A Neural Pathway for Nonreinforced Preference Change
Authors: Tom Schonberg, Leor N. Katz
Journal: Trends in Cognitive Sciences (2020)

Subjective preferences can be changed even in the absence of external reinforcement.Neural evidence from humans suggests that nonreinforced preference change involves areas in the visual cortex, parietal cortex, striatum, and prefrontal regions.We suggest that areas related to nonreinforced preference change promote the change in preference through mechanisms related to selective attention.We put forth a framework in which nonreinforced preference change takes place through attention-related areas in a circuit we term the dorsal value pathway (DVP).The DVP is complementary to the extensively studied pathway promoting externally reinforced preference change through dopamine signals, which we term the ventral value pathway (VVP). The DVP and VVP may operate in tandem to shape the unified representation of value in the brain. How is value processed in the brain to inform decision making? A plethora of studies describe how preferences are shaped by experience with external reinforcements. While research on this reinforced pathway is well established, far less research has explored the neural pathways promoting preference change in the absence of external reinforcements. Here, we review behavioral paradigms linking nonreinforced preference change with manipulations of stimulus exposure, response, and gaze position. Based on this work, we propose that several brain regions traditionally associated with selective attention constitute a pathway for nonreinforced preference change. Together, this nonreinforced pathway (termed here the dorsal value pathway; DVP) and the more famously studied reinforced pathway (ventral value pathway; VVP), interface with prefrontal regions of the primate brain to guide value-based decisions. How is value processed in the brain to inform decision making? A plethora of studies describe how preferences are shaped by experience with external reinforcements. While research on this reinforced pathway is well established, far less research has explored the neural pathways promoting preference change in the absence of external reinforcements. Here, we review behavioral paradigms linking nonreinforced preference change with manipulations of stimulus exposure, response, and gaze position. Based on this work, we propose that several brain regions traditionally associated with selective attention constitute a pathway for nonreinforced preference change. Together, this nonreinforced pathway (termed here the dorsal value pathway; DVP) and the more famously studied reinforced pathway (ventral value pathway; VVP), interface with prefrontal regions of the primate brain to guide value-based decisions. How are preferences constructed in the brain to guide value-based decisions? The most common way to assign value to goods is through rewarded experience, a framework that has been formalized by reinforcement learning (see Glossary) [1.Rangel A. et al.A framework for studying the neurobiology of value-based decision making.Nat. Rev. Neurosci. 2008; 9: 545-556Crossref PubMed Scopus (1339) Google Scholar,2.Sutton R. Barto A. Reinforcement Learning: An Introduction. MIT Press, 2018Google Scholar]. Within the reinforcement learning framework, choices that lead to positive outcomes are reinforced whereas those that lead to negative outcomes are avoided. Usage of external reinforcement has been described by learning theories as the association between a stimulus, either with or without a response, and an outcome (for review, see [3.Balleine B.W. et al.Multiple forms of value learning and the function of dopamine.in: Neuroeconomics. Elsevier, 2009: 367-387Crossref Scopus (73) Google Scholar]). The repeated association between a stimulus, response, and positive outcome, for example, leads to a positive change in preference towards that stimulus (Figure 1A ). However, there are other paths for preference change that do not rely on external reinforcements. In many situations, preferences towards stimuli (or goods) are changed in the absence of external reinforcement (Figure 1B). While decades of research have elucidated the neural mechanisms underpinning preference change due to external reinforcement [4.Gershman S.J. Uchida N. Believing in dopamine.Nat. Rev. Neurosci. 2019; 20: 703-714Crossref PubMed Scopus (90) Google Scholar, 5.Glimcher P.W. Correction for Glimcher, Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis.Proc. Natl. Acad. Sci. 2011; 108: 17568-17569Crossref Scopus (496) Google Scholar, 6.Niv Y. Reinforcement learning in the brain.J. Math. Psychol. 2009; 53: 139-154Crossref Scopus (369) Google Scholar], relatively little is known about the neural substrates related to preference change that occurs in its absence, despite the paramount importance of both in guiding value-based decisions. What are the factors that drive nonreinforced preference change? In this Opinion, we attempt to uncover the factors governing nonreinforced preference change and their underlying mechanisms. We review behavioral studies linking nonreinforced preference change with manipulations of exposure, response, and gaze position. We synthesize behavioral and neuroimaging studies in humans with neurobiological investigations in macaques to inform a framework within which a neural pathway exists for nonreinforced preference change, centered on areas traditionally associated with selective attention. We put forth that this pathway, together with the more famously studied pathway governing reinforced preference change [7.Schultz W. Neuronal reward and decision signals: from theories to data.Physiol. Rev. 2015; 95: 853-951Crossref PubMed Scopus (553) Google Scholar], interfaces with prefrontal regions of the primate brain to guide value-based decisions. Preferences can be manipulated in the absence of external reinforcement using several methods. Early evidence of nonreinforced preference change was reported by Zajonc [8.Zajonc R.B. Attitudinal effects of mere exposure.J. Pers. Soc. Psychol. 1968; 9: 1-27Crossref PubMed Scopus (3866) Google Scholar,9.Zajonc R.B. Mere exposure: a gateway to the subliminal.Curr. Dir. Psychol. Sci. 2001; 10: 224-228Crossref Scopus (820) Google Scholar], who experimentally manipulated exposure levels to Chinese-like calligraphy and unfamiliar faces, and found that stimuli presented more frequently were deemed more favorable. The effect was dubbed the mere-exposure effect, used to describe the positive attitude developed by people towards stimuli previously exposed to [8.Zajonc R.B. Attitudinal effects of mere exposure.J. Pers. Soc. Psychol. 1968; 9: 1-27Crossref PubMed Scopus (3866) Google Scholar,9.Zajonc R.B. Mere exposure: a gateway to the subliminal.Curr. Dir. Psychol. Sci. 2001; 10: 224-228Crossref Scopus (820) Google Scholar]. Indeed, marketing research had long linked exposure to favorable familiarity [10.Grimes A. Kitchen P.J. Researching mere exposure effects to advertising - theoretical foundations and methodological implications.Int. J. Mark. Res. 2007; 49: 191-219Crossref Scopus (33) Google Scholar]. A general framework suggested that preferences could be described in terms of memory [11.Weber E.U. Johnson E.J. Constructing preferences from memory.in: Lichtenstein S. Slovic P. The Construction of Preference. Cambridge University Press, 2006: 397-410Crossref Google Scholar], and indeed, the speed and ease of processing or remembering (fluency) have been tied to positive preference [12.Winkielman P. et al.The hedonic marking of processing fluency: implications for evaluative judgment the psychology of evaluation: affective processes in cognition and emotion.in: Musch J. Klauer K.C. The Psychology of Evaluation: Affective Processes in Cognition and Emotion. Lawrence Erlbaum Associates, 2003: 189-217Google Scholar, 13.Johnson E.J. et al.Aspects of endowment: a query theory of value construction.J. Exp. Psychol. Learn. Mem. Cogn. 2007; 33: 461-474Crossref PubMed Scopus (301) Google Scholar, 14.Mantonakis A. et al.Consumer memory, fluency, and familiarity.in: Handbook of Consumer Psychology. Routledge, 2015Google Scholar, 15.Mandler G. et al.Nonspecific effects of exposure on stimuli that cannot be recognized.J. Exp. Psychol. Learn. Mem. Cogn. 1987; 13: 646-648Crossref Scopus (270) Google Scholar], as well as to a wide class of decisions [16.Shadlen M.N. Shohamy D. Decision making and sequential sampling from memory.Neuron. 2016; 90: 927-939Abstract Full Text Full Text PDF PubMed Scopus (174) Google Scholar]. Nonreinforced preference change has also been linked to the act of making a choice, in and of itself. In choice-induced preference change paradigms, the mere act of choosing a stimulus or item (as opposed to receiving it, for example) was found to influence subsequent preference towards it [17.Izuma K. et al.Neural correlates of cognitive dissonance and choice-induced preference change.Proc. Natl. Acad. Sci. 2010; 107: 22014-22019Crossref PubMed Scopus (154) Google Scholar, 18.Voigt K. et al.Endogenous formation of preferences: choices systematically change willingness-to-pay for goods.J. Exp. Psychol. Learn. Mem. Cogn. 2017; 43: 1872-1882Crossref PubMed Scopus (16) Google Scholar, 19.Sharot T. et al.Is choice-induced preference change long lasting?.Psychol. Sci. 2012; 23: 1123-1129Crossref PubMed Scopus (66) Google Scholar]. The effect of choice on subsequent preference, similar to the effect of exposure, has also been suggested to involve memory mechanisms [20.Izuma K. Murayama K. Choice-induced preference change in the free-choice paradigm: a critical methodological review.Front. Psychol. 2013; 4Crossref PubMed Google Scholar,21.Voigt K. et al.Hard decisions shape the neural coding of preferences.J. Neurosci. 2019; 39: 718-726Crossref PubMed Scopus (21) Google Scholar], and in the context of perceptual decision making, has been suggested to recruit mechanisms for selective attention [22.Talluri B.C. et al.Confirmation bias through selective overweighting of choice-consistent evidence.Curr. Biol. 2018; 28: 3128-3135.e8Abstract Full Text Full Text PDF PubMed Scopus (72) Google Scholar]. A related means with which to influence preference towards stimuli in the absence of external reinforcement was recently developed using cue-approach training (CAT) [23.Schonberg T. et al.Changing value through cued approach: an automatic mechanism of behavior change.Nat. Neurosci. 2014; 17: 625-630Crossref PubMed Scopus (103) Google Scholar]. In CAT, stimuli such as food items that vary in subjective value are presented on the screen one after the other. A neutral cue (e.g., sound) appears after the presentation of some of the stimuli, and participants are instructed to respond (press a button) as fast as they can following the cue, without any feedback or external reinforcement to the response. Several studies found that the association of a cue and speeded response to an item (termed the Go item) led to pronounced preference changes in favor of the Go item, consistent across multiple stimulus categories and cue modalities [24.Chen Z. et al.Changing food choice through cued approach: impulsive or deliberative response?.Appetite. 2016; 101: 229Crossref Google Scholar, 25.Zoltak M.J. et al.Attention! Can choices for low value food over high value food be trained?.Appetite. 2018; 124: 124-132Crossref PubMed Scopus (22) Google Scholar, 26.Salomon T. et al.The cue-approach task as a general mechanism for long-term non-reinforced behavioral change.Sci. Rep. 2018; 8: 3614Crossref PubMed Scopus (21) Google Scholar], and lasting for months [23.Schonberg T. et al.Changing value through cued approach: an automatic mechanism of behavior change.Nat. Neurosci. 2014; 17: 625-630Crossref PubMed Scopus (103) Google Scholar,26.Salomon T. et al.The cue-approach task as a general mechanism for long-term non-reinforced behavioral change.Sci. Rep. 2018; 8: 3614Crossref PubMed Scopus (21) Google Scholar, 27.Salomon T. et al.Enhanced striatal and prefrontal activity is associated with individual differences in nonreinforced preference change for faces.Hum. Brain Mapp. 2020; 41: 1043-1060Crossref PubMed Scopus (7) Google Scholar, 28.Botvinik-Nezer R. et al.Enhanced bottom-up and reduced top-down fMRI activity is related to long-lasting nonreinforced behavioral change.Cereb. Cortex. 2020; 30: 858-874Crossref PubMed Scopus (11) Google Scholar]. Importantly, the absence of external reinforcement does not guarantee the absence of internal reinforcement, but current experimental designs lack the resolution to address this directly. In one of the first studies to link gaze to the formation of preferences in the absence of external reinforcement, subjects freely viewed pairs of stimuli and selected the one they preferred [29.Armel K.C. et al.Biasing simple choices by manipulating relative visual attention.Judgm. Decis. Mak. 2008; 3: 396-403Google Scholar]. Within a single trial, the stimulus gazed upon more often tended to be preferred by the subjects, demonstrating a correlation between gaze and preference. A causal demonstration then followed, whereby manipulations to the duration of stimulus presentation (and duration of subject gaze at the stimulus) influenced subjects' preference. The authors proposed that preferences are enhanced towards stimuli at the center of gaze by virtue of a gaze cascade, in which gaze directed towards an appetitive stimulus leads to increased attention towards it, which in turn, leads to the attraction of gaze. This line of work was followed by studies that manipulated the position of gaze position directly, which also influenced preference [29.Armel K.C. et al.Biasing simple choices by manipulating relative visual attention.Judgm. Decis. Mak. 2008; 3: 396-403Google Scholar,30.Lim S.-L. et al.The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention.J. Neurosci. 2011; 31: 13214-13223Crossref PubMed Scopus (216) Google Scholar]. One study found that the effect of gaze manipulations on preference was positive for appetitive items and negative for aversive items [29.Armel K.C. et al.Biasing simple choices by manipulating relative visual attention.Judgm. Decis. Mak. 2008; 3: 396-403Google Scholar], suggesting that the manipulation operates as a gain on value: amplifying the preference regardless of sign. Together, studies linking gaze with preference have suggested that the root of the observed gaze-dependent preference change effects is based on the deployment of overt attention [29.Armel K.C. et al.Biasing simple choices by manipulating relative visual attention.Judgm. Decis. Mak. 2008; 3: 396-403Google Scholar, 30.Lim S.-L. et al.The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention.J. Neurosci. 2011; 31: 13214-13223Crossref PubMed Scopus (216) Google Scholar, 31.Krajbich I. et al.Visual fixations and the computation and comparison of value in simple choice.Nat. Neurosci. 2010; 13: 1292-1298Crossref PubMed Scopus (771) Google Scholar, 32.Krajbich I. Accounting for attention in sequential sampling models of decision making.Curr. Opin. Psychol. 2019; 29: 6-11Crossref PubMed Scopus (38) Google Scholar]. Following the experimental approaches linking overt attention with preference change, a computational model termed the attentional drift diffusion model (aDDM) was developed whereby attention is proposed to guide valuation [31.Krajbich I. et al.Visual fixations and the computation and comparison of value in simple choice.Nat. Neurosci. 2010; 13: 1292-1298Crossref PubMed Scopus (771) Google Scholar]. The aDDM is based on the extensively studied drift diffusion model [33.Ratcliff R. et al.Diffusion decision model: current issues and history.Trends Cogn. Sci. 2016; 20: 260-281Abstract Full Text Full Text PDF PubMed Scopus (658) Google Scholar,34.Smith P.L. Ratcliff R. Psychology and neurobiology of simple decisions.Trends Neurosci. 2004; 27: 161-168Abstract Full Text Full Text PDF PubMed Scopus (784) Google Scholar] and describes how a relative decision value evolves over time, and crucially, how it is biased by overt attention such that ultimately, one item is valued more highly and preferred over another. The approach has influenced a number of groups [35.Vaidya A.R. Fellows L.K. Testing necessary regional frontal contributions to value assessment and fixation-based updating.Nat. Commun. 2015; 6: 10120Crossref PubMed Scopus (34) Google Scholar, 36.Thomas A.W. et al.Gaze bias differences capture individual choice behaviour.Nat. Hum. Behav. 2019; 3: 625-635Crossref PubMed Scopus (34) Google Scholar, 37.Molter F. et al.GLAMbox: a Python toolbox for investigating the association between gaze allocation and decision behaviour.PLoS One. 2019; 14e0226428Crossref PubMed Scopus (5) Google Scholar], but it should be noted that the locus of attention in the model is determined by overt attention (i.e., gaze position) and not by controlling for covert attention, which might sometimes be deployed elsewhere [38.Posner M.I. Orienting of attention.Q. J. Exp. Psychol. 1980; 32: 3-25Crossref PubMed Scopus (7041) Google Scholar]. Whether or not manipulations of covert attention influence preference similarly to manipulations of overt attention is an important open question. The relationship between gaze and preferences extends beyond studies that manipulate gaze explicitly, and is also evident in the aforementioned choice-induced preference change and CAT. For example, a recent study of choice-induced preference change found that subject gaze was preferentially allocated towards items that were subsequently preferred [21.Voigt K. et al.Hard decisions shape the neural coding of preferences.J. Neurosci. 2019; 39: 718-726Crossref PubMed Scopus (21) Google Scholar]. In fact, fixation duration was predictive of the magnitude of preference change. In CAT, unchosen Go items were found to attract gaze more than unchosen NoGo items during the test phase [23.Schonberg T. et al.Changing value through cued approach: an automatic mechanism of behavior change.Nat. Neurosci. 2014; 17: 625-630Crossref PubMed Scopus (103) Google Scholar]. However, these experimental designs lack the resolution to determine whether the manipulation caused a preference change which biased gaze, or a bias in gaze which led to a preference change. The underlying mechanisms across paradigms likely differ, but a commonality amongst them emerges downstream, where changes in both preference and gaze are observed. Notably, the timescale of preference change can vary considerably across paradigms. Gaze manipulations operate within a single trial, influencing preference on the order of seconds. In contrast, preference change induced by mere exposure, by choice, and by CAT, occur outside the duration of a trial and are thought to take place on substantially longer timescales. Indeed, the behavioral consequences of CAT last for months [26.Salomon T. et al.The cue-approach task as a general mechanism for long-term non-reinforced behavioral change.Sci. Rep. 2018; 8: 3614Crossref PubMed Scopus (21) Google Scholar], and those of choice-induced preference change, for years [19.Sharot T. et al.Is choice-induced preference change long lasting?.Psychol. Sci. 2012; 23: 1123-1129Crossref PubMed Scopus (66) Google Scholar]. Thus, it is possible that these long-term effects of preference change rely more heavily on memory-related mechanisms for value assignment [21.Voigt K. et al.Hard decisions shape the neural coding of preferences.J. Neurosci. 2019; 39: 718-726Crossref PubMed Scopus (21) Google Scholar,39.Chammat M. et al.Cognitive dissonance resolution depends on episodic memory.Sci. Rep. 2017; 7: 41320Crossref PubMed Scopus (19) Google Scholar], while short-term effects avail other mechanisms, potentially related to selective attention [22.Talluri B.C. et al.Confirmation bias through selective overweighting of choice-consistent evidence.Curr. Biol. 2018; 28: 3128-3135.e8Abstract Full Text Full Text PDF PubMed Scopus (72) Google Scholar,31.Krajbich I. et al.Visual fixations and the computation and comparison of value in simple choice.Nat. Neurosci. 2010; 13: 1292-1298Crossref PubMed Scopus (771) Google Scholar]. However, it is also possible that the differences in timescale are a consequence of task design. For example, the CAT design does not lend itself to within-trial manipulations, and, as far as we know, the effects of within-trial gaze manipulations have not been tested beyond the scope of the manipulated trial. Thus, it is currently unclear whether differences in timescale of effect across paradigms are indicative of underlying mechanism, or of differences in experimental design. The neural basis of preference change has been extensively studied and continues to be elucidated, but almost exclusively in response to external reinforcement and with reinforcement learning models [1.Rangel A. et al.A framework for studying the neurobiology of value-based decision making.Nat. Rev. Neurosci. 2008; 9: 545-556Crossref PubMed Scopus (1339) Google Scholar,4.Gershman S.J. Uchida N. Believing in dopamine.Nat. Rev. Neurosci. 2019; 20: 703-714Crossref PubMed Scopus (90) Google Scholar,7.Schultz W. Neuronal reward and decision signals: from theories to data.Physiol. Rev. 2015; 95: 853-951Crossref PubMed Scopus (553) Google Scholar,40.Lee D. et al.Neural basis of reinforcement learning and decision making.Annu. Rev. Neurosci. 2012; 35: 287-308Crossref PubMed Scopus (280) Google Scholar]. The brain regions involved in externally reinforced preference change constitute a pathway which we denote here as the ventral value pathway (VVP), due to the ventral location of the dopaminergic nuclei in the midbrain and their innervation to the ventral and dorsal striatum [41.Haber S.N. et al.Striatonigrostriatal pathways in primates form an ascending spiral from the shell to the dorsolateral striatum.J. Neurosci. 2000; 20: 2369-2382Crossref PubMed Google Scholar,42.Haber S.N. Knutson B. The reward circuit: linking primate anatomy and human imaging.Neuropsychopharmacology. 2010; 35: 4-26Crossref PubMed Scopus (2359) Google Scholar]. The representation of subjective value assigned to a stimulus or response has been extensively linked to activity in the ventromedial prefrontal cortex (vmPFC) and orbitofrontal cortex (OFC), which have been posited to represent value in a unified and common currency [43.Bartra O. et al.The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value.Neuroimage. 2013; 76: 412-427Crossref PubMed Scopus (1077) Google Scholar,44.Levy D.J. Glimcher P.W. The root of all value: a neural common currency for choice.Curr. Opin. Neurobiol. 2012; 22: 1027-1038Crossref PubMed Scopus (731) Google Scholar]. The vmPFC and OFC are considered late in the VVP hierarchy, and are thought to be read out by downstream areas that guide value-based choices and actions, such as the anterior cingulate cortex and related regions in the frontoparietal network [45.Kennerley S.W. et al.Optimal decision making and the anterior cingulate cortex.Nat. Neurosci. 2006; 9: 940-947Crossref PubMed Scopus (667) Google Scholar, 46.Gluth S. et al.Deciding when to decide: time-variant sequential sampling models explain the emergence of value-based decisions in the human brain.J. Neurosci. 2012; 32: 10686-10698Crossref PubMed Scopus (106) Google Scholar, 47.Hare T.A. et al.Transformation of stimulus value signals into motor commands during simple choice.Proc. Natl. Acad. Sci. 2011; 108: 18120-18125Crossref PubMed Scopus (234) Google Scholar, 48.Rodriguez C.A. et al.The neural basis of value accumulation in intertemporal choice.Eur. J. Neurosci. 2015; 42: 2179-2189Crossref PubMed Scopus (34) Google Scholar, 49.Pisauro M.A. et al.Neural correlates of evidence accumulation during value-based decisions revealed via simultaneous EEG-fMRI.Nat. Commun. 2017; 8: 15808Crossref PubMed Scopus (81) Google Scholar, 50.Rich E.L. Wallis J.D. Decoding subjective decisions from orbitofrontal cortex.Nat. Neurosci. 2016; 19: 973-980Crossref PubMed Scopus (193) Google Scholar]. While the VVP is one of the most actively researched circuits in neuroscience, research into the neural basis of preference change that is not externally reinforced is scarce. One functional magnetic resonance imaging (fMRI) study found that manipulating overt attention by restricting gaze position to one of two appetitive food items influenced subjects' preference towards the items [30.Lim S.-L. et al.The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention.J. Neurosci. 2011; 31: 13214-13223Crossref PubMed Scopus (216) Google Scholar]. A link was identified between the induced preference change and activity in ventral striatum, vmPFC, and visual regions. The vmPFC has also been implicated in the CAT paradigm across multiple different stimuli [23.Schonberg T. et al.Changing value through cued approach: an automatic mechanism of behavior change.Nat. Neurosci. 2014; 17: 625-630Crossref PubMed Scopus (103) Google Scholar,27.Salomon T. et al.Enhanced striatal and prefrontal activity is associated with individual differences in nonreinforced preference change for faces.Hum. Brain Mapp. 2020; 41: 1043-1060Crossref PubMed Scopus (7) Google Scholar,51.Bakkour A. et al.Neural mechanisms of cue-approach training.Neuroimage. 2017; 151: 92-104Crossref PubMed Scopus (17) Google Scholar]. Such fMRI results add an important dimension to the behavioral findings but were obtained only after the preference had already changed, during the choice phase. Thus, it is unclear whether the areas implicated in these studies are related to a change in nonreinforced preference specifically, or to a change in preference at large, irrespective to how it was formed. To uncover brain regions related to preference shaped by nonreinforced manipulations specifically, a recent fMRI study compared brain activation to snack-food items before versus after CAT, independent of choice [28.Botvinik-Nezer R. et al.Enhanced bottom-up and reduced top-down fMRI activity is related to long-lasting nonreinforced behavioral change.Cereb. Cortex. 2020; 30: 858-874Crossref PubMed Scopus (11) Google Scholar]. The study found that parietal regions decreased in fMRI signal following training, while the vmPFC and visual areas in the lateral occipital cortex increased. The modulation of visual areas is reminiscent of value-based modulations observed in early visual cortex [30.Lim S.-L. et al.The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention.J. Neurosci. 2011; 31: 13214-13223Crossref PubMed Scopus (216) Google Scholar,52.Serences J.T. Value-based modulations in human visual cortex.Neuron. 2008; 60: 1169-1181Abstract Full Text Full Text PDF PubMed Scopus (259) Google Scholar]. A second CAT study showed a correlation between fMRI signals in the striatum during training to the subsequent preference change, implicating the striatum in nonreinforced preference change [27.Salomon T. et al.Enhanced striatal and prefrontal activity is associated with individual differences in nonreinforced preference change for faces.Hum. Brain Mapp. 2020; 41: 1043-1060Crossref PubMed Scopus (7) Google Scholar]. Similar brain regions were implicated in choice-induced preference change studies with the addition of the hippocampus [17.Izuma K. et al.Neural correlates of cognitive dissonance and choice-induced preference change.Proc. Natl. Acad. Sci. 2010; 107: 22014-22019Crossref PubMed Scopus (154) Google Scholar,39.Chammat M. et al.Cognitive dissonance resolution depends on episodic memory.Sci. Rep. 2017; 7: 41320Crossref PubMed Scopus (19) Google Scholar], and in one instance, the preference change could be decoded from activity in dorsolateral PFC and precuneus, even before it had behaviorally manifest [21.Voigt K. et al.Hard decisions shape the neural coding of preferences.J. Neurosci. 2019; 39: 718-726Crossref PubMed Scopus (21) Google Scholar]. A recent study [53.McGinty V.B. et al.Orbitofrontal cortex value signals depend on fixation location during free viewing.Neuron. 2016; 90: 1299-1311Abstract Full Text Full Text PDF PubMed Scopus (63) Google Scholar] explored the response of value-encoding OFC neurons in freely-viewing monkeys where, while individual trials were associated with a reward, individual eye movements were not. Value encoding in OFC neurons was strongly modulated by gaze position, providing a neural-based explanation to the link between gaze position and value observed previously as reviewed earlier [29.Armel K.C. et al.Biasing simple choices by manipulating relative visual attention.Judgm. Decis. Mak. 2008; 3: 396-403Google Scholar,31.Krajbich I. et al.Visual fixations and the computation and comparison of value in simple choice.Nat. Neurosci. 2010; 13: 1292-1298Crossref PubMed Scopus (771) Google Scholar,54.Shimojo S. et al.Gaze bias both reflects and influences preference.Nat. Neurosci. 2003; 6: 1317-1322Crossref PubMed Scopus (578) Google Scholar]. In summary, the set of brain regions involved in nonreinforced preference change is gradually being uncovered, but it is still unclear whether the highlighted regions play a causal role in driving the change in preference itself or whether they reflect the end product of a process that has taken place elsewhere. In the following section we propose a number of mechanisms that might play a causal role in propagating the changes in preference observed during nonreinforced paradigms. It is unlikely that the various behavioral manipulations reviewed earlier are all supported by the exact same set of underlying mechanisms. However, the links between overt attention and preference change in multip