Abstract
Humans must understand their world in order to act on it. I develop this premise into a set of empirical claims concerning the organization of the mind—namely, claims about strategies that people use to bring evidence to bear on hypotheses, and to harness those hypotheses for predicting the future and making choices. By isolating these sense-making strategies, we can study which faculties of mind share common cognitive machinery.
My object in Chapter 1 is to make specific the claim that a common logic of explanation underlies diverse cognitive functions. In this dissertation, the empirical work focuses on causal inference and categorization—the core achievements of higher-order cognition—but there are rumblings throughout psychology, hinting that sense-making processes may be far more general. I explore some of these rumblings and hints.
In Chapters 2–4, I get into the weeds of the biases that afflict our explanatory inferences—necessary side effects of the heuristics and strategies that make it possible. Chapter 2 looks at the inferred evidence strategy—a way that reasoners coordinate evidence with hypotheses. Chapter 3 examines our preferences for simple and for complex explanations, arguing that there are elements in explanatory logic favoring simplicity and elements favoring complexity—opponent heuristics which are tuned depending on contextual factors. Chapter 4 studies the aftermath of explanatory inferences—how such inferences are used to predict the future. I show that these inferences are not treated probabilistically, but digitally, as certainly true or false, leading to distortions in predictions.
Chapter 5 considers the origins of these strategies. Given that children and adults are sometimes capable of sophisticated statistical intuition, might these heuristics be learned through repeated experiences with rational inference? Or might the converse be true, with our probabilistic machinery built atop an early-emerging heuristic foundation? I use the inferred evidence strategy as a case study to examine this question.
Chapters 6 and 7 are concerned with how these processes propagate to social cognition and action. Chapter 6 studies how all three of these strategies and associated biases—inferred evidence, opponent simplicity heuristics, and digital prediction—enter into our stereotyping behavior and our mental-state inferences. Chapter 7 looks at how explanatory inferences influence our choices, again using inferred evidence as a case study. We shall find that choice contexts invoke processes that operate on top of explanatory inference, which can lead to choices that are simultaneously less biased but also more incoherent.
In the concluding Chapter 8, I close with a meditation on the broader implications of this research program for human rationality and for probabilistic notions of rationality in particular. Even as our efforts to make sense of things can get us into trouble, they may be our only way of coping with the kinds of uncertainty we face in the world.
My object in Chapter 1 is to make specific the claim that a common logic of explanation underlies diverse cognitive functions. In this dissertation, the empirical work focuses on causal inference and categorization—the core achievements of higher-order cognition—but there are rumblings throughout psychology, hinting that sense-making processes may be far more general. I explore some of these rumblings and hints.
In Chapters 2–4, I get into the weeds of the biases that afflict our explanatory inferences—necessary side effects of the heuristics and strategies that make it possible. Chapter 2 looks at the inferred evidence strategy—a way that reasoners coordinate evidence with hypotheses. Chapter 3 examines our preferences for simple and for complex explanations, arguing that there are elements in explanatory logic favoring simplicity and elements favoring complexity—opponent heuristics which are tuned depending on contextual factors. Chapter 4 studies the aftermath of explanatory inferences—how such inferences are used to predict the future. I show that these inferences are not treated probabilistically, but digitally, as certainly true or false, leading to distortions in predictions.
Chapter 5 considers the origins of these strategies. Given that children and adults are sometimes capable of sophisticated statistical intuition, might these heuristics be learned through repeated experiences with rational inference? Or might the converse be true, with our probabilistic machinery built atop an early-emerging heuristic foundation? I use the inferred evidence strategy as a case study to examine this question.
Chapters 6 and 7 are concerned with how these processes propagate to social cognition and action. Chapter 6 studies how all three of these strategies and associated biases—inferred evidence, opponent simplicity heuristics, and digital prediction—enter into our stereotyping behavior and our mental-state inferences. Chapter 7 looks at how explanatory inferences influence our choices, again using inferred evidence as a case study. We shall find that choice contexts invoke processes that operate on top of explanatory inference, which can lead to choices that are simultaneously less biased but also more incoherent.
In the concluding Chapter 8, I close with a meditation on the broader implications of this research program for human rationality and for probabilistic notions of rationality in particular. Even as our efforts to make sense of things can get us into trouble, they may be our only way of coping with the kinds of uncertainty we face in the world.
| Original language | English |
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| Qualification | Ph.D. |
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| Award date | 1 May 2017 |
| Publication status | Published - 2017 |