Melbourne School of Psychological Sciences - Theses
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ItemHow we evaluate and overrule our perceptual decisions( 2020)To navigate the world safely, it is critical that we are able to rapidly evaluate and overrule about our perceptual judgements. This thesis investigated the cognitive processes which underlie two forms of decision evaluation: discrete ‘changes of mind’ (i.e. decision reversals) and continuous confidence judgements. Three specific research questions were posed and addressed. Firstly, what sources of sensory information influence the likelihood and speed with which we change our minds about a perceptual decision? Secondly, what are the moment-to-moment information processing dynamics that underlie decision reversals? And finally, can information which is seemingly extraneous to a perceptual decision, specifically the amount of physical effort invested into reporting a decision outcome, affect retrospective judgements of decision confidence? These research questions formed the basis of three studies, which make up the core empirical chapters of this thesis. Study 1 investigated whether ‘absolute’ sensory information affects change-of-mind behaviour, across two experiments. In both experiments, participants indicated which of two flickering grey squares was the brightest with a button press. Following each initial decision, the stimuli remained on screen for a brief period and participants were free to change their response. To manipulate absolute sensory evidence the overall brightness of the two squares was varied, while either their luminance difference (Experiment 1) or luminance ratio (Experiment 2) was held constant. In both experiments increases in absolute evidence led to faster, less accurate initial responses and slower changes of mind. Change-of-mind accuracy decreased when the luminance difference was held constant, but remained unchanged when the luminance ratio was fixed. To account for these findings, we examined the predictions of six models: three existing change-of-mind models and three alternative models which have previously been used to account for the effects of absolute evidence on one-off decisions. Overall, a leaky competing accumulator model best accounted for participants’ behaviour. This suggests that the biologically relevant features of leak and partial inhibition within a decision process may be important in accounting for change-of-mind behaviour. Study 2 investigated the information processing dynamics underlying initial decisions and changes of mind. In particular, this study addressed the outstanding question of whether information processed prior to a decision being made (‘pre-decisional information’) has any influence on the likelihood and speed with which that decision is later reversed. As in Study 1, participants indicated which of two flickering grey squares was the brightest. Following each decision, the stimulus briefly remained on screen and participants were free to change their response. Critically, with each screen refresh a random luminance value was added to the mean luminance value of each square. Using psychophysical reverse correlation, we then retrospectively examined the impact that this luminance noise had on participants’ decisions on a frame-by-frame basis. Strikingly, we found that even the very first frame of sensory evidence participants saw influenced the likelihood and speed of later decision reversals. This indicates that pre-decisional information can influence later change-of-mind behaviour, and challenges the most prominent model of perceptual changes of mind, the extended Diffusion Decision Model (extended DDM), which predicts a complete insensitivity to pre-decisional information. To account for our findings within the DDM framework, we developed a novel variant of the extended DDM in which initial sensory information exerts a long-lasting bias over ongoing evidence accumulation. When fit to just the behavioural response data alone, this model was able to recreate the information usage patterns we observed. This suggests that an initial 'snapshot' of sensory information may exert a long-lasting bias over later sensory evidence accumulation, thus influencing later self-corrective behavior. Finally, Study 3 investigated the effect of foregone physical effort expenditure on decision confidence judgements. In this study, a dynamic luminance discrimination task was again employed. However, participants reported their decisions by squeezing one of two hand-held dynamometers until a pre-specified force threshold was reached. To manipulate the amount of effort required to report a choice, we varied how hard participants needed to squeeze on each trial across three individually calibrated levels (low, medium, high). After each decision, participants gave a confidence rating on a continuous scale ranging from 0 (‘certainly incorrect’) to 100 (‘certainly correct’). It was found that when more effort had been invested into reporting a decision, participants were more confident that the decision was correct. Broadly put, this suggests that people are sensitive to a ‘motoric sunk cost effect’ whereby greater foregone effort expenditure leads to an inflated sense of decision confidence. Overall, these findings suggest that: a) change-of-mind decisions are sensitive to absolute as well as relative sources of sensory information, b) that initial, pre-decisional, sensory information can influence the speed and likelihood with which a decision is later reversed, and c) that additional sources of information beyond sensory evidence, specifically action dynamics, can feed into and/or modulate the processes which underlie self-evaluative behaviour. These findings are consistent with post-decisional evaluative behaviours arising out of a continued unfolding of the initial decision process, with time-varying dynamics, which receives top-down modulation from additional self-monitoring process(es).
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ItemThe Impact of Comorbidities and Expectations on Functional Neurological Disorder Symptoms (FND)( 2020)Functional Neurological Disorder (FND) is a condition that encompasses a wide spectrum of neurological symptoms that do not have an organic explanation. It is not clear why some patients develop a specific neurological symptom. In our study we considered the most frequent FND sub-types: functional motor disorders (FMD) and non-epileptic seizures (NES). It has been purported that individuals with risk factors, create strong expectations about body sensations based on clinical experiences such as disease, injury or surgery. These expectations would impact the nervous system which in turn lead to functional symptoms. These symptoms are inconsistent in frequency and evolution across time, and they are vulnerable to suggestion. Thus, there are no objective measures that can account for the symptoms. This thesis aims to determine whether previous clinical factors affecting different parts of the body have a relationship with the development of functional motor disorder (FMD) and non-epileptic seizures (NES), and to carry out a preliminary study of a measure capable of identifying perceptual differences in patients with functional weakness, the most reported symptom. In the first study we analysed the medical records of 108 FND patients (52 FMD and 56 NES), and in the second study we applied the Size-Weight Illusion (SWI) to 11 FND patients with functional weakness and 15 healthy controls (HC). It was found that patients with motor symptoms (FMD) had significantly higher rates of clinical factors that affected their limbs prior to the symptom onset than NES. Moreover, contrary to predicted, patients with NES had a similar rate of events that affected their head than FMD. However, NES had a higher rate of clinical factors during their lifetime than FMD, such as dissociative symptoms, suicidal ideation, and being victim of bullying, which affect the mind, and from the patients’ perspective they can be considered as located in the head. In the second study, FND and HC experienced a similar size-weight illusion. The severity and laterality of the symptom did not impact on the strength of the illusion, nor the dominance of the affected side. However, we propose that it is likely to find an effect in FND with a larger sample size. Otherwise, if similar results were found in future studies, the SWI might be a test that provides an objective assessment to confirm FND has a normal perception of weight relative to size.
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ItemBelief updating and information seeking in decision making under uncertainty( 2017)Adaptive goal-directed behaviour depends on a well-calibrated internal model of the environment. In particular, learning and decision making is most efficient when there is a good match between an agent’s model of the environment and true environmental contingencies. Accordingly, recent theories in cognitive neuroscience have proposed that a primary computational goal of the central nervous system in humans and other animals is the construction and maintenance of these internal models (also termed beliefs). The present thesis aimed to investigate the implications of this belief-centric perspective for the study of decision making under uncertainty, using computational modelling of behaviour in concert with analysis of human electroencephalography (EEG) data. Across one behavioural and three EEG studies, this thesis addressed two related questions concerning participants’ updating of beliefs. Study 1 and Study 2 sought to characterise the neural and cognitive processes by which participants incorporated new information into their beliefs (belief updating). Study 3 and Study 4 sought to investigate the nature of participants’ preferences for acquiring such information, and the neural substrates of this preference (information seeking). In Study 1, 18 healthy young adult participants completed a perceptual learning task—in which participants learned to identify the target contrast of a greyscale checkerboard stimulus using monetary feedback—while EEG was recorded. Formal comparison of computational cognitive models revealed that participants’ behaviour in this task was better explained by a model implementing Bayesian belief updating than by a model implementing a simpler win-stay lose-shift (WSLS) heuristic. Belief variables computed from this Bayesian model were then used as predictors in a single-trial regression analysis of event-related potential (ERP) data. This analysis revealed that the amplitude of the frontocentral P3 component of the ERP was positively associated with belief update size, and that the amplitude of the stimulus-preceding negativity component was negatively associated with belief uncertainty. These results provided evidence that belief update size and belief uncertainty had distinct neural signatures that could be tracked in single trials in specific ERP components. The results further suggested that the cognitive mechanisms underlying belief updating in this task could be described well within a Bayesian framework. Study 2 investigated the effect of task motivation on participants’ use of Bayesian versus heuristic task strategies, and upon the neural substrates of belief updating. Using a modified variant of the perceptual learning task from Study 1, Study 2 presented feedback to participants in the form of either monetary reward, or as affectively neutral instructional directives. Using model-based clustering based upon formal comparison of computational cognitive models, Study 2 identified two distinct participant subgroups. The first subgroup used Bayesian inference in the monetary condition, but switched to a heuristic strategy in the instructive feedback condition; by contrast, the second subgroup always used the Bayesian inference strategy, regardless of feedback condition. It was found that only the strategy-switching subgroup showed worse performance for instructive than for monetary feedback, whereas the Bayesian subgroup did not. This pattern of performance was reflected by similar differences between subgroups in neural encoding of feedback in two components of the event-related potential: the P3, and the late positive potential. These findings suggested that selection of Bayesian versus heuristic strategies in perceptual learning may depend critically on participants’ motivational state, and that individual differences in strategy-switching may underlie group-level differences in neural encoding of feedback. Study 3 investigated information seeking behaviour in two experiments, each with 40 healthy participants. In these experiments, participants completed a novel information-seeking task in which they could choose to pay a monetary cost to receive advance information about the outcome of a monetary lottery. Importantly, acquiring information did not alter lottery outcome probabilities. In the first experiment, it was found that participants were willing to incur considerable monetary costs to acquire payoff-irrelevant information about the lottery outcome. This behaviour was well explained by a computational cognitive model in which information preference resulted from aversion to temporally prolonged uncertainty. In the second experiment, it was found that preference for information was modulated by the rate of information delivery. These results strongly suggest that humans assign an intrinsic value to information, in a manner inconsistent with normative accounts of decision making under uncertainty. Finally, Study 4 investigated the neural substrates of the intrinsic valuation of information, using EEG data were recorded from 22 participants performing the information seeking task developed in Study 3. Behavioural results replicated the findings of Study 3. Analysis of ERPs elicited by informative cues revealed that the feedback-related negativity, an ERP component linked to reward processing, independently encoded both a reward prediction error and an information prediction error. These findings are consistent with the hypothesis that information seeking results from processing of information within neural reward circuits, and provide further evidence for an intrinsic valuation of information. Overall, these findings demonstrate that a belief-centric research perspective has substantial explanatory power for behavioural and neural data in human decision making under uncertainty.