papers & presentations

2021

1. Human scheduling of perceptual tasks

   Little, Daniel R., Steinberg, Ruby A., Huang, Linting, Eidels, Ami, and Lin, Deborah

   Preregistered 2021

   Link

2020

1. Further tests of sequence-sensitive models in a modified Garner task using separable dimensions

   Lin, Deborah, and Little, Daniel R.

   PsyArXiv 2020

   Abs Link Code

   In the study of perceptual categorization, a key distinction is made between integral and separable dimensions. Integral dimensions are often highly unanalyzable, while separable dimensions are highly analyzable and easy to attend in isolation. Little, Wang, and Nosofsky (2016) showed that when trial-by-trial responses are analyzed, a consistent pattern of sequential effects was found in a modified Garner paradigm using integral-dimension stimuli. The present experiments investigate whether these pronounced sequential effects are also found with separable-dimension stimuli. Two experiments using different separable dimensions were conducted. The results indicate that similar patterns of sequential effects were present for separable dimension stimuli, but, unlike for integral dimensions, the effect of a change in the irrelevant dimension in the filtering task was not found. Further, for separable dimensions, the overall pattern of sequential effects did not vary between the Garner tasks (i.e., control, correlated, and filtering). To explain these results, we fit a sequence-sensitive exemplar model and compared the fits of this model to a novel sequence-sensitive feature model, in which only the relevant feature influences the categorization decision. We find that this feature-based model provides a more compelling account of our separable dimension data, while the full exemplar model provides a better account of the integral dimension data. The findings of the present study provide a more complete understanding of perceptual categorization and add to the growing body of literature on the prevalence and critical implications of strong sequential effects in cognitive tasks.

2019

1. Response times and the exploration-exploitation trade-off

   Lin, Deborah J., Little, Daniel R., and Smith, Philip L.

   Australasian Mathematical Psychology Conference 2019

   Abs

   Often, decisions do not entail explicitly stated rewards and outcomes for each option but instead require learning about the decision environment from experience. Additionally, the decision environment can be stable or changing. These decisions can be formalised as a multi-armed bandit problem. In these bandit problems, humans have to navigate a trade-off between exploration (i.e., trying out different options) and exploitation (i.e., sticking with a familiar option) while continually tracking the decision environment in order to maximise reward over the entire period. Numerous cognitive models utilising partial to full information, and different underlying mechanisms, such as heuristics, Bayesian updating, and sequential sampling, have been developed to account for choice behaviour in these problems. At present, choice response times (RTs) have largely not been analysed or fit in the exploration-exploitation literature. This project aims to better constrain and discriminate between these models by using choices as well as RTs, and determine how effort, uncertainty, and reward influence explorative and exploitative behaviour. An experimental paradigm comprising a perceptual task instead of an economic decision task is used to ensure reliable RTs, and allow for fine-grained control over experimental parameters and participant goals. We present model simulations and demonstrate that this paradigm is a promising avenue for investigating the exploration-exploitation trade-off.

2017

1. Sequential effects in the Garner tasks

   Lin, Deborah, and Little, Daniel R.

   Proceedings of the 39th Annual Conference of the Cognitive Science Society 2017

   Abs Link

   The distinction between integral and separable dimensions is of central importance to understanding how humans integrate information from multiple stimulus sources. One approach to characterizing stimulus integrality is through a set of speeded categorization tasks most closely associated with the work of Wendell Garner. These tasks demonstrate that integral dimensions result in marked speed up or slow down in responding when there is correlated or irrelevant variation, respectively, compared with a baseline task. Little, Wang & Nosofsky (2016) recently found that the slow down or interference can be largely explained by a reduction in the number of direct repetitions in a modified Garner filtering task. In this paper, we examine a large sample of subjects tested on either separable or integral dimensions to determine the extent of and individual differences in the overall and sequential effects in the standard Garner tasks.

2. Further tests of sequential effects in a modified Garner task using separable dimensions

   Lin, Deborah, and Little, Daniel R.

   Australasian Mathematical Psychology Conference 2017

2015

1. The exemplar confusion model: An account of biased probability estimates in decisions from description

   Lin, Deborah, Donkin, Christopher, and Newell, Benjamin

   Proceedings of the 37th Annual Meeting of the Cognitive Science Society 2015

   Abs Link

   At the core of every decision-making task are two simple features; outcome values and probabilities. Over the past few decades, many models have developed from von Neumann’ and Morgenstern’s (1945) Expected Utility Theory to provide a thorough account of people’s subjective value and probability weighting functions. In particular, one such model that has been largely successful in both Psychology and Economics is Cumulative Prospect Theory (CPT; Tversky & Kahneman, 1992). While these models do fit people’s choice behavior well, few models have attempted to provide a psychological account for subjective value, probability weighting, and resulting choice behavior. In this paper, we focus on a memory confusion process as described in Hawkins et al.’s (2014) exemplar-based model for decisions from experience, the Exemplar Confusion (ExCon) model, and adapt it to account for biased probability estimates in decisions from description. Using Bayesian model selection techniques, we demonstrate that it is able to account for real choice data from a Rieskamp (2008) study using gains, losses, and mixed gambles, and performs at least as well as CPT.