Barnhardt TM, Chan JY, Ghoraani B, Wilcox T. Effects of Competition on Left Prefrontal and Temporal Cortex During Conceptual Comparison of Brand-Name Product Pictures: Analysis of fNIRS Using Tensor Decomposition.
Brain Sci 2025;
15:127. [PMID:
40002460 PMCID:
PMC11852890 DOI:
10.3390/brainsci15020127]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2024] [Revised: 01/17/2025] [Accepted: 01/20/2025] [Indexed: 02/27/2025] Open
Abstract
BACKGROUND/OBJECTIVES
Recent theories of the neurocognitive architecture of semantic memory have included a distinction between semantic control in the left inferior frontal gyrus (LIFG) and semantic representation in the left anterior temporal lobe (LATL). Support for this distinction has been found both in tasks in which high semantic selection demands have been instantiated and in tasks in which previous presentations of semantic information that compete with target information have been instantiated.
METHODS
In the current study, these manipulations were combined in a novel manner into a single task in which brand-name product pictures were used. Functional near-infrared spectroscopy (fNIRS) was used to measure hemodynamic activity and tensor decomposition, in addition to grand averaging, was used to analyze the fNIRS output.
RESULTS
Both analytic methods converged on the same set of findings. That is, in line with past studies, greater activity in the LIFG was observed in the competitive condition than in a repeated condition. However, unlike past studies, greater activity in the competitive condition was also observed in both the left and right anterior temporal lobes (ATLs).
CONCLUSIONS
While it was possible that the novel combination of high selection and competition into a single task unlocked a semantic selection mechanism in the bilateral ATL, a number of other post-hoc explanations were offered for this unusual finding, including a re-interpretation of the high-selection task as an ad hoc categorization task. Finally, the convergence of the tensor decomposition and grand averaging approaches on the same set of findings supported tensor decomposition as a viable approach to the analysis of fNIRS data.
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