A recent study choice examined the relationship between lateral inhibition and the engagement of prefrontal and parietal areas in intertemporal choices [76]. The authors adapted mechanisms such as lateral inhibition and leakage [16,38] to test how self-control processes emerge when choosing between a smaller–sooner versus a later–larger reward. Hierarchical Bayesian analysis of competing models found that the best account of the decision process was a dynamic, oscillatory feature-selection process [13,49] combined with active suppression of tempting, but inferior, choice options through lateral inhibition [15,16]. More refined single-trial analyses revealed that distinct subregions within the prefrontal cortex (i.e., the dmPFC and the left dlPFC) were associated with inhibition of the tempting but inferior sooner–smaller reward options, consistent with extant theories of cognitive control [77,78].

Future Neuroscientific Research on Value-Based Decisions

With a very few exceptions [76], the neurobiological plausibility of component processes in value-based decisions, such as attentional switching or lateral inhibition, is yet to be tested in a systematic and rigorous fashion. We propose that much additional effort will need to be taken to develop a new agenda of neuroscientific research on multi-attribute value-based decisions. This agenda should be guided by four principles. We have outlined three of these principles above: we need (i) increased reliance on neuroscientific methods such as EEG and MEG that allow capturing the rapidly evolving component processes of multi-attribute decision making, (ii) intensified crosstalk with animal research on evidence accumulation, and (iii) principled use of experimental designs and stimuli that quantify the processing of well-defined attributes (e.g., intertemporal choice options that are characterized by the attributes amount and delay of reward). In addition, we propose that decision neuroscientists need to employ state-of-the-art tools to bridge the gap between cognitive modeling and neural recordings [79–83]. In particular, novel methods have been developed that allow joint modeling of neural and behavioral data such that neural data can directly constrain cognitive models [55,84–86]. These approaches offer a hitherto missing opportunity to dissociate between competing cognitive models and their presumed component processes (which often make highly similar behavioral predictions) on the basis of neurobiological plausibility.

Concluding Remarks

Empirical research on value-based decisions, based on multi-attribute and multi-alternative choices, has produced a collection of puzzling choice context effects that have challenged traditional static theories for over 30 years (Figure 1B and Box 1). The challenging set of empirical regularities was finally successfully addressed by extending classic sequential sampling models of evidence-based decisions into value-based decisions with new mechanisms (Box 3) such as stochastic integration of attribute comparisons, attention weighting depending on attribute values, lateral inhibition, and nonlinear evaluation of comparisons. Although these additional mechanisms enabled computational models to capture important context effects behaviorally, many researchers began the difficult quest of justifying the additional complexity brought on by their inclusion. In the present review we have highlighted several key advantages produced by sequential sampling models of value-based decisions, including temporal evolution of preference, decision times, eye movements, and connections to neural data. Perhaps equally interesting is the discovery of context effects in evidence-based decision paradigms (Box 2), suggesting the possibility of a framework for unifying evidenceand value-based decision making. However, to construct such a framework, we believe that modern cognitive scientists will need to synthesize evidence across both value-based and evidence-based domains to identify when advanced mechanisms affect cognitive dynamics, while looking to the many technological advances for better appreciating the temporal aspects of the computations of the mind (see[176T$DIF] Outstanding Questions for future issues).

Outstanding Questions

Do the new findings of context effects in perception and inference tasks require switching from simpler models previously used in evidence-based tasks to adopting some of the more advanced mechanisms developed for value-based decisions?

Is it possible to form a single sequential sampling model that can be applied to both value-based and inferencebased tasks, or are these domains of application so different that different models are needed for each one?

The advanced mechanisms used in value-based sequential sampling models introduce a very high level of complexity in these models. How can we develop rigorously empirical tests for these complex models?

Similarly, how can we avoid that these advanced mechanisms make models too complex and impractical for application to field research in marketing and consumer behavior?

What methodological advances will be necessary to investigate complex decision dynamics? Both precise spatial and temporal information about neural computations are essential. Currently, methods for combining high spatial and temporal modalities (e.g., EEG and fMRI) are complicated to implement. How will development in sig- nal-processing techniques change cognitive theories of decision making?

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