Adaptive behavior in optimal sequential search

Sequential decision making-making a decision where available options are encountered successively-is a hallmark of everyday life. Such decisions require deciding to accept or reject an alternative without knowing potential future options. Prior work focused on understanding choice behavior by developing decision models that capture human choices in such tasks. We investigated people's adaptive behavior in changing environments in light of their cognitive strategies. We present two studies in which we modified (a) outcome variance and (b) the time horizon and provide empirical evidence that people adapt to both context manipulations. Furthermore, we apply a recently developed threshold model of optimal stopping to our data to disentangle different cognitive processes involved in optimal stopping behavior. The results from Study 1 show that participants adaptively scaled the values of the sampling distribution to its variance, suggesting that the value of an option is perceived in relative rather than absolute terms. The results from Study 2 suggest that increasing the time horizon decreases the initial acceptance level, but less strongly than would be optimal. Furthermore, for longer sequences, participants more weakly adjusted this acceptance threshold over time than for shorter sequences. Further correlations between individual estimates in each condition indicate that individual differences between the participants' thresholds remain fairly stable between the conditions, pointing toward an additive effect of our manipulations

The influence of reward magnitude on stimulus memory and stimulus generalization in categorization decisions

Reward magnitude is a central concept in most theories of preferential decision making and learning. However, it is unknown whether variable rewards also influence cognitive processes when learning how to make accurate decisions (e.g., sorting healthy and unhealthy food differing in appeal). To test this, we conducted 3 studies. Participants learned to classify objects with 3 feature dimensions into two categories before solving a transfer task with novel objects. During learning, we rewarded all correct decisions, but specific category exemplars yielded a 10 times higher reward (high vs. low). Counterintuitively, categorization performance did not increase for high-reward stimuli, compared with an equal-reward baseline condition. Instead, performance decreased reliably for low-reward stimuli. To analyze the influence of reward magnitude on category generalization, we implemented an exemplar-categorization model and a cue-weighting model using a Bayesian modeling approach. We tested whether reward magnitude affects (a) the availability of exemplars in memory, (b) their psychological similarity to the stimulus, or (c) attention to stimulus features. In all studies, the evidence favored the hypothesis that reward magnitude affects the similarity gradients of high-reward exemplars compared with the equal-reward baseline. The results from additional reward-judgment tasks (Studies 2 and 3) strongly suggest that the cognitive processes of reward-value generalization parallel those of category generalization. Overall, the studies provide insights highlighting the need for integrating reward- and category-learning theories

The role of perception in generalization

Reply to Zaman, Yu, & Verheyen (2023) The idiosyncratic nature of how individuals perceive, represent, and remember their surroundings and its impact on learning-based generalization. JEP

Similarity Drives Cooperation in Social Dilemmas

materials, data, analysis scripts and online supplement for the three studies of the paper "Similarity Drives Cooperation in Social Dilemmas" which resulted from a Project funded by German Israel Foundatio

The role of perception in generalization: Commentary on Zaman, Yu, & Verheyen (2023)

Stimulus generalization, or the transfer of learned responses between stimuli, is a critical ability for adaptation to everyday life. In a typical experiment, generalization is assessed by measuring responses to stimuli varying along a physical dimension. Variations in the gradient of learned responses are usually interpreted as differences in the underlying cognitive process of generalization. A recent study by Zaman, Yu, and Verheyen (2023) seeks to challenge this view, arguing that generalization is best modelled by perceptual factors and that individual differences in perception or ability to identify the stimuli, are primary drivers of generalization. In this commentary, we outline issues in the methodology and analysis of Zaman et al.'s study, and show that their key result is not robust to the addition of theoretically-informed alternative models. We conclude that the evidence is not strong enough to support their conclusions regarding the primacy of perceptual processes in generalization. We propose some ways forward for researchers in this field attempting to understand the psychological mechanisms underlying individual differences in stimulus generalization