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Dive into the research topics where Mel Win Khaw is active.

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Featured researches published by Mel Win Khaw.


Nature Communications | 2017

Reminders of past choices bias decisions for reward in humans

Aaron M. Bornstein; Mel Win Khaw; Daphna Shohamy; Nathaniel D. Daw

We provide evidence that decisions are made by consulting memories for individual past experiences, and that this process can be biased in favour of past choices using incidental reminders. First, in a standard rewarded choice task, we show that a model that estimates value at decision-time using individual samples of past outcomes fits choices and decision-related neural activity better than a canonical incremental learning model. In a second experiment, we bias this sampling process by incidentally reminding participants of individual past decisions. The next decision after a reminder shows a strong influence of the action taken and value received on the reminded trial. These results provide new empirical support for a decision architecture that relies on samples of individual past choice episodes rather than incrementally averaged rewards in evaluating options and has suggestive implications for the underlying cognitive and neural mechanisms.


PLOS ONE | 2015

The Measurement of Subjective Value and Its Relation to Contingent Valuation and Environmental Public Goods.

Mel Win Khaw; Denise A. Grab; Michael A. Livermore; Christian A. Vossler; Paul W. Glimcher

Environmental public goods—including national parks, clean air/water, and ecosystem services—provide substantial benefits on a global scale. These goods have unique characteristics in that they are typically “nonmarket” goods, with values from both use and passive use that accrue to a large number of individuals both in current and future generations. In this study, we test the hypothesis that neural signals in areas correlated with subjective valuations for essentially all other previously studied categories of goods (ventromedial prefrontal cortex and ventral striatum) also correlate with environmental valuations. We use contingent valuation (CV) as our behavioral tool for measuring valuations of environmental public goods. CV is a standard stated preference approach that presents survey respondents with information on an issue and asks questions that help policymakers determine how much citizens are willing to pay for a public good or policy. We scanned human subjects while they viewed environmental proposals, along with three other classes of goods. The presentation of all four classes of goods yielded robust and similar patterns of temporally synchronized brain activation within attentional networks. The activations associated with the traditional classes of goods replicate previous correlations between neural activity in valuation areas and behavioral preferences. In contrast, CV-elicited values for environmental proposals did not correlate with brain activity at either the individual or population level. For a sub-population of participants, CV-elicited values were correlated with activity within the dorsomedial prefrontal cortex, a region associated with cognitive control and shifting decision strategies. The results show that neural activity associated with the subjective valuation of environmental proposals differs profoundly from the neural activity associated with previously examined goods and preference measures.


bioRxiv | 2017

What’s past is present: Reminders of past choices bias decisions for reward in humans

Aaron M. Bornstein; Mel Win Khaw; Daphna Shohamy; Nathaniel D. Daw

We provide evidence that decisions are made by consulting memories for individual past experiences, and that this process can be biased in favor of past choices using incidental reminders. First, in a standard rewarded choice task, we show that a model that estimates value at decision-time using individual samples of past outcomes fits choices and decision-related neural activity better than a canonical incremental learning model. In a second experiment, we bias this sampling process by incidentally reminding participants of individual past decisions. The next decision after a reminder shows a strong influence of the action taken and value received on the reminded trial. These results provide new empirical support for a decision architecture that relies on samples of individual past choice episodes rather than incrementally averaged rewards in evaluating options, and has suggestive implications for the underlying cognitive and neural mechanisms.


Proceedings of the National Academy of Sciences of the United States of America | 2017

Normalized value coding explains dynamic adaptation in the human valuation process

Mel Win Khaw; Paul W. Glimcher; Kenway Louie

Significance Relative processing is a ubiquitous feature of neural and cognitive function. In perception, a prominent example of relative processing is adaptation, in which both sensory neural responses and resulting percepts depend on the history of past stimuli. Neurons in decision-related brain areas also adapt, representing value information relative to previous rewards, but whether adaptive value coding affects behavior is unknown. Here, we show adaptation in the subjective valuation process of human subjects, with values consistently dependent on the recent history of presented values. This adaptive valuation can be explained by divisive normalization, a canonical neural computation widely observed in sensory processing, offering a unifying biological mechanism for temporal context effects in perception and decision making. The notion of subjective value is central to choice theories in ecology, economics, and psychology, serving as an integrated decision variable by which options are compared. Subjective value is often assumed to be an absolute quantity, determined in a static manner by the properties of an individual option. Recent neurobiological studies, however, have shown that neural value coding dynamically adapts to the statistics of the recent reward environment, introducing an intrinsic temporal context dependence into the neural representation of value. Whether valuation exhibits this kind of dynamic adaptation at the behavioral level is unknown. Here, we show that the valuation process in human subjects adapts to the history of previous values, with current valuations varying inversely with the average value of recently observed items. The dynamics of this adaptive valuation are captured by divisive normalization, linking these temporal context effects to spatial context effects in decision making as well as spatial and temporal context effects in perception. These findings suggest that adaptation is a universal feature of neural information processing and offer a unifying explanation for contextual phenomena in fields ranging from visual psychophysics to economic choice.


National Bureau of Economic Research | 2017

Risk Aversion as a Perceptual Bias

Mel Win Khaw; Ziang Li; Michael Woodford

The theory of expected utility maximization (EUM) proposed by Bernoulli explains risk aversion as a consequence of diminishing marginal utility of wealth. However, observed choices between risky lotteries are difficult to reconcile with EUM: for example, in the laboratory, subjects’ responses on individual trials involve a random element, and cannot be predicted purely from the terms offered; and subjects often appear to be too risk averse with regard to small gambles (while still accepting sufficiently favorable large gambles) to be consistent with any utility-of-wealth function. We propose a unified explanation for both anomalies, similar to the explanation given for related phenomena in the case of perceptual judgments: they result from judgments based on imprecise (and noisy) mental representation of the decision situation. In this model, risk aversion is predicted without any need for a nonlinear utility-of-wealth function, and instead results from a sort of perceptual bias | but one that represents an optimal Bayesian decision, given the limitations of the mental representation of the situation. We propose a specific quantitative model of the mental representation of a simple lottery choice problem, based on other evidence regarding numerical cognition, and test its ability to explain the choice frequencies that we observe in a laboratory experiment.


Acta Psychologica | 2018

Continuous aesthetic judgment of image sequences

Mel Win Khaw; David A. Freedberg

Perceptual judgments are said to be reference-dependent as they change on the basis of recent experiences. Here we quantify sequence effects within two types of aesthetic judgments: (i) individual ratings of single images (during self-paced trials) and (ii) continuous ratings of image sequences. As in the case of known contrast effects, trial-by-trial aesthetic responses are negatively correlated with judgments made toward the preceding image. During continuous judgment, a different type of bias is observed. The onset of change within a sequence introduces a persistent increase in ratings (relative to when the same images are judged in isolation). Furthermore, subjects indicate adjustment patterns and choices that selectively favor sequences that are rich in change. Sequence effects in aesthetic judgments thus differ greatly depending on the continuity and arrangement of presented stimuli. The effects highlighted here are important in understanding sustained aesthetic responses over time, such as those elicited during choreographic and musical arrangements. In contrast, standard measurements of aesthetic responses (over trials) may represent a series of distinct aesthetic experiences (e.g., viewing artworks in a museum).


Cerebral Cortex | 2013

Decoding the Role of the Insula in Human Cognition: Functional Parcellation and Large-Scale Reverse Inference

Luke J. Chang; Tal Yarkoni; Mel Win Khaw; Alan G. Sanfey


Journal of Monetary Economics | 2017

Discrete Adjustment to a Changing Environment: Experimental Evidence

Mel Win Khaw; Luminita Stevens; Michael Woodford


Archive | 2018

Cognitive Imprecision and Small-Stakes Risk Aversion

Mel Win Khaw; Ziang Li; Michael Woodford


Data in Brief | 2017

Forecasting the outcome of a time-varying Bernoulli process: Data from a laboratory experiment

Mel Win Khaw; Luminita Stevens; Michael Woodford

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