Error Detection

Human behaviour is riddled with errors, and understanding how these errors can be minimised has been of great importance to a broad range of activities (see Rizzo, Ferrante & Bagnara, 1995). For example, human error is the biggest cause of accidents in the aviation and medical industries, as well as work related accidents more generally (Amalberti, 2013; Sarter &

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Alexander, 2000; Ghaferi, Birkmeyer & Dimick, 2009). However, successful organisations have been found to differ in their ability to detect and neutralise errors before they lead to irreversible consequences, and not in the initial amount of errors made (e.g., Ghaferi et al., 2009). Yet the propensity to perceive false feedback as accurate appears to demonstrate that on the individual level our ability to detect a mismatch in expected and actual information about the self is very limited, even when that information is collected and then changed in a controlled environment, with minimal environmental distractors and limited range of possible outcomes. Although the study of false feedback acceptance has been largely limited to feedback on personality traits and preferential choice, it seems to question the fundamental cognitive mechanisms that have been identified as necessary for error detection; (i) a feedback mechanism with some monitoring function that compares what is expected with what has occurred, and (ii) the ability of the cognitive system to catch a discrepancy between expectations and occurrences (Norman, 1981; Rizzo et al., 1995).

In light of the limitations of introspective abilities discussed in the previous section (e.g., Nisbett & Wilson, 1977) questioning the existence of an efficient error monitoring system may seem like a natural progression. Yet our inability to detect a mismatch between our stated preference and outcome are more surprising in the context of low level psychological processes such as motor control (e.g., Bernstein, 1967; Adams, 1971; Schmidt & White, 1972; Schmidt, 1975; Scott, 2004). To illustrate, consider a study by Fourneret and Jeannerod (1998) which required participants to trace a straight line to a target approximately 20cm away. However, instead of being able to see the action of their arm directly, participants were presented with the feedback of their action on screen with a computerised cursor that distorted the movement. The study found that participants adjusted their behaviour according to the feedback, in other words, if the feedback deviation was to the right they moved their arm towards the left to compensate for this discrepancy and vice versa. The findings necessitate the ability to monitor the discrepancy between expected and actual outcomes, as otherwise it would be impossible to adjust the behaviour.

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Although the study by Fourneret and Jeannerod (1998) reports the monitoring ability for real time processes that may appear to be distinct from the personality and choice literature, similarly successful error monitoring can also be observed in simple discrete decisions. For example, when choosing which button to press in response to a presented stimulus, participants successfully detect trials on which they select the wrong response even if no feedback is provided (Rabbitt, 1966). Indeed, error detection appears to be hard-wired within our cognition, with distinct neural processes dedicated to monitoring whether observed outcomes match our expectations (see Holroyd & Coles, 2002; Yeung, Botvinick & Cohen, 2004). More specifically electroencephalography (EEG) research has identified neural activity specific to error detection, termed the Error-Related Negativity (ERN; Gehring, 1992). The ERN is a sharp negative EEG signal that typically peaks 80-150 milliseconds after the motor response begins, and has been found in humans and non-human primates alike (e.g., Godlove et al., 2011) across a wide range of tasks (e.g., categorical discrimination – Gehring, Coles, Meyer & Donchin, 1995; flanker task – Jodo & Kayama, 1992; Go/No Go task – Ruchsow, Spitzer, Grön, Grothe & Kiefer, 2005; Stroop task – Masaki, Tanaka, Takasawa & Yamazaki, 2001). Additionally, the ERN can also be observed in response to negative feedback received after the task. For example, when participants were required to press a button when 1 second had elapsed following presentation of a warning stimulus, and received feedback as to whether they were in an appropriate accuracy range, if the feedback indicated that the response was not within the criterion (negative feedback) it elicited an ERN (Miltner, Braun & Coles, 1997). The ERN exhibited in response to negative feedback is often termed the feedback ERN (fERN), and has now been well documented in research (e.g., Holroyd, Hajcak & Larsen, 2006; Moser & Simons, 2009; San Martín, Manes, Hurtado, Isla & Ibañez, 2010).

Although to my knowledge neural activity of participants undergoing either the Barnum or the choice blindness paradigm is yet to be investigated, the very nature of wrong information being presented would, in theory, suggest that the same error-related neural activity should be anticipated. For

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the Barnum effect, it could be argued that error detection and therefore ERN are not present because the expectations of the outcome are unclear due to lack of transparency of how responses are translated to the actual personality profile, and therefore there is a lack of comparison point for the feedback seen. Indeed, measures which have a clear relationship between the response and personality generated are less susceptible to the Barnum effect (see Furnham & Schofield, 1987, for discussion). On the other hand, the wide range of domains in which the Barnum effect occurs suggests that at least some of the experiments should contain a perceivable mismatch between reality and feedback. Without an exploratory study of the neural activity, however, it is impossible to conclude whether no ERN is present, or whether it does occur but does not translate to a conscious response.

For choice blindness on the other hand, extrapolating findings from other choice tasks suggests that an ERN should be present. In fact, this may be the case on two accounts, first a direct mismatch of feedback to the action performed should result in ERN, second since presenting participants with the non-chosen alternative also entails presenting the less preferred outcome, or negative feedback, we can also anticipate a fERN. Without empirical research this is, of course, impossible to determine for sure, and the finding that the majority of people fail to notice when the feedback of their own choice is incorrect would suggest the opposite – since no error detection occurs it is unlikely that the associated neural activity does either. There is also another possibility, which is that the ERN does occur even for participants that fail to notice the error but it fails to reach the threshold necessary for the error to be detected in consciousness. It has indeed been reported that the ERN can be observed, albeit lower in strength, when participants make an error even when they are not consciously aware of this (e.g., Nieuwenhuis, Ridderinkhof, Blom, Band & Kok, 2001), suggesting that this may be a plausible hypothesis for the underlying neural activity in choice blindness.

Whether false feedback acceptance occurs because of an absence of any error-related neural activity, or because such activity fails to reach consciousness, it is nonetheless surprising that despite an established neural

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process in place to detect errors we often fail to do so. It is, of course, possible that the specificity of domains used in false feedback acceptance research may be responsible for the uniquely poor error detection rates exhibited by participants. However, as you will see in the following chapters the range of domains that are prone to false feedback acceptance is very broad (e.g., Hall et al., 2010; McLaughlin & Somerville, 2013; Richards & Merrens, 1971; Sagana et al., 2013; Wyman & Vyse, 2008), suggesting that it is implausible that every domain selected is uniquely prone to poor error detection. It is however, also very clear that false feedback is not perceived as accurate one hundred percent of the time, and that the proportion of trials on which people do accept false information about their characteristics and preferences varies systematically depending on the precise nature of the task at hand (e.g., Andersen & Nordvik, 2002; Poškus, 2014; Steenfeldt-Kristensen & Thornton, 2013; Somerville & McGowan, 2016). It is, therefore, crucial to investigate why error detection can be poor, and what factors are responsible for distinguishing between detected and undetected invalid feedback. This can not only help us understand how we operate on the cognitive and neural level, but also identify the best techniques that can be used to minimise error detection in real life settings.

So far, in discussing introspective abilities and error detection mechanisms, I have tried to discuss the characteristics of human cognition as a whole, extrapolating between domains and behaviour types to try and paint a comprehensive picture of how people process information. In the next section, I will narrow the field of discussion specifically to preferential choice, as this is the main domain choice blindness research set out to explore (e.g., Johansson et al., 2005, 2008; Hall et al., 2010). Accordingly, the next section excludes the contributions of Barnum effect literature from the discussion. It must be noted that since choice blindness has been demonstrated for recognition of previously seen stimuli (Sagana et al., 2013), individual characteristic ratings (Sagana et al., 2014a; Sauerland et al., 2013), and perception of psychological symptoms (Merckelbach et al., 2011), it is likely that preferential choice is simply a sub-section of choice and judgement

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more generally. Accordingly, much of the discussion can inform cognition beyond the confines of preferential choice alone.