Aba Szollosi’s research while affiliated with Corvinus University of Budapest and other places

The naiveté of the dominant ‘cognitive-miser’ metaphor of human thinking hampers theoretical progress in understanding how and why subtle behavioural interventions—‘nudges’—could work. We propose a reconceptualization that places the balance in agency between, and the alignment of representations held by, people and choice architects as central to determining the prospect of observing behaviour change. We argue that two aspects of representational (mis)alignment are relevant: cognitive (how people construe the factual structure of a decision environment) and motivational (the importance of a choice to an individual). Nudging thinkers via the alignment of representations provides a framework that offers theoretical and practical advances and avoids disparaging people’s cognitive capacities.

An unexamined assumption in many studies of learning and decision making is that people learn underlying probability distributions. However, the acquisition of distributional knowledge is rarely the focus of investigations. We report five experiments (N = 580 adults) that provide this focus and highlight the factors that impact people’s ability to accurately learn and reproduce underlying distributions. We find that people accurately reproduced the distribution only when either the environmental signal is strong (e.g., discrete bimodal distributions) or sufficient cues are provided to aid construction of mental representations (e.g., items from the modes in a noisy bimodal distribution are presented in different colors). We interpret these results in terms of participants testing and learning discrete rules corresponding to salient features of the environment rather than spontaneously representing entire distributions. As such, the findings challenge strong assumptions about the role of probability distribution knowledge in explanations of learning and decision making.

The naiveté of the dominant ‘cognitive-miser’ metaphor of human thinking hampers theoretical progress in understanding how and why subtle behavioral interventions – ‘nudges’ – could work. We propose a reconceptualization that places the balance in agency between, and the alignment of representations held by, people and choice architects as central to determining the prospect of observing behaviour change. We argue that two aspects of representational (mis)alignment are relevant: cognitive (how people construe the factual structure of a decision environment) and motivational (the importance of a choice to an individual). Nudging thinkers via the alignment of representations provides a framework that offers theoretical and practical advances and avoids disparaging people’s cognitive capacities.

We connect Conviction Narrative Theory to an account that views people as intuitive scientists who can flexibly create, evaluate, and modify representations of decision problems. We argue that without understanding how the relevant complex narratives (or indeed any representation, simple to complex) are themselves constructed, we also cannot know when and why people would rely on them to make choices.

An unexamined assumption in many studies of learning and decision making is that people learn underlying probability distributions. However, the acquisition of distributional knowledge is rarely the focus of investigations. We report five experiments (N = 580 adults) that provide this focus and highlight the factors that impact people’s ability to accurately learn and reproduce underlying distributions. We find that people accurately reproduced the distribution only when either the environmental signal is strong (i.e., non-noisy discrete bimodal distributions), or sufficient cues are provided to aid construction of mental representations (e.g., items from the modes in a noisy bimodal distribution are presented in different colours). Together the results challenge strong assumptions about the role of probability distribution-knowledge in explanations of learning and decision making.

Referring to probabilistic concepts (such as randomness, sampling, and probability distributions among others) is commonplace in contemporary explanations of how people learn and make decisions in the face of environmental unknowns. Here, we critically evaluate this practice and argue that such concepts should only play a relatively minor part in psychological explanations. To make this point, we provide a theoretical analysis of what people need to do in order to deal with unknown aspects of a typical decision-making task (a repeated-choice gamble). This analysis reveals that the use of probabilistic concepts in psychological explanations may and often does conceal essential, nonprobabilistic steps that people need to take to attempt to solve the problems that environmental unknowns present. To give these steps a central role, we recast how people solve these problems as a type of hypothesis generation and evaluation, of which using probabilistic concepts to deal with unknowns is one of many possibilities. We also demonstrate some immediate practical consequences of our proposed approach in two experiments. This perspective implies a shift in focus toward nonprobabilistic aspects of psychological explanations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Generalization does not come from repeatedly observing phenomena in numerous settings, but from theories explaining what is general in those phenomena. Expecting future behavior to look like past observations is especially problematic in psychology, where behaviors change when people's knowledge changes. Psychology should thus focus on theories of people's capacity to create and apply new representations of their environments.

Referring to probabilistic concepts (such as randomness, sampling, and probability distributions among others) is commonplace in contemporary explanations of how people learn and make decisions in the face of environmental unknowns. Here, we critically evaluate this practice and argue that such concepts should only play a relatively minor part in psychological explanations. To make this point, we provide a theoretical analysis of what people need to do in order to deal with unknown aspects of a typical decision-making task (a repeated-choice gamble). This analysis reveals that the use of probabilistic concepts in psychological explanations may and often does conceal essential, nonprobabilistic steps that people need to take to attempt to solve the problems that environmental unknowns present. To give these steps a central role, we recast how people solve these problems as a type of hypothesis generation and evaluation, of which using probabilistic concepts to deal with unknowns is one of many possibilities. We also demonstrate some immediate practical consequences of our proposed approach in two experiments. This perspective implies a shift in focus toward nonprobabilistic aspects of psychological explanations.

Generalization does not come from repeatedly observing phenomena in numerous settings, but from theories explaining what is general in those phenomena. Expecting future behavior to look like past observations is especially problematic in psychology, where behaviors change when people’s knowledge changes. Psychology should thus focus on theories of people’s capacity to create and apply new representations of their environments.