Post-response βγ power predicts the degree of choice-based learning in internally guided decision-making

Nakao, Takashi; Kanayama, Noriaki; Katahira, Kentaro; Odani, Misaki; Ito, Yosuke; Hirata, Yuki; Nasuno, Reika; Ozaki, Hanako; Hiramoto, Ryosuke; Miyatani, Makoto; Northoff, Georg

doi:10.1038/srep32477

Cited by 26 publications

(29 citation statements)

References 62 publications

(166 reference statements)

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“…The objective of decision making is therefore to reduce the error between one's past choices and present preferences; we refer to this general mechanism as coherency maximization. Consistent with patterns of repeatpurchasing observed in supermarket consumers 24 and studies of choice-induced preference change [11][12][13][14][15][16][17][18][19][20][21][22][23] , coherency maximization boosts the likelihood of past choices being repeated by shifting preferences towards the chosen item and away from rejected alternatives. As was shown in a simple simulation, this mechanism can elicit strong subjective preferences in the absence of extrinsic feedback.…”

Section: Discussionsupporting

confidence: 55%

“…As well as facilitating the inference of preferences, past choices also shape them. This has been demonstrated in studies of free choice, which show that after freely choosing an option, people tend to increase their subjective preference for it [11][12][13][14][15][16][17][18][19][20][21][22][23][24] . In the original free-choice paradigm, Brehm 11 asked participants to rate a set of items (e.g.…”

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confidence: 81%

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How Decisions and the Desire for Coherency Shape Subjective Preferences Over Time

Hornsby¹,

Love²

2019

Preprint

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Recent findings suggest a bidirectional relationship between preferences and choices such that what is chosen can become preferred. Yet, it is still commonly held that preferences for individual items are maintained, such as caching a separate value estimate for each experienced option. Instead, we propose that all possible choice options and preferences are represented in a shared, continuous, multidimensional space that supports generalization. Decision making is cast as a learning process that seeks to align choices and preferences to maintain coherency. We formalized an error-driven learning model that updates preferences to align with past choices, which makes repeating those and related choices more likely in the future. The model correctly predicts that making a free choice increases preferences along related attributes. For example, after choosing a political candidate based on trivial information (e.g., they like cats), voters' views on abortion, immigration, and trade subsequently shifted to match their chosen candidate.

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Section: Discussionsupporting

confidence: 55%

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confidence: 81%

How Decisions and the Desire for Coherency Shape Subjective Preferences Over Time

Hornsby¹,

Love²

2019

Preprint

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“…The question of temporal integration becomes even more powerful given that, on a psychological level, the self has been associated with the integration of different functions: sensory (Sui, He, & Humphreys, 2012;Sui, Rotshtein, & Humphreys, 2013), motor (Frings & Wentura, 2014), affective (Northoff et al, 2009), cognitive (Nakao et al, 2016;Nakao, Bai, Nashiwa, & Northoff, 2013;Nakao, Ohira, & Northoff, 2012), and social (Schilbach et al, 2013). Strikingly, these functions operate in different frequency ranges (Buzsáki, 2007) and their integration on the psychological level may ultimately be traced to temporal integration on the neuronal level.…”

mentioning

confidence: 99%

The temporal signature of self: Temporal measures of resting‐state EEG predict self‐consciousness

Wolff

Giovanni

Gómez‐Pilar

et al. 2018

Human Brain Mapping

Self Cite

100

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The self is the core of our mental life. Previous investigations have demonstrated a strong neural overlap between self‐related activity and resting state activity. This suggests that information about self‐relatedness is encoded in our brain's spontaneous activity. The exact neuronal mechanisms of such “rest‐self containment,” however, remain unclear. The present EEG study investigated temporal measures of resting state EEG to relate them to self‐consciousness. This was obtained with the self‐consciousness scale (SCS) which measures Private, Public, and Social dimensions of self. We demonstrate positive correlations between Private self‐consciousness and three temporal measures of resting state activity: scale‐free activity as indexed by the power‐law exponent (PLE), the auto‐correlation window (ACW), and modulation index (MI). Specifically, higher PLE, longer ACW, and stronger MI were related to higher degrees of Private self‐consciousness. Finally, conducting eLORETA for spatial tomography, we found significant correlation of Private self‐consciousness with activity in cortical midline structures such as the perigenual anterior cingulate cortex and posterior cingulate cortex. These results were reinforced with a data‐driven analysis; a machine learning algorithm accurately predicted an individual as having a “high” or “low” Private self‐consciousness score based on these measures of the brain's spatiotemporal structure. In conclusion, our results demonstrate that Private self‐consciousness is related to the temporal structure of resting state activity as featured by temporal nestedness (PLE), temporal continuity (ACW), and temporal integration (MI). Our results support the hypothesis that self‐related information is temporally contained in the brain's resting state. “Rest‐self containment” can thus be featured by a temporal signature.

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“…Recently, choice-based learning (CBL) has been proposed to describe the CIPC phenomenon [9][10][11][12][13], in which the values of both the chosen and rejected items are updated as if the own choice is regarded as a correct answer. The CBL model was first proposed by Akaishi et al [9] and revealed the tendency of people to make the same decision on perceptually ambiguous stimuli without feedback.…”

Section: Internally Guided Decision-making (Idm) and Computational Momentioning

confidence: 99%

“…Although Akaishi et al [9] used a perceptual decision-making task, which was more externally guided than internally guided [1], they proposed that the same mechanism can be applied to the CIPC in IDM. Based on Akaishi's model, Nakao et al [12,13] constructed the CBL model for IDM and conducted simulations to show that the behavioral index they used (i.e., change of decision consistency) is observed as the result of CBL.…”

Section: Internally Guided Decision-making (Idm) and Computational Momentioning

confidence: 99%

Computational modeling of Choice-Induced Preference Change: A Reinforcement-Learning-based approach

Jian-hong¹,

Hashimoto²,

Katahira³

et al. 2020

Preprint

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The value of an item is learned through a series of decision-making. The learning process has been investigated using decision-making tasks with a correct answer specified by the external environment (externally guided decision-making, EDM). In this case, people are required to adjust their choices based on feedback, and the learning process is generally explained by the reinforcement learning (RL) model. In addition to the RL in EDM, value is learned through internally guided decision-making (IDM), in which no correct answer defined by external circumstances is available and one has to decide based on one’s own internal criteria, such as preferences. In IDM, it has been believed that the value of chosen item is increased and that of rejected item is decreased, called choice-induced preference change (CIPC). An RL-based model called the choice-based learning (CBL) model has been proposed to describe CIPC, in which the values of chosen and rejected items are updated as if the own choice is regarded as a correct answer. However, the validity of the CBL model has not been confirmed by fitting the model to IDM behavioral data, since the differences of initial preferences among the items make it difficult to estimate the model parameters. The aim of the present study is to examine the CBL model in IDM. To avoid the problem of different initial preferences, we used a preference judgment task with novel contour shapes. Through simulations and a behavioral experiment with model-based analysis, we showed for the first time that the CBL model fits the IDM behavioral data well. Although frequently in preference change only the value of the chosen or rejected item has been reported using subjective preference ratings, we confirmed that both values were changed by applying the computational model to the IDM.

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Post-response βγ power predicts the degree of choice-based learning in internally guided decision-making

Cited by 26 publications

References 62 publications

How Decisions and the Desire for Coherency Shape Subjective Preferences Over Time

How Decisions and the Desire for Coherency Shape Subjective Preferences Over Time

The temporal signature of self: Temporal measures of resting‐state EEG predict self‐consciousness

Computational modeling of Choice-Induced Preference Change: A Reinforcement-Learning-based approach

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