore more reliance on other cognitive compensations or strategies (e.g., context) for agency judgment. Some schizophrenic symptoms might be excessive compensations for it since patients might be aware that their motor prediction or forward model is unoptimized. These results are discussed on the basis of a schematic interpretation (see Fig. 7), which would explain accuracy-confidence correlation in motor prediction and the schizotypal anomalous feeling of agency. 4. Discussion The current study examined the possibility that we are aware of our own accuracy in motor prediction (forward modeling) in motor control. This meta-monitoring ability was examined by focusing on response latency as a function of confidence. The accuracy-confidence correlation can be taken as a quantitative measure of the meta-monitoring process (Fleming & Lau, 2014) since it indicates a superior evaluating process over decisions that produce confidence. Healthy participants conducted a simple motor control task where the relationship between the accuracy and the confidence in mismatch (prediction error) detection task and also in self-other attribution task (agency) was examined. The results showed a negative correlation (when one’s decision is faster (= more confident), one is more likely to be accurate) both in mismatch detection and agency tasks, suggesting accuracy-confidence correlation. Response latency means that a person is aware of being unsure about the answer until s/he finally judges (Fetsch et al., 2014), suggesting that we can know that the accumulated evidence for the final judgment is still not sufficient. This is possible because we have a metacognitive ability over perceptual evidence (Fleming & Lau, 2014; Shea et al., 2014). Although the mechanism is still unclear (see below discussion Being aware of what and how?), the intrinsic and undissociable relationship between accuracy and confidence is the main idea of metacognition. If someone doesn’t have a metacognitive ability over perception, response latency should not be modulated. In such a case, at the moment any stimulus is presented, we should be able to respond to it immediately because we could not be aware that the decision is unsure or that stimulus is difficult to judge (Kiani et al., 2014). Furthermore, among healthy participants, higher schizotypal people were associated to lower accuracy as well as to longer latency (= less confidence) in both tasks. These relationships, especially between schizotypy and confidence (response latency) should be task-dependent (maybe only for motor prediction), not a general relationship between schizotypy and response latency, because schizotypal symptomatology is thought to be related to shorter latency for judgments in general (jumping to conclusion bias, Adams et al., 2013; Joyce et al., 2013; Juarez-Ramos et al., 2014). Shorter latency might be observed in higher cognitive functions where the following compensative process is overdriven (Fletcher & Frith, 2009). On the contrary, especially in a motor control task in relation to the sense of agency, patients with schizophrenia needed more time to evaluate or ‘‘feel” their own agency, where they showed a longer duration of erroneous external misattribution of their own movement (Werner et al., 2014). This is indeed congruent with the current results. In the current experiment, if schizotypal participants had difficulty in detecting mismatches without a metacognitive ability (i.e., less confidence), they should have answered ‘‘self” (or ‘‘no-mismatching”) without any delay since they didn’t detect a mismatch cue. The delayed latency for them indicates that they didn’t detect a mismatch cue at that moment, but at the same time, they knew the detection was not reliable and needed more evidence to judge, indicating that their metacognitive accuracy-confidence calibration still worked. These results suggest that we have a meta-monitoring process over our own forward model (i.e., a monitoring over selfmonitoring), where the accuracy of motor prediction and therefore of the felt agency are implicitly evaluated. Schizotypal people exhibited a deficit in motor prediction (i.e., less accuracy), but they might be aware of their inaccuracy (i.e., less confidence). In this sense, even schizotypal people have an intact meta-monitoring process over the forward model that could, however, exercise an excessive compensative strategy by higher cognitive functions (e.g., delusion and hallucination) (Synofzik et al., 2010; Werner et al., 2014) where their perceptual information could be devaluated (Teufel et al., 2015). Indeed, the relative comparison between agency and mismatch sensitivity revealed that higher schizotypal participants (especially hallucination proneness) exhibited lower sensitivity for mismatch detection than agency judgment. They seem to judge agency without enough perceptual evidence (Asai & Tanno, 2013). Given that sensitivity for mismatch should be higher than for agency since agency is susceptible to many cognitive factors (Synofzik et al., 2008) and especially since positively biased self-attribution of agency (detected small error is accepted for self-agency) is more adaptive than ‘‘accurate” self-other attribution (Miyazaki & Hiraki, 2006), higher sensitivity for agency means less reliance on perceptual information and less adaptive. Passivity symptoms including hallucination could be the result of less reliance on perception and therefore more reliance on other cognitive expectations, compensations, or strategies (e.g., context) for agency judgment. This is consistent with the notion that excessive compensation from perception to thought might entail schizophrenic symptoms (Fletcher & Frith, 2009).

96


4.1. Being aware of what and how? The current results might be interpreted as Fig. 7 shows. When we make a reaching movement, we can predict its sensory consequence by using the forward model (Wolpert & Miall, 1996). In the current experimental setting, participants could predict their own arm movement (i.e., spatio-temporal position) and could therefore detect the prediction error between the predicted (orange sphere) and actual cursor position (blue sphere) as shown in the lower part of Fig. 7. Those might be represented as probability distributions in our brain (Bays & Wolpert, 2007) since both prediction and actual sensory inputs include inevitable noise (Fig. 7 upper part). The distance between the two distributions (predictive and actual spatio-temporal position) is thought to be the prediction error (red1 arrows): large error means mismatch and otheragency. People with schizophrenia or schizotypal people might have a broad prediction with higher variance due to their unoptimized forward modeling (Fig. 7, right). Patients exhibited increased variability when predicting the visual feedback of their own movement in the absence of visual feedback (Synofzik et al., 2010). In addition, higher schizotypal participants pointed to a target more deviately without visual feedback (Asai et al., 2008), suggesting that their motor prediction is unoptimized and therefore becomes more distributed trial to trial. In this case, the detected error is unreliable because over-lapped distributions could cause detection errors (miss or false alarm). The current study indeed suggested the low discriminability (d0 ) between prediction (self) and actual cursor movement (self or other) in schizotypal participants. The point here is that such discriminability was correlated to response latency (see Fig. 3), suggesting that our own discriminability is meta-monitored implicitly. A person who knows that the detected error is unreliable needs more time to decide (Fig. 7, lower right). Then, the first question is what can people exactly know about their motor prediction? Two hypotheses are suggested here. One is the possibility that we can access (i.e., monitoring) the prediction distribution (orange one) derived from the motor command at the online basis. Since increased variability in motor prediction should entail following detection errors as mentioned above, knowing the distribution might mean knowing our own discriminability. The other is about detected error (red arrows). If people can know that the detected error itself is unreliable, this would mean low discriminability. This idea might be supported by the literature of SDT, where the intensity of perceptual experience could directly contribute to confidence rating (Koriat, 2008). It has been suggested that our motor control system includes several kinds of motor representations, some of which are available to awareness, some of which are not (Blakemore, Wolpert, & Frith, 2002). According to this paper, the ‘‘predicted state” seems to be available to awareness. This is related to the first hypothesis, though whether its distribution can be monitored or not is still unclear. The second hypothesis is related to the comparator in the system where the prediction error (discrepancy or distance between prediction distribution and actual outcome distribution) is computed. Whether the comparator is available to awareness is unclear (Blakemore et al., 2002), but many studies for schizophrenia have suggested that patients cannot detect mismatch (and agency) (Daprati et al., 1997; Fourneret et al., 2002; Franck et al., 2001; Hur et al., 2014; Johns et al., 2001; Knoblich et al., 2004; Waters et al., 2012; Werner et al., 2014). It has been assumed that their predictor is altered but the comparator is intact (c.f., Frith, 2005), where even this situation would produce detection errors. In this sense, their intact comparator could know the reliability of the detected error or perceptual intensity of error. For now, though it is difficult to say which one ‘‘we” have access to, the predictor or comparator, the first hypothesis might be favorable since some studies have indicated that patients are aware of their poor motor prediction even without the comparator process (e.g., reaching without visual feedback, Synofzik et al., 2010). This probabilistic interpretation is basically congruent with the predictive coding account. In predictive coding, confidence or ‘‘precision” is the inverse of variability or uncertainty (Adams et al., 2013). When we discuss meta-monitoring over forward-modeling, precision in the current experiment is about motor prediction or prior distribution. If this prior distribution is more variable (distributed wider, Fig. 7 left), posterior expectation (e.g., self-other criterion) will shift from the prior mean to data (sensory) mean. Prediction errors are ‘‘precision-weighted” in this way, where a mismatch between prediction with higher precision and sensory evidence will entail an amplified or salient prediction error (c.f., attentional gain, Picard & Friston, 2014). However again, ‘‘who” knows their prior distribution is more variable (i.e., reduced precision) in order to calculate the posterior criterion? Precision might be physiologically encoded by the post-synaptic gain of neurons reporting prediction error (NMDA and dopaminergic neuromodulation). If the precision of prior distribution is reduced in schizophrenia, this suggests that both trait and state abnormality in psychosis is related to the ‘‘controller” of post-synaptic gain (for details, see Adams et al., 2013). A further question here is how physiological (e.g., implicit gain) and psychological (e.g., explicit confidence) representations for precision are linked. Prior beliefs (probability distribution over some unknown state or attribute), that are concentrated over the most likely value, may or may not be consciously accessible (‘‘beliefs about beliefs”, Adams et al., 2013). This could be realized by a metacognitive representer, which might be the ‘‘self”. 4.2. ‘‘Self” as a meta-representation over motor control The current study indicated that we know our own agency on the basis of the detection of prediction error. This metarepresentative monitor over the sensorimotor system might work as a unified ‘‘self” in real-world functioning that can interact with the environment and the dysfunction of it might be the essential cause for schizophrenia (Koren et al., 2006; Moe & Docherty, 2014; Postmes et al., 2014). The sense of agency generally refers to the subjective experience of controlling one’s

1

For interpretation of color in Fig. 7, the reader is referred to the web version of this article.


97

own action. This sense is sometimes regarded as a postdictive illusion of causality (Wegner, 2003). However, agency has an important function in motor control (Asai, 2015). When we move our own body, we attribute that movement to ourselves and utilize that sensory information (e.g., visual feedback) to correct ‘‘our own” movement. This might be a function of agency—the mediator between sensory input and motor output (Asai, 2015). Knowing our own agency (or precision) can make that mediation smooth by weighting between our own internal signal (prior belief) including motor prediction and external signal (sensory evidence). In the case of schizophrenia, however, patients might put more weight on external signal (Synofzik et al., 2010) since they know that their motor prediction and then agency are incorrect. As a result, they seem to develop a strategy of not utilizing sensory feedback in their motor control (Asai & Tanno, 2013) since they know that the felt agency over that feedback is incorrect. The compensative process could be escalated from perception to thought (e.g., hallucination and delusion) in terms of a hierarchic Bayesian framework (Fletcher & Frith, 2009), producing a high confidence in their weird belief as a by-product (Joyce et al., 2013), without any error correction process. The current study suggests that we have a meta-monitoring process over our own forward model, where the accuracy of motor prediction and therefore of the felt agency are implicitly evaluated. This could help us understand the relationship among perception (mismatch detection), attribution (agency judgment), and even symptoms (hallucination). For future studies, it is necessary to examine whether other predictive processes aside from motor prediction can be monitored. Though metacognition studies have suggested our meta-monitoring ability over some cognitive functions, as far as I know the current paper has suggested that ability over a predictive process (motor prediction itself or detection of prediction error) for the first time. The predictive coding approach has been suggested as a way to understand the general principle of computation in the brain. Precise monitoring over such computation might be essential for us to consider how reliable detected prediction error is. That in turn could determine whether or not the following compensative process should be exercised, and one might call this ability ‘‘(self-) consciousness” (Fleming & Lau, 2014; Grimaldi, Lau, & Basso, 2015; Soto & Silvanto, 2014).

Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.concog. 2017.03.001.

References Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D., & Friston, K. J. (2013). The computational anatomy of psychosis. Frontiers in Psychiatry, 4, 47. http://dx. doi.org/10.3389/fpsyt.2013.00047. Apps, M. A., & Tsakiris, M. (2014). The free-energy self: A predictive coding account of self-recognition. Neuroscience and Biobehavioral Reviews, 41, 85–97. http://dx.doi.org/10.1016/j.neubiorev.2013.01.029. Asai, T. (2015). Feedback control of one’s own action: Self-other sensory attribution in motor control. Consciousness and Cognition. Asai, T. (2016). Self is ‘‘other”, other is ‘‘self”: Poor self-other discriminability explains schizotypal twisted agency judgment. Psychiatry Research, 246, 593–600. http://dx.doi.org/10.1016/j.psychres.2016.10.082. Asai, T., Mao, Z., Sugimori, E., & Tanno, Y. (2011). Rubber hand illusion, empathy, and schizotypal experiences in terms of self-other representations. Consciousness and Cognition, 20(4), 1744–1750. http://dx.doi.org/10.1016/j.concog.2011.02.005. Asai, T., Sugimori, E., & Tanno, Y. (2008). Schizotypal personality traits and prediction of one’s own movements in motor control: What causes an abnormal sense of agency? Consciousness and Cognition, 17(4), 1131–1142. http://dx.doi.org/10.1016/j.concog.2008.04.004. Asai, T., Sugimori, E., & Tanno, Y. (2009). Schizotypal personality traits and atypical lateralization in motor and language functions. Brain and Cognition, 71(1), 26–37. http://dx.doi.org/10.1016/j.bandc.2009.03.007. S0278-2626(09)00047-5 [pii]. Asai, T., Sugimori, E., & Tanno, Y. (2011b). A psychometric approach to the relationship between hand-foot preference and auditory hallucinations in the general population: Atypical cerebral lateralization may cause an abnormal sense of agency. Psychiatry Research. http://dx.doi.org/10.1016/j. psychres.2011.02.014. S0165-1781(11)00160-0 [pii]. Asai, T., Sugimori, E., & Tanno, Y. (2011a). Auditory verbal hallucination in schizophrenic patients and the general population: The sense of agency in speech. In F. Columbus (Ed.), Hallucinations: Types, stages and treatments. NY: Nova Science Publishers. Asai, T., & Tanno, Y. (2007). The relationship between the sense of self-agency and Schizotypal personality traits. Journal of Motor Behavior, 39(3), 162–168. Asai, T., & Tanno, Y. (2008). Highly schizotypal students have a weaker sense of self-agency. Psychiatry and Clinical Neurosciences, 62(1), 115–119. Asai, T., & Tanno, Y. (2013). Why must we attribute our own action to ourselves? Auditory hallucination like-experiences as the results both from the explicit self-other attribution and implicit regulation in speech. Psychiatry Research, 207(3), 179–188. http://dx.doi.org/10.1016/j.psychres.2012.09.055. Bays, P. M., & Wolpert, D. M. (2007). Computational principles of sensorimotor control that minimize uncertainty and variability. Journal of Physiology, 578 (Pt 2), 387–396. http://dx.doi.org/10.1113/jphysiol.2006.120121. Blakemore, S. J., Frith, C. D., & Wolpert, D. M. (2001). The cerebellum is involved in predicting the sensory consequences of action. NeuroReport, 12(9), 1879–1884. Blakemore, S. J., Smith, J., Steel, R., Johnstone, C. E., & Frith, C. D. (2000). The perception of self-produced sensory stimuli in patients with auditory hallucinations and passivity experiences: Evidence for a breakdown in self-monitoring. Psychological Medicine, 30(5), 1131–1139. Blakemore, S. J., Wolpert, D. M., & Frith, C. D. (1998). Central cancellation of self-produced tickle sensation. Nature Neuroscience, 1(7), 635–640. Blakemore, S. J., Wolpert, D. M., & Frith, C. D. (2002). Abnormalities in the awareness of action. Trends in Cognitive Sciences, 6(6), 237–242. Blanke, O., Slater, M., & Serino, A. (2015). Behavioral, neural, and computational principles of bodily self-consciousness. Neuron, 88(1), 145–166. http://dx. doi.org/10.1016/j.neuron.2015.09.029. Brooks, J. X., Carriot, J., & Cullen, K. E. (2015). Learning to expect the unexpected: Rapid updating in primate cerebellum during voluntary self-motion. Nature Neuroscience, 18(9), 1310–1317. http://dx.doi.org/10.1038/nn.4077. Brown, H., Adams, R. A., Parees, I., Edwards, M., & Friston, K. (2013). Active inference, sensory attenuation and illusions. Cognitive Processing, 14(4), 411–427. http://dx.doi.org/10.1007/s10339-013-0571-3. Bulot, V., Thomas, P., & Delevoye-Turrell, Y. (2007). A pre-reflective indicator of an impaired sense of agency in patients with Schizophrenia. Experimental Brain Research, 183(1), 115–126. http://dx.doi.org/10.1007/s00221-007-1027-8. Carvalho, A. X. Y., Albuquerque, P. H. M., de Almeida Junior, G. R., & Guimaraes, R. D. (2009). Spatial hierarchical clustering. Revista Brasileira de Biometria, 27 (3), 411–442.

98


Daprati, E., Franck, N., Georgieff, N., Proust, J., Pacherie, E., Dalery, J., & Jeannerod, M. (1997). Looking for the agent: An investigation into consciousness of action and self-consciousness in schizophrenic patients. Cognition, 65(1), 71–86. David, N., Gawronski, A., Santos, N. S., Huff, W., Lehnhardt, F. G., Newen, A., & Vogeley, K. (2008). Dissociation between key processes of social cognition in autism: Impaired mentalizing but intact sense of agency. Journal of Autism and Developmental Disorders, 38(4), 593–605. http://dx.doi.org/10.1007/ s10803-007-0425-x. David, N., Newen, A., & Vogeley, K. (2008). The ‘‘sense of agency” and its underlying cognitive and neural mechanisms. Consciousness and Cognition, 17(2), 523–534. http://dx.doi.org/10.1016/j.concog.2008.03.004. S1053-8100(08)00035-4 [pii]. DePaulo, B. M., Charlton, K., Cooper, H., Lindsay, J. J., & Muhlenbruck, L. (1997). The accuracy-confidence correlation in the detection of deception. Personality and Social Psychology Review, 1(4), 346–357. http://dx.doi.org/10.1207/s15327957pspr0104_5. Desantis, A., Roussel, C., & Waszak, F. (2014). The temporal dynamics of the perceptual consequences of action-effect prediction. Cognition, 132(3), 243–250. http://dx.doi.org/10.1016/j.cognition.2014.04.010. Fetsch, C. R., Kiani, R., & Shadlen, M. N. (2014). Predicting the accuracy of a decision: A neural mechanism of confidence. Cold Spring Harbor Symposia on Quantitative Biology, 79, 185–197. http://dx.doi.org/10.1101/sqb.2014.79.024893. Fleming, S. M., & Lau, H. C. (2014). How to measure metacognition. Frontiers in Human Neuroscience, 8, 443. http://dx.doi.org/10.3389/fnhum.2014.00443. Fletcher, P. C., & Frith, C. D. (2009). Perceiving is believing: A Bayesian approach to explaining the positive symptoms of schizophrenia. Nature Reviews Neuroscience, 10(1), 48–58. http://dx.doi.org/10.1038/nrn2536. Ford, J. M., Dierks, T., Fisher, D. J., Herrmann, C. S., Hubl, D., Kindler, J., ... van Lutterveld, R. (2012). Neurophysiological studies of auditory verbal hallucinations. Schizophrenia Bulletin. http://dx.doi.org/10.1093/schbul/sbs009. Fourneret, P., de Vignemont, F., Franck, N., Slachevsky, A., Dubois, B., & Jeannerod, M. (2002). Perception of self-generated action in schizophrenia. Cognitive Neuropsychiatry, 7(2), 139–156. http://dx.doi.org/10.1080/13546800143000212. JW5TTPF9CFN9L3UW [pii]. Fourneret, P., & Jeannerod, M. (1998). Limited conscious monitoring of motor performance in normal subjects. Neuropsychologia, 36(11), 1133–1140. Franck, N., Farrer, C., Georgieff, N., Marie-Cardine, M., Dalery, J., d’Amato, T., & Jeannerod, M. (2001). Defective recognition of one’s own actions in patients with schizophrenia. American Journal of Psychiatry, 158(3), 454–459. Friston, K. (2010). Is the free-energy principle neurocentric? Nature Reviews Neuroscience, 11(8), 605. http://dx.doi.org/10.1038/nrn2787-c2. Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: The brain as a phantastic organ. Lancet Psychiatry, 1(2), 148–158. http://dx.doi.org/10.1016/S2215-0366(14)70275-5. Frith, C. (2005). The neural basis of hallucinations and delusions. Comptes Rendus Biologies, 328(2), 169–175. http://dx.doi.org/10.1016/j.crvi.2004.10.012. Frith, C. D., Blakemore, S. J., & Wolpert, D. M. (2000a). Abnormalities in the awareness and control of action. Philosophical Transactions of the Royal Society BBiological Sciences, 355(1404), 1771–1788. Frith, C. D., Blakemore, S. J., & Wolpert, D. M. (2000b). Explaining the symptoms of schizophrenia: Abnormalities in the awareness of action. Brain Research Reviews, 31(2–3), 357–363. Frith, C. D., & Done, D. J. (1989). Experiences of alien control in schizophrenia reflect a disorder in the central monitoring of action. Psychological Medicine, 19 (2), 359–363. Gallagher, S. (2000). Philosophical conceptions of the self: Implications for cognitive science. Trends in Cognitive Sciences, 4(1), 14–21. Green, D. S. J. (1966). Signal detection theory and psychophysics. New York, NY: Wiley. Grimaldi, P., Lau, H., & Basso, M. A. (2015). There are things that we know that we know, and there are things that we do not know we do not know: Confidence in decision-making. Neuroscience and Biobehavioral Reviews, 55, 88–97. http://dx.doi.org/10.1016/j.neubiorev.2015.04.006. Haggard, P., Martin, F., Taylor-Clarke, M., Jeannerod, M., & Franck, N. (2003). Awareness of action in schizophrenia. NeuroReport, 14(7), 1081–1085. http://dx. doi.org/10.1097/01.wnr.0000073684.00308.c0. Hauser, M., Knoblich, G., Repp, B. H., Lautenschlager, M., Gallinat, J., Heinz, A., & Voss, M. (2011). Altered sense of agency in schizophrenia and the putative psychotic prodrome. Psychiatry Research, 186(2–3), 170–176. http://dx.doi.org/10.1016/j.psychres.2010.08.003. Hauser, M., Moore, J. W., de Millas, W., Gallinat, J., Heinz, A., Haggard, P., & Voss, M. (2011). Sense of agency is altered in patients with a putative psychotic prodrome. Schizophrenia Research, 126(1–3), 20–27. http://dx.doi.org/10.1016/j.schres.2010.10.031. Hur, J. W., Kwon, J. S., Lee, T. Y., & Park, S. (2014). The crisis of minimal self-awareness in schizophrenia: A meta-analytic review. Schizophrenia Research, 152 (1), 58–64. http://dx.doi.org/10.1016/j.schres.2013.08.042. Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys (CSUR), 31(3), 264–323. Johns, L. C., Rossell, S., Frith, C., Ahmad, F., Hemsley, D., Kuipers, E., & McGuire, P. K. (2001). Verbal self-monitoring and auditory verbal hallucinations in patients with schizophrenia. Psychological Medicine, 31(4), 705–715. Joyce, D. W., Averbeck, B. B., Frith, C. D., & Shergill, S. S. (2013). Examining belief and confidence in schizophrenia. Psychological Medicine, 43(11), 2327–2338. http://dx.doi.org/10.1017/S0033291713000263. Juarez-Ramos, V., Rubio, J. L., Delpero, C., Mioni, G., Stablum, F., & Gomez-Milan, E. (2014). Jumping to conclusions bias, BADE and feedback sensitivity in schizophrenia and schizotypy. Consciousness and Cognition, 26, 133–144. http://dx.doi.org/10.1016/j.concog.2014.03.006. Kaneko, T., & Tomonaga, M. (2011). The perception of self-agency in chimpanzees (Pan troglodytes). Proceedings of the Royal Society of London B: Biological Sciences, 278(1725), 3694–3702. http://dx.doi.org/10.1098/rspb.2011.0611. Kanemoto, M., Asai, T., Sugimori, E., & Tanno, Y. (2013). External misattribution of internal thoughts and proneness to auditory hallucinations: The effect of emotional valence in the Deese-Roediger-McDermott paradigm. Frontiers in Human Neuroscience, 7, 351. http://dx.doi.org/10.3389/fnhum.2013.00351. Kiani, R., Corthell, L., & Shadlen, M. N. (2014). Choice certainty is informed by both evidence and decision time. Neuron, 84(6), 1329–1342. http://dx.doi.org/ 10.1016/j.neuron.2014.12.015. Knoblich, G., & Kircher, T. T. (2004). Deceiving oneself about being in control: Conscious detection of changes in visuomotor coupling. Journal of Experimental Psychology: Human Perception and Performance, 30(4), 657–666. http://dx.doi.org/10.1037/0096-1523.30.4.657. Knoblich, G., Stottmeister, F., & Kircher, T. (2004). Self-monitoring in patients with schizophrenia. Psychological Medicine, 34(8), 1561–1569. http://dx.doi. org/10.1017/s0033291704002454. Koren, D., Seidman, L. J., Goldsmith, M., & Harvey, P. D. (2006). Real-world cognitive–and metacognitive–dysfunction in schizophrenia: A new approach for measuring (and remediating) more ‘‘right stuff”. Schizophrenia Bulletin, 32(2), 310–326. http://dx.doi.org/10.1093/schbul/sbj035. Koriat, A. (2008). Subjective confidence in one’s answers: The consensuality principle. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34 (4), 945–959. http://dx.doi.org/10.1037/0278-7393.34.4.945. Koriat, A. (2011). Subjective confidence in perceptual judgments: A test of the self-consistency model. Journal of Experimental Psychology: General, 140(1), 117–139. http://dx.doi.org/10.1037/a0022171. Koriat, A., & Ackerman, R. (2010). Choice latency as a cue for children’s subjective confidence in the correctness of their answers. Developmental Science, 13 (3), 441–453. http://dx.doi.org/10.1111/j.1467-7687.2009.00907.x. Koriat, A., & Sorka, H. (2015). The construction of categorization judgments: Using subjective confidence and response latency to test a distributed model. Cognition, 134, 21–38. http://dx.doi.org/10.1016/j.cognition.2014.09.009. Kouider, S., Long, B., Le Stanc, L., Charron, S., Fievet, A. C., Barbosa, L. S., & Gelskov, S. V. (2015). Neural dynamics of prediction and surprise in infants. Nature Communications, 6, 8537. http://dx.doi.org/10.1038/ncomms9537. Lak, A., Costa, G. M., Romberg, E., Koulakov, A. A., Mainen, Z. F., & Kepecs, A. (2014). Orbitofrontal cortex is required for optimal waiting based on decision confidence. Neuron, 84(1), 190–201. http://dx.doi.org/10.1016/j.neuron.2014.08.039. Launay, G., & Slade, P. (1981). The measurement of hallucinatory predisposition in male and female prisoners. Personality and Individual Differences, 2(3), 221–234.


99

Lawson, R. P., Rees, G., & Friston, K. J. (2014). An aberrant precision account of autism. Frontiers in Human Neuroscience, 8, 302. http://dx.doi.org/10.3389/ fnhum.2014.00302. Lederman, S. J., Klatzky, R. L., Abramowicz, A., Salsman, K., Kitada, R., & Hamilton, C. (2007). Haptic recognition of static and dynamic expressions of emotion in the live face. Psychological Science, 18(2), 158–164. http://dx.doi.org/10.1111/j.1467-9280.2007.01866.x. Legrand, D. (2007). Pre-reflective self-as-subject from experiential and empirical perspectives. Consciousness and Cognition, 16(3), 583–599. http://dx.doi. org/10.1016/j.concog.2007.04.002. Lemaitre, A. L., Luyat, M., & Lafargue, G. (2016). Individuals with pronounced schizotypal traits are particularly successful in tickling themselves. Consciousness and Cognition, 41, 64–71. http://dx.doi.org/10.1016/j.concog.2016.02.005. Lochmann, T., & Deneve, S. (2011). Neural processing as causal inference. Current Opinion in Neurobiology, 21(5), 774–781. http://dx.doi.org/10.1016/ j.conb.2011.05.018. Macmillan, N. A. C. C. D. (2004). Detection theory: A user’s guide. Hillsdale, NJ: Lawrence Erlbaum. Mahalanobis, P. C. (1936). On the generalized distance in statistics. Proceedings of the National Institute of Sciences (India), 2, 49–55. Miall, R. C., & Wolpert, D. M. (1996). Forward models for physiological motor control. Neural Networks, 9(8), 1265–1279. Miyazaki, M., & Hiraki, K. (2006). Delayed intermodal contingency affects young children’s recognition of their current self. Child Development, 77(3), 736–750. Moe, A. M., & Docherty, N. M. (2014). Schizophrenia and the sense of self. Schizophrenia Bulletin, 40(1), 161–168. http://dx.doi.org/10.1093/schbul/sbt121. Nielsen, T. I. (1963). Volition: A new experimental approach. Scandinavian Journal of Psychology, 4(1), 225–230. Picard, F., & Friston, K. (2014). Predictions, perception, and a sense of self. Neurology, 83(12), 1112–1118. http://dx.doi.org/10.1212/ WNL.0000000000000798. Pickering, M. J., & Clark, A. (2014). Getting ahead: Forward models and their place in cognitive architecture. Trends in Cognitive Sciences, 18(9), 451–456. http://dx.doi.org/10.1016/j.tics.2014.05.006. Postmes, L., Sno, H. N., Goedhart, S., van der Stel, J., Heering, H. D., & de Haan, L. (2014). Schizophrenia as a self-disorder due to perceptual incoherence. Schizophrenia Research, 152(1), 41–50. http://dx.doi.org/10.1016/j.schres.2013.07.027. Raine, A. (1991). The SPQ: A scale for the assessment of schizotypal personality based on DSM-III-R criteria. Schizophrenia Bulletin, 17(4), 555–564. Raine, A., & Benishay, D. (1995). The SPQ-B: A brief screening instrument for schizotypal personality disorder. Journal of Personality Disorders, 9(4), 346–355. Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87. http://dx.doi.org/10.1038/4580. Repp, B. H., & Knoblich, G. (2007). Toward a psychophysics of agency: Detecting gain and loss of control over auditory action effects. Journal of Experimental Psychology: Human Perception and Performance, 33(2), 469–482. http://dx.doi.org/10.1037/0096-1523.33.2.469. Robinson, M. D., Johnson, J. T., & Herndon, F. (1997). Reaction time and assessments of cognitive effort as predictors of eyewitness memory accuracy and confidence. Journal of Applied Psychology, 82(3), 416–425. Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends in Cognitive Sciences, 17(11), 565–573. http://dx.doi.org/10.1016/j. tics.2013.09.007. Sevdalis, V., & Keller, P. E. (2014). Know thy sound: Perceiving self and others in musical contexts. Acta Psychologica (Amst), 152, 67–74. http://dx.doi.org/ 10.1016/j.actpsy.2014.07.002. Sharps, M. J., & Martin, S. S. (2002). ‘‘Mindless” decision making as a failure of contextual reasoning. Journal of Psychology, 136(3), 272–282. http://dx.doi.org/ 10.1080/00223980209604155. Shea, N., Boldt, A., Bang, D., Yeung, N., Heyes, C., & Frith, C. D. (2014). Supra-personal cognitive control and metacognition. Trends in Cognitive Sciences, 18(4), 186–193. http://dx.doi.org/10.1016/j.tics.2014.01.006. Shergill, S. S., Samson, G., Bays, P. M., Frith, C. D., & Wolpert, D. M. (2005). Evidence for sensory prediction deficits in schizophrenia. American Journal of Psychiatry, 162(12), 2384–2386. Smith, V. L., Kassin, S. M., & Ellsworth, P. C. (1989). Eyewitness accuracy and confidence: Within- versus between-subjects correlations. Journal of Applied Psychology, 74(2), 356–359. Soto, D., & Silvanto, J. (2014). Reappraising the relationship between working memory and conscious awareness. Trends in Cognitive Sciences, 18(10), 520–525. http://dx.doi.org/10.1016/j.tics.2014.06.005. Stahl, A. E., & Feigenson, L. (2015). Cognitive development. Observing the unexpected enhances infants’ learning and exploration. Science, 348(6230), 91–94. http://dx.doi.org/10.1126/science.aaa3799. Stanislaw, H., & Todorov, N. (1999). Calculation of signal detection theory measures. Behavior Research Methods, Instruments, & Computers, 31(1), 137–149. Sugimori, E., Asai, T., & Tanno, Y. (2009). Reliability and validity of the auditory hallucination-like experience scale. Shinrigaku Kenkyu, 80(5), 389–396. Sugimori, E., Asai, T., & Tanno, Y. (2011a). Sense of agency over speech and proneness to auditory hallucinations: The reality-monitoring paradigm. Quarterly Journal of Experimental Psychology, 64(1), 169–185. http://dx.doi.org/10.1080/17470218.2010.489261. Sugimori, E., Asai, T., & Tanno, Y. (2011b). Sense of agency over thought: External misattribution of thought in a memory task and proneness to auditory hallucination. Consciousness and Cognition, 20(3), 688–695. http://dx.doi.org/10.1016/j.concog.2010.12.014. Synofzik, M., Thier, P., Leube, D. T., Schlotterbeck, P., & Lindner, A. (2010). Misattributions of agency in schizophrenia are based on imprecise predictions about the sensory consequences of one’s actions. Brain, 133(Pt 1), 262–271. http://dx.doi.org/10.1093/brain/awp291. awp291 [pii]. Synofzik, M., Vosgerau, G., & Newen, A. (2008). Beyond the comparator model: A multifactorial two-step account of agency. Consciousness and Cognition, 17 (1), 219–239. http://dx.doi.org/10.1016/j.concog.2007.03.010. S1053-8100(07)00026-8 [pii]. Teufel, C., Kingdon, A., Ingram, J. N., Wolpert, D. M., & Fletcher, P. C. (2010). Deficits in sensory prediction are related to delusional ideation in healthy individuals. Neuropsychologia. http://dx.doi.org/10.1016/j.neuropsychologia.2010.10.024. S0028-3932(10)00450-1 [pii]. Teufel, C., Subramaniam, N., Dobler, V., Perez, J., Finnemann, J., Mehta, P. R., ... Fletcher, P. C. (2015). Shift toward prior knowledge confers a perceptual advantage in early psychosis and psychosis-prone healthy individuals. Proceedings of the National Academy of Sciences of the United States of America. http://dx.doi.org/10.1073/pnas.1503916112. Toyoda, H., & Ikehara, K. (2011). An improved method using k-means to determine the optimal number of clusters, considering the relations between several variables. Shinrigaku Kenkyu, 82(1), 32–40 (in Japanese). Uithol, S., & Paulus, M. (2014). What do infants understand of others’ action? A theoretical account of early social cognition. Psychological Research Psychologische Forschung, 78(5), 609–622. http://dx.doi.org/10.1007/s00426-013-0519-3. Ward, J. H. J. R. (1963). Hierarchical grouping to optimize an objective function. Journal of American Statistical Association, 58, 236–244. Waters, F., Woodward, T., Allen, P., Aleman, A., & Sommer, I. (2012). Self-recognition deficits in schizophrenia patients with auditory hallucinations: A metaanalysis of the literature. Schizophrenia Bulletin, 38(4), 741–750. http://dx.doi.org/10.1093/schbul/sbq144. Wegner, D. M. (2003). The mind’s best trick: How we experience conscious will. Trends in Cognitive Sciences, 7(2), 65–69. S1364661303000020 [pii]. Weiss, C., Tsakiris, M., Haggard, P., & Schutz-Bosbach, S. (2014). Agency in the sensorimotor system and its relation to explicit action awareness. Neuropsychologia, 52, 82–92. http://dx.doi.org/10.1016/j.neuropsychologia.2013.09.034. Werner, J. D., Trapp, K., Wustenberg, T., & Voss, M. (2014). Self-attribution bias during continuous action-effect monitoring in patients with schizophrenia. Schizophrenia Research, 152(1), 33–40. http://dx.doi.org/10.1016/j.schres.2013.10.012. Wolpe, N., Ingram, J. N., Tsvetanov, K. A., Geerligs, L., Kievit, R. A., Henson, R. N., & Rowe, J. B. (2016). Ageing increases reliance on sensorimotor prediction through structural and functional differences in frontostriatal circuits. Nature Communications, 7, 13034. http://dx.doi.org/10.1038/ncomms13034. Wolpert, D. M. (1997). Computational approaches to motor control. Trends in Cognitive Sciences, 1(6), 209–216. Wolpert, D. M., Ghahramani, Z., & Jordan, M. I. (1995). An internal model for sensorimotor integration. Science, 269(5232), 1880–1882. Wolpert, D. M., & Miall, R. C. (1996). Forward models for physiological motor control. Neural Networks, 9(8), 1265–1279. Wolpert, D. M., Miall, R. C., & Kawato, M. (1998). Internal models in the cerebellum. Trends in Cognitive Sciences, 2(9), 338–347.

Know thy agency in predictive coding: Meta-monitoring ... - Google Sites

Know thy agency in predictive coding: Meta-monitoring ... - Google Sites

Suggest Documents

Predictive coding in agency detection

Know thy enemy

Predictive coding in agency detection - PURE - Aarhus Universitet

Predictive coding in agency detection - PURE - Aarhus Universitet

PREDICTIVE CODING

Know Thy Source Code - CEUR Workshop Proceedings

Know Thy Students: Providing Aggregate Student ...

Complementing predictive coding

Know Thy Impact: Visible Learning in Theory and Practice

Honor thy symbionts - Google Sites

PACKET LOSS CONCEALMENT IN PREDICTIVE AUDIO CODING ...

Application of Predictive Coding in Neuroevolution

ERASURE RECOVERY IN PREDICTIVE CODING ... - CiteSeerX

Know thy enemy: Hizbullah, 'terrorism' and the politics of perception

Know Thy Student! Combining Learning Analytics and Critical ... - Eric

Know Thy Impact: Teaching, Learning and Leading (Spring ... - Ontario

Know Thy Sensor: Trust, Data Quality, and Data Integrity ... - CiteSeerX

Know Thy Neighbor: Costly Information Can Hurt Cooperation ... - PLOS

NoN-Greedy Routing Algorithms Are Optimal or Know thy Neighbor's ...

Know thy Neighbor's Neighbor: Better Routing for Skip-Graphs and ...

Deficits in Predictive Coding Underlie Hallucinations in Schizophrenia

Interpersonal predictive coding, not action ... - Leonhard Schilbach

Predictive Image Coding With Adaptive Wavelet ...

Electrophysiological correlates of predictive coding ... - Semantic Scholar