On the effect of perceived patterns in decisions from sampling.

Abstract

Many real-life choices are based on previous experiences. Research devoted to these decisions from experience has typically employed static settings, where the probability of a given outcome is constant across trials. However, recent studies of repeated choice suggest that people tend to follow perceived patterns of outcomes even when true patterns do not exist (i.e., in static settings). Here, we examine whether the tendency to follow perceived patterns above and beyond external incentives also characterizes decisions from sampling. To this aim, we modified the static sampling paradigm to include a fixed sequence of outcomes while the incentive dictate disregarding the sequence. In two studies, we found a strong tendency to follow the fixed pattern of outcomes. This tendency was evident not only in sampling choices where following the pattern required additional effort and did not provide additional information. The same tendency was also evident in participant's final consequential choices, where following the pattern impaired financial returns. The results were replicated after ensuring comprehension of the task, doubling the expected payoffs, and under a partial-feedback design. Overall, our results suggest that decisions from sampling, like repeated consequential choice, reflect a strong tendency to follow perceived environmental regularities. Our results are consistent with the assumption that during free sampling and during consequential choice, most participants respond to when (i.e., on which specific trials) each of the options is better rather than to which option is better overall (i.e., implies a more attractive distribution of outcomes).

On the Effect of Practice on Exploration and Exploitation of Options and Strategies

“…Yet, the sampling paradigm does not allow to distinguish between exploration of options and exploration between sampling strategies. For example, high rates of alternation between two choice options might imply a participant’s attempt to either randomly explore the two options (e.g., Hills and Hertwig, 2010 ; Gonzalez and Dutt, 2016 ), or exploit a strategy that follows a specific pattern (see Cohen and Teodorescu, 2021 ; also see Schulze et al, 2020 for a similar observation in a probability matching task).…”

“…These latter tasks include the binary clicking paradigm ( Barron and Erev, 2003 ), the sampling paradigm ( Hertwig et al, 2004 ; Weber et al, 2004 ), the Iowa Gambling Task ( Bechara et al, 1997 ), and probability learning tasks. These tasks typically present to participants only 2–4 options to choose from, and involve a relatively simple, static payoff rule ( Cohen and Teodorescu, 2021 ). Accordingly, in these tasks exploration of new options is limited to the first few trials, precluding observations of behaviors that reflect under- and over- exploration of new alternatives.…”

On the descriptive value of the reliance on small-samples assumption

“…The clearest boundary, in the context of pure decisions from experience, involves environments with easy-to-detect dynamic structures as illustrated by the thought experiment described in Table 4 (following Plonsky et al, 2015). While the noisy-sampler model (with the parameters estimated above based on YCNE's data) predicts a Top-rate (at trial 100) of only 29%, it is natural to assume most human subjects will quickly learn to select Top after a sequence of four losses (see related observation in Cohen & Teodorescu, 2021). We believe that the observation that the noisy-sampler model provides useful description of YCNE results, but fails to describe the likely behavior in Table 4's thought experiment, can shed light on the underlying processes.…”

The Future of Decisions From Experience: Connecting Real-World Decision Problems to Cognitive Processes

“…For example, when the underlying odds of winning or losing in a DfE task are stationary, participants have been assumed to also treat the task as stationary. Yet recent evidence suggests that participants behave as if the environment is dynamic, and they develop higher-order mental models and concepts about upcoming outcomes, resulting in identifiable search and choice patterns ( Barron & Leider, 2010 ; Clotfelter & Cook, 1993 ; Cohen & Teodorescu, 2021 ; Oskarsson et al, 2009 ; Szollosi et al, 2019 ; but see the discussion in Ashby et al, 2017 ; Plonsky & Teodorescu, 2020b ; Yechiam et al, 2020 ). Expectations about upcoming rewards can originate from identifying certain regularities in the environment, such as that larger rewards are less likely to be received than smaller rewards (the risk-reward regularity ; see Pleskac & Hertwig, 2014 ).…”