Iowa Gambling Task performance in individuals with schizophrenia: the role of general versus specific cognitive abilities

Objective: We aimed to explore how specific cognitive processes, such as attention and executive functions, account for variance in decision-making measured by Iowa Gambling Task (IGT) performance among individuals with schizophrenia spectrum disorders.

Methods: Adults (N = 65, M_age = 25.4) with schizophrenia spectrum disorders participating in a clinical trial (registered at clinicaltrials.gov NCT03048695) completed the IGT, neuropsychological tests of attention, response inhibition, mental flexibility, working memory, and planning, as well as subtests from the Wechsler tests of intelligence to estimate IQ. Associations between performance on specific tasks, a composite score of executive function and attention, and IGT performance measured in two ways, one using the total net score, decks (C+D) – (A+B) and the other as preference for decks with more frequent gains than losses, decks (B+D) – (A+C), were analyzed with correlational and hierarchical regression analysis controlling for estimated IQ and psychotic symptoms, measured by the Positive and Negative Syndrome Scale.

Results: In the regression analyses, the strongest predictor of IGT performance measured as the total net score was estimated IQ (b = 1.43, p <.001). Neither specific cognitive tasks nor the composite score of executive functioning significantly contributed to explaining variance in IGT total net score beyond IQ and symptoms of psychosis. However, IQ and symptoms of psychosis did not predict tendency towards selecting decks with different gain-to-loss frequency, whereas poorer composite executive functioning predicted a pattern of selecting decks A and C with more frequent losses, (b = 8.30, p <.05).

Discussion: The results suggest that both IQ and executive functions contribute to IGT performance, but in distinct ways. Whereas lower IQ may contribute to overall more disadvantageous decision-making, poorer executive functioning may contribute to a more risk-aversive decision-making style. A clinical implication may be that individuals with schizophrenia and lower IQ or poorer executive functioning will have a higher need for support and interventions targeting decision-making.

1 Introduction

Schizophrenia is commonly recognized as one of the most severe mental disorders, characterized by hallucinations and delusions (i.e., positive symptoms) as well as diminished emotional expressions, avolition, and social withdrawal (i.e., negative symptoms) (1, 2). The lifetime prevalence of schizophrenia in the population is between 0.7% and 0.9% (3, 4). Despite the low prevalence, schizophrenia represents a heavy burden in terms of healthcare costs and years lived with disability (5, 6). Thus, research aimed at better understanding the multidimensional nature of schizophrenia and factors associated with heterogeneity in functioning is important for clinical care.

Despite existing treatment for psychotic symptoms, a substantial portion of individuals with schizophrenia experience reduced real-world function (7, 8). As cognition has proved a significant predictor of function, it has become an important treatment target (9, 10). Individuals with schizophrenia commonly present with poorer cognitive performance across a range of different domains (11). Lower pre-onset IQ predicts a higher risk of schizophrenia onset (12, 13). However, despite this association between lower IQ and schizophrenia risk, there is considerable variation in cognitive performance among individuals with schizophrenia, and almost one-fourth (22%) perform averagely or above averagely on tests of IQ (14). Beyond this deficit in global cognitive abilities, individuals with schizophrenia also commonly display difficulties across a range of different specific cognitive tasks, including attention, inhibition, working memory, shifting, and planning (15–20). These cognitive processes are commonly referred to as executive functions, an umbrella term for cognitive processes involved in the control of cognition, emotion, and behaviors (17, 21). The cognitive difficulties with executive functions and IQ have been found to be unrelated to positive symptoms and only weakly or moderately related to negative symptoms (22–24). Thus, the cognitive difficulties represent an important clinical domain, independent of the core symptoms of schizophrenia. Furthermore, demonstrating the importance of executive functions for clinical outcomes, studies have found that difficulties with executive functions in individuals with schizophrenia predict more functional impairments and internalizing difficulties later in life (25, 26).

Another domain where individuals with schizophrenia display poorer performance than their healthy counterparts is decision-making (27, 28). Individuals with schizophrenia more often display risk-taking behaviors, including among others substance-use and criminal offences (29–31). These risk-taking behaviors may be tied to decision-making processes (32). The Iowa Gambling Task (IGT; 33) is one laboratory task that has been extensively used in the study of decision-making processes in clinical and non-clinical populations (27, 28, 34–36). The IGT is a simulation of a situation where participants can win and lose money by drawing cards without knowing up front which decks of cards are more beneficial. The implicit nature of the task separates it from explicit tasks where probabilities are made explicit from start. Decision-making under implicit contingencies is considered to include a “hot” aspect, referring to the affective response participants have to the choice options (33, 37). The somatic marker hypothesis suggests that reactivation of bodily responses (e.g. increase in heart rate or sweat) to previous losses help guide decision-making (38, 39). Explicit tasks are not assumed to cause the same affective responses, but are rather considered “cold” in the sense that participants can make rational decisions about risk and benefit based on the known probabilities of the task (37).

Two meta-analyses have demonstrated that individuals with schizophrenia exhibit poorer performance on the IGT, i.e. they chose the disadvantageous decks more often (27, 28). This finding indicate that individuals with schizophrenia have difficulties in deciphering the risk/reward contingencies of the task and may struggle to adjust their strategy based on feedback. Generally, IGT performance has inconsistently been found to be positively related with IQ and executive functions (34). However, in one study of healthy individuals, IQ could account for around 40% of the variance in IGT performance and attention and shifting could account for 37% and 17% of the variance in IGT performance, respectively (40). Similarly, one of the meta-analysis of IGT performance in individuals with schizophrenia found a significant positive correlation with IQ (r = .20) and working memory (r = .22), whereas evidence for an association with overall executive functioning was inconclusive (27). The other meta-analysis of IGT performance in individuals with schizophrenia, however, found that whereas higher IQ was associated with placing lesser weight on immediate gain and increased weighting of gain-to-loss frequency in healthy controls, these effects were attenuated in individuals with schizophrenia (28). Furthermore, whereas higher IQ was associated with higher net scores towards the middle of the task (block 3 of 5) across individuals with schizophrenia and healthy controls, this effect was attenuated in individuals with schizophrenia earlier in the task (block 2) (28). A possible explanation for why IQ becomes more influential later during the IGT for individuals with schizophrenia may be that difficulties with executive function make them use more trials to decipher the contingencies of the task and correct their strategy accordingly. Whereas executive functions within the typical range may have a small or negligible impact on IGT performance (34), difficulties with executive functions may have a stronger impact on IGT performance (34, 41). First, difficulties with attending to the task (attention) make it difficult to code relevant information, and if coded, difficulties with holding the relevant information in mind to decipher the contingencies (working memory) may hinder the use of that information. Furthermore, if attending and holding the relevant information in mind, the participants still must inhibit the prepotent response to go for the decks with largest gains (response inhibition), shift focus from one deck to another (shifting) and plan a strategy as the relevant contingencies are deciphered (planning). Thus, difficulties with one or more executive functions may come into play when completing the IGT.

Overall, few studies (k = 6) of individuals with schizophrenia have examined the relationship between executive functions and performance on the IGT and related tasks (27). Moreover, the studies that have examined this relationship have often focused on complex tasks tapping a range of different executive functions at the same time, such as the Wisconsin Card Sorting Task, or just a specific executive function like working memory (27, 41). In order to gain a more refined understanding of how executive functions may contribute to IGT performance among individuals with schizophrenia, our aim was to examine the impact of a range of different executive functions including attention, response inhibition, shifting, working memory, and planning on IGT performance. We expected the different executive functions to have an impact on IGT performance beyond estimated IQ, and expected a composite score of all executive functions to have the most notable impact (16, 42). We also controlled for positive and negative symptoms, as previous studies have found a negative association between negative symptoms and decision-making (27). Before examining our main aim, we examined whether our participants with schizophrenia displayed the expected improvement from block 1 and onwards (i.e., learning trajectory), and their deck preferences.

2 Materials and methods

2.1 Procedures

Data for the current analysis was collected as part of a baseline assessment in a randomized controlled trial examining the effects of the metacognitive strategy training, Goal Management Training for executive functions, in a sample of persons with schizophrenia (43). The trial was preregistered at clinicaltrials.gov (NCT03048695) and approved by the Regional Committee for Medical and Health Research Ethics in Norway (2015/2118) prior to commencement. All participants gave informed consent in writing. The study took place at a regional hospital in Norway 2017-2021 and participants were recruited with the help of treating clinicians.

For this analysis, only the baseline data from participants with a schizophrenia spectrum disorder according to the criteria in the Diagnostic and Statistical Manual of Mental Disorders, DSM-IV-TR (44) were utilized. The diagnostic evaluation was performed by a clinical psychologist under supervision from a specialist in psychiatry using the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) Axis 1 disorders, SCID I (45). A specialist in clinical neuropsychology supervised the cognitive assessment.

Inclusion criteria were age 16 - 69 years and subjective complaints of executive function difficulties. Exclusion criteria were (1) having received treatment for psychosis for longer than five years, (2) ongoing substance abuse, (3) neurological disease or traumatic brain injury, or (4) severe intellectual disability (IQ <70).

2.2 Participants

The participants in the sample (n = 65) comprised 40% females and 60% males who were aged 16 – 44 years (M_age = 25.4, SD = 6.5). The majority, 86.2%, were of European descent. See Table 1 for a description of the sample.

Table 1

Table 1. Sample characteristics.

2.3 Measures

2.3.1 Estimated IQ

The Matrix Reasoning and Vocabulary subtests from Wechsler Abbreviated Scale of Intelligence (WASI) or the General Ability Index from Wechsler’s Adult Intelligence Scale, 4th edition (WAIS-IV) were used as estimates of IQ (46, 47). Estimated IQ was used as a control variable in the regression analysis.

2.3.2 Symptoms of psychosis

The severity of psychotic symptoms was assessed at the time of testing, using the Structured Clinical Interview for the Positive and Negative Syndrome Scale for Schizophrenia (SCI-PANSS), which includes a structured interview with participants, supplemental information from caregivers, and clinical observations made by mental health professionals (48). Symptom severity is measured on a scale ranging from 1 (absent) to 7 (extreme). A score of 4 is considered above the psychotic threshold for the items covering hallucinations and delusions. The inter-rater reliability of the Norwegian version of the instrument is adequate when it is performed by trained clinicians (49). In the present study the total score for seven positive symptom items and the total score for seven negative symptoms according to the original scale was utilized, as this allows for comparison with previous studies. Positive and negative symptoms were controlled for in the regression analysis.

2.3.3 Response inhibition and shifting

The time raw scores on the Color-Word Interference Test (CWIT), from the Delis-Kaplan Executive Function System (D-KEFS (50), condition 3 (CW3; response inhibition) and condition 4 (CW4; shifting) were used as measures of response inhibition and shifting, respectively. In the CWIT, the participant is presented with color words with dissonant ink (e.g., the word “red” written with blue ink) and asked to name the dissonant color (condition 3) instead of reading the word, or to switch back-and-forth between reading the word and naming the dissonant color (condition 4). The CWIT condition 3 and 4 have shown adequate test-retest reliability (r = .52 to.90) in a general population sample (50, 51) as well as discriminative validity in differentiating between populations with and without difficulties with executive functions (15, 52). CW3 and CW4 were used as independent variables in the regression analyses independent variables in the regression analyses to examine the influence of response inhibition and shifting on IGT performance.

2.3.4 Attention

The detectability score of the Conners Performance Test, 3^rd edition (CPT-3), was used as a measure of an individual’s sustained attention and discriminative processing, offering insight into their executive functions regarding focus and response consistency (53). In the CPT-3, participants were instructed to respond quickly to letters appearing on a screen by pressing a button for all letters appearing, while refraining from pressing the button when the letter X appeared. The test comprises 360 trials, whereof 20% of them present the letter X. The detectability score is calculated as the ratio of incorrect responses to the non-target (i.e., the letter X) divided by correctly identified targets. The CPT-3 scores have shown good test-retest reliability (r ≥.74) in a general population sample (53) as well as discriminative validity in differentiating between populations with and without attention deficits (54, 55). CPT detectability was used as an independent variable in the regression analyses to examine the IGT performance.

2.3.5 Planning

The Tower test from the Delis-Kaplan Executive Function System (D-KEFS (50); was used as a measure of planning. In the Tower test, the participant is asked to move five disks in order to reproduce a target tower, varying in complexity, across three pegs. The participant is only allowed to move one disk at a time and is instructed to use as few moves as possible. We used the total achievement score, which is comprised by a combination of the time the participants use and the number of moves. The Tower test has shown weak test-retest reliability (r = .41 to.51) in a general population sample (50). However, the test has shown convergent validity through significant association with the Tower of London test (56) and discriminative validity in differentiating between individuals with and without traumatic brain injury (57). The Tower total achievement score was used as an independent variable in the regression analyses to examine the influence of planning on IGT performance.

2.3.6 Working memory

The Letter-Number Sequencing (LNS) test from the Wechsler Intelligence Scale for Children (58) was used as a measure of working memory. In the LNS test, the participant is asked to recall a sequence of letters and numbers read aloud by the test administrator and thereafter repeated the numbers in ascending order and the letters in alphabetic order. The LNS has shown adequate test-retest reliability (r ≥.69) (59–61) as well as discriminative validity in differentiating between populations with and without difficulties with executive functions (62) and predictive validity in relation to occupational attainment in patients with psychosis (63). The LNS total score was used as an independent variable in the regression analyses of whether working memory contributed to IGT performance.

2.3.7 Composite score of executive functioning

In addition to the test scores on the specific cognitive tasks, we also calculated a composite score of executive functioning for use in the analyses. The composite measure of executive functioning was added because there is an ongoing debate about whether executive functioning should be considered a unidimensional or multidimensional construct (64, 65). Furthermore, several studies have suggested that composite measures of executive functioning have greater clinical utility and higher predictive value and reliability (16, 66, 67). A composite measure also fits with the proposition that different specific cognitive processes may contribute to explaining individual differences in IGT performance (36). We created the composite score by converting all test scores to standardized scores (i.e., Z-scores) and then adding the Z-scores into a composite score of executive functioning. Higher scores on the composite reflect poorer executive functioning. The composite score of executive functioning was considered the primary independent variable in the regression analyses to examine the contribution of executive functioning to performance on the IGT.

2.3.8 Decision-making

A computerized version of the IGT, the IGT version 2 (33, 68), was used as a measure of decision-making. The participants were instructed that the goal of the task was to maximize their gains through choosing cards form the different decks (A, B, C, and D). In the IGT, the participant is presented with four decks of cards with varying gains and losses and is asked to maximize gain by choosing between cards from the four decks across five blocks of 20 trials. Two of the decks (A and B) are associated with larger gains but even greater losses, leading to a net loss over time, whereas the two other decks (C and D) are associated with smaller gains but even smaller losses, leading to a net gain over time. Decks A and C are characterized by frequent and smaller losses, whereas deck B and D is characterized by less frequent but larger losses. The participant is not informed about these probabilities and must decipher the contingencies of the task themselves. The gain-to-loss structure of the task can be seen in Table 2. The participants were instructed that some decks may be more beneficial than others, but not which decks. The task ended after five blocks of 20 trials (a total of 100 trials). We collected information about the participants total net score, as well as net scores of the five blocks, and the number of responses to each deck. The IGT has shown adequate internal consistency across blocks (α = .75) in previous studies (32, 69) and is considered to have adequate construct validity in terms of differentiating between clinical and non-clinical populations (70). The test-retest reliability of the IGT has been questioned as one study found the IGT to display low test-retest reliability (r = .26-.27) over a three-week period (71). However, a recent study found similar rank-order stability (r = .25) over an eight-year period across a clinical and non-clinical population (32), suggesting at least some stability in performance over time.

Table 2

Table 2. Gain-to-loss structure of Iowa Gambling Task, version 2. First twenty draws.

Two measures of performance on the IGT were used in the analysis: The total net score and a measure of gain-to-loss frequency. The total net score, often referred to as expectancy value, is calculated by subtracting the number of draws from decks with lower expectancies of winning money in the long term due to larger losses (the so-called ‘bad’ decks A and B) from the number of draws from decks with higher long-term win expectancies (the so-called ‘good’ decks C and D). As a measure of preference for decks with high gain-to-loss frequency, the number of draws from decks A and C were subtracted from draws from decks B and D. Both the net score and the gain-to-loss frequency were used as the dependent variable in separate regression analyses of the contribution of executive functions on IGT performance, controlling for estimated IQ and symptoms of psychosis.

2.4 Data analyses

We performed all analyses in SPSS version 29. First, we used repeated-measures analysis of variance (ANOVA) to examine the learning trajectory across the five blocks of the IGT and deck preferences across the four decks of the IGT. Second, we examined bivariate correlations between the different cognitive processes, IGT performance, and psychosis symptoms using Pearson’s product-moment correlation coefficient (r). Third, we examined the contribution of each of the specific cognitive processes to IGT performance beyond estimated IQ and psychosis symptoms in hierarchical regression analyses. In step 1, we entered psychosis symptoms and estimated IQ as predictors to control for these effects in the subsequent step. In step 2, we entered the specific cognitive tasks, as well as a composite measure of executive functioning, in separate analyses. We used the increase in explained variance (ΔR²) to determine model fit and set the significance level (α) at p ≤.05. We assumed missing data were missing at random and dealt with missingness using listwise deletion. A post hoc power analysis showed that all stepwise regression analyses achieved an acceptable power of ≥.79 to detect a medium increase in explained variance (f² = .15, ΔR² = .13) from step 1 to step 2 (72, 73). An f² of respectively 0.02, 0.15, and 0.35 were considered a small, medium, and large effect size (74).

After finding a significant effect of estimated IQ on the IGT total net score, we did a post hoc visual inspection of the learning trajectory across IGT blocks according to IQ. We divided participants into three groups based on their normed IQ estimates; 1) a group with an average estimated IQ of the normative mean of 100 or above, 2) a group with estimated IQ between 99 and 1 SD below the normative mean (85), and 3) a group with an average estimated IQ between 1 and 2 SD below the normed mean (70–84).

3 Results

3.1 Learning trajectory and deck preferences

See Table 3 for descriptive statistics. The repeated-measures ANOVA with block as within-subject factor showed an overall significant effect of block (Pillai’s Trace = .233, F (4, 60) = 4.565, p = .003). As can be seen in Figure 1, pairwise comparisons showed that the net score in block 1 differed significantly from the net score of all other blocks (p ≤.009), whereas there were no significant differences in net score between blocks 2, 3, 4, and 5 (p ≥.336). Figure 2 illustrates mean number of responses to the IGT decks in the sample. The repeated-measures ANOVA with deck as a within-subject factor showed an overall significant effect of deck (Pillai’s Trace = .633, F (4, 60) = 35.137, p <.001). Deck B received significantly more responses compared to deck A (MD = 13.61, p <.001) and deck C (MD = 8.39, p = .003). Deck D received significantly more responses compared to deck A (MD = 11.67, p <.001) and deck C (MD = 6.45, p = .010). Deck C received significantly more responses compared to deck A (MD = 5.22, p = .005). This suggests an emphasis on gain-to-loss frequency, as the two decks where participants win more often and lose more seldom (B and D) were chosen significantly more often than decks where losses occur more frequently (A and C).

Table 3

Table 3. Descriptive statistics on the measures included in the current study.

Figure 1

Figure 1. Net scores across blocks on the Iowa Gambling Task (IGT). Error bars display 95% confidence intervals.

Figure 2

Figure 2. Number of responses to each deck on the Iowa Gambling Task (IGT). Error bars display 95% confidence intervals. (A) and (B) = Disadvantageous decks with high gains, but even higher losses. (C) and (D) = Advantageous decks with low gains, but even lower losses. Decks B and D have less frequent losses (18:2 per block), compared to decks (A) and (C) (10:10 per block).

3.2 Cognitive processes and IGT performance

Table 4 presents the bivariate correlations between the different specific cognitive tasks, the composite score of executive functioning, estimated IQ, psychosis symptoms, and IGT performance (total net score and gain-to-loss frequency). IGT total net score correlated significantly with estimated IQ, response inhibition (CW3), and the composite score of executive functioning. IGT gain-to-loss frequency correlated significantly with the composite score of executive functioning. Table 5 presents the results from the stepwise regression analyses with the IGT total net score as the dependent variable. After adding psychosis symptoms and estimated IQ in step 1, none of the cognitive tasks nor the composite score of executive functioning significantly contributed to increased explained variance in the IGT total net score in step 2. Estimated IQ was the only significant predictor of the IGT total net score, and consistently predicted this score across all analyses with differing executive tasks. In step 1, psychosis symptoms and estimated IQ together explained 28% of the variance in IGT performance (F (3, 55) = 8.492, p <.001, R² = .317, Adjusted R² = .279, f² = .46). As can be seen in the illustration in Figure 3, those with lower estimated IQ showed little or no progression in terms of increasing net scores across blocks compared to those with higher estimated IQ who clearly increased their net score from block 2 onwards. Table 6 presents the results from the stepwise regression analyses with the IGT gain-to-loss frequency as the dependent variable. In this analysis, IQ and symptoms of psychosis did not explain variation in IGT gain-to-loss frequency, (F (3, 49) = .139, p = .936, R² = .008, Adjusted R² = -.052, f² = .01). However, adding the composite measure of executive functioning in step 2 significantly increased the explanatory power of the model with a small, but significant change in explained variance (ΔF (4, 48) = 5.256, p.026, R² = .106, Adjusted R² = .032, ΔR² = .098, f² = .12). The individual components of executive functioning did not reach statistical significance when entered as predictors of IGT gain-to-loss frequency.

Table 4

Table 4. Correlation-matrix displaying the bivariate correlations between cognitive tasks, IQ, psychosis symptoms, and IGT performance.

Table 5

Table 5. Results from stepwise regression analyses with Iowa Gambling Task (IGT) performance measured by the total net score as dependent variable.

Figure 3

Figure 3. Net scores on the Iowa Gambling Task (IGT) across block for different levels of estimated intelligence quotient (IQ). Error bars display 95% confidence intervals.

Table 6

Table 6. Results from stepwise regression analyses with Iowa Gambling Task (IGT) performance measured as selection of decks with higher gain-to-loss frequency as the dependent variable.

4 Discussion

The current study aimed at examining the impact of executive functions on IGT performance in individuals with schizophrenia. Contrary to our expectations, the findings show that executive functions did not contribute to IGT performance beyond estimated IQ when using the preference for advantageous decks (C+D) as the measure of IGT performance. However, executive function, and not IQ, predicted patterns of preference for decks with differing frequency of wins and losses, as participants with poorer overall performance on executive function tasks where more likely to choose the decks with more frequent losses (A+C). Thus, the findings support the notion that decision-making and executive functions are relatively separate cognitive domains (34). Yet both general and specific cognitive abilities may contribute during decision making processes.

When it comes to learning trajectory and deck preferences, our findings diverge from previous findings on two issues. First, Betz and colleagues concluded in their meta-analysis that the healthy controls displayed a steep learning trajectory on the IGT, whereas individuals with schizophrenia did not improve during the task (28). On the contrary, our findings showed that individuals with schizophrenia displayed the typical learning trajectory on the IGT; they used the first block to decipher the contingencies and thereafter performed significantly better in subsequent blocks. The lack of a control group prevented us from comparing the learning trajectory of individuals with schizophrenia to the learning trajectory of healthy controls. Thus, a clear conclusion about the divergence in findings compared to the meta-analysis by Betz and colleagues is not possible. However, it may be that individuals with schizophrenia display improvement during the task, but that they improve significantly less than healthy controls. This interpretations is in line with findings from an earlier study of IGT in participants with schizophrenia, where the steepest learning curve was seen from block one to block two, but the participants with schizophrenia held a level of advantageous to disadvantageous deck somewhat below the performance of healthy participants in the last four blocks (75).

Second, Betz and colleagues (28) also concluded that whereas deck preferences among healthy controls were driven by gain-to-loss frequency, shown by the number of responses to decks B and D, deck preferences among individuals with schizophrenia were driven mainly by immediate gains, shown by the number of responses to decks A and B. Similarly, the meta-analysis by Woodrow and colleagues (27) found that individuals with psychosis placed a greater weight on gains over losses compared to healthy controls, which typically results in choosing the decks with greater gains, i.e., deck A and B. In contrast, our findings showed a within-group preference for deck B and D relative to A and C, a preference pattern consistent with the typical preferences of healthy controls (28, 35). This finding is in line with at least one other study of persons with schizophrenia using the IGT (75). In the present study, those participants with poorer executive function showed a greater preference for deck A and C. Again, the lack of a control group prevented us from investigating whether the magnitude of the preferences differs between individuals with schizophrenia and healthy controls. Nonetheless, it is interesting that the current sample, comprising young adults with schizophrenia spectrum disorders having subjective executive functioning difficulties, display a rather typical learning trajectory and deck preferences on the task as a group, whereas atypical preference for decks with more frequent losses were seen more frequently among participants with larger general cognitive challenges or executive functioning difficulties. Still, it should be noted that in terms of risk, deck B and D are the two decks that have the highest risk within the disadvantageous and advantageous decks, respectively (36). Thus, the results are consistent with the notion of individuals with schizophrenia being risk-taking in their decision-making. However, risk-taking decision-making on the IGT may be the norm also for healthy controls (41). Deck B and D share the same properties in terms of gain-to-loss frequency, and an increasing number of studies suggest that gain-to-loss frequency may drive decision-making on the IGT in both healthy and psychiatric populations (41, 76). If this is the case, also healthy participants may more often than previously assumed be using a “win-stay, lose-shift”-strategy, indicating that they are not able to guard against poor long-term outcomes (77).

Our research explored the influence of IQ on IGT performance and found that higher IQ scores predicted better performance, consistent with past studies suggesting that the IGT reflects cognitive rather than emotional processes (78). This finding underscores the necessity of considering IQ as a potential confounding variable when using the IGT across several populations, both healthy and clinical. Exploring the relationship between IQ and decision-making in healthy populations could provide further insights. Future studies should incorporate control comparisons to determine whether the relationship between IQ and decision-making abilities is unique to schizophrenia or more broadly applicable.

When it comes to the impact of cognitive abilities on IGT performance, our hypothesis that specific cognitive abilities like executive functions would be associated with IGT performance beyond general cognitive abilities (i.e., IQ) was partially supported. Estimated IQ was the only significant correlate of IGT total net score in multivariate analyses, whereas overall executive functioning was a significant correlate of IGT gain-to-loss frequency. Our findings align with previous studies demonstrating an association between IQ and IGT performance, and lend support to a smaller number of studies showing an associations between executive functioning and IGT performance in people with schizophrenia (27, 28). Still, a great proportion of the variance in IGT performance remains unaccounted for after investigating symptoms of psychosis, estimated IQ and a broad battery of executive functioning tests. This supports the notion that IGT measures some other rather specific cognitive ability that may (or may not) be of clinical relevance (34).

Of interest, poorer overall executive functions predicted more draws from deck A and C relative to deck B and D. This is interesting because the overall pattern in the sample was a preference for deck B and D, and because deck A and C have a lower gain-to-loss frequency and is thus associated with lower risk than deck B and D (36). Consequently, our findings suggest that individuals with SZ and poorer executive functions exhibit a more risk-aversive decision-making style compared to individuals with SZ and better executive functions, after controlling for IQ. As such, it may be that risk-taking behaviors in individuals with SZ are more deliberate than previously thought and may not be a consequence of difficulties with regulating one’s own behavior and emotions (75). Interestingly, it has been argued that individuals on the autism spectrum is more deliberate in their decision-making style and thus, more risk-aversive (79, 80). As individuals with SZ and individuals on the autism spectrum display similar performance on executive functions, and an impairment compared to healthy controls (15), this may suggest that poorer executive functions (at least sometimes) contribute to a more risk-aversive decision-making style. Thus, whereas IQ may show a similar association with IGT performance across healthy and clinical populations (78), executive functions may be differentially associated with IGT performance across healthy and clinical populations (40). However, this hypothesis needs to be examined in future studies including a healthy comparison group.

The varied findings related to executive functions (i.e., net score versus gain-loss frequency) can be interpreted in light of the clinical cases of damage to the ventromedial prefrontal cortex (vmPFC) that were central to the development of the IGT (33). In these cases, Bechara and colleagues describe that the patients usually have intact intellectual (i.e., IQ) and problem-solving (i.e., executive functions) abilities, but exhibit a rather severe impairment in real-life decision-making – and on the IGT (33). The extent to which specific cognitive abilities play a role in determining IGT performance may depend on the pathophysiology of cortical networks involved in particular disorders. For example, one study found that executive functions were related to IGT performance among healthy participants and participants with lesions to the dorsolateral prefrontal cortex (dlPFC) but not among participants with lesions to the vmPFC (81). Reduced motivation and pleasure (anhedonia) are common negative symptoms of schizophrenia and found to be related to dampened decision value signals in the vmPFC (82). However, pathology of the dlPFC has been assumed to be central to the cognitive difficulties of individuals with schizophrenia (83). Thus, the differential role that executive functions play in overall IGT performance versus gain-to-loss frequency may indicate that different cortical regions are involved in the choices between advantageous versus disadvantageous decks and risky versus less-risky decks. It is important to note that whereas poorer executive functions may contribute to a risk-aversive decision-making style (i.e., more frequent draws from deck A and C), this decision-making style do not entail poorer overall decision-making (i.e., net score). In sum, current evidence implies that several cortical networks are involved in the pathophysiology of schizophrenia, complicating the interpretation of IGT results in this population (39, 83, 84). Thus, future research must continue to develop task paradigms aimed at teasing out specific cognitive processes. Importantly, to increase clinical relevance it is also essential to combine measures at the physiological, neuropsychological, and behavioral levels in the same studies.

Although IGT performance is assumed to be a proxy of real-life decision-making, the evidence of IGT being associated with real-life outcomes is limited (32, 70). Some evidence has suggested that IGT performance is related to social functioning in individuals with schizophrenia and risk-taking in everyday life in young adults with ADHD (27, 32). However, a recent study of decision-making tasks similar to the IGT found low ecological validity of the tasks across two general population samples (85). The decision-making tasks were not related to risk-taking in everyday life, operationalized as preventive health behaviors (e.g., wearing a mask) during the COVID-19 pandemic (85). Thus, more research is needed to establish clear links between IGT performance and clinical outcomes (e.g., psychotic relapse, remission, occupational functioning etc.) among individuals with schizophrenia.

4.1 Strengths and limitations

The strengths of the current study include a well-characterized clinical sample who had undergone testing with a comprehensive neuropsychological test battery. A major limitation of the current study is the lack of a control group preventing us from comparing the IGT performance of individuals with schizophrenia to that of healthy controls matched on IQ or another clinical group. Furthermore, the requirement that participants had to report subjective executive functioning difficulties to participate in the clinical trial means that our participants may have larger cognitive deficits on average compared to the general population of individuals with schizophrenia. At the same time, based on established norms, few participants scored in the clinical range on the neuropsychological tests (43, 86). Thus, the sample may not be representative of the whole population of individuals with schizophrenia spectrum disorders. Furthermore, due to the limited samples size, we did not control for sex differences in the regression analysis. However, a recent meta-analysis found that males tend to perform better than females on the IGT (87). In future studies, larger samples or pooling of data would help clarify whether sex differences are relevant to the relationships between general and specific cognitive functioning and decision-making. In our study, 69.2% of participants received antipsychotic treatment. According to the systematic review and meta-analysis by Woodrow et al. (27), low-dose antipsychotic treatment showed no impairments in decision-making, while medium to high doses and one antipsychotic-free study demonstrated moderate impairments, suggesting a possible curvilinear relationship. Consequently, the type and dosage of antipsychotic medication in our study may have influenced IGT performance. While the overall impact of second generation antipsychotic medication on cognitive functions is thought to be minor, the exact relationships are largely unknown (88). A last limitation is the cross-sectional nature of the study. Since IGT testing only took place at baseline, we could not examine how treatment may have affected IGT performance, or how IGT performance may affect the clinical course of individuals with schizophrenia. Furthermore, we lacked a measure of risk-taking in everyday life preventing us from establishing ecological validity.

4.2 Conclusion and clinical implications

The current study showed that individuals with schizophrenia improve across blocks in the IGT and display the typical preference for decks with highly frequent gains, and low frequent losses. Estimated IQ was strongly related to IGT total net score (choosing decks that are advantageous in the long run), whereas specific cognitive abilities contributed to the explained variance in choosing decks with more frequent gains than losses similar to how healthy participants often approach the task. In the real world, individuals with schizophrenia who possess higher cognitive functioning (i.e., estimated IQ and/or executive functioning) may be better equipped to weigh risks and benefits, anticipate the consequences of their actions, and engage in more adaptive decision-making processes. Moreover, knowledge of how these individuals approach risk and make decisions can assist caregivers and health professionals in creating environments that minimize potential harm while encouraging autonomy and independence. Clinical implications include the importance of taking both general cognitive difficulties, executive difficulties and decision-making difficulties into account when providing health care to individuals with schizophrenia, for instance by adapting information to aid informed treatment decisions. Standardized clinical assessment ought to, at the very least, include measures of general abilities and executive functioning, but preferably also measures that can shed light on real-life decision-making.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Regional Committee for Medical and Health Research Ethics in South-Eastern Norway (2015/2118). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

SO: Formal analysis, Methodology, Writing – original draft. MØ: Conceptualization, Funding acquisition, Project administration, Writing – review & editing. IH: Investigation, Methodology, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the South-Eastern Norway Health Authority under Grant number 2017012; Innlandet Hospital Trust under Grant number 150602 and 150648; and University of Oslo under Grant number 353139.

Acknowledgments

The authors would like to thank collaborators Jan Stubberud, Elisabeth Haug and Torill Ueland. We also thank Evelyn Robsahm, Kari Veisten and Tina Sveum Engh for assistance with data collection and entry.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abbreviations

ADHD, Attention-Deficit/Hyperactivity Disorder; CPT-3, Connors Performance Test, 3^rd edition; CW3, Color Word Interference Test, condition 3; CW4, Color Word Interference Test, condition 4; CWIT, Color Word Interference Test; D-KEFS, Delis-Kaplan Executive Function System; dlPFC, Dorsolateral prefrontal cortex; DSM-IV-TR, Diagnostic and Statistical Manual of Mental Disorders, 4^th edition; IGT, Iowa Gambling Task; IQ, Intelligence Quotient; LNS, Letter-Number Sequencing test; PANSS Negative, Positive and Negative Syndrome Scale for Schizophrenia, Negative symptoms subscale; PANSS Positive, Positive and Negative Syndrome Scale for Schizophrenia, Positive symptoms subscale; SCI-PANSS, Positive and Negative Syndrome Scale for Schizophrenia; vmPFC, Ventromedial prefrontal cortex; WAIS-IV, Wechsler’s Adult Intelligence Scale, 4^th edition; WASI, Wechsler Abbreviated Scale of Intelligence.

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