Standardization of decision-making skills but persistent impulsivity after chronic stimulant exposure in ADHD patients

Standardization of decision-making skills but persistent impulsivity after chronic stimulant exposure in ADHD patients Francisco Jos´ e Lobato-Camacho * , Juan Pedro Vargas , Juan Carlos L´ opez Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Calle Camilo Jos´ e Cela, S/N, 41018 Sevilla, Spain ARTICLE INFO Keywords: ADHD Decision-making Memory Long-term stimulant treatment Impulsivity Pattern separation ABSTRACT Attention deficit hyperactivity disorder (ADHD) is commonly associated with deficits in executive function. Even though attention, hyperactivity, and impulsivity are the more distinctive symptoms, impairment in other cognitive processes, for instance memory, could be due to the interferences from these symptoms. However, it remains unclear whether information processing errors made by individuals with ADHD arise primarily from impulsive responding or reflect a more fundamental difference in how they process information, potentially due to compensatory mechanisms developed throughout childhood. This study analyzes pattern separation (distinguishing similar stimuli), recognition memory, decision-making, and impulsivity in both ADHD-diagnosed and non-diagnosed youth population. We further examined possible treatment effects by dividing the ADHD group into three cohorts based on stimulant medication duration. We evaluate their response latency and responses utilizing the signal detection theory method. While ADHD participants exhibited poorer recognition memory compared to controls, this pattern did not show a statistically significant difference in pattern separation. Additionally, both processes improved with longer treatment duration within the ADHD group, leading to decreased error commission. Decision-making analyses revealed sex-specific response strategies within the ADHD group, but both groups showed similar adjustment to task difficulty. However, the ADHD group responses were notably faster, associated with a higher error rate. Additionally, response times varied depending on the stimulus type, suggesting potential differences in how the ADHD group processed information compared to the control group. These findings collectively point towards a possible difference in information management in ADHD, that is also characterized by faster, but less accurate, processing. 1. Introduction Executive functions are skills that allow us to achieve goals (LobatoCamacho et al., 2023; Santa-Cruz and Rosas, 2017). Poor development of these functions, as a low capacity in control process, reduces a variety of higher-level skills, leading to serious problems mainly in childhood (Faraone et al., 2021). These failures are usually associated with working memory deficits, as errors committed seem to be related to a dysfunctional central executive. Selective attention and its control systems have been linked to executive functions. In fact, attention deficit hyperactivity disorder (ADHD) is characterized by deficits in executive functions, particularly in control systems. Furthermore, ADHD is behaviorally marked by a triad of inattention, hyperactivity, and impulsivity symptoms, potentially stemming from executive dysfunction (American Psychiatric Association, 2022). Although all three symptoms negatively impact childhood, inattention generally persists throughout life, significantly hindering learning processes (Faraone et al., 2015, 2021). Specifically, difficulties in memory encoding and processing details are common manifestations of inattention (Skodzik et al., 2017). This circumstance leads to an increase in error rates in tasks such as continuous performance tests, stopsignal tasks, and go/no-go tasks, which may also be linked to impulsive symptoms as they can result from hasty decision-making (Patros et al., 2016; Serrano-Barroso et al., 2021, 2022). However, it remains unclear whether these increased error rates are attributable to impulsivity, attentional deficits, or a combination of both. Currently, there is uncertainty about the causes of symptoms in ADHD. Potential explanations include deficits in specific neurotransmitters (Del Campo et al., 2011) or dysfunctions in neuronal circuits (Yoo et al., 2018). Supporting this hypothesis, significant differences have been identified in the resting-state functional network and anatomical connectivity in individuals with ADHD (Konrad and * Corresponding author. E-mail addresses: [email protected] (F.J. Lobato-Camacho), [email protected] (J.P. Vargas), [email protected] (J.C. L´ opez). Contents lists available at ScienceDirect Pharmacology, Biochemistry and Behavior journal homepage: www.elsevier.com/locate/pharmbiochembeh https://doi.org/10.1016/j.pbb.2025.173986 Received 4 January 2025; Received in revised form 18 February 2025; Accepted 25 February 2025 Pharmacology, Biochemistry and Behavior 249 (2025) 173986 Available online 26 February 2025 0091-3057/© 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/bync-nd/4.0/ ). Eickhoff, 2010; Yoo et al., 2018). In this context, it has been reported that the failure on attenuation of the resting-state in functional network is associated with momentary lapses in attention, manifested by longer reaction times and less accurate performance in attention control tasks (Sutcubasi et al., 2020). The integration of findings from structural and functional connectivity studies suggests that the reduced global efficiency of brain networks observed in ADHD may be related to a loss of long-range connections. These findings indicate that interference between neural networks could be an underlying factor in cognitive process dysfunction (Konrad and Eickhoff, 2010). Consequently, learning difficulties could be due to deficits in cognitive processes rather than impulsivity or attentional deficits. Thus, it is essential to determine if commission errors in ADHD patients are driven by impulsiveness, attentional deficits, or differences in processing information. Analyzing discrepancies between recognition memory and attention to detail could help elucidate this question. A concept closely related with attention to detail is pattern separation (Lacy et al., 2010). This ability allows us to encode similar stimuli distinctly in our memory, preventing them from overlapping and interfering with each other (Das et al., 2014). Essentially, it ensures clear and separate storage of similar information. In sum, this function allows us distinguishing between different kind of objects belongings the same class or concept. In contrast, recognition memory focuses on identifying previously encountered stimuli (Das et al., 2019). In recognition memory, we can thoroughly assess its encoding and recovery by facilitating the presence of the stimulus (Hockley, 2022). Tests aimed at measuring these cognitive processes often show how the commission of errors is clearly related to ADHD patients. This fact is usually justified by waiting impulsivity or the tendency to respond before the appearance of the target (Robinson et al., 2009). A faster response might significantly distort the learning assessment, as it implies a lack of analysis of the options. Therefore, this may not reflect the amount of learning reached and has a negative impact on ADHD (Bari and Robbins, 2013). Standard memory recognition tasks make it difficult to discern whether related issues are due to impulsiveness, attentional deficits, or it is due to a different way of processing the information. In the present study, we have analyzed potential differences in information processing between young people with ADHD diagnoses and a control group. We focused on three key cognitive processes: pattern separation (the ability to distinguish similar stimuli), recognition memory (the ability to identify previously encountered items), and decision-making adjustments (how individuals adapt their strategies under varying cognitive demand). To explore these processes, we evaluated response times and response patterns. We employed tasks targeting pattern separation and object recognition memory to compare information processing in both groups. Furthermore, for the ADHD group, we evaluated the impact of stimulant treatment on task performance over time to examine whether treatment duration affected their information processing. 2. Method 2.1. Sample This study included 96 participants, equally divided into two groups: an ADHD group and a neurotypical development (ND) (Table 1). The ADHD group were recruited from the AireLibre Foundation in Huelva, Spain. Notably, this sample was previously used in a different study (Lobato-Camacho et al., 2024b); however, and given the implications of the former, we have extended the analysis of different processes in the present study. All participants were diagnosed with ADHD by the Andalusian Public Health. They presented moderate ADHD symptoms, including difficulties in the school environment, peer relationships and family dynamics. The ND group were randomly recruited from the La Campi˜ na Educational Institute in Arahal, Spain. All participants provided written informed consent before participating in the study. The study was conducted following the guidelines of the Declaration of Helsinki and received approval from the Comit´ e de ´ Etica de la Universidad de Sevilla (code: 2099-N22). Participants in the ADHD group were under stimulant medication, with 39 taking methylphenidate and 9 lisdexamfetamine. The primary mechanism of action of lisdexamfetamine involves increasing levels of dopamine and norepinephrine in the brain, a mechanism it shares with methylphenidate (Faraone and Author, 2018). These neurotransmitters play a crucial role in regulating attention, focus, and impulse control, thus explaining their therapeutic role in ADHD. Both lisdexamfetamine and methylphenidate demonstrate similar profiles in terms of efficacy, tolerability, and safety, providing comparable therapeutic benefits for individuals with ADHD (Faraone and Author, 2018). To investigate the potential impact of treatment duration, participants were categorized into three groups based on treatment length (Table 1): cohort one <24 months, cohort two between 25 and 72 months, and cohort three between 73 and 120 months. This categorization aligns with previous research suggesting a stronger medication effect on certain symptoms during the initial 24 months of intensive treatment, with a diminishing effect observed thereafter (Harpin et al., 2016). To analyze the lower effect after 24 months of treatment, 48month intervals were established from that moment. 2.2. Materials We used the Mnemonic Similarity Task (MST) program, version 0.96 (Stark et al., 2019). The MST test is a well-established tool for assessing episodic memory and hippocampal function by measuring the ability to recall specific details of past experiences (Lobato-Camacho et al., 2024c; Stark et al., 2023). This test is particularly valuable for evaluating the effectiveness of cognitive and pharmacological interventions to improve memory (Stark et al., 2023). 2.3. Procedure The MST consisted of two consecutive phases, administered without a break. Participants responded to images presented for two seconds each on a computer screen and responded via keyboard input. 2.4. Learning phase During the learning phase, participants classified 192 images of everyday objects. They had to indicate if the object could be considered as an inside home object, pressing the number one key in the keyboard or outside home pressing the number two. This phase aimed to induce incidental learning. All participants viewed the same set of images. 2.5. Recognition phase Once the learning phase ended, the participants performed a surprise memory test. They were asked to classify 192 images as “old” (seen before, pressing the number one in the keyboard), “similar” (Images seen before with a detail change, pressing the number two), or “new” (not previously encountered, pressing the number three). This set included 64 ‘target’ images (identical to those in the learning phase), 64 Table 1 Age, gender and treatment duration in ADHD cohorts. N Age (Mean ±SE) Treatment time (Months, Mean ± SE) Female (n) Male (n) ND 48 14.29 ±0.31 –26 22 ADHD 48 13.85 ±0.34 45.66 ±4.56 14 34 Cohort 1 19 13.30 ±0.52 15.10 ±1.76 6 13 Cohort 2 19 13.01 ±0.60 51.15 ±3.09 5 14 Cohort 3 10 14.90 ±0.61 93.30 ±4.27 3 7 F.J. Lobato-Camacho et al. Pharmacology, Biochemistry and Behavior 249 (2025) 173986 2 ‘foil’ images (completely new), and 64 ‘lure’ images (target images with a detail change). The program randomly chose the images grouped as target, lure, and foil. All participants completed the test during a single session. The task lasted one hour, and it was driven by an expert clinical psychologist. Participants received printed instructions and could ask questions or request clarification. Both groups undertook the tests between 6:00 PM and 8:00 PM. Participants with ADHD continued with their usual medication regimen. This is, all participants received their treatment once daily in the morning, before attending school, at approximately 8:00 AM. Both participants taking methylphenidate and those taking lisdexamfetamine were prescribed doses adjusted to their age and weight in extended-release formulations. 2.6. Variables Independent variables included sex, age, and ADHD diagnosis. Treatment duration was also included as an independent variable for the ADHD group. As dependent variables, we analyzed the response time (RT) and its variability to evaluate waiting impulsivity. Additionally, we evaluated the response bias (β) and discrimination (d’) in pattern separation and recognition memory tasks. The discrimination index (d’) and the response bias (β) was calculated as: dʹ=Hit–False Alarm ß=e(dʹ−c) Hits and false alarms (FA) in the pattern separation was calculated based on responses to target and lures stimuli (see Table annex 1). Hit =Target Old (Target Old) + (Target Similar) FA =Lure Old (Lure Old) + (Lure Similar) Hits and FA in the recognition memory was derived from responses to foil and target stimuli (see Table annex 1). Hit =Foil New (Foil New) + (Foil Old) FA =Target New (Target New) + (Target Old) 2.7. Analysis of data We analyzed participant responses to the different stimuli to compare information processing in ADHD and ND groups. To estimate the effect size, Cohen’s d statistic was used, which measures the difference between the means of two groups in standard deviations. The ADHD group exhibited a more liberal response bias than the ND group in recognition memory, as indicated by beta (ß) values (t 94 =2.24, p =0.013, Cohen’s d =0.450, Fig. 1). In addition, ND group discriminated better than ADHD group as showed by d’ (t 94 =2.29, p =0.011, Cohen’s d =0.524, Fig. 1). ADHD committed a higher number of misses (t 94 =2.16, p =0.016, Cohen’s d =0.446) and false alarms (FA), (t 94 = 1.98, p =0.025, Cohen’s d =0.409; see table annex 2, 3). In contrast, no significant differences were observed in ß between the groups in the pattern separation index (ISP) (t 94 =0.31, p =0.378, Fig. 1), indicating a similar conservative strategy in both groups. In addition, ND displayed a higher d’ value (t 94 =1.52, p =0.065, Cohen’s d =0.313, Fig. 1) due to the fewer FA, although this difference was not statistically significant. 2.8. Treatment Effects in ADHD To explore the effect of the treatment, we further analyzed the discrimination capacity among three ADHD cohorts. Cohort one exhibited a lower d’ compared to cohorts two and three in recognition memory (F 2,47 =3.52, p =0.038, Cohen’s d =0.364; Fisher’s LSD test p < 0.05, Fig. 2); however, no differences were found in β (F 2.47 =0.13, p = 0.879, Fig. 2), suggesting a general trend towards a liberal criterion across all groups. Regarding ISP, the cohort one exhibited significantly lower d’ compared to cohorts two and three (F 2,47 =6.28, p =0.003, Cohen’s d =0.922; Fisher’s LSD test p < 0.05, Fig. 2) due to fewer HITs (cohort 1 =70.0 %, cohort 2 =82.6 % and cohort 3 =82.0 %). Although the ANOVA was not significant, specific differences in β were found between cohort one and the other cohorts according to post hoc test (F 2,47 =2.53, p =0.090, Cohen’s d =0.628; Fisher’s LSD test p < 0.05, Fig. 2), this could suggest that there are trends or patterns that warrant further investigation with a larger sample size. According this find, cohort one displayed a neutral criterion, while cohorts two and three showed a trend towards a more conservative one. Regression analysis indicated a positive relationship between treatment duration and recognition memory, as measured by the d’ value (F 47 =6.53, p =0.015, C =0.352, R 2 =12.43, Fig. 2), showing a Fig. 1. Distribution of performance metrics in recognition memory and pattern recognition tasks. Panels A and B illustrate the distribution of d’ and β indices, respectively, for recognition memory. Panels C and D present the same metrics for pattern separation. The ADHD group exhibited lower d’ values (mean = 2.66, SE =0.16) compared to the ND group (mean =3.17, SE =0.14) in recognition memory (A), indicative of poorer discrimination performance. In contrast (C), no significant differences in d’ were found between groups for pattern discrimination (ADHD mean =0.92, SE =0.06; ND mean =1.08, SE = 0.07). Beta values (β) showed a similar pattern, with higher values for the ADHD group in recognition memory (B) and no significant differences in pattern separation (D). F.J. Lobato-Camacho et al. Pharmacology, Biochemistry and Behavior 249 (2025) 173986 3 consistent upward trend as the treatment advanced, that is, the more time under treatment, the better results in recognition memory. ß showed similar results, with a trend to conservative criterion (F 47 = 5.69, p =0.021, C =0.328, R 2 =11.01, Fig. 2). In ISP, we also found a significant correlation in d’ (F 47 =8.59, p =0.005, C =0.397, R 2 = 15.73, Fig. 2), and ß (F 47 =4.06, p =0.049, C =0.283, R 2 =8.11, Fig. 2), following a trend similar to that observed in recognition memory task. These findings suggest that longer treatment duration could be associated with improved performance in both memory tasks and response strategies. 2.9. Response time analysis Due to the lack of normality in response times, we have used nonFig. 2. Performance metrics by cohort and task. Panels A and B present d’ and β values, respectively, for recognition memory across three cohorts. Panels C and D display the same metrics for pattern recognition. Panels E, F, G, and H illustrate the regression models for d’ and β in recognition memory and pattern recognition, respectively. Data are presented as mean ±standard error (SE). F.J. Lobato-Camacho et al. Pharmacology, Biochemistry and Behavior 249 (2025) 173986 4 parametric tests. Therefore, to compare the distributions of two independent groups, the Mann-Whitney U Test was used, and to compare three or more independent groups, the Kruskal-Wallis H Test was employed. To assess response waiting impulsivity, we compared RT between ADHD and ND groups. As expected, the ADHD group exhibited significantly faster overall response times compared to the ND group (W = 2.796, p < 0.001, Cohen’s d =0.617, Table 2). This suggests a potential response bias in the ADHD group. We further analyzed RT between the cohorts, examining both global response times and variability. To estimate the equality between variances, Levene’s test was used. Here, we found a significant effect of cohort on response times. Participants in cohort one displayed significantly slower and more variable RT compared to cohorts two and three (H =15.12, p < 0.001, Cohen’s d =0.078; Levene’s =7.24, p < 0.001, Table 2). This suggests potential differences in processing speed across cohorts. To understand underlying response strategies, we analyzed RT for target, lure, and foil stimuli separately. The ND group exhibited significantly different RT for each stimulus type (H =137.92, p < 0.001, Cohen’s d =0.246; Fisher’s LSD test, p =0.01; Fig. 3). This indicates a nuanced response process, with RT increasing progressively (target < lure <foil). In contrast, the ADHD group presented significantly faster RT to target stimulus compared to lure and foil stimuli, but no differences between lure and foil response time (H =70.56, p < 0.001, Cohen’s d =0.176, Fisher’s LSD test p =0.01; Fig. 3). This suggests a less differentiated response process in the ADHD group, potentially reflecting impulsivity or difficulty in distinguishing between stimuli. 2.10. Age and gender results We conducted a regression analysis to examine the influence of age on task performance within the ADHD group. We found no significant relationship between age and either ISP (d’: F 47 =0.02; β: F 47 =0.01, both ps >0.9) or recognition memory (d’: F 47 =3.28, p =0.08; β: F 47 = 3.21, both ps >0.08). In addition, no differences were found between groups in mean age as noted in Table 1 (t 95 =0.944, p =0.347) or among the ADHD cohorts (F 2,47 =1.73, p =0.189). Analyses of sex differences group revealed no significant differences in d’ (F 3.95 =0.89, p =0.449) or ß (F 3.95 =1.14, p =0.337) in ISP. However, an interesting result emerged regarding recognition memory. Females in the ADHD group displayed significantly better recognition memory than males (F 3.95 =4.06, p =0.009, Fisher’s LSD Test p < 0.05, Cohen’s d =0.760). This difference appears to be driven by a more conservative response bias in males, who exhibited a tendency to miss targets compared to females (t 46 =1.76, p =0.013, Cohen’s d =0.544). 3. Discussion Processing details involves briefly focusing on parts of the stimuli that we consider essential (Posner et al., 2020). Previous studies suggests that inattention in ADHD is linked to difficulties in detail processing, potentially due to increased distractibility (Fassbender et al., 2009; Gumenyuk et al., 2005). In this study, we analyzed the ability of individuals diagnosed with ADHD to discriminate between similar stimuli, focusing on attention to detail through pattern separation. We expected the ADHD group would exhibit impaired pattern separation due to deficits in detail processing. However, our findings revealed no significant differences between the ADHD and ND groups in pattern separation. This finding is relevant since pattern separation depends on the ability to attend and encode details between similar objects. These results contrast with those from our previous study, which found a performance difference in a sample of young people with ADHD in ISP compared to a ND control group (Lobato-Camacho et al., 2024b). Here, we analyzed discrimination and response criterion in both ADHD and ND samples. Despite previous findings suggesting a clear advantage for the ND group, data obtained in the present study points to similar discrimination and response criteria in both groups when the difficulty of the test increase. This is an interesting result because it could indicate a possible ceiling effect in ND group due to the complexity of the task. This issue is being analyzed in depth by our group. In the same vein, we analyzed how the duration of treatment influenced pattern separation by examining performance across cohorts. The analysis revealed enhanced discrimination in ISP as treatment duration increased. This finding aligns with previous research demonstrating the beneficial impact of stimulant treatment on pattern separation after 24 months of treatment (Lobato-Camacho et al., 2024b). Our results, in conjunction with those of Mikl´ os et al., 2019 suggest that the lack of differentiation between medicated and unmedicated children in distraction tasks may be attributed to insufficient treatment duration. Extended stimulant therapy appears necessary to achieve substantial improvements in discrimination between similar stimuli, potentially indicating enhanced attention to detail and reduced distractibility. On the other hand, we also analyzed responses to recognition memory. Individuals with ADHD demonstrated lower discrimination capacity on this task. This observation is consistent with previous research indicating impairment in recognition memory across both verbal and object tasks within this population (Andersen et al., 2013; Lobato-Camacho and Faísca, 2024a) Additionally, this analysis revealed an interesting sex-based difference within the ADHD group. Females displayed greater discrimination between stimuli compared to males. This finding is consistent with a recent meta-analysis, which indicated that samples with a higher percentage of women with ADHD exhibit greater object recognition memory (Lobato-Camacho & Luís Faísca, 2024). This could potentially be linked to possible sex differences in ADHD diagnosis, leading to potentially divergent symptomatology in females (Carucci et al., 2023; Hinshaw et al., 2022; Young et al., 2020). Likewise, the analysis revealed that discrimination in recognition memory improved notably with increasing treatment duration within the ADHD cohort. This finding appears to contradict previous research in humans, where a single dose of stimulants did not improve object recognition memory (Rhodes et al., 2006). It is important to note that some comparative studies in animals suggest a potential deterioration in recognition memory with prolonged stimulants use (LeBlanc-Duchin and Taukulis, 2009). These discrepancies could be due to several factors, including dosage variations across studies and species (Foley et al., 2019; Ross et al., 2020; Rozenek et al., 2019; Vargas et al., 2016). Table 2 Median response time (ms) and standard deviation for each group (ADHD, ND) across the entire test (Global RT) and for each stimulus type (target, lure, foil). Note that the ADHD group includes both the overall mean and the specific scores by treatment cohorts. Global RT Target Lure Foil Group median SD median SD median SD median SD ND 2064 1336 1946 1317 2052 1328 2193 1344 ADHD 1496 1382 1409 1214 1522 1301 1552 1594 Cohort 1 1511 1530 1438 1312 1549 1337 1550 1872 Cohort 2 1504 1275 1433 1166 1538 1186 1561 1450 Cohort 3 1440 1270 1341 1098 1488 1132 1537 1243 F.J. Lobato-Camacho et al. Pharmacology, Biochemistry and Behavior 249 (2025) 173986 5 Even though our findings showed improvement in recognition memory, the ADHD group still performed significantly lower than the normative group. It has been noted that improvements in object recognition memory may take longer than 24 months to reach performance levels similar to the normative group (Lobato-Camacho et al., 2024b). Therefore, some participants in our study might not yet have reached their peak treatment benefit. 3.1. Response bias Decision-making is an executive function that involves selecting from multiple options and considering their results and consequences. An alteration in this process has been observed in ADHD patients (Groen et al., 2013). Our analysis of the response strategy used during discrimination tests suggests a trend towards committing more misses in the ADHD group than the normative group, although the inclination was only significant in recognition memory assessment. To analyze the evolution of response bias, we further compared response strategy across treatment cohorts. Interestingly, no significant differences emerged between the cohorts in recognition memory task. This suggests that while ADHD may broadly impact decision-making, the specific response strategies and biases employed appear to be relatively consistent. However, we found a relationship between treatment time and response bias, where longer treatment time was related to a lower number of misses. Regarding pattern separation, we found a difference between cohort one and cohorts two and three, where cohort one performed the task with a greater trend towards misses. In line with this finding, we also found a relationship between a longer treatment time and a response bias less oriented towards misses. In both cases, it seems that the treatment had some influence on the response bias in both tests, although it seems to be more evident in pattern separation. Interestingly, this task turned out to be more cognitively challenging for both groups, which may make small improvements more observable. While previous studies suggest that children with ADHD diagnosis may struggle to adjust their response strategies in regard to cognitive load (Sergeant and van der Meere, 1988), our study offers a different perspective. We found that the ADHD group exhibited an adjustment of response bias to cognitive load similar to ND group. Both groups found it more difficult to discriminate in the pattern separation task where the response bias was related to a trend towards producing fewer misses. In contrast, discrimination in the recognition memory task was better than in pattern separation, with a response bias favoring fewer false alarms. Intriguingly, our analysis of response times revealed a significant difference: in the pattern separation task, the ADHD group was slower in responding to lure stimuli compared to target stimuli. This suggests that lure stimulus discrimination may pose a greater cognitive challenge for this group, adjusting their response time to the task’s difficulty (Sergeant and van der Meere, 1988). Within the ADHD group, our analysis revealed an interesting sex difference in response strategy for the recognition memory task. Females exhibited a bias towards committing more false alarms than males, although female showed a higher recognition memory index. These data suggest a more liberal criterion in recognition memory. In contrast, there were no significant differences in response latency between both groups. Neither did we find sex differences in response strategy for the pattern separation task, where discrimination rates were similar between males and females. These results, together with the findings in stimulus discrimination, provide valuable insights into how the effects of treatment on basic Fig. 3. Cumulative response time distributions. Panels A and B display cumulative response time distributions for ADHD and neurotypical (ND) groups, respectively. Panels C-E present cumulative response time distributions for the ADHD group across target, lure, and foil stimuli. Panels F – H depict the same distributions for the ND group. * p < 0.01. F.J. Lobato-Camacho et al. Pharmacology, Biochemistry and Behavior 249 (2025) 173986 6 cognitive processes could influence more complex cognitive functions, such as decision-making. Specifically, we observed that treatment may enhance the ability to discriminate between response alternatives, which is central to effective decision-making. Intriguingly, the ADHD group adjusted their responses strategically regarding the cognitive load of the task. However, we did not detect a direct influence of treatment on this specific response bias adjustment. 3.2. Response times Impulsivity, defined as the tendency to respond prematurely, can be exacerbated in ADHD patients (Jim´ enez-Soto et al., 2020, 2022). To study this aspect of impulsivity, we analyzed RT in the samples. Rather than imposing a specific time limit for responses, we informed the participants that once pictures disappeared, a new one would not be shown until they made a choice. This approach allowed us to observe natural response trends. Our analysis revealed significantly lower RT in the ADHD group. This finding is consistent with previous studies where children with ADHD were found to be moderately faster than ND children (Kofler et al., 2013). As a result of this decrease in RT, the ADHD group showed a higher error rate than the ND group. In general, the ADHD group responded before the disappearance of the stimulus, indicating haste in their responses, contrary to the ND group. Our results are in line with previous work, such as Van Dessel et al. (2019), who found heightened waiting impulsivity and reduced inhibitory control in children with ADHD compared to a normative sample. Their study linked waiting impulsivity to parent-reported ratings of hyperactivity/impulsivity and was a significant predictor of ADHD symptomatology status (Van Dessel et al., 2019). To explore the evolution between the cohorts, we examined the RT and its variability in their responses. RT variability has been proposed as an underlying trait in ADHD (Kofler et al., 2013). It has been argued to reflect a subset of abnormally slow responses during tasks, exhibiting increased RT variability across a wide range of tasks, including behavioral inhibition (Kofler et al., 2013). We found a slower response time in cohort one, although the effect size was small. However, we found less variability in response times in cohorts two and three associated with a decrease of slow responses. This could be due to difficulty in inhibiting inappropriate responses, causing them to take longer to give correct and appropriate responses (Grandjean et al., 2021). In support of this view, cohorts two and three made a lower percentage of errors in their responses. This decrease in errors likely reflects the better discrimination between stimuli found in the cohorts that received longer treatment. Therefore, our findings suggest that stimulant treatment might have reduced differences between the ADHD and ND groups, even though it does not seem to address waiting impulsivity as effectively as basic cognitive processes. 3.3. Response process The analysis of RT in the ND group revealed a clear distinction between the three stimuli. This variation could be related to a serial analysis process based on the stimuli encoded during learning. Following a chronological order, the lowest RT was observed for target stimuli explicitly presented during learning. In case of direct recognition not being successful, the RT increased as participants potentially analyzed the similarity or familiarity of the stimulus to previously encoded images, potentially leading to the selection of similar lures. Finally, the highest RT emerged for foils, suggesting classification as entirely novel stimuli. This pattern aligns with the Yonelinas et al. (2010) dual-process model, where recollection serves as a highthreshold process for retrieving specific details, and familiarity acts as a continuous variable guiding decision when recollection fails. Regarding the performance of the ADHD group, our analysis revealed a significant difference in RT to target stimuli compared to lure and foil stimuli. This contrast with the normative group, where differences were observed in the RT to the lure and foil stimuli. This discrepancy could be due to different stimulus processing in the ADHD group. While the initial recovery process may be similar to the normative group (as seen in target recovery), lures and foils might be subsequently managed in parallel or categorized within the same category. The possibility of processing in parallel, combined with shorter RT and the similar performance observed in the ND group, is consistent with our findings in the pattern separation task. Conversely, categorizing different stimuli as similar could indicate an interference process, resulting in lower performance compared to the normative group, as seen in the recognition memory task. These results suggest potential differences in underlying brain mechanisms for stimulus management between the groups, supporting the possibility of neural network variations in individuals with ADHD and ND samples (Konrad and Eickhoff, 2010; Yoo et al., 2018). Despite a thorough search, we found a lack of studies aimed at analyzing this issue. It is imperative to expand this type of analysis to fully understand differences in information processing within the ADHD population. 4. Conclusions Our study revealed several key findings regarding the cognitive processes in ADHD compared to a ND sample. Overall, our findings indicate different information processing patterns in individuals with ADHD compared to ND controls. While ADHD participants demonstrated deficits in recognition memory, attention to detail and a tendency towards impulsive responding, stimulant treatment appeared to ameliorate some cognitive difficulties, particularly in improving discrimination abilities and reducing errors. However, further investigation is warranted to fully understand the underlying mechanisms and to develop more targeted interventions for ADHD. Open practices statement The datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request. CRediT authorship contribution statement Francisco Jos´ e Lobato-Camacho: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Juan Pedro Vargas: Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition. Juan Carlos L´ opez: Writing – review & editing, Validation, Supervision, Resources, Project administration, Funding acquisition. Informed consent statement Informed consent was obtained from all subjects involved in the study. Institutional review board statement The study was approved by the Comit´ e de ´ Etica de la Investigaci´ on de la Universidad de Sevilla, dependent Comit´ e Coordinador de ´ Etica de la Investigaci´ on Biom´ edica de Andalucía, Junta de Andalucía (Spain) with the code (2099-N22). Funding Gobierno de Espa˜ na. Ministerio de Ciencia e Innovaci´ on y Universidades: PID2019-110739GB-I00/AEI/10.13039/501100011033 y PID2023-149901NB-I00. F.J. Lobato-Camacho et al. Pharmacology, Biochemistry and Behavior 249 (2025) 173986 7 Declaration of competing interest The authors have nothing to disclose. Acknowledgments To the Aire Libre Association of Families with ADHD of Huelva (Spain) and its therapy and manager teams for their selfless collaboration in this work and their significant contribution to improving the quality of life of people diagnosed with ADHD. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.pbb.2025.173986. Data availability Data will be made available on request. References American Psychiatric Association, 2022. Diagnostic and statistical manual of mental disorders. Diagnostic and Statistical Manual of Mental Disorders. https://doi.org/ 10.1176/APPI.BOOKS.9780890425787. Andersen, P.N., Egeland, J., Øie, M., 2013. Learning and memory impairments in children and adolescents with attention-deficit/hyperactivity disorder. J. Learn. 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