Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Alcohol Alcohol . 2025 Apr 15;60(3):agaf017. doi: 10.1093/alcalc/agaf017 Search in PMC Search in PubMed View in NLM Catalog Add to search Effect of acute alcohol consumption in a novel rodent model of decision-making Atanu Giri Atanu Giri 1 Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Data curation, Formal analysis, Methodology, Software, Visualization, Writing - original draft, Writing - review & editing Find articles by Atanu Giri 1, # , Cory N Heaton Cory N Heaton 2 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Formal analysis, Visualization, Writing - original draft, Writing - review & editing Find articles by Cory N Heaton 2, # , Serina A Batson Serina A Batson 3 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Formal analysis, Investigation, Validation, Writing - original draft Find articles by Serina A Batson 3, # , Andrea Y Macias Andrea Y Macias 4 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Formal analysis, Investigation, Validation, Writing - original draft Find articles by Andrea Y Macias 4, # , Neftali F Reyes Neftali F Reyes 5 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Investigation, Validation Find articles by Neftali F Reyes 5, # , Alexis A Salcido Alexis A Salcido 6 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Investigation, Validation Find articles by Alexis A Salcido 6, # , Luis D Davila Luis D Davila 7 Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Data curation, Formal analysis, Software Find articles by Luis D Davila 7, # , Lara I Rakocevic Lara I Rakocevic 8 Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Formal analysis, Software Find articles by Lara I Rakocevic 8, # , Dirk W Beck Dirk W Beck 9 Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Formal analysis, Software Find articles by Dirk W Beck 9, # , Raquel J Ibañez Alcalá Raquel J Ibañez Alcalá 10 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Formal analysis, Investigation, Resources, Software Find articles by Raquel J Ibañez Alcalá 10, # , Safa B Hossain Safa B Hossain 11 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Formal analysis, Investigation, Validation Find articles by Safa B Hossain 11, # , Paulina Vara Paulina Vara 12 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Investigation Find articles by Paulina Vara 12 , Sabrina M Drammis Sabrina M Drammis 13 Artificial Intelligence Laboratory, Department of Computer Science, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, United States Find articles by Sabrina M Drammis 13 , Kenichiro Negishi Kenichiro Negishi 14 National Institute on Drug Abuse, 251 Bayview Blvd, Baltimore, MD 21224, United States Formal analysis, Investigation Find articles by Kenichiro Negishi 14 , Laura E O’Dell Laura E O’Dell 15 Department of Psychology, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Writing - review & editing Find articles by Laura E O’Dell 15 , Adrianna E Rosales Adrianna E Rosales 16 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Project administration Find articles by Adrianna E Rosales 16 , Travis M Moschak Travis M Moschak 17 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Funding acquisition, Writing - review & editing Find articles by Travis M Moschak 17 , Ki A Goosens Ki A Goosens 18 Departments of Psychiatry, Pharmacological Sciences, and Medicine, Center for Translational Medicine and Pharmacology, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, United States Conceptualization, Funding acquisition, Writing - review & editing Find articles by Ki A Goosens 18, ✉ , Alexander Friedman Alexander Friedman 19 Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 20 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Visualization, Writing - original draft, Writing - review & editing Find articles by Alexander Friedman 19, 20, ✉ Author information Article notes Copyright and License information 1 Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 2 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 3 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 4 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 5 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 6 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 7 Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 8 Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 9 Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 10 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 11 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 12 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 13 Artificial Intelligence Laboratory, Department of Computer Science, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, United States 14 National Institute on Drug Abuse, 251 Bayview Blvd, Baltimore, MD 21224, United States 15 Department of Psychology, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 16 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 17 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 18 Departments of Psychiatry, Pharmacological Sciences, and Medicine, Center for Translational Medicine and Pharmacology, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, United States 19 Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States 20 Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States ✉ Corresponding authors. Alexander Friedman, Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. E-mail: [email protected] ; Ki Goosens, Departments of Psychiatry, Pharmacological Sciences, and Medicine, Center for Translational Medicine and Pharmacology, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, United States. E-mail: [email protected] # Atanu Giri, Cory N. Heaton, Serina A. Batson, Andrea Y. Macias, Neftali F. Reyes, Alexis A. Salcido, Luis D. Davila, Lara I. Rakocevic, DirkW. Beck, Raquel Ibañez Alcalá and Safa B. Hossain have contributed equally to the manuscript. Roles Atanu Giri : Data curation, Formal analysis, Methodology, Software, Visualization, Writing - original draft, Writing - review & editing Cory N Heaton : Formal analysis, Visualization, Writing - original draft, Writing - review & editing Serina A Batson : Formal analysis, Investigation, Validation, Writing - original draft Andrea Y Macias : Formal analysis, Investigation, Validation, Writing - original draft Neftali F Reyes : Investigation, Validation Alexis A Salcido : Investigation, Validation Luis D Davila : Data curation, Formal analysis, Software Lara I Rakocevic : Formal analysis, Software Dirk W Beck : Formal analysis, Software Raquel J Ibañez Alcalá : Formal analysis, Investigation, Resources, Software Safa B Hossain : Formal analysis, Investigation, Validation Paulina Vara : Investigation Kenichiro Negishi : Formal analysis, Investigation Laura E O’Dell : Writing - review & editing Adrianna E Rosales : Project administration Travis M Moschak : Funding acquisition, Writing - review & editing Ki A Goosens : Conceptualization, Funding acquisition, Writing - review & editing Alexander Friedman : Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Visualization, Writing - original draft, Writing - review & editing Received 2024 Dec 9; Revised 2025 Mar 20; Accepted 2025 Mar 22; Collection date 2025 May. © The Author(s) 2025. Medical Council on Alcohol and Oxford University Press. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://academic.oup.com/pages/standard-publication-reuse-rights ) PMC Copyright notice PMCID: PMC12167758 PMID: 40229991 Previous version available: This article is based on a previously available preprint posted on bioRxiv on September 26, 2024: " Effect of Acute Alcohol Consumption in a Novel Rodent Model of Decision Making ". Abstract Aims We sought to explore how acute alcohol exposure alters decision-making in rats performing an approach-avoid decision-making task. Increasing concentrations of alcohol were mixed with decreasing concentrations of sucrose to mimic mixed/sweetened alcoholic beverages. Methods Rats were trained on an apparatus in which different concentrations of sucrose were available in four different corners of the arena. During daily sessions, a tone signaled each trial start, followed by illumination (15 lux, blue LEDs) of a single corner port, indicating the potential availability of sucrose at that location. The rat (one rat per arena, both females and males) then chose to approach the lit corner to have the solution dispensed or avoid it, with no solution being dispensed. We examined how the decisions to pursue sucrose rewards shifted with the addition and subsequent removal of ethanol from the sucrose ports. Results Males were greatly affected by the introduction of alcohol into the task environment, shifting their approach preference to solutions containing higher alcohol concentrations rather than maintaining the prior preference for high-sucrose-concentration solutions. In contrast, females’ choice patterns and task performance remained largely unchanged. We also explore a method for identifying changes in decision-making tendencies during and after alcohol consumption within individual subjects. Conclusions This research explores the introduction of alcohol in varying concentrations with sucrose solutions during an approach-avoid task, with male decision-making and behavioral patterns significantly impacted. We also explore a novel approach for identifying individual adaptations of decision-making behavior when alcohol becomes available, which could be expanded upon in future research. Keywords: decision-making, acute alcohol, conflict task, non-conflict task, psychometric function, RECORD Introduction The consumption of alcohol is widespread in multiple different demographics within the USA ( Centers for Disease Control and Prevention (CDC) 2023 ) and around the world ( Degenhardt et al. 2008 , Rehm 2016 ) with alcoholic beverages greatly varying in their alcohol content and flavor. While alcohol consumption behavior is incredibly complicated and driven by several factors ( Young-Wolff et al. 2011 ), one important factor is the taste of the alcohol ( Bachmanov et al. 2003 , Duffy et al. 2004 , Youngentob and Glendinning 2009 ), with sweetened alcohol being linked to increased consumption ( Ayoub et al. 2020 , Wakabayashi et al. 2021 ). Alcohol use also impacts decision-making and, according to some findings, increases the likelihood that risky decisions are selected ( Fein et al. 2004 , Lane et al. 2004 , George et al. 2005 , Noël et al. 2007 , Bidwell et al. 2013 , Kornreich et al. 2013 , Brevers et al. 2014 , Wallin-Miller et al. 2017 , Aguirre et al. 2020 , Burnette et al. 2021 ). However, other studies examining risk-taking and alcohol consumption have found that alcohol does not increase risk-taking behavior ( Mitchell et al. 2011 , Bernhardt et al. 2019 , Horn et al. 2022 , Karlsson et al. 2022 ), thus alcohol’s influence on risky decision-making remains an active research topic of interest. Decision-making is also a useful tool for comparing the impact of alcohol consumption across individuals or groups and probing alcohol-induced changes in other cognitive functions ( Weissenborn and Duka 2003 , Field et al. 2010 , Dry et al. 2012 , Van Skike et al. 2019 ). Thus, it is critical to explore how decision-making changes with the presentation of alcohol–sucrose solutions at varying concentrations and how subsequent decisions may be influenced by prior alcohol-related choices. Here, we use our novel task environment ( Ibáñez Alcalá et al. 2024 ) to explore how decision-making changes when a substance like alcohol is added as a choice that can be approached or avoided. Various models have been employed to study the impact of alcohol on behavior in rodents. Self-administration tasks are often used to study voluntary alcohol consumption; however, these tasks typically offer only two amounts of alcohol linked to an operant response ( Beckwith and Czachowski 2016 ). The conditioned place preference model provides an opportunity to assess choice behavior for a neutral versus alcohol-paired chamber on a test day following conditioning sessions. However, this model lacks clearly defined time points when the animal makes decisions, making it challenging to analyze specific moments of choice ( Lucke-Wold 2011 ). The runway model is effective in assessing motivated behavior; however, it also lacks the ability to assess discrete choice points, complicating the understanding of underlying neuronal mechanisms during decision-making ( Pandy and Khan 2016 ). The Iowa Gambling Task is used to evaluate risk-taking behavior and complements our task by offering additional perspectives on decision-making under uncertain conditions ( Spoelder et al. 2015 ). Another method used to examine alcohol is the two-way access protocol, where an illuminated light signals the availability of a retractable sipper, then subjects have time to drink before the sipper retracts. Measures like time spent at the sipper and consumption of ethanol are recorded, tracking time and condition ( McCane et al. 2014 , Linsenbardt et al. 2019 ). To expand on the current rodent models, the present study aimed to develop a novel task paradigm that combines etiological validity with a task offering distinct choices, similar to self-administration but with multiple decision points across different contexts, enabling us to explore how alcohol in a decision-making task environment will change choice and decision-related behavior. To achieve this, we utilized our published REward-COst in Rodent Decision-making (RECORD) system to create a versatile model for studying the effects of acute alcohol consumption on decision-making ( Ibáñez Alcalá et al. 2024 ). Using the RECORD system ( Ibáñez Alcalá et al. 2024 ), which administers four different solutions that can be approached or avoided during an experimental session, we examined approach-avoid decision-making in rats across different timeframes and between two different conditions, non-conflict and conflict. During non-conflict sessions, the LEDs signaling solution dispensation were set to 15 lux for all 40 trials of a behavioral session. Conflict sessions consisted of 20 non-conflict trials in which light intensity was set to 15 lux and 20 conflict trials in which light intensity was set to 320 lux. The conflict arises from the desire to approach the appetitive reward and the desire to avoid the punishing bright light. The alcohol task, similarly to the non-conflict condition, set LEDs to 15 lux throughout the session, but offered solutions that contained alcohol and sucrose mixed in inverse concentrations (higher sucrose solutions were mixed with lower alcohol solutions and vice versa). By providing sucrose and alcohol in inverse concentrations, we wanted to examine if rats would retain their preference for the high-sucrose solutions or shift to approaching solutions containing higher alcohol concentrations during acute exposure conditions. To describe the relative timeframe when an experiment took place, we use “pre-alcohol” for sessions performed prior to alcohol exposure, proximal-post-alcohol for 1–4 days after a period of alcohol sessions, and distal-post-alcohol for after alcohol sessions were no longer ran up until the experiment’s end (~2 months later). In summation, subjects performed pre-alcohol non-conflict and pre-alcohol conflict sessions before alcohol exposure with conflict sessions having trials that set LED light intensity to 320 lux. Alcohol offered alcoholic solutions. Proximal-post-alcohol non-conflict and proximal-post-alcohol conflict were conflict and non-conflict sessions performed shortly after a period of alcohol task performance. Finally, distal-post-alcohol non-conflict and distal-post-alcohol conflict sessions were performed for 2 months after alcohol was removed. We found that alcohol significantly changed the approach rate of rats within the task environment ( Fig. 1 ), and that these differences were primarily due to changes in male approach rates ( Fig. 2 ). These changes in approach rate persisted after alcohol was removed from the task environment ( Figs. 3 and 4 ). Figure 1. Open in a new tab Overview and general analysis of behavioral tasks. Pre-alcohol non-conflict (a), pre-alcohol conflict (b), and alcohol (c) tasks utilizing the RECORD setup where one of four solutions are dispensed into a bowl surrounded by LED lights per trial (40 trials per session). Setup of the experimental tasks (d) and individual trials (e) used in the study. (f) Example of psychometric function from a singular pre-alcohol non-conflict session, fitted with a 4-parameter logistic model ( f ( x ) = d + ( a − d )/(1 + ( x / c ) b )). Individual ( n = 20) psychometric functions demonstrating approach rate in pre-alcohol non-conflict (g), pre-alcohol conflict (h, left), and alcohol tasks (h, right). Each line represents the psychometric function of an individual animal in a single session. For clarity, 20 representative sessions were randomly selected for visualization. (i) No significant difference is observed in cumulative distribution of inflection points between pre-alcohol non-conflict and alcohol (KS test, P = .08) or distal-post-alcohol non-conflict tasks (KS test, P = .75). (j) When comparing alcohol to pre-alcohol non-conflict (MANOVA, Wilks’ Λ = .9340, F (4, 268) = 4.73, P = .0011) and distal-post-alcohol non-conflict to pre-alcohol non-conflict (MANOVA, Wilks’ Λ = .9449, F (4, 170) = 2.48, P = .0459), approach rates are significantly different Figure 2. Open in a new tab Sex differences during acute alcohol task performance. Approach rates for individual sessions for all rats ( n = 10) in pre-alcohol non-conflict (a) and alcohol (b) tasks, separated by sex. For clarity, 10 representative sessions were randomly selected for visualization. (c) Compared to pre-alcohol non-conflict, alcohol inflection point shifted significantly left in males (top, KS test, P = .004), but not females (bottom, KS test, P = .96). There were no significant differences for either sex comparing pre-alcohol non-conflict to distal-post-alcohol non-conflict (male: P = .17; female: P = .64). (d) Approach rate is significantly different between pre-alcohol non-conflict and alcohol for males (MANOVA, Wilks’ Λ = .8156, F (4, 126) = 7.12, P < .0001) but not females (MANOVA, Wilks’ Λ = .9746, F (4, 137) = .89, P = .04701). (e) Mean daily (e.g. per session) alcohol consumption values for males and females collapsed across four concentrations. There was no significant difference between the groups (unpaired t -test: P = .9207). (f) Psychometric functions for males and females across four alcohol concentrations. MANOVA did not detect a significant difference between the two sexes (Wilks’ Λ = .5496, F (4, 13) = 2.66, P = .0802). (g) Mean alcohol consumption values for males and females collapsed across four concentrations, normalized by body weight. Females consumed significantly more alcohol than males (unpaired t -test: P < .0001). (h) Psychometric functions for males and females across four alcohol concentrations, normalized by body weight. MANOVA revealed a significant difference between two sexes (Wilks’ Λ = .0326, F (4, 13) = 96.37, P < .0001). (i) Based on figure 1 of Livy (2003) , where rats were gavaged with 0.567 g of ethanol, resulting in a blood alcohol concentration (BAC) of 85 mg/dL, we estimated the BAC for each animal. Given that each session lasted ~35 min and the half-life of ethanol in rat blood exceeds 1 h ( Abel 1982 ), this provided a basis for our estimation Figure 3. Open in a new tab Alcohol affects proximal cost benefit tasks. (a) Psychometric plots of approach rates for individual sessions of rats ( n = 20) in proximal-post-alcohol non-conflict and proximal-post-alcohol conflict tasks. For clarity, 20 representative sessions were randomly selected for visualization. (b) Psychometric plots for individual sessions of all rats ( n = 20) in distal-post-alcohol non-conflict and distal-post-alcohol conflict tasks. (c) A significant shift of inflection points in distal-post-alcohol conflict compared to pre-alcohol conflict (KS test, P < .0001). (d) Approach rates were significantly different across all task types (pre-alcohol non-conflict vs. proximal-post-alcohol non-conflict: MANOVA, Wilks’ Λ = .9292, F (4, 225) = 4.29, P = .0023; pre-alcohol non-conflict vs. distal-post-alcohol non-conflict: MANOVA, Wilks’ Λ = .9449, F (4, 170) = 2.48, P = .0459; pre-alcohol conflict vs. proximal-post-alcohol conflict: MANOVA, Wilks’ Λ = .8109, F (4, 176) = 10.26, P < .0001; pre-alcohol conflict vs. distal-post-alcohol conflict: MANOVA, Wilks’ Λ = .9438, F (4, 171) = 2.55, P = .0413). (e) The bar plot shows the mean difference in approach rates between 320 lux (high-cost trials) and 15 lux (low-cost trials) performed during same session/day for two experimental groups, pre-alcohol conflict, and proximal-post-alcohol conflict. Statistical analysis using the Wilcoxon rank-sum test indicated that the difference in approach rates between the two light levels was statistically significant ( P = .008) in both tasks, suggesting that light level significantly influenced approach behavior in the pre-alcohol conflict and proximal-post-alcohol conflict tasks Figure 4. Open in a new tab Sex-dependent effects of alcohol observed in proximal tasks. Approach rates for individual sessions for all rats (male = 10, female = 10) in proximal-post-alcohol non-conflict task (a) and distal-post-alcohol non-conflict task (b) separated by sex. For clarity, 10 representative sessions were randomly selected for visualization. (c) No significant change in inflection point is observed across tasks or sex. (d) The difference in approach rates is statistically significant between pre-alcohol non-conflict and proximal-post-alcohol non-conflict in males (MANOVA, Wilks’ Λ = .8044, F (4, 100) = 6.08, P = .0002) but not in females (MANOVA, Wilks’ Λ = .9457, F (4, 120) = 1.72, P = .1498). Approach rates for individual sessions for all rats (10 males, 10 females) in proximal-post-alcohol conflict (e) and distal-post-alcohol conflict (f), separated by sex. (g) Proximal-post-alcohol conflict inflection point is significantly changed compared to pre-alcohol conflict in both males (pre-alcohol conflict vs. proximal-post-alcohol conflict: KS test, P < .0001) and females (pre-alcohol conflict vs. proximal-post-alcohol conflict: KS test, P = .02). (h) Differences in approach rate is statistically significant between pre-alcohol conflict and proximal-post-alcohol conflict across both males (MANOVA, Wilks’ Λ = .6389, F (4, 88) = 12.43, P < .0001) and females (MANOVA, Wilks’ Λ = .8362, F (4, 83) = 4.06, P = .0047). Approach rates between pre-alcohol conflict and distal-post-alcohol conflict are significant in males (MANOVA, Wilks’ Λ = .8116, F (4, 92) = 5.34, P = .0007) but not in females (MANOVA, Wilks’ Λ = .9017, F (4, 74) = 2.02, P = .1008). Methods Animal ethics statement The project received approval for all protocols from the University of Texas at El Paso Institutional Animal Care and Use Committee and followed the Guide for Care and Use of Laboratory Animals (IACUC reference number: A-202009-1). Animals Male and female Long Evans rats were housed in ventilated cages under a 12:12 light/dark cycle at 22 ± 2°C and 50 ± 10% humidity with two rats of the same sex per cage. They had access to food and water ad libitum while in the home cage . Male rats weighed 542.8 ± 42.4 g on average while female rats weighed 299.9 ± 25.7 g on average. Rats performed behavioral sessions during the dark phase of the light cycle. RECORD framework We leveraged the RECORD system to introduce alcohol as a component of cost–benefit decision-making within a foraging-like environment ( Ibáñez Alcalá et al. 2024 ). On average, training rats to perform RECORD tasks took 9 weeks. The RECORD arena is divided into four quadrants, each distinguished by a different floor pattern (we used diagonal, grid, horizontal, and radial patterns). Diagonal, grid, horizontal, and radial refer to the floor patterns printed on the maze floor for rats to use as tactile/visual cues to discriminate between the quadrants. Rewards were delivered into a “reward zone”; each quadrant had one reward zone and consistently delivered the same sucrose concentration as a reward during tasks (9% diagonal, 5% grid, 2% horizontal, 0.5% radial). Dimmable LED lights are embedded in each reward zone to signal the active port on each trial and to serve as a cost associated with the reward offered. In general, sessions were run over the course of 5 days with no sessions run over the weekend. Non-conflict and conflict tasks During the non-conflict tasks, the LEDs at each reward zone were set to emit at 15–20 lux. The animals were 11.05 ± .22 months old on average at the beginning of the non-conflict task. The conflict task, in contrast, sets the LEDs to emit at a maximum of 320 lux which rats find aversive (figure 2b from Ibáñez Alcalá et al. 2024 ). The animals were 11.85 ± 0.37 months old on average at the beginning of the conflict task. During a conflict task, each reward zone was pseudo-randomly and equally active for low or high cost (lux level) for each trial (50% low cost: 50% high cost). Acute alcohol task Alcohol was mixed with the pre-existing sucrose concentrations and offered with non-conflict task LED brightness (15–20 lux). Solutions were mixed as follows: (a) 9 g of sucrose, 1 mL of Everclear brand alcohol (95%), and 99 mL of water; (b) 5 g of sucrose, 4 mL of Everclear brand alcohol, and 96 mL of water; (c) 2 g of sucrose, 10 mL of Everclear brand alcohol, and 90 mL of water; and (d) 0.5 g of sucrose, 20 mL of Everclear brand alcohol, and 80 mL of water ( Ibáñez Alcalá et al. 2024 ). Experimental sessions, aside from the differences in reward solutions, were similar to the pre-alcohol non-conflict task. Twenty rats (10 males and 10 females) participated in 158 sessions for this task, with 77 sessions conducted by males and 81 by females. All rats ran the same number of behavioral sessions; however, some sessions had missing data due to problems during data preprocessing or storage and, thus, were unable to be analyzed. On average, males consumed 0.47 ± 0.16, 0.36 ± 0.14, 0.41 ± 0.07, and 0.12 ± 0.02 g/kg alcohol under 0.5%, 2%, 5%, and 9% sucrose conditions, respectively. In contrast, females consumed 0.76 ± 0.34, 0.63 ± 0.23, 0.79 ± 0.08, and 0.25 ± 0.02 g/kg under 0.5%, 2%, 5%, and 9% sucrose conditions, respectively. Brief overview of all task types We used three task types: a non-conflict task ( Fig. 1a ), a conflict task ( Fig. 1b ), and an alcohol task ( Fig. 1c ). First, pre-alcohol non-conflict (115 sessions total) and pre-alcohol conflict (125 sessions total) tasks ran for 4 weeks. After this period of conflict and non-conflict task performance, 158 sessions of the alcohol task were completed over 5 weeks. Proximal-post-alcohol conflict and proximal-post-alcohol non-conflict tasks were defined as running between 1 and 4 days after the alcohol task. Lastly, distal-post-alcohol non-conflict and distal-post-alcohol conflict tasks were performed for 2 months without alcohol offers after the alcohol task concluded ( Fig. 1d ). Each animal was limited to one task type per day. Each trial began with a tone at the 4-s mark, signaling the start of the trial, followed by the activation of the offer indicated by an LED light. The animal had 6 s to reach the reward compartment to avail the reward. If the animal entered the vicinity of the reward compartment, the reward was dispensed and available for 7 s, during which the animal could consume it. Afterward, the LED light was turned off and the excess solution drained onto an absorbent pad beneath the arena. Trials were separated by a 28-s inter-trial interval ( Fig. 1e ). After finishing the session, rats were returned to their home cage. Twenty rats (10 males and 10 females) performed all experimental conditions and timeframes ( Supplemental Fig. S1b ). Spatiotemporal behavioral dynamics Features derived from behavioral data, such as approach rate, collected during experimental sessions were used to analyze individual and group task performance ( Ibáñez Alcalá et al. 2024 ). In this paper, we analyzed a new feature, “Time in reward zone,” which quantifies how long each rat spent within the active reward zones during a trial. Behavioral data and approach data were collected using high-speed cameras and custom codes that are provided in the first RECORD paper ( Ibáñez Alcalá et al. 2024 ). The codes allowed trials to progress automatically by detecting whether an animal approached the reward zone, triggered or withheld solution dispensation, and timed the next trial. Psychometric function shape analysis To analyze the psychometric function for each session of individual animals, we fit the data using the function . This function characterizes the sigmoidal relationship between the stimulus intensity, represented by the concentration of sucrose on the -axis, and approach rate. In this equation, and represent the upper and lower horizontal asymptotes, respectively, indicating the maximum and minimum response levels. Variables and are the growth rate parameter and inflection point, respectively. For robust analysis, we only included sessions where individual animals completed at least 40 trials, disregarding any sessions with fewer trials. This threshold ensures the reliability of the fitted psychometric functions by providing sufficient data points for accurate curve fitting. Goodness of fit ( ) was used to determine how well the data fit the sigmoidal equation. The psychometric function representing a session was considered sigmoidal if the . Statistical analysis All statistical analyses were performed using MATLAB (R2022a) software. The cumulative probability density of inflection points between groups was compared using the two-sample Kolmogorov–Smirnov (KS) test. To address the issue of unbalanced sample sizes across different conditions (alcohol task performers vs. non-alcohol task performers), a non-parametric bootstrapping method with 1000 iterations was employed during the KS test. Multivariate analysis of variance (MANOVA) was used to evaluate the effects of group (e.g. pre-alcohol non-conflict vs. proximal-post-alcohol non-conflict) on approach rates across four concentrations. MANOVA was chosen because it accounts for the interdependence among the dependent variables (approach rates at different concentrations) and allows for a simultaneous evaluation of group differences across all concentrations. This approach controls Type I error associated with multiple comparisons and improves statistical power by leveraging the relationships among the dependent variables. Additionally, MANOVA aligns with the study’s objective to identify overall patterns of group differences in approach behavior. The “fraction of sigmoid” for an animal within a particular health group was calculated by dividing the number of sigmoid sessions by the total number of sessions. Most rats met or exceeded a threshold of 0.7, meaning >70% of sessions were sigmoidal. Rats who had <70% of sessions that formed sigmoidal functions were considered to “respond” to alcohol exposure. The Wilcoxon rank-sum test was used to compare approach rates between different light conditions (e.g. L3 vs. L1) within experimental groups. This non-parametric test was chosen because it does not assume normality and is suitable for comparing distributions between two independent samples. The Kruskal–Wallis test was employed to examine differences in approach rates across trial sections within the same experimental condition and sucrose concentration, allowing for the detection of significant differences across trial sections. A chi-square test was performed to assess whether there was a significant difference in the proportion of rats who were above versus below 0.7 between the two sexes within the same health group. Results Alcohol alters psychometric functions The psychometric functions of approach rate typically form a sigmoidal function (individual example Fig. 1f ). During pre-alcohol non-conflict ( Fig. 1g ) and pre-alcohol conflict ( Fig. 1h , left), tasks performed by the rats before being introduced to alcohol, the individual psychometric functions generated during behavioral sessions typically had the highest approach rates for 9% sucrose. We then wanted to examine how the introduction of alcohol into the task environment (see Materials and methods: Acute alcohol task) affected approach preferences. We observed an increased approach rate for lower sucrose (consequently, higher alcohol) concentrations and reduction in approach rate for 9% sucrose concentrations in some individuals ( Fig. 1h , right). To examine if there was a change in solution preference across the three task types, we looked at the distribution of inflection points since the inflection point measures which solution the subject switches from approaching or avoiding 50% of the time. While there were no significant differences between inflection point distribution across task types ( Fig. 1i ), the approach rate was significantly different across task types. With alcohol, the approach rate for every solution was higher than the other conditions except for 9%, the highest sucrose concentration solution that had the lowest alcohol concentration ( Fig. 1j , MANOVA alcohol compared to pre-alcohol non-conflict P = .001). Comparing distal-post-alcohol non-conflict task performance to pre-alcohol non-conflict shows that rats would approach 5% and 9% solutions significantly more often after alcohol exposure than they did before ( Fig. 1j , MANOVA pre-alcohol non-conflict vs. post-alcohol non-conflict P = .05, Supplemental Fig. S1a ). There were no significant differences identified in either the number of sessions across task types or number of trials completed by the subjects between the sexes ( Supplemental Fig. S1b, c ). When examining approach rate across all alcohol task sessions, we found no significant shift in performance, which suggests that rats develop their preference for alcohol approach patterns quickly after it is introduced into the task environment ( Supplemental Fig. S1d ). Overall, we introduced alcohol into an approach-avoid decision-making paradigm. We paired the sucrose concentrations that rats were already exposed to with alcohol in a manner that would allow rats to mostly avoid it (since the 9% sucrose solutions only contained 1% alcohol compared to 5% sucrose with 4% alcohol, 2% sucrose with 10% alcohol, and 0.5% sucrose with 20% alcohol). Despite the ability to mostly avoid alcohol after it was introduced into the task environment, we found an increased approach rate for solutions with higher alcohol content and a slight decrease in approach rate for 9% sucrose concentrations when alcohol was available. Then, after alcohol was removed from the approach-avoid environment, rats started approaching 5% sucrose and 9% sucrose even more than they had pre-alcohol, potentially indicating increased reward seeking with the removal of alcohol. Sex differences in sensitivity to alcohol We then looked at individual functions formed during pre-alcohol non-conflict and alcohol tasks split by sex, which indicated that males generated more altered psychometric functions after alcohol exposure compared to females ( Fig. 2a, b , top male, bottom female for all figures). Using inflection point distributions, we wanted to investigate whether there were sex differences in approach preference during alcohol exposure. In males, there was a significant change in inflection point distributions during alcohol task performance ( Fig. 2c , top, P = .004) while there was no significant change observed in females ( Fig. 2c , bottom). Similarly, examining approach rate elucidated prominent differences across task types in males. During the alcohol task, males approached lower sucrose concentrations significantly more and 9% sucrose less than during pre- and post-alcohol non-conflict ( Fig. 2d , top, P < .0001, Supplemental Fig. S1b ). Females had no significant differences in approach rate across task types ( Fig. 2d , bottom). During the alcohol task, females and males drank the same amount of alcohol (male 0.74 g, female 0.73 g), but, adjusting for weight, females consumed significantly more alcohol compared to males (male 1.35 g/kg, female 2.43 g/kg, P < .0001, Fig. 2e–h ). We estimated blood alcohol concentration (BAC) by taking the average from comparing the alcohol consumed by our rats to data from Livy et al., where rodents underwent a gavage procedure with 0.567 g of ethanol administered and a BAC of 85 mg/dL was measured ( Livy et al. 2003 ); this led to our estimation of male BAC being 1.77 ± 0.14 g/dL while BAC was 3.17 ± 0.27 g/dL in females ( Fig. 2i ). Additionally, the half-life of alcohol in rodents has been found to be over an hour; thus, we did not factor half-life into our estimation since a behavioral session is finished well before an hour passes ( Abel 1982 ). Looking at the differences between sexes in approach-avoid decision-making suggests that males change their decision-making tendencies after alcohol is introduced as a component of the decision while females largely maintain consistent approach patterns. Particularly, males seem to approach solutions with higher alcohol concentrations, potentially because they perceive the mixtures of sucrose with alcohol as more rewarding than sucrose alone. Furthermore, despite males significantly altering their decision-making tendencies, females still consumed more alcohol than males when normalized for weight. Rats change their behavioral tendencies when performing the alcohol task We examined measures that may indicate conditioned place preference. When plotting the location of subjects in the maze during the first 5 s of each trial for proximal-post-alcohol non-conflict, rodent locations are dispersed across all four reward zones, though there seems to be a preference for the higher sucrose concentration zones. In contrast, during distal-post-alcohol non-conflict sessions, rodents go toward the higher sucrose concentration reward zones and primarily start at the 9% sucrose zone ( Supplemental Fig. S2a ). We then looked at approach rates across sessions with trials split into blocks of 10 (the first 10 trials were combined, 11 to 20 were combined, and so forth) for proximal-post-alcohol non-conflict task performance compared to distal-post-alcohol non-conflict. Across this session progression, we found that for the first 10 trials of a session, the approach rate of 0.5% and 2% sucrose (formerly the highest alcohol concentration solutions) had a significantly increased approach rate compared to later trials ( Supplemental Fig. S2b , left, 0.05% first 10 trials, P = 0.0008, 2% sucrose first 10 trials, P = .0008, Kruskal–Wallis test) consistent with a moderate place preference. This elevation in approach rate is not observed during distal-post-alcohol non-conflict task performance ( Supplemental Fig. S2b , right), suggesting that the place preference does not persist. We also examined how much time rats would spend in each reward zone across sessions, split into blocks of 10 trials, comparing proximal-post-alcohol non-conflict and distal-post-alcohol non-conflict. Similarly to approach rate, rodents were in the formerly high alcohol concentration zones for longer periods of time during the first 10 trials of proximal-post-alcohol non-conflict task performance ( Supplemental Fig. S2c , left, 0.5% first 10 trials, P = .007, 2% first 10 trials, P = .005), and this difference is not significant for distal-post-alcohol non-conflict task performance ( Supplemental Fig. S2c , right). Alcohol alters time spent in reward zones With the RECORD task environment, we are also capable of looking at the subject’s behavior throughout the behavioral session. One behavior of interest was the time that rats would spend in different reward zones ( Supplemental Fig. S3a ) because rats may find the new solutions interesting and spend time in the reward zone without approaching the solution itself. After alcohol was added to the task, rats generally spent more time in the 0.5%, 2%, and 5% reward zones compared to pre-alcohol non-conflict ( Supplemental Fig. S3b , MANOVA, P = .0002) and is not significantly different when comparing pre- and post-alcohol non-conflict. Additionally, the inflection point distribution for time spent in reward zone, an indicator of which solution a rat will spend more time in a reward zone for, shifts toward lower sucrose solutions for both alcohol and post-alcohol non-conflict compared to pre-alcohol non-conflict ( Supplemental Fig. S3c , KS test, P = .001). We then looked at time in reward zones split across sexes and similar to approach rate found that males significantly changed their behavior during alcohol and post-alcohol non-conflict tasks in both average time in reward zones ( Supplemental Fig. S3d top, MANOVA, P < .0001) and their inflection point distributions ( Supplemental Fig. S3e top, KS test, P = .003). Females, in contrast, had no significant differences across task types in either average approach time or inflection point distribution ( Supplemental Fig. S3d, e , bottom). This analysis shows that after alcohol, rats spend more time in lower sucrose concentration reward zones. These significant differences seem to be solely driven by male rats changing their behavioral preference for the lower sucrose concentration reward zones because female rats have no significant differences in either average time in reward zone or inflection point distributions. Identifying shifts in behavioral task performance Looking at different behavioral measures compared to before and during alcohol task performance, we found some indication that the alcohol task affects how the rodents behave while performing approach-avoid decision-making. When we examined the distance traveled at a session level, as rats performed more sessions, rats would gradually travel further during a session compared to earlier sessions ( Supplemental Fig. S4a , left, P < .0001, Kruskal–Wallis). During the alcohol task, this trend flips and rats start out traveling further initially, and as they performed more sessions gradually traveled less throughout a session ( Supplemental Fig. S4a , right, P = .02). Examining time spent in the center of the arena, we observe some different tendencies between pre-alcohol non-conflict and alcohol task performance, though there are no significant differences observed across session progression ( Supplemental Fig. S4b ). We then looked at these behavioral measures for trial progression. The distance rats traveled generally decreases during both pre-alcohol ( Supplemental Fig. S4c , left, P = .007, Kruskal–Wallis) and alcohol task performance ( Supplemental Fig. S4c , right, P < .0001), though it seems that rats travel further during alcohol for all trials. Interestingly, alcohol seems to overall reduce the time spent in reward zones; however, during later trials, rats spend more time in the center compared to the initial trials ( Supplemental Fig. S4d , right, P < .0001). We wanted to see if we could identify rats that demonstrated greater changes in approach-avoid decision-making. To do so, we looked at the formation of sigmoid functions during task performance and found that some subjects will form sigmoid functions during pre-alcohol non-conflict and conflict tasks which become non-sigmoidal during alcohol task performance (e.g. Supplemental Fig. S4e ); in contrast, other subjects retain sigmoid functions throughout all three task types (e.g. Supplemental Fig. S4f ). Across all tasks, animals are considered alcohol “responders” when the proportion of sigmoid-shaped functions formed during sessions fell <0.7/70% [a threshold determined in our initial publication where we develop and validated the RECORD system ( Ibáñez Alcalá et al. 2024 )]. “Non-responders,” those who retain >70% of sessions as sigmoidal, in contrast, seem to retain more consistent functions across timeframes. Using these criteria, seven males ( Supplemental Fig. S4 g left) and two females were considered responders ( Supplemental Fig. S4 g right) which is a significant difference between sexes ( Supplemental Fig. S4 h, chi-square test, P = .025). Furthermore, only one male and three females had <70% of sigmoidal sessions during both pre-alcohol ( Supplemental Fig. S4b, c ) and pre-alcohol conflict tasks ( Supplemental Fig. S4d, e ). With this analysis, we show that sigmoid function formation may be an indicator of whether a subject changes approach-avoid decision-making behavior with alcohol present. It also highlights a significant difference between male and female response patterns after alcohol exposure, where females tend to retain sigmoid-shaped response functions. Interestingly, this approach may identify a subset of females who are vulnerable to changes in decision-making with alcohol. Alcohol effects on the proximal cost–benefit tasks To determine if the differences in approach-avoid decision-making observed during alcohol task performance were short-term or long-term effects, we split post-alcohol task performance between the first 4 days after the alcohol task ended, termed “proximal-post-alcohol” ( Fig. 3a ), and sessions performed after those 4 days up to the end of the experiment (2 months post-alcohol) were termed “distal-post-alcohol” ( Fig. 3b ). When we examined infection point distributions to see if approach preferences shifted for pre-alcohol, proximal-, and distal-post-alcohol non-conflict, there were no significant differences ( Fig. 3c , top). However, the inflection point distribution was significantly shifted toward lower sucrose solutions when comparing pre-alcohol conflict to proximal-post-alcohol conflict which then disappears when comparing pre-alcohol to long abstinence conflict ( Fig. 3c , bottom, KS test, P < .0001). Average approach rates were significantly different across all non-conflict and conflict conditions, indicating that alcohol task exposure changes approach-avoid decision-making in a manner that persists over the course of the experiment ( Fig. 3d , MANOVA: pre-alcohol non-conflict vs. proximal-post-alcohol non-conflict P = .0023, pre-alcohol non-conflict vs. distal-post-alcohol non-conflict P = .046, pre-alcohol conflict vs. proximal-post-alcohol conflict P < .0001, and pre-alcohol conflict vs. distal-post-alcohol conflict P = .041). We have found that rats, during proximal-post-alcohol task performance, approach more during conflict tasks especially for the higher sucrose concentration offers. When analyzing the approach of high-cost versus low-cost trials, we also found that during proximal-post-alcohol task performance, rats start to approach high-cost offers significantly more than before ( Fig. 3e ). This suggests that exposure to the alcohol paradigm changes the perception of cost. Overall, this demonstrates that alcohol changes which offers subjects choose to approach both shortly after alcohol exposure and in the long term. Generally, this is shown by the increased preference for higher alcohol concentration solutions, though in non-conflict it appears to also increase approach for reward offers even more for high-sucrose solutions like 9% during distal-post-alcohol sessions while proximal-post-alcohol sessions are more similar to alcohol sessions with the reduced preference for high-sucrose solutions. This trend is also seen during conflict sessions where proximal-post-alcohol sessions have approach rates for 9% sucrose solutions compared to both other conditions, though distal-post-alcohol seems much closer to pre-alcohol sessions than alcohol sessions. We also speculate that increased approach after alcohol exposure could stem from an insensitivity to the cost stimulus or could be because high-intensity LEDs become a stronger cue (rather than serving as a cost like prior to alcohol) for the subject while performing behavioral sessions. Sex differences are observed during the proximal tasks We then separated proximal- and distal-post-alcohol tasks by sex to look for differences in decision-making across the two timeframes (individual functions for male and female proximal/distal-post-alcohol sessions, Fig. 4a, b ). Non-conflict inflection point distributions are not significantly different, indicating that the shift in approach-avoid decision-making is due to the presence of alcohol ( Fig. 4c ). Males have significantly different approach rates when comparing proximal-post-alcohol non-conflict to pre-alcohol non-conflict, though this significance is not present for distal-post-alcohol non-conflict ( Fig. 4d , top, MANOVA, P = .0002), and recapitulating prior data, females have no significant differences across non-conflict task timeframes ( Fig. 4d , bottom). We also split proximal-post-alcohol ( Fig. 4e ) and distal-post-alcohol conflict ( Fig. 4f ) sessions by sex. Interestingly, when comparing shifts in approach preference, we found both males ( Fig. 4g , top, KS test, P < .0001) and females ( Fig. 4g , bottom, KS test, P = .02) had significantly altered inflection point distributions between pre-alcohol and proximal-post-alcohol conflict sessions. Similarly, both males ( Fig. 4g , top, MANOVA, P < .0001) and females ( Fig. 4g , bottom, P = .005) had significantly different approach rates when comparing pre-alcohol and proximal-post-alcohol conflict sessions, though only males had approach rates that remained significantly different during distal-post-alcohol conflict ( Fig. 4g , top, P = .0007). This analysis show that males have altered decision-making even after alcohol has been removed from the task environment for up to 2 months. Females also show significantly different decision-making during proximal-post-alcohol conflict performance but lose that significant difference in decision-making after an extended break from alcohol exposure. Time spent in reward zone altered in the proximal tasks We also examined the time subjects spent in different reward zones splitting between proximal-post-alcohol versus distal-post-alcohol and conflict versus non-conflict conditions. Pre-alcohol non-conflict was significantly different from proximal-post-alcohol non-conflict (MANOVA, P = .021) and lost this significance when compared to distal-post-alcohol non-conflict ( Supplemental Fig. S7a , top). In contrast, proximal-post-alcohol and distal-post-alcohol conflict times in reward zones are significantly different from pre-alcohol conflict. We then investigated inflection point distribution across the different task types to see how approach preference changed for each timeframe or condition. Both proximal-post-alcohol (KS test, P < .0001) and distal-post-alcohol non-conflict tasks ( P = .02) were significant from pre-alcohol non-conflict sessions ( Supplemental Fig. S7b , top). During conflict conditions, only proximal-post-alcohol had a significantly different inflection point distribution from pre-alcohol conflict ( Supplemental Fig. S7b , bottom, KS test, P < .0001). When evaluating effects of time spent in reward zones by sex, males but not females demonstrated increased time spent in 0.5%, 2%, and 5% sucrose during both proximal-post-alcohol non-conflict ( Supplementary Fig. S7c top, MANOVA, P = .0021) and distal-post-alcohol non-conflict ( Supplementary Fig. S7c top, MANOVA, P = .047). Males also exhibit shifted density in proximal-post-alcohol non-conflict ( Supplemental Fig. S7d top, KS test, P = .003) but not in distal-post-alcohol non-conflict tasks. This trend of increased time spent in 0.5%, 2%, and 5% sucrose was conserved, and males dominated in proximal-post-alcohol conflict ( Supplemental Fig. S7e top, MANOVA, P < .0001) and distal-post-alcohol conflict ( Supplemental Fig. S7e bottom, MANOVA, P < .0001) tasks. This is further supported by the significantly altered inflection point distributions (depicted through cumulative density functions) in males performing proximal-post-alcohol ( Supplemental Fig. S7f top, KS test, P < .0001) and distal-post-alcohol conflict sessions ( Supplemental Fig. S7f top, KS test, P < .028). This analysis shows that the time spent in reward zones generally differed after alcohol exposure and between conflict or non-conflict sessions. Non-conflict only had significantly different average time in reward zones during proximal-post-alcohol sessions that became non-significant over time; however, inflection point distributions remained significantly different for non-conflict in both proximal and distal-post-alcohol conditions. In contrast, conflict sessions had significantly different average times in reward zones in both the proximal and distal-post-alcohol sessions but only had a significantly different inflection point distribution during proximal-post-alcohol conflict trials that is non-significant over distal-post-alcohol conditions. When split by sex, this behavior corroborates what has been found, with males demonstrating more significant differences across average time in reward zones and inflection point distributions with females having no significant differences in the time they spent in reward zones across condition and timeframe. Identification of shifted task performance patterns in proximal tasks We were interested in determining whether responder populations in proximal non-conflict and conflict tasks were sex exclusive, and a chi-square test was used to quantify the difference in fraction of a sigmoid. Notably, half the males in proximal-post-alcohol non-conflict shifted their behavioral strategies away from sigmoidal while females did not ( Supplementary Fig. S6a, b , chi-square test, P = .0098). When comparing responder males and females in the context of distal-post-alcohol non-conflict, neither group was significant ( Supplementary Fig. S7g, h ). During proximal-post-alcohol conflict sessions, more males ( n = 5) than females ( n = 2) were classified as alcohol responders, but the two were not statistically different ( Supplementary Fig. S6c, d , chi-square test, P = .16). Interestingly, during distal-post-alcohol conflict sessions, both males and females exhibit equal numbers of alcohol-responsive subjects ( Supplemental Fig. S7i, j , chi-square test, P = 1.0). This analysis demonstrates a potential method for identifying and quantifying individuals who may be more affected by the introduction of alcohol into the decision-making context. It also provides an interesting contrast to the earlier analysis on non-conflict task, where there was no significant difference in responders between males and females. Discussion Overall, we explored how introducing alcohol into an approach-avoid task environment changes reward approach preference. When alcohol-containing solutions were available, approach rates for lower sucrose concentration solutions (the solutions with higher alcohol concentrations) increased ( Fig. 1 ). This increased approach for higher alcohol concentrations was primarily observed in male rats ( Fig. 2 ). The preference for lower sucrose concentrations persisted for a few days after alcohol was removed from the reward solutions ( Fig. 3 ) primarily driven by male rats retaining higher approach rates for the lower sucrose reward solutions ( Fig. 4 ). Post-alcohol task performance (sessions with sucrose-only solutions performed 60 days after the alcohol task was conducted) becomes non-significantly different from controls for some measures (almost all distal-post-alcohol inflection point measures), but some lingering differences in task performance remained, particularly in male approach rates. It is interesting that the male decision-making was more perturbed by alcohol than female decision-making, given that females typically consumed twice as much alcohol as the males. One reason for the shift in preference toward the moderate alcohol solutions could be because males found the combination of sucrose and alcohol more pleasurable than solutions predominantly one or the other. Female rodents could also have more stable decision-making tendencies in this paradigm, whereas males may be more easily influenced to change how they make decisions when new information is available. Many other rodent paradigms looking at alcohol use and decision-making examine binge drinking patterns or chronic (2–3 months of exposure) alcohol consumption ( Strong et al. 2010 , Jury et al. 2017 ). These studies often aim to understand how sucrose impacts alcohol pursuit, typically using water–ethanol mixtures without sucrose ( Crabbe et al. 2009 , Hwa et al. 2011 ). In contrast, our study takes the opposite approach, investigating how the presence of alcohol alters the pursuit of appetitive rewards. Specifically, we explore how introducing alcohol into a sucrose-based reward system shifts decision-making and reward preferences. In our task, we found that presenting a sucrose–alcohol mixture into the maze caused a significant change in how males perform the task, where they approached 2% and 5% sucrose more than in the non-alcohol task conditions. Females in our task did not demonstrate many significant differences whether alcohol was present or not. This research, potentially due to the approach-avoid style task, mixture of alcohol with sucrose, and short-term nature of the alcohol exposure, finds a greater shift in male decision-making and behavior with minimal shifts observed in female rodents. One reason for the shift in male decision-making and not female decision-making despite females consuming more ethanol for their bodyweight could be due to female rats metabolizing alcohol faster than their male counterparts ( Thomasson 2002 ). Sex differences were also found when mice were allowed to choose between water or an alcohol solution containing 20% alcohol and water within their home cage. After measuring baseline drinking patterns, the researchers added quinine to the alcohol solution to see if the addition of an aversive taste would cause mice to drink less of the alcohol solution. Female mice drank significantly more of the alcohol solution even after the quinine was added leading to the conclusion that females do not suppress alcohol intake after an aversive component is added to it while males more readily suppress their alcohol intake to avoid the aversive taste ( Arnold et al. 2023 ). Another sex difference for alcohol use is the reason for using alcohol to begin with: females may drink alcohol to avoid negative internal states while males may be motivated by the rewarding properties of alcohol ( Becker et al. 2012 , Zachry et al. 2019 ). This could partially explain the prior quinine study results because females may be more willing to ignore a physical aversive taste for the alleviation of other negative consequences while males lose the desire to pursue a solution that is less rewarding. These findings would generally align with what we observed in males if male rats do find the mixture of sucrose and alcohol more rewarding than sucrose alone. If so, then the observation that males, when alcohol is removed, approach rewards significantly more than even before alcohol was administered potentially suggests heightened reward sensitivity. Overall, the increased approach after alcohol also shows that rats in general, after alcohol exposure, become less sensitive to cost compared to before. In future work, an interesting dynamic to examine would be to use alcohol–sucrose solutions for conflict tasks as well as non-conflict. We speculate that females might approach alcohol at the same rate as before or more, demonstrating a resistance to aversive cues similar to that of published studies using the quinine task ( Arnold et al. 2023 ), while males will decrease their approach once there is more of a cost to the reward though the reward itself remains unchanged. However, it is also possible that the cost being a separate component of the reward may have different effects than making the solution itself more aversive. A variety of adaptations to the RECORD system could be made to further examine the impacts of alcohol consumption after acute exposure, chronic alcohol consumption, and other paradigms on approach-avoid decision-making behavior. The first could be training subjects to react to the light as a cue without the signaling tone that we had used here. The tone could then be introduced to examine its impact as a priming/occasion setting cue as rats associate it with the onset of the task. Another change that could be made is to have multiple feeder zones cued during a session (with different color LEDs or perhaps different tones), with one cue indicating the “correct” port for solution dispensation and the other cue indicating the “incorrect” port where no solution would be dispensed. This could then allow for examining the cognitive effects of acute alcohol exposure short term or long term. Aside from system adaptations, an important consideration for future experiments is to determine BAC after a session of this task. We attempted to draw blood from some of the rats after they performed a behavioral session; however, the rats would not perform the behavioral task during the subsequent session, potentially due to a negative association with the task and getting their blood drawn. Thus, future experiments may try to leverage a chronically implanted catheter to make blood collection easier. Another option may be to collect blood alcohol concentrations during/right before the weekend to put space between the behavioral task and blood being drawn. Future work exploring acute alcohol consumption could use a similar task setup alongside in vivo recording techniques to uncover correlated neural activity as the task is performed. Exploring these neuronal recordings and other alcohol factors, like delivering chronic alcohol, could give further insight into the physiological activities underlying acute/social alcohol consumption. We are particularly interested in investigating regions within a cortico-basal ganglia–substantia nigra compacta circuit [that includes the prefrontal cortex, prelimbic cortex, striatum (split between the striosomes and matrix), lateral habenula, and substantia nigra compacta] that we have modeled previously ( Beck et al. 2024 ). Initially, we intend on starting by trying to isolate the striosomal role in decision-making during and after acute alcohol use. Work in the dorsal striatum has suggested that it becomes a center for behavioral control as opposed to the prefrontal cortex during addiction ( DePoy et al. 2013 ). The dorsal striatum has also been implicated in the development of habitual behavior during alcohol-related lever pressing ( Corbit et al. 2014 ). Fast spiking interneurons within the dorsal striatum have been found to attenuate compulsive alcohol consumption ( Patton et al. 2021 ) and that activity within the dorsal striatum after adolescent alcohol use may correlate to future risky behavior ( Jones et al. 2016 ). Thus, eliciting the effect of alcohol on striosomal neurons may provide an avenue to further clarify some fundamental mechanisms that contribute to the development and maintenance of alcohol use along with the associated changes in decision-making. Additionally, our task could introduce cost by increasing the intensity of the LEDs during the acute alcohol task which would provide insight into how alcohol is valued and whether acute alcohol presentation skews the perception of the cost. With the RECORD task framework, there could easily be 16 different combinations to explore by offering the four different alcohol solutions with four different cost levels. This type of task structure has been demonstrated to enable various analytical approaches including heat maps across reward, cost, and approach rates, which would be incredibly useful to examine with acute alcohol being implemented as a choice that could be selected. This approach enables the exploration of how alcohol influences choice in a foraging-like task environment and implements alcohol as part of the task paradigm that factors into decision-making. Supplementary Material SI_figs_final_agaf017 si_figs_final_agaf017.pdf (1MB, pdf) Acknowledgements We thank Dr Abhijit Mandal for his valuable suggestions regarding statistical analyses. We also thank the summer research students in our laboratory, Martin J. Villalobos and Juan R. Anaya, for their valuable help with data collection. Contributor Information Atanu Giri, Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Cory N Heaton, Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Serina A Batson, Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Andrea Y Macias, Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Neftali F Reyes, Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Alexis A Salcido, Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Luis D Davila, Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Lara I Rakocevic, Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Dirk W Beck, Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Raquel J Ibañez Alcalá, Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Safa B Hossain, Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Paulina Vara, Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Sabrina M Drammis, Artificial Intelligence Laboratory, Department of Computer Science, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, United States. Kenichiro Negishi, National Institute on Drug Abuse, 251 Bayview Blvd, Baltimore, MD 21224, United States. Laura E O’Dell, Department of Psychology, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Adrianna E Rosales, Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Travis M Moschak, Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Ki A Goosens, Departments of Psychiatry, Pharmacological Sciences, and Medicine, Center for Translational Medicine and Pharmacology, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, United States. Alexander Friedman, Computational Science Program, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States; Department of Biological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, United States. Author contributions Alexander Friedman (Conceptualization [equal], Data curation [equal], Formal analysis [equal], Funding acquisition [equal], Methodology [equal], Project administration [equal], Supervision [lead], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Atanu Giri (Data curation [equal], Formal analysis [equal], Methodology [equal], Software [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Cory Heaton (Formal analysis [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Andrea Macias (Formal analysis [equal], Investigation [equal], Validation [equal], Writing—original draft [equal]), Serina Batson (Formal analysis [equal], Investigation [equal], Validation [equal], Writing—original draft [equal]), Neftali Reyes (Investigation [equal], Validation [equal]), Alexis Gutierrez (Investigation [equal], Validation [equal]), Luis Davila (Data curation [equal], Formal analysis [equal], Software [equal]), Lara Rakocevic (Formal analysis [equal], Software [equal]), Dirk Beck (Formal analysis [equal], Software [equal]), Raquel Ibanez-Alcala (Formal analysis [equal], Investigation [equal], Resources [equal], Software [equal]), Safa Hossain (Formal analysis [equal], Investigation [equal], Validation [equal]), Paulina Vara (Investigation [supporting]), Sabrina Drammis (Formal analysis), Kenichiro Negishi (Formal analysis [equal], Investigation [equal]), Laura O’Dell (Writing—review & editing [supporting]), Adrianna Rosales (Project administration [equal]), Travis Moschak (Funding acquisition [equal], Writing—review & editing [equal]), and Ki Goosens (Conceptualization [equal], Funding acquisition [equal], Writing—review & editing [equal]). Conflict of interest : None declared. Funding NSF-CAREER (2235858), NIH-NIDA (R01DA058653), U-RISE T34GM145, G-RISE T32GM144919, NIH Science Education Partnership Award (1R25GM132959-05). Data availability The data used within this manuscript are available at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YVPYUJ . All codes used for this manuscript can be accessed with the following link: https://github.com/atanugiri/Alcohol-Project/tree/main \. References Abel EL. Fetal Alcohol Syndrome-Volume III: Animal Studies . Boca Raton, FL: CRC Press, 1982. [ Google Scholar ] Aguirre CG, Stolyarova A, Das K. et al. Sex-dependent effects of chronic intermittent voluntary alcohol consumption on attentional, not motivational, measures during probabilistic learning and reversal. PloS One. 2020;15:e0234729. 10.1371/journal.pone.0234729. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Arnold ME, Butts AN, Erlenbach TR. et al. Sex differences in neuronal activation during aversion-resistant alcohol consumption. Alcohol Clin Exp Res. 2023;47:240–50. 10.1111/acer.15006. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ayoub SM, Minhas M, Lapointe T. et al. Effects of high fructose corn syrup on ethanol self-administration in rats. Alcohol. 2020;87:79–88. 10.1016/j.alcohol.2020.05.003. [ DOI ] [ PubMed ] [ Google Scholar ] Bachmanov AA, Kiefer SW, Molina JC. et al. Chemosensory factors influencing alcohol perception, preferences, and consumption. Alcohol Clin Exp Res. 2003;27:220–31. 10.1097/01.ALC.0000051021.99641.19. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Beck DW, Heaton CN, Davila LD. et al. Model of a striatal circuit exploring biological mechanisms underlying decision-making during normal and disordered states. bioRxiv. 2024. 10.1101/2024.07.29.605535. [ DOI ] [ Google Scholar ] Becker JB, Perry AN, Westenbroek C. Sex differences in the neural mechanisms mediating addiction: a new synthesis and hypothesis. Biol Sex Differ. 2012;3:14. 10.1186/2042-6410-3-14. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Beckwith SW, Czachowski CL. Alcohol-preferring P rats exhibit elevated motor impulsivity concomitant with operant responding and self-administration of alcohol. Alcohol Clin Exp Res. 2016;40:1100–10. 10.1111/acer.13044. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Bernhardt N, Obst E, Nebe S. et al. Acute alcohol effects on impulsive choice in adolescents. J Psychopharmacol. 2019;33:316–25. 10.1177/0269881118822063. [ DOI ] [ PubMed ] [ Google Scholar ] Bidwell LC, MacKillop J, Murphy JG. et al. Biphasic effects of alcohol on delay and probability discounting. Exp Clin Psychopharmacol. 2013;21:214–21. 10.1037/a0032284. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Brevers D, Bechara A, Cleeremans A. et al. Impaired decision-making under risk in individuals with alcohol dependence. Alcohol Clin Exp Res. 2014;38:1924–31. 10.1111/acer.12447. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Burnette EM, Grodin EN, Ghahremani DG. et al. Diminished cortical response to risk and loss during risky decision making in alcohol use disorder. Drug Alcohol Depend. 2021;218:108391. 10.1016/j.drugalcdep.2020.108391. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Centers for Disease Control and Prevention (CDC) . Behavioral Risk Factor Surveillance System Survey Data . Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2023. [ Google Scholar ] Corbit LH, Nie H, Janak PH. Habitual responding for alcohol depends upon both AMPA and D2 receptor signaling in the dorsolateral striatum. Front Behav Neurosci. 2014;8:301. 10.3389/fnbeh.2014.00301. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Crabbe JC, Metten P, Rhodes JS. et al. A line of mice selected for high blood ethanol concentrations shows drinking in the dark to intoxication. Biol Psychiatry. 2009;65:662–70. 10.1016/j.biopsych.2008.11.002. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Degenhardt L, Chiu WT, Sampson N. et al. Toward a global view of alcohol, tobacco, cannabis, and cocaine use: findings from the WHO World Mental Health Surveys. PLoS Med. 2008;5:e141. Edited by P. Hay. 10.1371/journal.pmed.0050141. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] DePoy L, Daut R, Brigman JL. et al. Chronic alcohol produces neuroadaptations to prime dorsal striatal learning. Proc Natl Acad Sci. 2013;110:14783–8. 10.1073/pnas.1308198110. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Dry MJ. et al. Dose-related effects of alcohol on cognitive functioning. PloS One. 2012;7:e50977. Edited by J.E. Mendelson. 10.1371/journal.pone.0050977. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Duffy V, Peterson J, Bartoshuk L. Associations between taste genetics, oral sensation and alcohol intake. Physiol Behav. 2004;82:435–45. 10.1016/j.physbeh.2004.04.060. [ DOI ] [ PubMed ] [ Google Scholar ] Fein G, Klein L, Finn P. Impairment on a simulated gambling task in long-term abstinent alcoholics. Alcohol Clin Exp Res. 2004;28:1487–91. 10.1097/01.alc.0000141642.39065.9b. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Field M, Wiers RW, Christiansen P. et al. Acute alcohol effects on inhibitory control and implicit cognition: implications for loss of control over drinking. Alcohol Clin Exp Res. 2010;34:1346–52. 10.1111/j.1530-0277.2010.01218.x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] George S, Rogers RD, Duka T. The acute effect of alcohol on decision making in social drinkers. Psychopharmacology (Berl). 2005;182:160–9. 10.1007/s00213-005-0057-9. [ DOI ] [ PubMed ] [ Google Scholar ] Horn TL, Whelan JP, Weil GT. Does acute alcohol consumption increase risk-taking while gambling? A systematic review and meta-analysis. Addiction. 2022;117:2780–90. 10.1111/add.15896. [ DOI ] [ PubMed ] [ Google Scholar ] Hwa LS, Chu A, Levinson SA. et al. Persistent escalation of alcohol drinking in C57BL/6J mice with intermittent access to 20% ethanol: escalated alcohol after intermittent access. Alcohol Clin Exp Res. 2011;35:1938–47. 10.1111/j.1530-0277.2011.01545.x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ibáñez Alcalá RJ, Beck DW, Salcido AA. et al. RECORD, a high-throughput, customizable system that unveils behavioral strategies leveraged by rodents during foraging-like decision-making. Commun Biol. 2024;7:822. 10.1038/s42003-024-06489-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jones SA, Cservenka A, Nagel BJ. Binge drinking impacts dorsal striatal response during decision making in adolescents. Neuroimage. 2016;129:378–88. 10.1016/j.neuroimage.2016.01.044. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jury NJ, DiBerto JF, Kash TL. et al. Sex differences in the behavioral sequelae of chronic ethanol exposure. Alcohol. 2017;58:53–60. 10.1016/j.alcohol.2016.07.007. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Karlsson H, Persson E, Perini I. et al. Acute effects of alcohol on social and personal decision making. Neuropsychopharmacology. 2022;47:824–31. 10.1038/s41386-021-01218-9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Kornreich C, Brevers D, Ermer E. et al. Polysubstance dependent patients display a more utilitarian profile in moral decision-making than alcohol-dependent patients, depressive patients and controls. Drug Alcohol Depend. 2013;132:434–40. 10.1016/j.drugalcdep.2013.03.005. [ DOI ] [ PubMed ] [ Google Scholar ] Lane SD, Cherek DR, Pietras CJ. et al. Alcohol effects on human risk taking. Psychopharmacology (Berl). 2004;172:68–77. 10.1007/s00213-003-1628-2. [ DOI ] [ PubMed ] [ Google Scholar ] Linsenbardt DN, Timme NM, Lapish CC. Encoding of the intent to drink alcohol by the prefrontal cortex is blunted in rats with a family history of excessive drinking. eneuro. 2019;6:ENEURO.0489-18.2019. 10.1523/ENEURO.0489-18.2019. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Livy DJ, Parnell SE, West JR. Blood ethanol concentration profiles: a comparison between rats and mice. Alcohol. 2003;29:165–71. 10.1016/S0741-8329(03)00025-9. [ DOI ] [ PubMed ] [ Google Scholar ] Lucke-Wold B. The varied uses of conditioned place preference in behavioral neuroscience research: an investigation of alcohol administration in model organisms. Impulse: The Premier Undergraduate Neuroscience Journal (Columbia, S.C.). 2011;8:1–11. http://impulse.appstate.edu/sites/impulse.appstate.edu/files/Lucke_Wold.pdf . [ PMC free article ] [ PubMed ] [ Google Scholar ] McCane AM, Czachowski CL, Lapish CC. Tolcapone suppresses ethanol intake in alcohol-preferring rats performing a novel cued access protocol. Alcohol Clin Exp Res. 2014;38:2468–78. 10.1111/acer.12515. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Mitchell MR, Vokes CM, Blankenship AL. et al. Effects of acute administration of nicotine, amphetamine, diazepam, morphine, and ethanol on risky decision-making in rats. Psychopharmacology (Berl). 2011;218:703–12. 10.1007/s00213-011-2363-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Noël X, Bechara A, Dan B. et al. Response inhibition deficit is involved in poor decision making under risk in nonamnesic individuals with alcoholism. Neuropsychology. 2007;21:778–86. 10.1037/0894-4105.21.6.778. [ DOI ] [ PubMed ] [ Google Scholar ] Pandy V, Khan Y. Design and development of a modified runway model of mouse drug self-administration. Sci Rep. 2016;6:21944. 10.1038/srep21944. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Patton MS, Heckman M, Kim C. et al. Compulsive alcohol consumption is regulated by dorsal striatum fast-spiking interneurons. Neuropsychopharmacology. 2021;46:351–9. 10.1038/s41386-020-0766-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Rehm J. How should prevalence of alcohol use disorders be assessed globally?: Global estimates for alcohol use disorders. Int J Methods Psychiatr Res. 2016;25:79–85. 10.1002/mpr.1508. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Spoelder M, Lesscher HM, Hesseling P. et al. Altered performance in a rat gambling task after acute and repeated alcohol exposure. Psychopharmacology (Berl). 2015;232:3649–62. 10.1007/s00213-015-4020-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Strong MN, Yoneyama N, Fretwell AM. et al. “Binge” drinking experience in adolescent mice shows sex differences and elevated ethanol intake in adulthood. Horm Behav. 2010;58:82–90. 10.1016/j.yhbeh.2009.10.008. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Thomasson HR. Gender differences in alcohol metabolism. In: Galanter M. et al. (eds.), Recent Developments in Alcoholism . Boston, MA: Springer US, 2002, 163–79. 10.1007/0-306-47138-8_9. [ DOI ] [ PubMed ] [ Google Scholar ] Van Skike CE, Goodlett C, Matthews DB. Acute alcohol and cognition: remembering what it causes us to forget. Alcohol. 2019;79:105–25. 10.1016/j.alcohol.2019.03.006. [ DOI ] [ PubMed ] [ Google Scholar ] Wakabayashi KT, Greeman EA, Barrett ST. et al. The sugars in alcohol cocktails matter. ACS Chem Nerosci. 2021;12:3284–7. 10.1021/acschemneuro.1c00526. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wallin-Miller KG, Chesley J, Castrillon J. et al. Sex differences and hormonal modulation of ethanol-enhanced risk taking in rats. Drug Alcohol Depend. 2017;174:137–44. 10.1016/j.drugalcdep.2017.01.023. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Weissenborn R, Duka T. Acute alcohol effects on cognitive function in social drinkers: their relationship to drinking habits. Psychopharmacology (Berl). 2003;165:306–12. 10.1007/s00213-002-1281-1. [ DOI ] [ PubMed ] [ Google Scholar ] Youngentob SL, Glendinning JI. Fetal ethanol exposure increases ethanol intake by making it smell and taste better. Proc Natl Acad Sci. 2009;106:5359–64. 10.1073/pnas.0809804106. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Young-Wolff KC, Enoch M-A, Prescott CA. The influence of gene–environment interactions on alcohol consumption and alcohol use disorders: a comprehensive review. Clin Psychol Rev. 2011;31:800–16. 10.1016/j.cpr.2011.03.005. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zachry JE, Johnson AR, Calipari ES. Sex differences in value-based decision making underlie substance use disorders in females. Alcohol Alcohol. 2019;54:339–41. 10.1093/alcalc/agz052. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials SI_figs_final_agaf017 si_figs_final_agaf017.pdf (1MB, pdf) Data Availability Statement The data used within this manuscript are available at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YVPYUJ . All codes used for this manuscript can be accessed with the following link: https://github.com/atanugiri/Alcohol-Project/tree/main \. Articles from Alcohol and Alcoholism (Oxford, Oxfordshire) are provided here courtesy of Oxford University Press ACTIONS View on publisher site PDF (1.7 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top