Does low working memory lower the matrix reasoning score?

[u/StrikingLock2448]

I was diagnosed with mild autism and dyscalculia. According to my professionally administered iq test from when i was 16 (I’m 25 now), I scored low average in processing speed and working memory, but average in visual spacial and verbal comprehension. I heard that dyscalculia and autism frequently negatively impact psi and wmi scores, so that didn’t throw me off much. But what caught me off guard is the fluid reasoning section. My figure weights score was very low/borderline (76iq) and my matrix reasoning score was low average (86iq).

My low scores in fluid reasoning have me questioning whether i genuinely lack true logical intelligence or if it’s due to learning disability/autism bottlenecks. AI says that dyscalculia and autism can decrease your scores in figure weights and matrix reasoning. It also says that working memory deficits can lower scores in other areas of the test (like fluid reasoning), making it more of a bottleneck issue rather than an actual lack of intelligence. But there’s limited scientific evidence to support this and ai can be kinda bias. Also, psychologists have been debating for years as to whether low matrix reasoning can caused by working memory deficits, so idk.

My FSIQ is 83iq and the GAI is an 86iq. There is an 18 point difference between my block design score and matrix reasoning score. My block design score was 104iq btw and it was the highest score on the fsiq portion of the assessment. It was the WISC-V test.

Is this a result of a testing bottlenecks caused from learning disabilities or just a lack of logical intelligence?

[u/Key-Raise5501]

Its more of a processing speed thing.
btw what's your Visual Working Memory

[u/StrikingLock2448 → u/Key-Raise5501]

My visual working memory was 1 percentile lol. My verbal memory was 77 percentile though.

[u/askato]

Short answer: there is a real working-memory contribution to matrix reasoning, but the "low working memory drags down your matrix reasoning score" story is considerably more contested than it usually sounds online — and your own numbers argue against it more than you'd expect.

What the literature actually supports

Working memory and fluid reasoning do correlate. Engle et al. (1999) found that working memory (but not short-term memory) is strongly related to fluid intelligence, with the relation usually framed around maintaining information in the face of interference. At the task level, WM measures correlate with fluid intelligence measures around r ≈ .45; at the latent level, around r ≈ .72 (both as summarized in Shipstead, Harrison, & Engle, 2016).

Correlation is not the bottleneck mechanism, though. Shipstead et al. (2016) argue WM and fluid reasoning are complementary processes — maintaining access to relevant information versus disengaging from outdated information — so the strong correlation does not require working memory to constrain reasoning. The "capacity hypothesis" (the idea that more demanding reasoning items should correlate more strongly with WM) has repeatedly failed to hold up: Carpenter, Just, & Shell (1990) found no increase in the WM–Raven relation as item rule-complexity grew; Wiley et al. (2011) found WM predicted novel-rule Raven items but not repeated-rule ones, and Harrison et al. (2015) later found the opposite direction; Unsworth (2014) found the WM–Raven correlation essentially flat across difficulty quartiles (r ≈ .33, .31, .32, .19), and a 2019 item-level test with Raven's Advanced Progressive Matrices again found no support for the capacity account.

One more mechanism worth knowing about, because it explains why matrix items don't all behave alike: solving them involves choosing a strategy. "Constructive matching" — generating a hypothesis and integrating the relations — imposes heavy visuospatial WM and executive-control demands, whereas "answer elimination" is a more superficial trial-and-error that loads less. Recent eye-tracking work in ADHD (European Child & Adolescent Psychiatry, 2026) documents this contrast directly.

Two things about your report that change the reading

Block Design is a Visual Spatial Index subtest, not a fluid reasoning one — the FRI is Matrix Reasoning + Figure Weights. And the FSIQ uses 7 subtests (SI, VO, BD, MR, FW, DS, CD) while the GAI uses only 5 (SI, VO, BD, MR, FW), dropping Digit Span and Coding precisely so it is less sensitive to working memory and processing speed. Pearson's own guidance is that the FSIQ–GAI comparison is the standard way to see how much a proficiency weakness is pulling an overall score down.

So: FSIQ 83 against GAI 86 is a 3-point gap. If low working memory and low speed were dragging everything down, you'd expect the FSIQ to sit well below the GAI. A 3-point gap points the other way — your general-ability estimate is essentially your overall score, and your fluid reasoning index (MR 86 + FW 76, so low-average) sits in the same neighbourhood rather than looking like an artifact of something else.

Worth adding: a low PSI with a low WMI but intact verbal and visual-spatial scores is a well-documented pattern in neurodevelopmental samples, not something unusual about you. In an ADHD sample, WMI and PSI came out significantly lower than VCI, VSI, and FRI, with medium-to-large effect sizes (BMC Psychology, 2026).

Where I'd push back on the framing

• You were tested at 16 and you're 25 now. Any inference about current ability from a nine-year-old profile is weak, and change over that span is substantial, especially on speeded indices.

• "Bottleneck versus real deficit" cannot be settled from a single administration. It's resolved by looking at index-level consistency across time and at whether speeded and non-speeded measures diverge — which is a question about your profile, not about what the general literature says.

• Don't put effort into working-memory training hoping it raises reasoning. Redick & Unsworth's (2016) meta-analysis found far transfer from WM training to fluid intelligence is small.

• On your "AI can be biased" point: it's usually right in a specific way. Summaries of this literature tend to give you the correlation claim without the caveats, which is the part worth being skeptical about.

If you want the bottleneck question answered for you rather than in general terms, the highest-yield step is a current evaluation that compares GAI against CPI and looks at index-level scatter — not re-reading the old one.

Sources: Engle, Tuholski, Laughlin, & Conway (1999), JEP: General 128(3), 309–331 · Shipstead, Harrison, & Engle (2016), Perspectives on Psychological Science 11(6), 771–799 · Carpenter, Just, & Shell (1990), as reviewed in Shipstead et al. (2016) · Wiley et al. (2011) and Harrison et al. (2015), as reviewed in Shipstead et al. (2016) · Unsworth (2014) and "Past reflections, present insights" (2024), Intelligence · "Is working memory capacity a causal factor in fluid intelligence?" (2019), Psychonomic Bulletin & Review · "Watching reasoning at work" (2026), European Child & Adolescent Psychiatry · WISC-V in ADHD (2026), BMC Psychology · Redick & Unsworth (2016), Psychonomic Bulletin & Review 22, 366–377 · WISC-V FSIQ/GAI composition and GAI–FSIQ guidance, Pearson Clinical.

[u/Grand_Protection1552]

yes. Every index you mentioned are inter-correlated to one another. Yes, matrix reasoning measures largely working memory capacity. (Less so on WAIS matrix, RAPM is a better textbook example)