Introduction

In chapter 2 autism and dyslexia were characterised as disorders of modular functioning, and mental retardation as a disorder of central processing. Although a modular deficit is thought to result in specific disorders such as the two discussed here, this is not to say that central processes remain unaffected. The functioning of the central systems is dependent on receiving correct inputs from the modules. If a module is selectively damaged, then certain modular output representations will be lacking in the central systems.

In the case of autism, it has been suggested that the specific deficit in the ToM module leads to the pervasive communication and language impairments (Frith & Happé, 1994), and may even explain the poor performance on standard IQ tests (75% of people with autism are mentally retarded; Wing, 1993). Standard intelligence assessments involve both pragmatic and communicative elements, which cause difficulties for individuals with autism. Extraneous task demands such as these may explain some of the prominent peaks and troughs on standard IQ assessments that children and adults with autism consistently show (e.g. Harris, Handleman & Burton, 1990; McDonald, Mundy, Kasari & Sigman, 1989; see Lincoln, Allen & Kilman, 1995 for review). Deficits in understanding experimenter’s intention (theory of mind; Frith, 1989), for example, may underlie poor performance on the Comprehension subtest (Happé, 1994d). The typical pattern further includes better performance IQ than verbal IQ (Lord & Schopler, 1988). Even when this does not apply, selective impairment on certain subtests (e.g. Comprehension; Asamow, Tanguay, Bott & Freeman, 1987) and superior performance on others (e.g. Block Design; Shah & Frith, 1993) are commonly found (see Happé, 1994d for review). Indeed, a verbal - performance discrepancy does not appear sufficient to describe the spiky IQ profile in autism; performance on Picture Arrangement (performance scale) is often poor, while Digit Span (verbal scale) is good. Certainly, autism seems to flout the premise of standard IQ tests, which include a set of heterogeneous subtests in order to

extract the factor common to success across these tasks (general intelligence, or ‘g ’; Spearman, 1904).

Autism further challenges notions of general intelligence (assessed by tests such as the WISC) by the frequent presentation of savant skills; areas of surprising talent in otherwise low-functioning individuals (Hermelin & O ’Connor, 1983). Savant abilities are perhaps ten times more common in autism than in other forms of mental handicap, with an estimated incidence of 1 in 10 for the best-known skills such as music, drawing, mnemonism and calendar calculation (Rimland & Hill, 1984). The incidence in autism of some sort of skill out of line with general development (e.g. jigsaw constmction, hyperlexia, memory for routes) is probably higher still.

It may be these islets of ability, along with the uneven profile of skills, which have led to the impression of good or superior intelligence in even apparently retarded children with autism. Thus, in the first report of autism, Kanner (1943) concluded that; “The astounding vocabulary of the speaking children, the excellent memory...and the precise recollection of complex patterns and sequences, bespeak good intelligence.”. Despite this impression of hidden intellectual strengths, on standard psychometric assessments most children with autism fall in the retarded range, and these assessments have proven stable over time (Lockyer & Rutter, 1970; Lord & Schopler, 1989; Freeman, Ritvo, Needleman & Yokota, 1985), and predictive of later achievement (Venter, Lord & Schopler, 1992; Gillberg & Steffenburg, 1987).

Dyslexia, unlike autism, is characterised by at least average performance on standard IQ assessments. However, a spiky profile across the range of subtests has been observed (Vargo et al., 1995). There is often a knowledge component required in standard intelligence assessments, so that acquired knowledge and general intelligence (i.e. crystallised and fluid intelligence; Horn and Cattell,

1966) are confounded. Given that reading is probably the most important route to knowledge acquisition in literate societies, children with reading difficulties may be penalized on some intelligence tests. There is some empirical support for this argument. Siegel (1992) observed that 54% of her subjects in the 7 to 8 year age range were diagnosed as dyslexic, but only 38% in the 13 to 16 age range. This decrease reflects a drop in IQ scores due to reading disability. Stanovich (1986) found that correlations between IQ and reading increased with age. On the other

hand, reduced performance on other subtests that are relatively independent of knowledge may be a direct consequence of the modular deficit in phonology. The particular pattern that has been observed across the subscales of the WISC has been termed the ACID profile (Vargo et. al., 1995), with weaker performance on the Arithmetic, Coding, Information and Digit Span subtests. A deficit in representation of the phonological code is thought responsible for the reduced performance on the Coding, Digit Span and Arithmetics subtests, while the trough on the Information subtest is most likely to stem from reading difficulties, in terms of slower knowledge acquisition through the written word (a developmental rather than on-line effect of poor phonological representations).

Children with mild mental retardation, as opposed to autism and dyslexia, generally have low IQs with a flat profile across the subtests. The only criterion for a classification of mild mental retardation is low IQ test performance; mental retardation is unlikely to represent a unified disorder at the cognitive level, because its diagnosis is not theory-driven. For the present purposes it is assumed that mental retardation of no known etiology simply presents the lower end of the normal continuum of individual differences in central processing (i.e. intelligence or ‘g’).

Based on the hypothesis that autism and dyslexia constitute modular disorders it is predicted that central processing assessed by a measure uncontaminated by knowledge factors and competencies in pragmatic and communicative aspects of language (which rely directly or indirectly on intact modular output representations), should be unaffected. That is to say, if groups of children with autism or dyslexia are compared with groups of normal children on such a measure, no significant differences should obtain. This is despite the fact that, for children with autism at least, large differences on conventional measures of IQ are expected. On the other hand, given the hypothesis that mental retardation consitutes a disorder of central processing, groups of children with mental retardation should show significantly impaired performance compared to normal controls. A model that conceptualises intelligence, in particular the concept of general intelligence as synonymous with central processing was introduced in chapter 2. Anderson (1992) suggests a basic processing mechanism (BPM) that determines the rate of knowledge acquisition, and thus represents the speed of central processing. Individual differences in this speed are assumed to

be innate and stable throughout development, and are thus reflected in intelligence measurements (the ‘g’ factor; Spearman, 1904). Anderson (1992) suggests a simple empirical measure of speed of information processing called inspection time (IT). This knowledge-free parameter has consistently been found to correlate with IQ (see section 3.3). The IT measure is thus ideally suited to examine the thesis that autism and dyslexia can be characterised by intact central processing capacities (i.e. domain-specific deficits only), while learning difficulties should be characterised by a deficit in central processing (i.e. domain- general deficit).