Tracking two concurrent talkers involves both acoustic and cognitive challenges.

When talkers overlapped acoustically, spatial separation improved intelligibility.

When acoustic overlap was low, separation provided no benefit or was detrimental.

Working memory abilities best predicted performance when acoustic overlap was low.

Results are best explained in a data-limit vs. resource-limit framework.

An understanding of how listeners divide their attention between two simultaneous talkers requires modelling the interaction between acoustic factors (energetic masking) and cognitive processes (control of auditory attention). The impact of spatial separation between the two talkers on this interaction is unclear, since separation is likely to create both acoustic benefits (release from energetic masking) and cognitive costs (increased demands on spatial attentional control). To explore this question, we manipulated the degree of energetic masking (high vs. low) and spatial separation (collocated to dichotic) between two simultaneous talkers. When energetic masking was high (Experiment 1, unmanipulated talker voices), transcription performance improved monotonically from collocated to dichotic, owing to a gradual release from energetic masking. When energetic masking was low (Experiment 2, bandpass-filtered talker voices), the benefit of spatial separation disappeared; performance even worsened in the dichotic condition. Additionally, across both experiments, individual differences in working memory best predicted transcription performance in conditions where energetic masking was low. These results suggest that energetic masking is the dominant challenge during divided-attention listening, but that the contribution of cognitive control and working memory can be observed when energetic masking is reduced, at least in the context of the current paradigm. The findings are discussed in light of Norman and Bobrow’s (1975) concept of data-limited vs. resource-limited tasks, which we propose is a promising framework for reinterpreting existing results from speech-in-noise perception research.

Speech-in-noise

Masking

Divided attention

Working memory

Individual differences

Cognitive listening

All stimuli, data and analysis code are available at https://osf.io/vznhu/.

© 2023 The Author(s). Published by Elsevier Inc.