Goal neglect and knowledge chunking in the construction of novel behaviour

Abstract

Task complexity is critical in cognitive efficiency and fluid intelligence. To examine functional limits in task complexity, we examine the phenomenon of goal neglect, where participants with low fluid intelligence fail to follow task rules that they otherwise understand. Though neglect is known to increase with task complexity, here we show that - in contrast to previous accounts - the critical factor is not the total complexity of all task rules. Instead, when the space of task requirements can be divided into separate sub-parts, neglect is controlled by the complexity of each component part. The data also show that neglect develops and stabilizes over the first few performance trials, i.e. as instructions are first used to generate behaviour. In all complex behaviour, a critical process is combination of task events with retrieved task requirements to create focused attentional episodes dealing with each decision in turn. In large part, we suggest, fluid intelligence may reflect this process of converting complex requirements into effective attentional episodes.

Keywords: Chunking; Cognitive control; Goal neglect; Intelligence; Working memory.

Fig. 1

Sample stimuli for a series of trials (top to bottom) from the Duncan et al. (2008) feature match task. Actual stimuli were in colour (solid shape = red, dashed shape = green, dotted shape = blue).

Fig. 2

Sample trial stimuli from all tasks used in Experiments 1 and 2. All stimuli are shown in complex form and represent a regular trial. Actual stimuli were in colour.

Fig. 3

Sample stimuli from one sub-task in Experiment 1. Stimuli are shown in both simple (A) and complex (B) form. Actual stimuli were in colour.

Fig. 4

Experiment 1. Relationship of response decision accuracy to Culture Fair IQ for both critical (diamonds) and regular (open circles) trials. Each point represents data from a single participant averaged across all tasks.

Fig. 5

Experiment 1. Histograms of sub-task performance for critical (A and B) and regular (C and D) trials for low IQ (<100) and high IQ (>100) participants. Bars represent percentage of sub-tasks with performance (response accuracy) in bins.

Fig. 6

Experiment 1. Mean response decision accuracies (% correct) and frequency of major performance failures (MPF) for critical (A and B) and regular (C and D) trials as a function of same sub-task and other sub-task complexity.

Fig. 7

Sample print out accompanying task instructions (Task 1 from Fig. 2, complex–simple condition). Vehicles (A) are in complex form and books (B) are in simple form. Actual print-outs were in colour.

Fig. 8

Experiment 2. Relationship of response decision accuracy to Culture Fair IQ for the chunked (A) and interleaved (B) instructions group in both critical (diamonds) and regular (open circles) trials. Each point represents data from a single participant averaged across all tasks.

Fig. 9

Experiment 2: Critical trials. Mean response decision accuracies (% correct) and frequency of major performance failures (MPF) for the chunked instruction group (A and B) and the interleaved instructions group (C and D) as a function of same sub-task and other sub-task complexity.

Fig. 10

Experiment 2: Regular trials. Mean response decision accuracies (% correct) and frequency of major performance failures (MPF) for the chunked instruction group (A and B) and the interleaved instructions group (C and D) as a function of same sub-task and other sub-task complexity.

Fig. 11

Pooled data. Probability of following correct strategy (dotted line), probability of following incorrect strategy (solid line) and response time (dashed line), plotted as a function of trial number for critical trials (A) and regular trials (B). Data are averaged across all cases of major performance failure in Experiments 1 and 2.