Data shows that many inventors continue to expend resources on their inventions even after they have received expert advice suggesting that they cease effort. Using a sample of inventors seeking outside advice from a Canadian evaluative agency, this paper examines how overconfidence, optimism, and illusion of control explain this fact. While overconfidence did not have a significant effect on inventor's decisions, illusion of control and optimism did have an effect. An additional interesting finding is that the more time people have spent working on inventions, the more likely they are to discount this expert advice.

I present behavioural data contributing to existing research that (implicit) self-predictions are overly reliant on current intentions at the time of the decision (Koehler & Poon, 2006). Results are consistent with previous findings that self-predictions are often insensitive to translatability cues and overly influenced by desirability cues. We show that although participants typically benefit from a reminder, it is undervalued at the time of the decision (Experiment 1 & 3a) as participants are not willing to pay for a reminder service, unless it is offered free of charge (Experiment 2). Our findings also show that participants fail to incorporate temporal delay sufficiently in their opt-in decisions, even though temporal delay was found to be a significant predictor return behaviour (Experiments 1, 2 & 3b). Instead, decisions were found to be highly influenced by desirability factors (Experiments 1 & 2) which were not significant predictors of task completion. Finally, using a construal manipulation intended to induce participants to think about the decision options in either a concrete or abstract way influenced decisions (Experiment 3a), and subsequently influenced how much participants benefitted from the reminder in task completion (Experiment 3b).

Decisions among alternatives that do not fit rigorous numerical frameworks are common. Such decisions, in which the various aspects of the alternatives are considered simultaneously, are called a tradeoff studies. Tradeoff studies may be more common than optimization problems, but are not generally formalized in written form.Tradeoff studies are broadly recognized and mandated as the method for considering many criteria simultaneously. They are the primary method for making a decision among alternatives listed in the Software Engineering Institute's Capability Maturity Model Integration (CMMI) Decision Analysis and Resolution (DAR) process.The field of Decision Making can explain why the mechanics of tradeoff studies are approached with underconfidence, and can also help eliminate biases from the tradeoff process. Many conclusions obtained from Judgment and Decision Making (JDM), Cognitive Science and Experimental Economics can be used to shed light on various aspects of the tradeoff process. Of course, since many experiments were designed to reveal truths about choice at a basic level, they do not exactly model the processes of tradeoff studies. The technique used to compare the basic experiments and tradeoff studies will be abstraction.Abstraction noun 1. a general concept formed by extracting common features from specific examples, 2. the process of extracting the underlying essence.What follows is a union of the fields of tradeoff studies and cognitive decision making. Because these two areas have never before been explicitly unified, I have produced some unfinished areas in which specific research needs to be done. At this stage, the work of unification must necessarily be conducted at an abstract level.

I present behavioural data contributing to existing research that (implicit) self-predictions are overly reliant on current intentions at the time of the decision (Koehler & Poon, 2006). Results are consistent with previous findings that self-predictions are often insensitive to translatability cues and overly influenced by desirability cues. We show that although participants typically benefit from a reminder, it is undervalued at the time of the decision (Experiment 1 & 3a) as participants are not willing to pay for a reminder service, unless it is offered free of charge (Experiment 2). Our findings also show that participants fail to incorporate temporal delay sufficiently in their opt-in decisions, even though temporal delay was found to be a significant predictor return behaviour (Experiments 1, 2 & 3b). Instead, decisions were found to be highly influenced by desirability factors (Experiments 1 & 2) which were not significant predictors of task completion. Finally, using a construal manipulation intended to induce participants to think about the decision options in either a concrete or abstract way influenced decisions (Experiment 3a), and subsequently influenced how much participants benefitted from the reminder in task completion (Experiment 3b).

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Two studies were conducted in which we examined the relation between shyness and facial expression processing in children. In Study 1, facial expression recognition was examined by asking 97 children ages 12 to 14 years to identify six different expressions displayed at 50% and 100% intensity, as well as a neutral expression. In Study 2, the focus shifted from the recognition of emotions to the interpretation of emotions. In this study, 123 children aged 12 to 14 years were asked a series of questions regarding how they would perceive different facial expressions. Findings from Study 1 showed that, in the case of shy boys, higher levels of shyness were related to lower recognition accuracy for sad faces displayed at 50% intensity. However, in most cases, shyness was not related to facial expression recognition. The results from Study 2 suggested broader implications for shy children. The findings of Study 2 demonstrated that shyness is predictive of biased facial expression interpretation and that rejection sensitivity mediates this relation. Overall the results of these two studies add to the research on facial expression processing in shy children and suggest that cognitive biases in the way facial expressions are interpreted may be related to shy children’s discomfort in social situations.

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Rumination is a well-established vulnerability factor for major depressive disorder (MDD) that may exert deleterious effects both independently and in interaction with life stress, and may contribute to the negative memory biases associated with MDD. Chapter 1 examines the role of both momentary ruminative self-focus (MRS) and stress-reactive rumination (SRR) as predictors of increases in depressive symptoms utilizing a smartphone ecological momentary assessment (EMA) design. SRR, but not MRS, independently predicted increases in depressive symptoms. Interactions emerged between negative life events (NLEs) and both MRS and SRR, such that experiencing higher levels of NLEs and rumination at an observation predicted greater increases in depressive symptoms. The results suggest that rumination levels in response to stress vary within individuals and can have an important effect on depressed mood. Chapter 2 tests the hypotheses that 1) engaging in greater SRR relative to an individual’s mean would lead to deeper encoding and improved retrieval of stressors, and 2) this biased memory for negative autobiographical information would predict increases in depressive symptoms over time. NLEs followed by increased SRR relative to individuals’ means were significantly more likely to be recalled two weeks later. In addition, a significant interaction emerged between the number of NLEs experienced and proportional recall of those events, such that individuals who endorsed and recalled greater numbers of stressors during the EMA week displayed increased depressive symptoms at follow-up. These findings support the role of rumination and memory biases as vulnerability factors for depression, and suggest potential clinical benefits of modifying ruminative response styles to daily stressors. / Psychology

Cultural artifacts, such as language, survive and replicate by passing from mind to mind. Cultural evolution always proceeds by an inductive process, where behaviors are never directly copied, but reverse engineered by the cognitive mechanisms involved in learning and production. I will refer to this type of evolutionary change as inductive evolution and explain how this represents a broader class of evolutionary processes that can include both neutral and selective evolution. This thesis takes a mechanistic approach to understanding the forces of evolution underlying change in culture over time, where the mechanisms of change are sought within human cognition. I define culture as anything that replicates by passing through a cognitive system and take language as a premier example of culture, because of the wealth of knowledge about linguistic behaviors (external language) and its cognitive processing mechanisms (internal language). Mainstream cultural evolution theories related to social learning and social transmission of information define culture ideationally, as the subset of socially-acquired information in cognition that affects behaviors. Their goal is to explain behaviors with culture and avoid circularity by defining behaviors as markedly not part of culture. I take a reductionistic approach and argue that all there is to culture is brain states and behaviors, and further, that a complete explanation of the forces of cultural change can not be explained by a subset of cognition related to social learning, but necessarily involves domain-general mechanisms, because cognition is an integrated system. Such an approach should decompose culture into its constituent parts and explore 1) how brains states effect behavior, 2) how behavior effects brain states, and 3) how brain states and behaviors change over time when they are linked up in a process of cultural transmission, where one person's behavior is the input to another. I conduct several psychological experiments on frequency learning with adult learners and describe the behavioral biases that alter the frequencies of linguistic variants over time. I also fit probabilistic models of cognition to participant data to understand the inductive biases at play during linguistic frequency learning. Using these inductive and behavioral biases, I infer a Markov model over my empirical data to extrapolate participants' behavior forward in cultural evolutionary time and determine equivalences (and divergences) between inductive evolution and standard models from population genetics. As a key divergence point, I introduce the concept of non-binomial cultural drift, argue that this is a rampant form of neutral evolution in culture, and empirically demonstrate that probability matching is one such inductive mechanism that results in non-binomial cultural drift. I argue further that all inductive problems involving representativeness are potential drivers of neutral evolution unique to cultural systems. I also explore deviations from probability matching and describe non-neutral evolution due to inductive regularization biases in a linguistic and non-linguistic domain. Here, I offer a new take on an old debate about the domain-specificity vs -generality of the cognitive mechanisms involved in language processing, and show that the evolution of regularity in language cannot be predicted in isolation from the general cognitive mechanisms involved in frequency learning. Using my empirical data on regularization vs probability matching, I demonstrate how the use of appropriate non-binomial null hypotheses offers us greater precision in determining the strength of selective forces in cultural evolution.