Session 1: Thursday 11 February, 9am-10am
Ms. Tehilla Ostrovsky
Shi Xian Liew
Numerous studies show that under certain situations people seek information when it cannot alter future outcomes (known as non-instrumental information seeking) while in other situations they deliberately ignore available information that could (known as deliberate ignorance). Recent work by Gigerenzer and Garcia-Retamero (2017) suggested that the decision to seek or avoid information is related to risk preferences via a regret avoidance mechanism. Specifically, people who deliberately avoid information are more risk-averse because acquiring information may elicit a feeling of (future) regret. Across three studies, we manipulated the risk structure of monetary gambles to test different predictions from this core hypothesis. We find limited evidence to support them. We also fit and compare various formalizations of Gigerenzer and Garcia-Retamero’s (2017) model to our data, finding little support for their central claim. We conclude with a discussion of future experimental and modeling work.
Richard M. Shiffrin
Eight initially novel objects with four features were learned by three participants over about 70 sessions in a variety of present-absent search tasks. This article analyzes and models trials with a single object presented for test. The features of the object were presented simultaneously, or successively at rates fast enough that the objects appeared to be simultaneous (ISIs were 16, 33, or 50 ms). Classification of a test object as target or foil required a conjunction of two features. When successively presented, features diagnostic for target presence could arrive first or last, and vice versa for features diagnostic for foil presence. Two results were particularly important: 1) The order in which target-diagnostic or foil-diagnostic features appeared produced large changes in accuracy and response times; 2) Simultaneous feature presentation produced lower accuracy than sequential presentation with target-diagnostic features arriving first, despite the delay in such features arriving. The results required a dynamic model for perception and decision. The model has features perceived at independent times. It accumulates evidence at each moment based on the particular features perceived up to that time, and the diagnosticities of those features for classifying the test object as target or foil. The model also assumes that configurations of features provide evidence as processing continues: When all four features of an object are perceived the evidence points without error to the correct response. The results and modeling support the view that perceptual and decision processes operate concurrently and interactively during identification, recognition, and classification of well-learned objects, rather than in successive stages.
Richard M. Shiffrin
The dynamic processes operating between object presentation and an identification/classification response less than a second later are explored with a novel empirical procedure coupled with Hidden Markov Modeling. Each object has two binary features, one perfectly predictive of the binary response, the other 75% predictive. Control conditions present a single feature. In the experimental conditions the two features are either simultaneous or sequential in time, with either feature first, and the two features can be consistent (indicate the same response) or inconsistent. The participant starts a trial by moving a cursor upward, triggering a stimulus presentation, and then moves the cursor to one of two response regions in the upper right or left. The cursor movements are analyzed with a Hidden Markov Model to infer for each ten ms. of each trial in each condition for each participant the features that have been perceived and the evidence accumulated by that moment, thereby unfolding the dynamic processes of perception and decision.
We present a unified model of the dynamics of goal-directed motivation and decision making. The model—referred to as the GOAL architecture—provides a quantitative framework for integrating theories of goal pursuit and for relating their predictions to different types of data. The GOAL architecture proposes that motivation changes over time according to three gradients that capture the effects of the distance to the goal (i.e., the progress remaining), the time to the deadline, and the rate of progress required to achieve the goal. We use the model to integrate and compare six theoretical perspectives that make different predictions about how these dynamics unfold when pursuing approach and avoidance goals. We use the architecture within a hierarchical Bayesian framework to analyze data from the naturalistic context of professional basketball. The results show that people rely on all three gradients when making goal-directed resource allocation decisions, but that the relative influence of the gradients depends on the type and importance of the goal. Our findings suggest that goal pursuit unfolds in a complex manner that cannot be accounted for by any one previous theoretical perspective, but that is well-characterized by our unified framework. This research highlights the importance of theoretical integration for understanding motivation and decision-making during goal pursuit.
When making decisions about products or services, consumers weigh up the competing options by assessing the individual attributes of each alternative. The decision strategies in the literature vary in their complexity and the assumptions about attribute processing. One assumption includes processing attributes in serial or parallel or integrating attribute values early in the decision into a utility for each option. Another assumption of these decision strategies is whether the decision can be made on partial information or only by exhaustively processing all attributes. I have classified the decision strategies by these assumptions and used the methods of Systems Factorial Technology to discriminate between the various strategy sets. Previous work has applied this in veridical choice experiments, but the current work extends this into a preferential choice scenario.
The attraction effect is one of the most prominent phenomena in behavioural science and has drawn considerable attention in many fields. However, studies which directly investigate the cognitive mechanisms underlying the attraction effect with process-tracing methods remain uncommon. The present study is among the first to examine the attraction effect with multiple process-tracing methods, that is, with mouse-tracking and reason listing. Methodologically, this addresses the need for triangulation and improves validity. Theoretically, this study investigates whether different explanations of the attraction effect converge, and assumed roles of attentional patterns and distinct reasons in cognitive models. Results showed that, for attentional patterns, the target was attended to more frequently and for a longer duration, and transitions between the target and the decoy were more prevalent than other types of transitions, both of which support previous findings. For reasoning, results showed that reasons supporting the chosen option were generated in greater quantity and earlier, which supports Query Theory (Johnson et al., 2007). Finally, with mouse movement data divided into discrete stages for each distinct reason, we studied how information sampling and decision queries affect each other. Results generally support existing models, while the data opens up the possibilities of hybrid process models.
Dr. Jerome Busemeyer
Quantum probability theory has successfully provided accurate descriptions of behavior in the areas of judgment and decision making, and here we apply the same principles to a category learning task using overlapping, information-integration (II) categories. Since II categories lack verbalizable descriptions, unlike rule-based (RB) categories, we assert that an II categorization decision is constructed out of an indefinite state and characterized by quantum probability theory, whereas an RB categorization decision is read out from a definite state and governed by classical probability theory. In our experiment, participants learn to categorize simple, visual stimuli as members of either category S or category K during an acquisition phase, and then rate the likelihood on a scale of 0 to 5 that a stimulus belongs to one category and subsequently perform the same likelihood rating for the other category during a transfer phase. Following the principle of complementarity in quantum theory, we expect the category likelihood ratings to exhibit order effects in this experiment. Although we have just begun to collect data, so far a quantum random walk model has more successfully captured the responses from participants compared to an analogous Markov random walk model with the same number of parameters.