title: Neural correlates of the food/non-food visual distinction.
abstract: An evolutionarily ancient skill we possess is the ability to distinguish between food and non-food. Our goal here is to identify the neural correlates of visually driven 'edible-inedible' perceptual distinction. We also investigate correlates of the finer-grained likability assessment. Our stimuli depicted food or non-food items with sub-classes of appealing or unappealing exemplars. Using data-classification techniques drawn from machine-learning, as well as evoked-response analyses, we sought to determine whether these four classes of stimuli could be distinguished based on the patterns of brain activity they elicited. Subjects viewed 200 images while in a MEG scanner. Our analyses yielded two successes and a surprising failure. The food/non-food distinction had a robust neural counterpart and emerged as early as 85 ms post-stimulus onset. The likable/non-likable distinction too was evident in the neural signals when food and non-food stimuli were grouped together, or when only the non-food stimuli were included in the analyses. However, we were unable to identify any neural correlates of this distinction when limiting the analyses only to food stimuli. Taken together, these positive and negative results further our understanding of the substrates of a set of ecologically important judgments and have clinical implications for conditions like eating-disorders and anhedonia.
authors.affiliation: Massachusetts Institute of Technology, Cambridge, MA 02139, United States. Electronic address: tsourk@mit.edu.
authors.forename: Kleovoulos
authors.initials: K
authors.lastname: Tsourides
authors.affiliation: Rutgers State University of New Jersey, Piscataway, NJ 08854, United States.
authors.forename: Shahriar
authors.initials: S
authors.lastname: Shariat
authors.affiliation: Massachusetts Institute of Technology, Cambridge, MA 02139, United States; Singapore University of Technology and Design, Singapore 138682, Singapore.
authors.forename: Hossein
authors.initials: H
authors.lastname: Nejati
authors.affiliation: Indian Institute of Technology, Delhi, New Delhi 110016, India.
authors.forename: Tapan K
authors.initials: TK
authors.lastname: Gandhi
authors.affiliation: Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
authors.forename: Annie
authors.initials: A
authors.lastname: Cardinaux
authors.affiliation: Ohio State University, Columbus, OH 43218, United States.
authors.forename: Christopher T
authors.initials: CT
authors.lastname: Simons
authors.affiliation: Singapore University of Technology and Design, Singapore 138682, Singapore.
authors.forename: Ngai-Man
authors.initials: NM
authors.lastname: Cheung
authors.affiliation: Rutgers State University of New Jersey, Piscataway, NJ 08854, United States.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Pavlovic
authors.affiliation: Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
authors.forename: Pawan
authors.initials: P
authors.lastname: Sinha
mesh_terms: D000293:Adolescent; D000328:Adult; D001143:Arousal / Q000502:physiology; D002540:Cerebral Cortex / Q000502:physiology; D004192:Discrimination, Psychological / Q000502:physiology*; D001068:Feeding and Eating Disorders / Q000503:physiopathology; D005260:Female; D005502:Food; D005518:Food Preferences / Q000502:physiology; D006801:Humans; D000069550:Machine Learning; D015225:Magnetoencephalography; D008297:Male; D010364:Pattern Recognition, Visual / Q000502:physiology*; D013223:Statistics as Topic; D055815:Young Adult
keywords: Edibility; Image representation; Likability; MEG; Machine learning; Visual distinction