Understanding the buyer decision processes has been one of the core subjects for marketing researchers. Decision-making has been referred as a psychological construct, which could be inferred from the behavior as a commitment to an action. The main scope of this project has been centered at a blended approach between economic and psychological models of decision-making with giving more credit to the possible psychological and physiological counterparts of the buying process. The technological developments and interdisciplinary studies especially in the last decade have paved a valuable path for various research tools that could contribute to the classical models of marketing. The contribution of neuroscientific knowledge and methods has provided a considerable insight for the question what is happening inside the brain. Since neuroscience has targeted at investigating the human brain with respect to internal and external factors, an applied form of neuroscience, neuromarketing, has been useful for the marketing research in general and for modeling the buying processes in specific. Thepresent empirical study could be accepted as one of these initial steps and possible contributions in testing the role of neuroscientific methods in buyers’ decision processes and providing a useful insight in interpreting the prefrontal brain dynamics during their possible buying decisions. Buying decisions have been claimed to be “malleable” (Schwartz, 2004) associated with the available information beneath instead of having well-defined preferences (Bettman, Luce, & Payne 1998). It has been demonstrated that excessive prices have an impact on increasing the insular activity and decreasing the activity in the medial prefrontal cortex, which have been shown to be active during monetary decision-making processes (Knutson et al., 2007). It has also been argued that affect factors and unfair prices have a direct interrelationship (Xia, Monroe, & Cox, 2004). These findings have been in line with the somatic markers hypothesis by Bechara and Damasio (2005) who argue that our brains predict the possible outcomes in a setting depending on our interoceptive emotional signals prior to decision-making. These emotional signals have been considered to have direct guidance during decision-making processes like purchasing. Reward processingmagnitude and valence have been associated with economic transactions by using event-related potentials (Yeung&Sanfey, 2004). Prefrontal cortex has been found to be a critical region that is observed to be active during various cognitive processes including decision-making, executive functions and reasoning. The decision-making processes could generally be divided into two main segments: (1) cognitive and (2) emotional counterparts and prefrontal regions could simply be correlated with the cognitive aspects rather than the emotional side which is mostly considered to be driven by the subcortical regions. The neuroscientific studies in the literature shed light into this dissociation with various empirical findings such that nucleus accumbens activation correlates with reward anticipation, and medial prefrontal cortex (MPFC) activation correlates with gain outcomes (Knutson et al., 2001). Option valuation, probability estimation, reward and cost anticipation and strategy formation have been some of the decision processes that are considered to be executed by the prefrontal cortex. Medial prefrontal cortex has been shown to have a direct role in the calculation of values (or valuation) of options like the statement of the preferences with the presented product and it has been demonstrated that the activation in medial PFC has been in correlation with the subjective product preferences (Paulus & Frank, 2003). The preference judgments between healthy and unhealthy food items have also been shown to activate the medial prefrontal cortex during the valuation of the options (Hare et al., 2011). This valuation of options have been shown to be not restricted to product valuation but rather it is possible to speak of a general valuation system that is also effective in valuation of the monetary rewards (Bastenet al., 2010). Functional near-infrared spectroscopy (fNIRS) method has been utilized to investigate various cognitive faculties. Izzetoglu et al. (2007) used fNIR in a series of experiments to study brain activations during several types of tasks that measure cognitive output. They used a videogame-like task called the Warship Commander Task in which the participants tried to manage varying numbers of airplanes and the amount of cognitive load of the participants was quantified by fNIR. In other experiments, n-back task was used to investigate working memory load on the prefrontal cortex and anagram problems of varying difficulty were used to measure brain activations correlated to problem solving efforts. fNIR was also used in a visual oddball paradigm to assess attention levels of the participants. Across this series of studies, fNIR was shown to be a reliable method for quantification of prefrontal cortex activations. The change in the blood flow has already been shown to illustrate the activation level. Kumagai (2012) investigated personal product preference in fNIR study. Subjects were asked which one of the two products presented they would prefer and then were shown the same products consecutively before making a final decision. Researchers were able to classify product preferences by analyzing blood flow changesvia fNIR.A similar study was conducted by Luu and Chau (2009) to detect product preference via fNIR in a single-trial task. Subjects evaluated two possible products to state their preference and a single-trial task was adequate to analyze fNIR signal for preference detection. We have aimed to observe the neural correlates of buying versus non-buying decisions by using optic neuroimaging method, functional near infrared spectroscopy (fNIR). To the best of our knowledge, no study has directly investigated the fNIR correlates of buying versus non-buying. We have mostly adapted the experimental design of Knutson et al. (2007) that could be accepted as a realistic model for an idealized case of a possible buying decision by which the participants see a product image, then its price (under the product image) and finally the decision screen by which the participant has to respond positively or negatively to the buying decision. There have been four main hypotheses for this empirical study. Firstly, the preferences and buying decisions of the participantsduring the experiment are highly correlated with the consumption rates in their lives.The participants in both experiments are likely to choose the products, which they declare in the subjective reports. Secondly, the reaction times of the purchasing versus not purchasing decisions differ depending on the final decisions of the participants.Thirdly, the preference versus non-preference decisions of the participants causes different brain activation patterns via fNIR. Fourthly, the buying versus non-buying decisions of the participants causes different brain activation patterns via fNIR. The buying decisions would elicit more activation in the prefrontal regions due to the possible involvement of monetary-centered decisions as well as the contribution of working memory in these higher-order processes. This empirical project consists of two experiments. First one has been done without a real purchasing outcome –the participants were choosing to buy the products or not but they were not given the products at the end of the experiment. Whereas in the second experiment, the participants had the chance to buy the products they selected to buy. 78 products were used in the task and the total duration of the experiment was 26 minutes.The products consisted of 3 main groups: food, cleaning and personal care products. There were 39 products in the food group (e.g. milk, cheese, coke), 17 products in the cleaning group (e.g. detergents) and 22 products in the personal care group (e.g. deodorant, shampoo, toothpaste). The prices of the products were taken from the supermarkets around Ankara and Istanbul.28 participants have participated in the first round of the experiment, which has done without actual buying endowment but instead participants have pretended to buy the products that they actually preferred. The participants for this experiment were undergraduate students from the Middle East Technical University and they were paid 10 Turkish Liras for their participation in the experiment. The second experiment was performed with the same experimental design but this time each of the participants were given 10 TL for their participation and 40 TL to purchase the products they prefer during the experiment. 11 participants (6 males and 5 females) have attended to this second experiment. The participants were also told that if they do not purchase any products or they do not spend all of the money, they would be able to get half of the unspent money. Since the products have been presented with their actual market prices, it has been much more likely and advantageous for the participants to spend all of their money.The data of one of the participants was excluded due to handedness, and 2 of them were excluded due to excessive artifacts. After the completion of the task, each participant filled out a survey about their frequency of use (Range;1: Never – 5: Frequently) for each of the 78 products. The data obtained from 16 channels via fNIR at a temporal resolution of 2 Hz consists of 4 main parameters: Oxygenation (oxy), Total Hemoglobin (hbt), Hemoglobin (hb) and Hemoglobin 2 (hb2). Each reading is taken for 4 stages (fixation, picture, price, decision). The main underlying assumption has been to observe an increase in the relative concentrations of oxy and hbt values depending on the higher PFC activation during the product demonstration when compared to the fixation screen (that is presented before each block). Almost all of the channels illustrate a difference on average between buying and non-buying conditions. We have also expected to observe a significant difference in activation levels between purchasing and non-purchasing decisions. The processed data has been statistically analyzed with SPSS 20.0. Repeated Measures (RM) ANOVA has been performed in order to observe if the buying versus non-buying decisions had a statistically significant effect on the fNIR signals that are averaged for each block of product, price and decision screens. The independent variable has been the binary buying decisions of the participants. The results of the RM ANOVA test have demonstrated that the first experiment done with preference of the products do not implicate any significant difference between buying and non-buying decisions. Thus our third hypothesis was based on observing a significant change in prefrontal activations due to participants’ product preferences (versus non-preferences). Thus it is more likely that the non-monetary simulated “buying” decisions, which do not actually end with purchasing products, do not elicit sufficient activation in the prefrontal regions to provide a detectable difference between preferred and non-preferred cases. Whereas the same statistical test performed for the results of the second experiment implicate significant difference for the buying versus non-buying decisions for 2 of 16 voxels: V1, V8. For N=11, significant levels of activation was observed on Voxel 1, F(1.00, 9.00) = 8.35 , p < 0.2 (Greenhouse-Geisser corrected) and Voxel 8, F(1.00, 9.00) = 5.50 , p < .05 (Greenhouse-Geisser corrected). The waveforms averaged over 11 subjects show a significant separation among purchase and no purchase decisions especially in Voxels 1 and 8. This finding has been a support for our fourth hypothesis, which has been a modification of the third one. Our third and fourth hypothesis has hold that the buying decisions versus non-buying decisions elicit higher activation among the prefrontal cortices of the participants when the decisions end with the actual purchasing decision. To sum up, this empirical study could be accepted as a step for the specific research field of buying decision processes and neuromarketing research in general. The obtained results clearly implicate that specific prefrontal regions –both lateral and medial- might be activated differently depending on the final buying decisions via the optic neuroimaging device, fNIR. Beside the technical limitations such as the appropriate presentation time, this study performed with fNIR method could be used as a baseline work for understanding the psychological and physiological dynamics of buying decisions and in the short-term several factors can also be investigated with this method. Therefore, it might also be possible for researchers to adapt this methodology for the sector-specific marketing research especially for pricing in the long-run.