A Neural Pathway for Nonreinforced Preference Change

When not responding to food changes food value: The role of timing

Establishing behavior change toward appetitive foods can be crucial to improve people's health. Food go/no-go training (GNG), in which people respond to some food items and not to other food items depending on the presentation of a go or no-go cue, is a means to establish behavior change. GNG changes the perceived value of food items and food consumption. After GNG, no-go items are rated as less attractive than go and/or untrained items, an empirical phenomenon called the NoGo-devaluation-effect. This effect is not always found, however. One theory-based explanation for these inconsistent results may be found in the timing of the go and no-go cues, which is also inconsistent across studies. Hence, in the present work we conducted two experiments to examine the possible role of go and no-go cue presentation timing in eliciting the NoGo-devaluation-effect. In Experiment 1, we presented the food items before the presentation of go/no-go cues, whereas we reversed this order in Experiment 2. As predicted, the NoGo-devaluation-effect was obtained in Experiment 1. This effect was absent in Experiment 2. Moreover, recognition memory for stimulus-action contingencies moderated the devaluation effect in Experiment 1, but not in Experiment 2. These results show that NoGo devaluation is dependent on the timing of the NoGo cue, which has theoretical and applied implications for understanding how and when go/no-go training influences food consumption. We propose that the value of food items is updated during go/no-go training to minimize prediction errors, and that this updating process is boosted by attention.

Neural correlates underlying preference changes induced by food Go/No-Go training

Consistently not responding to appetitive foods during food go/no-go training could change individuals' food choices and sometimes even body weight, however, fewer studies have explored the neural pathways underlying the effects of food go/no-go training. In this study, we scanned eighty-six female participants using functional magnetic resonance imaging and investigated the neural bases of preference changes in a binary food choice task following action (e.g., go) or inaction (e.g., no-go) toward distinct foods within a food go/no-go training paradigm. In line with prior behavioral work, we found that participants' food preferences changed as a function of food go/no-go training, with participants choosing more "go" over "no-go" foods for consumption following training. At a neural level, preference changes were inversely associated with frontoparietal and salience network activity when choosing go (vs. no-go) foods. Additionally, task-related functional connectivities from the inferior parietal lobule to the pre-supplementary motor cortex, dorsolateral prefrontal cortex, and dorsal anterior cingulate cortex were related to these preference changes. Together, current work supports that food go/no-go training reliably changes people's preferences. More importantly, our findings suggest that a neural pathway centered on areas traditionally associated with selective attention may interface with prefrontal regions to guide preference changes induced by food go/no-go training, though future studies using other tasks (e.g., passive viewing tasks) are still needed to test this potential neural mechanism.

Neural correlates of changing food choices while bypassing values

Current theories suggest that altering choices requires value modification. To investigate this, normal-weight female participants' food choices and values were tested before and after an approach-avoidance training (AAT), while neural activity was recorded during the choice task using functional magnetic resonance imaging (fMRI). During AAT, participants consistently approached low- while avoiding high-calorie food cues. AAT facilitated low-calorie food choices, leaving food values unchanged. Instead, we observed a shift in indifference points, indicating the decreased contribution of food values in food choices. Training-induced choice shifts were associated with increased activity in the posterior cingulate cortex (PCC). In contrast, the medial PFC activity was not changed. Additionally, PCC grey matter density predicted individual differences in training-induced functional changes, suggesting anatomic predispositions to training impact. Our findings demonstrate neural mechanisms underlying choice modulation independent of valuation-related processes, with substantial theoretical significance for decision-making frameworks and translational implications for health-related decisions resilient to value shifts.

Value certainty and choice confidence are multidimensional constructs that guide decision-making

The degree of certainty that decision-makers have about their evaluations of available choice alternatives and their confidence about selecting the subjectively best alternative are important factors that affect current and future value-based choices. Assessments of the alternatives in a given choice set are rarely unidimensional; their values are usually derived from a combination of multiple distinct attributes. For example, the taste, texture, quantity, and nutritional content of a snack food may all be considered when determining whether to consume it. We examined how certainty about the levels of individual attributes of an option relates to certainty about the overall value of that option as a whole and/or to confidence in having chosen the subjectively best available option. We found that certainty and confidence are derived from unequally weighted combinations of attribute certainties rather than simple, equal combinations of all sources of uncertainty. Attributes that matter more in determining choice outcomes also are weighted more in metacognitive evaluations of certainty or confidence. Moreover, we found that the process of deciding between two alternatives leads to refinements in both attribute estimations and the degree of certainty in those estimates. Attributes that are more important in determining choice outcomes are refined more during the decision process in terms of both estimates and certainty. Although certainty and confidence are typically treated as unidimensional, our results indicate that they, like value estimates, are subjective, multidimensional constructs.

Memory for individual items is related to nonreinforced preference change

It is commonly assumed that memories contribute to value-based decisions. Nevertheless, most theories of value-based decision-making do not account for memory influences on choice. Recently, new interest has emerged in the interactions between these two fundamental processes, mainly using reinforcement-based paradigms. Here, we aimed to study the role memory processes play in preference change following the nonreinforced cue-approach training (CAT) paradigm. In CAT, the mere association of cued items with a speeded motor response influences choices. Previous studies with this paradigm showed that a single training session induces a long-lasting effect of enhanced preferences for high-value trained stimuli, that is maintained for several months. We hypothesized that CAT increases memory of trained items, leading to enhanced accessibility of their positive associative memories and in turn to preference changes. In two preregistered experiments, we found evidence that memory is enhanced for trained items and that better memory is correlated with enhanced preferences at the individual item level, both immediately and 1 mo following CAT. Our findings suggest that memory plays a central role in value-based decision-making following CAT, even in the absence of external reinforcements. These findings contribute to new theories relating memory and value-based decision-making and set the groundwork for the implementation of novel nonreinforced behavioral interventions that lead to long-lasting behavioral change.

A Preferential Role for Ventromedial Prefrontal Cortex in Assessing "the Value of the Whole" in Multiattribute Object Evaluation

Everyday decision-making commonly involves assigning values to complex objects with multiple value-relevant attributes. Drawing on object recognition theories, we hypothesized two routes to multiattribute evaluation: assessing the value of the whole object based on holistic attribute configuration or summing individual attribute values. In two samples of healthy human male and female participants undergoing eye tracking and functional magnetic resonance imaging (fMRI) while evaluating novel pseudo objects, we found evidence for both forms of evaluation. Fixations to and transitions between attributes differed systematically when the value of pseudo objects was associated with individual attributes or attribute configurations. Ventromedial prefrontal cortex (vmPFC) and perirhinal cortex were engaged when configural processing was required. These results converge with our recent findings that individuals with vmPFC lesions were impaired in decisions requiring configural evaluation but not when evaluating the sum of the parts. This suggests that multiattribute decision-making engages distinct evaluation mechanisms relying on partially dissociable neural substrates, depending on the relationship between attributes and value.SIGNIFICANCE STATEMENT Decision neuroscience has only recently begun to address how multiple choice-relevant attributes are brought together during evaluation and choice among complex options. Object recognition research makes a crucial distinction between individual attribute and holistic/configural object processing, but how the brain evaluates attributes and whole objects remains unclear. Using fMRI and eye tracking, we found that the vmPFC and the perirhinal cortex contribute to value estimation specifically when value was related to whole objects, that is, predicted by the unique configuration of attributes and not when value was predicted by the sum of individual attribute values. This perspective on the interactions between subjective value and object processing mechanisms provides a novel bridge between the study of object recognition and reward-guided decision-making.

Does Attention Increase the Value of Choice Alternatives?

A growing recognition of the role of attention in decision-making has been driven by both the technology of eye tracking and the development of models that explicitly incorporate attention. One result of this convergence is the arresting claim that attention, by itself, can increase the perceived value of a decision alternative. In this review, we cover the origins of that claim, its empirical foundation, and the reasoning that supports it. The conclusion is that, to date, there is not sufficient evidence to support the claim. Alternative explanations for the extant evidentiary base are discussed, as is the balance between the bottom-up influence of empirical evidence and the top-down commitment to a conceptual framework.

Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks-with fixed scalings-to attention, transformers, dynamic convolutions, and consciousness priors-which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence.